diff --git a/README.md b/README.md
new file mode 100644
index 0000000..967a340
--- /dev/null
+++ b/README.md
@@ -0,0 +1,17 @@
+# BasisOfLearning
+
+In this project we will learn deep learning and learn its application. 
+
+I have added the requisite files on weekly in the respecive folders for the weeks. The content for their respective week are as follows :-
+
+Week1 : Anaconda installation, Introduction to Jupyter Notebooks, Numpy, Pandas and Matplotlib.
+
+
+Note : Content for the other weeks will be added as the project progresses.
+
+Team Members:
+
+1. Kumar Kanishk Singh
+2. Aditya Yadav
+3. Priyanshu Meena
+4. Vibhansh Bhatia
\ No newline at end of file
diff --git a/Sakt Launde/Mid-Term Evaluation/Exploratory Data Analysis.docx b/Sakt Launde/Mid-Term Evaluation/Exploratory Data Analysis.docx
new file mode 100644
index 0000000..897dc7b
Binary files /dev/null and b/Sakt Launde/Mid-Term Evaluation/Exploratory Data Analysis.docx differ
diff --git a/Sakt Launde/Week 1/Anaconda Installation.tex b/Sakt Launde/Week 1/Anaconda Installation.tex
new file mode 100644
index 0000000..9c661b4
--- /dev/null
+++ b/Sakt Launde/Week 1/Anaconda Installation.tex	
@@ -0,0 +1,25 @@
+Anaconda is an open-source software that contains Jupyter, spyder, etc. that are used for large data processing, data analytics, heavy scientific computing and Machine Learning.
+
+To start working we have to install it on our PC. Here is the installation process:
+
+1. We go www.anaconda.com and download the latest version of Anaconda.
+
+2. After the download is complete, we run the setup.
+
+3. License Agreement: Now we have to read and agree to the License Agreement by clicking on "I Agree". 
+   It is important that you understand the License Agreement before clicking "I agree".
+
+4. Select Installation Type: Select the installation type as "Just Me".
+
+5. Choose Install Location: Now we select the install location by typing the location address or by browsing. 
+   Remember that you need to have atleast 3.5 GB of disk space free at the location. 
+   Also, choose a folder without a 'space' character in its name to avoid any problems in the future.
+
+6. Advanced Installation Option: Tick both the boxes "Add Anaconda to my PATH environment variable" and "Register Anaconda as my default Python 3.9". 
+   Then click "Install".
+
+7. Installing: Now we patiently wait while the installation process completes.
+
+8. Once the installation is complete, we click on "Finish" and close the setup.
+
+Now with Anaconda installed on our system we just need to open Anaconda Navigator to use the tools Anaconda has to offer.
\ No newline at end of file
diff --git a/w1/CalDataset.ipynb b/Sakt Launde/Week 1/CalDataset.ipynb
similarity index 99%
rename from w1/CalDataset.ipynb
rename to Sakt Launde/Week 1/CalDataset.ipynb
index 3fc536e..1dffc72 100644
--- a/w1/CalDataset.ipynb
+++ b/Sakt Launde/Week 1/CalDataset.ipynb	
@@ -1,1756 +1,1756 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "894e2229",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import tarfile\n",
-    "import urllib.request\n",
-    "\n",
-    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
-    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
-    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
-    "\n",
-    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
-    "    if not os.path.isdir(housing_path):\n",
-    "        os.makedirs(housing_path)\n",
-    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
-    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
-    "    housing_tgz = tarfile.open(tgz_path)\n",
-    "    housing_tgz.extractall(path=housing_path)\n",
-    "    housing_tgz.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3e47d403",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'ntpath' from 'C:\\\\Users\\\\beserkarray\\\\anaconda3\\\\envs\\\\spooder01\\\\lib\\\\ntpath.py'>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "5e1817e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fetch_housing_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "b983bd98",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "def load_housing_data(housing_path=HOUSING_PATH):\n",
-    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
-    "    return pd.read_csv(csv_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "82ead2e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing = load_housing_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4c1a60e8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-122.23</td>\n",
-       "      <td>37.88</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>322.0</td>\n",
-       "      <td>126.0</td>\n",
-       "      <td>8.3252</td>\n",
-       "      <td>452600.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-122.22</td>\n",
-       "      <td>37.86</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>7099.0</td>\n",
-       "      <td>1106.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>1138.0</td>\n",
-       "      <td>8.3014</td>\n",
-       "      <td>358500.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-122.24</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1467.0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>496.0</td>\n",
-       "      <td>177.0</td>\n",
-       "      <td>7.2574</td>\n",
-       "      <td>352100.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1274.0</td>\n",
-       "      <td>235.0</td>\n",
-       "      <td>558.0</td>\n",
-       "      <td>219.0</td>\n",
-       "      <td>5.6431</td>\n",
-       "      <td>341300.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>1627.0</td>\n",
-       "      <td>280.0</td>\n",
-       "      <td>565.0</td>\n",
-       "      <td>259.0</td>\n",
-       "      <td>3.8462</td>\n",
-       "      <td>342200.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.85</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>919.0</td>\n",
-       "      <td>213.0</td>\n",
-       "      <td>413.0</td>\n",
-       "      <td>193.0</td>\n",
-       "      <td>4.0368</td>\n",
-       "      <td>269700.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.84</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>2535.0</td>\n",
-       "      <td>489.0</td>\n",
-       "      <td>1094.0</td>\n",
-       "      <td>514.0</td>\n",
-       "      <td>3.6591</td>\n",
-       "      <td>299200.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.84</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>3104.0</td>\n",
-       "      <td>687.0</td>\n",
-       "      <td>1157.0</td>\n",
-       "      <td>647.0</td>\n",
-       "      <td>3.1200</td>\n",
-       "      <td>241400.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>-122.26</td>\n",
-       "      <td>37.84</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>2555.0</td>\n",
-       "      <td>665.0</td>\n",
-       "      <td>1206.0</td>\n",
-       "      <td>595.0</td>\n",
-       "      <td>2.0804</td>\n",
-       "      <td>226700.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>-122.25</td>\n",
-       "      <td>37.84</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>3549.0</td>\n",
-       "      <td>707.0</td>\n",
-       "      <td>1551.0</td>\n",
-       "      <td>714.0</td>\n",
-       "      <td>3.6912</td>\n",
-       "      <td>261100.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "0    -122.23     37.88                41.0        880.0           129.0   \n",
-       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
-       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
-       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
-       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
-       "5    -122.25     37.85                52.0        919.0           213.0   \n",
-       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
-       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
-       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
-       "9    -122.25     37.84                52.0       3549.0           707.0   \n",
-       "\n",
-       "   population  households  median_income  median_house_value ocean_proximity  \n",
-       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
-       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
-       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
-       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
-       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
-       "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
-       "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
-       "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
-       "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
-       "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "f669df7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 20640 entries, 0 to 20639\n",
-      "Data columns (total 10 columns):\n",
-      " #   Column              Non-Null Count  Dtype  \n",
-      "---  ------              --------------  -----  \n",
-      " 0   longitude           20640 non-null  float64\n",
-      " 1   latitude            20640 non-null  float64\n",
-      " 2   housing_median_age  20640 non-null  float64\n",
-      " 3   total_rooms         20640 non-null  float64\n",
-      " 4   total_bedrooms      20433 non-null  float64\n",
-      " 5   population          20640 non-null  float64\n",
-      " 6   households          20640 non-null  float64\n",
-      " 7   median_income       20640 non-null  float64\n",
-      " 8   median_house_value  20640 non-null  float64\n",
-      " 9   ocean_proximity     20640 non-null  object \n",
-      "dtypes: float64(9), object(1)\n",
-      "memory usage: 1.6+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "housing.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "abd50e81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<1H OCEAN     9136\n",
-       "INLAND        6551\n",
-       "NEAR OCEAN    2658\n",
-       "NEAR BAY      2290\n",
-       "ISLAND           5\n",
-       "Name: ocean_proximity, dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing[\"ocean_proximity\"].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "0803d1d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20433.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "      <td>20640.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>-119.569704</td>\n",
-       "      <td>35.631861</td>\n",
-       "      <td>28.639486</td>\n",
-       "      <td>2635.763081</td>\n",
-       "      <td>537.870553</td>\n",
-       "      <td>1425.476744</td>\n",
-       "      <td>499.539680</td>\n",
-       "      <td>3.870671</td>\n",
-       "      <td>206855.816909</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.003532</td>\n",
-       "      <td>2.135952</td>\n",
-       "      <td>12.585558</td>\n",
-       "      <td>2181.615252</td>\n",
-       "      <td>421.385070</td>\n",
-       "      <td>1132.462122</td>\n",
-       "      <td>382.329753</td>\n",
-       "      <td>1.899822</td>\n",
-       "      <td>115395.615874</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-124.350000</td>\n",
-       "      <td>32.540000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.499900</td>\n",
-       "      <td>14999.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-121.800000</td>\n",
-       "      <td>33.930000</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>1447.750000</td>\n",
-       "      <td>296.000000</td>\n",
-       "      <td>787.000000</td>\n",
-       "      <td>280.000000</td>\n",
-       "      <td>2.563400</td>\n",
-       "      <td>119600.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>-118.490000</td>\n",
-       "      <td>34.260000</td>\n",
-       "      <td>29.000000</td>\n",
-       "      <td>2127.000000</td>\n",
-       "      <td>435.000000</td>\n",
-       "      <td>1166.000000</td>\n",
-       "      <td>409.000000</td>\n",
-       "      <td>3.534800</td>\n",
-       "      <td>179700.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>-118.010000</td>\n",
-       "      <td>37.710000</td>\n",
-       "      <td>37.000000</td>\n",
-       "      <td>3148.000000</td>\n",
-       "      <td>647.000000</td>\n",
-       "      <td>1725.000000</td>\n",
-       "      <td>605.000000</td>\n",
-       "      <td>4.743250</td>\n",
-       "      <td>264725.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>-114.310000</td>\n",
-       "      <td>41.950000</td>\n",
-       "      <td>52.000000</td>\n",
-       "      <td>39320.000000</td>\n",
-       "      <td>6445.000000</td>\n",
-       "      <td>35682.000000</td>\n",
-       "      <td>6082.000000</td>\n",
-       "      <td>15.000100</td>\n",
-       "      <td>500001.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
-       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
-       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
-       "std        2.003532      2.135952           12.585558   2181.615252   \n",
-       "min     -124.350000     32.540000            1.000000      2.000000   \n",
-       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
-       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
-       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
-       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
-       "\n",
-       "       total_bedrooms    population    households  median_income  \\\n",
-       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
-       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
-       "std        421.385070   1132.462122    382.329753       1.899822   \n",
-       "min          1.000000      3.000000      1.000000       0.499900   \n",
-       "25%        296.000000    787.000000    280.000000       2.563400   \n",
-       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
-       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
-       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
-       "\n",
-       "       median_house_value  \n",
-       "count        20640.000000  \n",
-       "mean        206855.816909  \n",
-       "std         115395.615874  \n",
-       "min          14999.000000  \n",
-       "25%         119600.000000  \n",
-       "50%         179700.000000  \n",
-       "75%         264725.000000  \n",
-       "max         500001.000000  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "e7a2ccfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kSZIkSdJAJo8kSZIkSZI0kMkjSZIkSZIkDWTySJIkSZIkSQOZPJIkSZI09pK8Jcn9ST7ZU/Y/knwqySeSvCfJk3vWXZhke5I7kzy7p/yUJLe2dW9IkhWuiiRNHJNHkiRJkibBpcDGOWXXACdW1XcDfw9cCJDkeOBM4IS2zxuTHND2uQTYBBzXHnOPKUmaw+SRJEmSpLFXVX8LfHFO2Qeqak97ej2wri2fAVxeVQ9W1V3AduDUJEcCh1TVdVVVwFuBF6xIBSRpgh046gAkSZIkaRm8DHhnWz6aLpk0a2cre6gtzy3vK8kmulZKTE1NMTMzM/DFd+/ePe/6Sbea62fdJtNqrhv0r9/mk/b03XYl3geTR5IkSZImWpJfAfYAb5st6rNZzVPeV1VtBbYCbNiwoaanpwfGMDMzw3zrJ91qrp91m0yruW7Qv37nbrmq77Y7zpruW76cTB5JkiRJmlhJzgGeB5zeuqJB16LomJ7N1gH3tvJ1fcolSfNwzCNJkiRJEynJRuBVwPOr6p97Vl0JnJnkoCTH0g2MfWNV7QIeSHJam2XtbOCKFQ9ckiaMLY8kSZIkjb0k7wCmgcOT7AReTTe72kHANV0uiOur6j9V1W1JtgG303VnO7+qHm6HejndzG0HA1e3hyRpHiaPJEmSJI29qnpxn+I3z7P9RcBFfcpvAk5cxtAkadWz25okSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkaeiSHJPkw0nuSHJbkgta+WuS3JPklvZ4Ts8+FybZnuTOJM/uKT8lya1t3RuSZBR1kiRJktaKA0cdgCRpTdgDbK6qjyV5InBzkmvaut+tqt/p3TjJ8cCZwAnAUcAHkzytqh4GLgE2AdcD7wM2AlevUD0kSZKkNceWR5KkoauqXVX1sbb8AHAHcPQ8u5wBXF5VD1bVXcB24NQkRwKHVNV1VVXAW4EXDDd6SZIkaW3bZ8ujJMfQfTj/N8DXga1V9XtJDgPeCawHdgAvqqovtX0uBM4DHgZeUVXvb+WnAJcCB9N9W3xB+/AvSVojkqwHngHcADwT+NkkZwM30bVO+hJdYun6nt12trKH2vLc8n6vs4muhRJTU1PMzMwsaz1GbfNJe/Yqmzq4fzmw6uq/e/fuVVen+Vjf1W2t1VeSNHkW0m1tUFeDc4Frq+riJFuALcCr7GogSRokyROAdwGvrKqvJrkE+HWg2s/XAi8D+o1jVPOU711YtRXYCrBhw4aanp5ecvzj5NwtV+1VtvmkPbz21v6X9h1nTQ85opU1MzPDavudzsf6rm5rrb6SpMmzz25r83Q1OAO4rG12Gd/sNmBXA0nSXpI8mi5x9LaqejdAVd1XVQ9X1deBPwFObZvvBI7p2X0dcG8rX9enXJIkSdKQLGrA7DldDaaqahd0CaYkR7TNJqqrwWpvJmz9xtdCupZMcv0WwvqtHW1GtDcDd1TV63rKj5y9lgA/AXyyLV8JvD3J6+hasR4H3FhVDyd5IMlpdNeis4HfX6l6SJIkSWvRgpNHfboaDNy0T9nYdjVY7c2Erd/46tflBB7ZtWSS67cQ1m9NeSbwUuDWJLe0sl8GXpzkZLrrwQ7gZwCq6rYk24Db6bpPn9+6PwO8nG+On3c1dn+WJEmShmpByaN+XQ2A+2a/MW5d0u5v5XY1kCQ9QlV9hP5fIrxvnn0uAi7qU34TcOLyRSdJkiRpPvsc82hQVwO6LgXntOVzgCt6ys9MclCSY/lmV4NdwANJTmvHPLtnH0mSJEmSJI2hhbQ8GtTV4GJgW5LzgLuBF4JdDSRJkiRJklaTfSaP5ulqAHD6gH3saiBJkiRp2SR5C/A84P6qOrGVHQa8E1hPN3bei6rqS23dhcB5wMPAK6rq/a38FL75hfb7gAvabNCSpAH22W1NkiRJksbApcDGOWVbgGur6jjg2vacJMcDZwIntH3emOSAts8ldDM7H9cec48pSZrD5JEkSZKksVdVfwt8cU7xGcBlbfky4AU95ZdX1YNVdRewHTi1TfRzSFVd11obvbVnH0nSACaPJEmSJE2qqTYxD+3nEa38aOCzPdvtbGVHt+W55ZKkeSxkwGxJkiRJmiT9xmytecr7HyTZRNfFjampKWZmZga+4O7du+ddP+lWc/2s22RazXWD/vXbfNKevtuuxPtg8kiSJEnSpLovyZFVtat1Sbu/le8EjunZbh1wbytf16e8r6raCmwF2LBhQ01PTw8MZGZmhvnWT7rVXD/rNplWc92gf/3O3XJV3213nDXdt3w52W1NkiRJ0qS6EjinLZ8DXNFTfmaSg5IcSzcw9o2ta9sDSU5LEuDsnn0kSQPY8kiSJEnS2EvyDmAaODzJTuDVwMXAtiTnAXcDLwSoqtuSbANuB/YA51fVw+1QL6ebue1g4Or2kCTNw+SRJEmSpLFXVS8esOr0AdtfBFzUp/wm4MRlDE2SVj27rUmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjpw1AFIq936LVeNOgRJkiRJkvabLY8kSZIkSZI0kMkjSZIkSZIkDWS3NWnM9HZz23zSHs7dchU7Ln7uCCOSJEmSJK1ltjySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJA1dkmOSfDjJHUluS3JBKz8syTVJPt1+Htqzz4VJtie5M8mze8pPSXJrW/eGJBlFnSRJkqS1wuSRJGkl7AE2V9V3AacB5yc5HtgCXFtVxwHXtue0dWcCJwAbgTcmOaAd6xJgE3Bce2xcyYpIkiRJa82Bow5AkrT6VdUuYFdbfiDJHcDRwBnAdNvsMmAGeFUrv7yqHgTuSrIdODXJDuCQqroOIMlbgRcAV69UXSRJkrRv67dc1bd8x8XPXeFItBxMHkmSVlSS9cAzgBuAqZZYoqp2JTmibXY0cH3Pbjtb2UNteW55v9fZRNdCiampKWZmZpavEmNg80l79iqbOrh/ObDq6r979+5VV6f5WN/Vba3VV5I0eUweSZJWTJInAO8CXllVX51nuKJ+K2qe8r0Lq7YCWwE2bNhQ09PTi453nJ3b59u8zSft4bW39r+07zhresgRrayZmRlW2+90PtZ3dVtr9ZUkTR7HPJIkrYgkj6ZLHL2tqt7diu9LcmRbfyRwfyvfCRzTs/s64N5Wvq5PuSRJkqQhMXkkSRq6NiPam4E7qup1PauuBM5py+cAV/SUn5nkoCTH0g2MfWPr4vZAktPaMc/u2UeSJEnSENhtTZK0Ep4JvBS4NcktreyXgYuBbUnOA+4GXghQVbcl2QbcTjdT2/lV9XDb7+XApcDBdANlO1i2JEmSNEQmjyRJQ1dVH6H/eEUApw/Y5yLgoj7lNwEnLl90kiRJWopBM6tp9bDbmiRJkqSJluTnk9yW5JNJ3pHksUkOS3JNkk+3n4f2bH9hku1J7kzy7FHGLkmTwOSRJEmSpImV5GjgFcCGqjoROAA4E9gCXFtVxwHXtuckOb6tPwHYCLwxyQGjiF2SJsU+k0dJ3pLk/iSf7Cl7TZJ7ktzSHs/pWdc3i5/klCS3tnVvyDzzM0uSJEnSIhwIHJzkQOBxdDNxngFc1tZfBrygLZ8BXF5VD1bVXcB24NSVDVeSJstCxjy6FPgD4K1zyn+3qn6nt2BOFv8o4INJntYGOb0E2ARcD7yPLsvvIKeSJEmS9ltV3ZPkd+gmXvgX4ANV9YEkU22WTqpqV5Ij2i5H092TzNrZyvaSZBPdPQxTU1PMzMwMjGP37t3zrp90q7l+1m3pNp+0Z8HbLlc8q/n3Bv3rN+h9Xon3YZ/Jo6r62yTrF3i8b2TxgbuSbAdOTbIDOKSqrgNI8la6zL/JI0mSJEn7rY1ldAZwLPBl4C+SvGS+XfqUVb8Nq2orsBVgw4YNNT09PfCgMzMzzLd+0q3m+lm3pTt3EQNm7zhrelleczX/3qB//Qa9z8v1ns5nKbOt/WySs4GbgM1V9SUGZ/Efastzy/taTIZ/qdZitnI1mYT6LSYLP9fUwd3+417H/TUJv7+lWO31kyRpTPwIcFdVfQ4gybuBHwDuS3Jka3V0JHB/234ncEzP/uvourlJkgbY3+TRJcCv02Xofx14LfAyBmfxF5zdh8Vl+JdqLWYrV5NJqN9isvBzbT5pD6+99cAVySSPwiT8/pZitddPkqQxcTdwWpLH0XVbO53uC+6vAecAF7efV7TtrwTenuR1dENtHAfcuNJBS9Ik2a/kUVXdN7uc5E+A97ang7L4O9vy3HJJkiRJ2m9VdUOSvwQ+BuwB/o7ui+gnANuSnEeXYHph2/62JNuA29v257cxWiVJA+xX8mi2+Wd7+hPA7ExsfbP4VfVwkgeSnAbcAJwN/P7SQpckSZIkqKpXA6+eU/wgXSukfttfBFw07Li0NOv7tODfcfFzRxCJpH0mj5K8A5gGDk+yk+6kPJ3kZLquZzuAn4F9ZvFfTjdz28F0A2U7WLYkSZIkSdKYW8hsay/uU/zmebbvm8WvqpuAExcVnSRJ6vvNqyRJkrRSHjXqACRJkiRJkjS+TB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBDhx1AJIkSZIkLcT6LVf1Ld9x8XNXOBJpbTF5tMrMPZluPmkP06MJRZIkSZK0igxK3mn1M3k0ofynlSRJkiRJK8ExjyRJkiRJkjSQLY8kSZJGwHE7JEnSpLDlkSRJkiRJkgYyeSRJGrokb0lyf5JP9pS9Jsk9SW5pj+f0rLswyfYkdyZ5dk/5KUlubevekCQrXRdJkiRprTF5JElaCZcCG/uU/25Vndwe7wNIcjxwJnBC2+eNSQ5o218CbAKOa49+x5QkSZK0jEweSZKGrqr+FvjiAjc/A7i8qh6sqruA7cCpSY4EDqmq66qqgLcCLxhKwJIkSZK+wQGzJUmj9LNJzgZuAjZX1ZeAo4Hre7bZ2coeastzy/tKsomulRJTU1PMzMwsb+QraPNJexa03dTBg7ed5Pr3s3v37omv02J+V6uhvothfSVJGi8mjyRJo3IJ8OtAtZ+vBV4G9BvHqOYp76uqtgJbATZs2FDT09NLDHd0zh0wK9dcm0/aw2tv7X9p33HW9DJGNHozMzNM8u8UBv9e+/2uVkN9F8P6SpI0Xuy2Jkkaiaq6r6oerqqvA38CnNpW7QSO6dl0HXBvK1/Xp1ySJEnSEJk8kiSNRBvDaNZPALMzsV0JnJnkoCTH0g2MfWNV7QIeSHJam2XtbOCKFQ1akiRJWoPstiZJGrok7wCmgcOT7AReDUwnOZmu69kO4GcAquq2JNuA24E9wPlV9XA71MvpZm47GLi6PSRJa1ySJwNvAk6ku668DLgTeCewnu4686I2th5JLgTOAx4GXlFV71/xoLXmrR/Uffni565wJP0Nik9rk8kjSdLQVdWL+xS/eZ7tLwIu6lN+E92NgSRJvX4P+Ouq+g9JHgM8Dvhl4NqqujjJFmAL8KokxwNnAicARwEfTPK0ni8qJElz2G1NkiRJ0sRKcgjwg7QvJarqX6vqy8AZwGVts8uAF7TlM4DLq+rBqroL2M43x92TJPVhyyNJkiRJk+zbgc8Bf5rk6cDNwAXAVBsvj6raleSItv3RwPU9++9sZXtJsgnYBDA1NcXMzMzAIHbv3j3v+kk3ivptPmnPgrddSmyj+t0Nqt9yxrKUui3m/V+M5arfWvyfW4m/mUFMHkmSJEmaZAcC3wP8XFXdkOT36LqoDZI+ZdVvw6raCmwF2LBhQ01PTw886MzMDPOtn3SjqN+5ixhzZ8dZ0/v9OqP63Q2q31LqMtdS6raY938xlqt+a/F/biX+Zgax25okSZKkSbYT2FlVN7Tnf0mXTLpvdmbP9vP+nu2P6dl/HXDvCsUqSRPJ5JEkSZKkiVVV/wR8Nsl3tqLT6WbsvBI4p5WdA1zRlq8EzkxyUJJjgeOAG1cwZEmaOHZbk9aIcZ8KVJIkaQl+Dnhbm2ntM8BP0n1Rvi3JecDdwAsBquq2JNvoEkx7gPOdaU2S5mfySJIkSdJEq6pbgA19Vp0+YPuLgIuGGZNW1jh/UTooNmmSmDySJEmSJGmNMrmlhXDMI0mSJEmSJA1k8kiSJEmSJEkD7bPbWpK3AM8D7q+qE1vZYcA7gfXADuBFVfWltu5C4DzgYeAVVfX+Vn4KcClwMPA+4IKqquWtjrQ6jXMfbkmSJEnS6raQMY8uBf4AeGtP2Rbg2qq6OMmW9vxVSY4HzgROAI4CPpjkaW32gkuATcD1dMmjjcDVy1WR1cw+qJIkSZKk1cAvxifTPrutVdXfAl+cU3wGcFlbvgx4QU/55VX1YFXdBWwHTk1yJHBIVV3XWhu9tWcfSZIkSZIkjan9nW1tqqp2AVTVriRHtPKj6VoWzdrZyh5qy3PL+0qyia6VElNTU8zMzOxnmPu2e/fuoR5/OWw+ac9+7zt1MGNfv6VYC7+/+fZfTN0HHWeU798k/P6WYrXXT5IkSdLasL/Jo0HSp6zmKe+rqrYCWwE2bNhQ09PTyxJcPzMzMwzz+Mvh3CV0W9t80h5eNOb1W4q18Pt77a2D/013nDW95DgWc4zlNgm/v6VY7fWTJEmStDbsb/LoviRHtlZHRwL3t/KdwDE9260D7m3l6/qUawXYp1SSJEnSuHKMV2n87XPMowGuBM5py+cAV/SUn5nkoCTHAscBN7Yubg8kOS1JgLN79pEkSZIkSdKY2mfLoyTvAKaBw5PsBF4NXAxsS3IecDfwQoCqui3JNuB2YA9wfptpDeDldDO3HUw3y5ozrUmSJEmShqZfqyZ7YEiLt8/kUVW9eMCq0wdsfxFwUZ/ym4ATFxWdJEmSJEmSRmq5B8yWJEmSJEn7ydZSGkcmj6Q1zouTJEmSJGk+Jo8kSZIkSVoG4zJz3Gwcm0/aw7lt2S+ItRQmjyRJkiRJGmODklImhLRSTB5JkiRJktaMuYmY2dY5JmKkwUweSZIkSZI0gRbTTW5cutRpMpk8kiRJGiP9PtxfuvHxI4hEkiSpY/JIkjR0Sd4CPA+4v6pObGWHAe8E1gM7gBdV1ZfauguB84CHgVdU1ftb+SnApcDBwPuAC6qqVrIukiRpdVrMuEK24tFa86hRByBJWhMuBTbOKdsCXFtVxwHXtuckOR44Ezih7fPGJAe0fS4BNgHHtcfcY0qSJElaZiaPJElDV1V/C3xxTvEZwGVt+TLgBT3ll1fVg1V1F7AdODXJkcAhVXVda2301p59JEmSJA2J3dYkSaMyVVW7AKpqV5IjWvnRwPU92+1sZQ+15bnlfSXZRNdKiampKWZmZpYv8hW2+aQ9C9pu6uDB205y/fvZvXv3WNbp1nu+slfZSUc/qe+2C/29wvjWd1isryRJ48XkkSRp3KRPWc1T3ldVbQW2AmzYsKGmp6eXJbhROHeB4ypsPmkPr721/6V9x1nTyxjR6M3MzDCOv9N+v6tB7/1Cf6/QDZg9jvUdlnH9/Q7LWqvvsLQuzjcB91TV8/ZnbD1JUn92W5Mkjcp9rSsa7ef9rXwncEzPduuAe1v5uj7lkiQBXADc0fN8f8bWkyT1YcsjSdKoXAmcA1zcfl7RU/72JK8DjqIbGPvGqno4yQNJTgNuAM4Gfn/lw5YkjZsk64DnAhcB/7kVnwFMt+XLgBngVfSMrQfclWQ7cCpw3QqGvCY5Q5k0uWx5JEkauiTvoPtQ/p1JdiY5jy5p9Kwknwae1Z5TVbcB24Dbgb8Gzq+qh9uhXg68iW4Q7X8Arl7RikiSxtXrgV8Cvt5T9oix9YDesfU+27PdvGPoSZJseSRJWgFV9eIBq04fsP1FdN8ezy2/CThxGUOTJE24JM8D7q+qm5NML2SXPmV9x9BbzOQLq33g8+Wo32ImClhJ8004AfD7b7tir7LNJw0zouWzr7qNk8X+fa3F/7lRToxi8kiSJEnSJHsm8PwkzwEeCxyS5M9pY+u1GT0XMrbeXhYz+cJqH/h8Oeq3mIkCVtJ8E05Muomq261f26tox8XPHbj5WvyfG/Q/tBITo9htTZIkSdLEqqoLq2pdVa2nGwj7Q1X1Er45th7sPbbemUkOSnIsbWy9FQ5bkibKhKQgJUmSJGlRLga2tXH27gZeCN3Yeklmx9bbwyPH1pMk9WHySJIkSdKqUFUzdLOqUVVfYJFj60mS+jN5JEmSJEmSxs76QWP8zDMW0jCOIcc8kiRJkiRJ0jxseSRJkiRJktaUQS2S+rGVkskjSZIWxabPkiRJWmvstiZJkiRJkqSBbHmkvdh8T5IkSZIkzbLlkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsgBsyVJkiRJ0sRYv+UqNp+0h3PnTPbkhE7DY/JIkiRJkrSsFjODs6TxZ/JIkiRJkiRNvGElLQcddy21dDJ5JE2wfiextXQCkyRJkiQN35IGzE6yI8mtSW5JclMrOyzJNUk+3X4e2rP9hUm2J7kzybOXGrwkSZIkSZKGazlmW/uhqjq5qja051uAa6vqOODa9pwkxwNnAicAG4E3JjlgGV5fkiRJkiRJQzKMbmtnANNt+TJgBnhVK7+8qh4E7kqyHTgVuG4IMUiSJI0FB42VJEmTbqnJowI+kKSAP66qrcBUVe0CqKpdSY5o2x4NXN+z785Wtpckm4BNAFNTU8zMzCwxzMF279491OMvh80n7dnvfacOHrz/oHov5vVG/d6t5d/fIJP0e52E399SrPb6abI4RpokaaH6XTM2n7TnGy0EJK09S00ePbOq7m0JomuSfGqebdOnrPpt2JJQWwE2bNhQ09PTSwxzsJmZGYZ5/OVw7hK+sdx80h5ee2v/X/OOs6aX/HqDjrFS1vLvb5BJ+r1Owu9vKVZ7/SRJ0triFxHS2rWk5FFV3dt+3p/kPXTd0O5LcmRrdXQkcH/bfCdwTM/u64B7l/L6kiRJWhinGZYkSftrv5NHSR4PPKqqHmjLPwr8N+BK4Bzg4vbzirbLlcDbk7wOOAo4DrhxCbFLkiRJ0qpnix9pPK2l/82ltDyaAt6TZPY4b6+qv07yUWBbkvOAu4EXAlTVbUm2AbcDe4Dzq+rhJUUvSZIkSRoZJwWQ1ob9Th5V1WeAp/cp/wJw+oB9LgIu2t/XlCStPkl2AA8ADwN7qmpDksOAdwLrgR3Ai6rqS237C4Hz2vavqKr3jyBsSZIkac141KgDkCQJ+KGqOrmqNrTnW4Brq+o44Nr2nCTHA2cCJwAbgTcmOWAUAUuSJElrhckjSdI4OgO4rC1fBrygp/zyqnqwqu4CttNN1iBJWqOSHJPkw0nuSHJbkgta+WFJrkny6fbz0J59LkyyPcmdSZ49uuglaTIsabY1SZKWQQEfSFLAH1fVVmCqqnYBtNk7j2jbHg1c37Pvzla2lySbgE0AU1NTzMzMLEuwm0/a07d8uY6/mNeca+rghW8Lw4152Hbv3j2W8S/m/V+Mxdb31nu+slfZ5pP6bzuO7+O4/n6HZa3Vdwj2AJur6mNJngjcnOQa4Fy6VqwXJ9lC14r1VXNasR4FfDDJ0xyPVZIGM3kkrTIOWqgJ9MyqurcliK5J8ql5tk2fsuq3YUtCbQXYsGFDTU9PLzlQgHMHTXd+1vIcfzGvOdfmk/bw2lsXfmkfZszDNjMzw3L9TpfTQn9Xi3Xpxscvqr6LiWMc/w7G9fc7LGutvsutfdkw+4XDA0nuoPti4Qxgum12GTADvIqeVqzAXUlmW7Fet7KRryw/I0paCpNHWhXW0hSJ0mpTVfe2n/cneQ/dB/j7khzZWh0dCdzfNt8JHNOz+zrg3hUNWJI0tpKsB54B3MAKt2IdZguyfq0aF/taS20ZudjWrZPEuk2mca3bcp0H+p1TRtECfpbJI2keg76hMTElLY8kjwce1b4pfjzwo8B/A64EzgEubj+vaLtcCbw9yevouhocB9y44oFrVRrnLyJuvecrfVsTjUt80jhI8gTgXcArq+qrSb/Gqt2mfcqW3Ip1mC3I+v7/L7LV4FJbRi62desksW6TaWzrduvX+hYv9prd75wyihbws8bwndYkMbkiaYmmgPe0D/gHAm+vqr9O8lFgW5LzgLuBFwJU1W1JtgG3041xcf5qGqPCLgWStH+SPJoucfS2qnp3K564VqxeBySNK5NHkqSRqarPAE/vU/4F4PQB+1wEXDTk0CRJEyLdNxBvBu6oqtf1rLIVqyQtE5NHa5jfbDyS74ckSdJEeibwUuDWJLe0sl+mSxrZilXSWJj0XjsmjyRJGsAP4JI0/qrqI/QfxwhsxSpJy8Lk0RjxJmV5TXpmV5IkSZKkcWDySJIkSZImjF88S1pJjxp1AJIkSZIkSRpftjySlpHfAEmSVpLXHUmStBJseSRJkiRJkqSBbHmkkXNga0mSJEnSWjTofnjzSXs4d4xaGNvySJIkSZIkSQOZPJIkSZIkSdJAdlvTUPRremc3NEmSJEmSJo/JI60YZ4SRtBaNczLdMef2zfdIkiTJ5JEkSVpj/DJDkiRpcUweSZK0DExISJIkabVywGxJkiRJkiQNZMsjaT/YwkCStBY5BpQkSWuTySNNlLkfWjeftIdzTeRI0kQw8SBJkjSZTB5pzbHVkCQN32o/11o/SZK0lpg8GgE/kGnc2TpA0mrhNVeSJGnpTB5pbPmBX5Kk4fN6Kw2X/2OSVgOTR5Ik6REWc6MzLi0SvTmTJEkaHpNHkiStMBMdWsuG2TW637HHJcEpSdIkM3kkSZKkJTFpI0nS6ramkkf9PthsPmkP0ysfirSqDOumwYG7JUlL5bVEkqSlW7XJI7sESKPlh3VpbVjp6+3s620+aQ/neq2XJElaEas2ebTSvFHWWmBSVtIweA1dnbxmSJK0eqx48ijJRuD3gAOAN1XVxSsdw0ryg5MkLb+1di2R1qphfo5ynCZ5LZGkhVvR5FGSA4A/BJ4F7AQ+muTKqrp9JeOYazHfeJoMkpam93+ot9vJYj6wO8bS2jau1xItP6+5a8ug64M0DF5LJGlxVrrl0anA9qr6DECSy4EzgLE8SfuhVVo5/r9pESbqWiJpcoziWuQXFCPjtUSSFiFVtXIvlvwHYGNV/VR7/lLg+6rqZ+dstwnY1J5+J3DnEMM6HPj8EI8/atZvslm/yTaoft9WVU9Z6WBWizG9loyL1f4/1Wst1RWs72q3P/X1WrIEQ7qWrPa/29VcP+s2mVZz3WBl6rfga8lKtzxKn7K9sldVtRXYOvxwIMlNVbVhJV5rFKzfZLN+k22112+Exu5aMi7W0t/cWqorWN/Vbq3Vd0ws+7Vktf8eV3P9rNtkWs11g/Gr36NW+PV2Asf0PF8H3LvCMUiSJpvXEknSUnktkaRFWOnk0UeB45Icm+QxwJnAlSscgyRpsnktkSQtldcSSVqEFe22VlV7kvws8H66KTHfUlW3rWQMfaz2Lg3Wb7JZv8m22us3EmN6LRkXa+lvbi3VFazvarfW6jtyQ7qWrPbf42qun3WbTKu5bjBm9VvRAbMlSZIkSZI0WVa625okSZIkSZImiMkjSZIkSZIkDbRmkkdJXpjktiRfT7Khp/xZSW5Ocmv7+cN99r0yySdXNuLFWWz9kjwuyVVJPtX2u3h00e/b/vz+kpzSyrcneUOSflOyjoV56vctST6cZHeSP5izz4tb/T6R5K+THL7ykS/cftbxMUm2Jvn79rf671c+8oXZn/r1bDP25xiNlySPTXJjko+3v7tfm7P+F5LUuJ8XFmq++ib5uSR3tvLfHmWcy2VQfZOcnOT6JLckuSnJqaOOdbkkOSDJ3yV5b3t+WJJrkny6/Tx01DEupz71/R/tOveJJO9J8uQRh6hFSrKxnYu2J9ky6niWIslbktzf+9lktfxPJjmmfS67o51fL2jlE1+/ea4dE1+3Wav5WpFkR7u3uyXJTa1srOq3ZpJHwCeB/x/wt3PKPw/8eFWdBJwD/FnvyiT/P2D3ikS4NPtTv9+pqn8LPAN4ZpIfW5FI98/+1O8SYBNwXHtsXIE499eg+v0f4L8Cv9BbmORA4PeAH6qq7wY+AfzsCsS5FIuqY/MrwP1V9TTgeOBvhhrh0uxP/SbpHKPx8iDww1X1dOBkYGOS06D7YAw8C7h7dOEtu771TfJDwBnAd1fVCcDvjDDG5TTo9/vbwK9V1cnAr7bnq8UFwB09z7cA11bVccC17flqMre+1wAntmv63wMXjiQq7ZckBwB/CPwY3eeVFyc5frRRLcml7P25ebX8T+4BNlfVdwGnAee339VqqN+ga8dqqNus1X6t+KGqOrmqZr+IHqv6rZnkUVXdUVV39in/u6q6tz29DXhskoMAkjwB+M/Ab6xcpPtnsfWrqn+uqg+3bf4V+BiwbuUiXpzF1i/JkcAhVXVddaPCvxV4wcpFvDjz1O9rVfURugREr7TH45MEOAS4d+7+42Q/6gjwMuA323Zfr6rPDznM/bY/9Zukc4zGS3Vmk46Pbo/ZGTB+F/ilnucTb576vhy4uKoebNvdP6IQl9U89S268z3Akxjz8/5CJVkHPBd4U0/xGcBlbfkyxvgavlj96ltVH6iqPe3p9YzxZzL1dSqwvao+0z5XX073NzyRqupvgS/OKV4V/5NVtauqPtaWH6BLRBzNKqjfPNeOia8brL1rRTNW9VszyaMF+vfA381+CAV+HXgt8M+jC2lZza0fAK1p9I/TZTMnWW/9jgZ29qzb2cpWhap6iO6m6Va6m4fjgTePNKhl1tNk/9eTfCzJXySZGmVMQ7DazjFaQa3p9i3A/cA1VXVDkucD91TVx0cb3fLrV1/gacD/leSGJH+T5HtHGuQyGlDfVwL/I8ln6VpZrZbWKa+nS3h+vadsqqp2QXezBxwxgriG5fXsXd9eLwOuXrFotByOBj7b83xVfe5sVt3/ZJL1dD0wbmCV1G/AtWNV1I3Vf60o4APphmLZ1MrGqn6rKnmU5INJPtnnsc/Mf5ITgN8CfqY9Pxl4alW9Z7hRL9xy1q+n/EDgHcAbquozw4l8YZa5fv3GNxrpt/BLqV+fYz2aLnn0DOAoum5rI7+JWM46AgfSffP6v6vqe4DrGHGXlGX+HZ7MmJ1jNFmq6uHWfWkdcGqS76br6vmrIw1sSPrU90S688ShdF0PfhHY1lpjTrwB9X058PNVdQzw86yCLw2SPI+ue/LNo45lJeyrvkl+ha5bzdtWNDAt1dh97tT8WuvvdwGvrKqvjjqe5TLg2jHx1si14pntnufH6LpT/uCoA5rrwFEHsJyq6kf2Z7/WBO49wNlV9Q+t+PuBU5LsoHufjkgyU1XTyxHr/ljm+s3aCny6ql6/xPCWbJnrt5NHNvlex4ib9+9v/QY4uR3zHwCSbGMM+vgucx2/QNciZza58hfAect4/EVb5vqN3TlGk6mqvpxkhq5p87HAx1v+ZB3wsSSnVtU/jTDEZdVT34105/p3t+7JNyb5OnA48LkRhris5tT3HLrxHqA7J75p0H4T5JnA85M8B3gscEiSPwfuS3JkVe1qXdFXRZdEBtS3ql6S5BzgecDp7W9ak2MncEzP85F/7hyCVfM/2b6EfRfwtqp6dyteNfWDva4dq6Fuq/5aMTsUS1Xdn+Q9dN1hx6p+q6rl0f5oXWOuAi6sqv89W15Vl1TVUVW1Hvh3wN9P4k3doPq1db9BN2bCK1c+suUxz+9vF/BAugFVA5wNXDGaKIfiHuD4JE9pz5/FIwePm3jtg/NfAdOt6HTg9pEFtMxWyzlGo5HkKbNdO5McDPwIXbfdI6pqffu72gl8z2pIHA2o76eA/wnMziL6NOAxdBMpTLR56nsv8H+3zX4Y+PRIAlxGVXVhVa1rf7NnAh+qqpcAV9Ily2g/V8U1fFB9k2wEXgU8v6rsyjx5Pgocl+TYJI+h+91eOeKYltuq+J9s9wVvBu6oqtf1rJr4+s1z7Zj4uq32a0WSxyd54uwy8KN0k/GMV/2qak08gJ+g+yD9IHAf8P5W/l+ArwG39DyOmLPveuCTo67DctaP7huRoks4zJb/1KjrsZy/P2AD3T/dPwB/AGTU9Vhs/dq6HXSDFu5u2xzfyv9T+/19gi7J8i2jrscQ6vhtdLOXfYJuTK5vHXU9lrN+PevH/hzjY7wewHcDf9f+Nz4J/GqfbXYAh4861mHWly5Z9Oet7GN0s8yMPN4h1vffATcDH6cbo+OUUce6zPWeBt7blr+lnfc/3X4eNur4hlzf7XRj5sx+lvmjUcfnY9G/z+fQzZT3D8CvjDqeJdblHcAu4KH2ueW81fI/2c6j1c6vs/9vz1kN9Zvn2jHxdZtTz1V3rQC+vV3bP043CdSvjGP90oKSJEmSJEmS9rLmu61JkiRJkiRpMJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkjRiSWaS/NR+7vutSXYnOWC545LA5JFWuSSXJvmNUcchSRofC7k2JJlOsnMZX7OSPHW5jidJWtuS7EjyI7PPq+ruqnpCVT08yri0epk80sjNPfEt17aSpMnltUGSJGl8mDyS5khy4KhjkCStHXYxkKTx076YuDDJ7Um+lORPkzy2rfvpJNuTfDHJlUmO6tmvkrwiyWeSfD7J/0jyqLbuNUn+vGfb9W37ve4/knxHkg8l+UI7ztuSPLmt+zPgW4G/al3VfmnusZIc1WL7Yov1p3uO/Zok25K8NckDSW5LsmFIb6VWCZNHGqkBJ77ntxPYl1u/3+8atG0r/4sk/5TkK0n+NskJi4xhOsnOJK9K8k/AnyY5KMnrk9zbHq9PclDPPvu6YPy/ST7dTsa/3k7+1yX5ajtRP6Zte3iS97a6fjHJ/5q9uEjSWjUO14aeWH65fWjfkeSsnvKDkvxOkruT3Jfkj5Ic3LP+F5PsateQl8055qVJLknyviRfA34oyXe1en251fP5Pds/qX3A/1ySf0zyX3puRM5N8r+T/G7b9zNJfqCVfzbJ/UnO6TnWc9LdCD2Q5J4kv7A/74skrRFnAc8GvgN4GvBfkvww8JvAi4AjgX8ELp+z308AG4DvAc4AXsbipb3OUcB3AccArwGoqpcCdwM/3rqq/Xaf/d8B7Gz7/wfgvyc5vWf981vcTwauBP5gP2LUGuJNqkZq7okP+J90J7pXAk8B3kd3Q/CYeU6SVwPHAUcAHwPeth+h/BvgMODbgE3ArwCnAScDTwdOBf4LwAIvGBuBU9oxfgnYSnfxOQY4EXhx224z3Un9KcAU8MtA7Uf8krRqjNm14XDgaOAcYGuS72zrfovuRuJk4Kltm18FSLIR+AXgWS2Gfl3q/iNwEfBE4Abgr4APtHh/Dnhbz2v9PvAk4NuB/xs4G/jJnmN9H/AJ4FuAt9Ndk763xfUS4A+SPKFt+2bgZ6rqiXTXow8t+l2RpLXjD6rqs1X1Rbpz9ovpPtO/pao+VlUPAhcC359kfc9+v1VVX6yqu4HX883P/gtWVdur6pqqerCqPge8ju4asE9JjgH+HfCqqvo/VXUL8CbgpT2bfaSq3tfGSPozunseaSCTRxo3/w9wVTtRPgT8DnAw8AODdqiqt1TVA+3k/Rrg6UmetMjX/Trw6nZy/he6i8J/q6r728n61/jmyXahF4yvVtVtwCeBD1TVZ6rqK3Q3NM9o2z1El4D6tqp6qKr+V1WZPJKkRxrVtQHgv7Zrw98AVwEvShLgp4GfbzcHDwD/HTiz7fMi4E+r6pNV9bX2+nNdUVX/u6q+TpeAegJwcVX9a1V9CHgv8OJ0Xdr+H+DCVp8dwGt55A3AXVX1p+0G4J10X1T8txb3B4B/pUskQXfdOT7JIVX1par62H68J5K0Vny2Z/kf6VrxHNWWAaiq3cAX6L5EmG+/RUlyRJLLWyvRrwJ/TveFxkIcBcxen3rj6I3xn3qW/xl4bBy+Q/MweaRxM/dk/HW6k+/R/TZOckCSi5P8Qzup7mirFnpinfW5qvo/g+LgkSf9hVww7utZ/pc+z2e/Af4fwHbgA62rwZZFxi1Ja8Gorg1fasmfWbPXgqcAjwNubl3Fvgz8dSufjXfujcNcveuPAj7b6tW7z9Et5sew9zVpvmsOVTXouvPvgecA/5jkb5J8f5/YJEmdY3qWvxW4tz2+bbYwyePpWn7es4/9AL5Gd/2Y9W/mee3fpOuR8N1VdQhdS9L0rJ/vC+d7gcOSPHFOHPcM2F7aJ5NHGge9J765J+PQnXzv6bMtdM3+z6DrEvAkYP3srkuIYa84eORJfyEXjIW9aPct8uaq+nbgx4H/PKcvsiStVeNwbTi0neNnzV4LPk+XkDmhqp7cHk9qXewAdrH3jcNcc+t3zJwx72Y/5H+errXQ3GvSft0AVNVHq+oMuu5x/xPYtj/HkaQ14vwk65IcRje8xDvpugf/ZJKT042J+t+BG1rL0Fm/mOTQ1n3sgrYfwC3ADyb51tYa9sJ5XvuJwG7gy0mOBn5xzvr76Loz76WqPgv8f8BvJnlsku8GzmP/unBLgMkjjYfeE9824LlJTk/yaLoxgR6kO/nN3Ra6k+qDdC1/Hkd38l4O76AbEO8pSQ6nG8didmaEhVwwFiTJ85I8td0IfRV4uD0kaa0bl2vDryV5TJL/C3ge8BethdCfAL+b5AiAJEcneXZPvOcmOT7J44BX7+M1bqD7NvqXkjw6yTTdFwqXt65o24CLkjwxybcB/5lvXpMWrNXjrCRPat3/Zq87kqT+3k43Ht1n2uM3qupa4L8C76L7suA7+Ga35VlXADfTJYuuohtvjqq6hi6R9Im2/r3zvPav0Q24/ZV2jHfPWf+bdPcrXx4w+cGL6b48uRd4D90QHdfsq8LSICaPNA6+ceKj+7D8ErrBQT/fnv94Vf3r3G3bSfKtdM337wFuB65fpph+A7iJ7sR+K91gq78BsMALxkIdB3yQ7luF64A3VtXMUgKXpFViHK4N/wR8ie6D99uA/1RVn2rrXkXX7fj61jXug8B3AlTV1XQDpH6obTPvoNStHs8HfqzV743A2T2v9XN0yaXPAB+hu5l5y37W6aXAjhbzf6J7XyVJ/X20qo5vLUzPqap/BqiqP6qq76iqw6rqeVW1c85+76uqb6+qb2m9DL6RqK+q89vxnlpVf1JVqao9bd10Vb2pLd9WVadUNxnEyVX12qpa13OcK6rqW9uxfqeqdsw51s4W22Et1j/q2fc1VfWSnueP2FfqJ47NK0mSJEnSNyXZAfxUVX1wkfsVcFxVbR9KYNKI2PJIkiRJkiRJA5k80pqQ5JeT7O7zuHrUsUmSRsNrgyRpkKpav9hWR22/2OpIq5Hd1iRJK6I1/36AboDePVW1oc1e8k66AR13AC+qqi+17S+kmxnkYeAVVfX+Vn4KcClwMPA+4ILyYiZJkiQNzdgnjw4//PBav379ovf72te+xuMf//h9b7hCximecYoFjGdfjGd+4xTPQmO5+eabP19VT1mBkMZKSx5tqKrP95T9NvDFqro4yRbg0Kp6VZLj6WY9PBU4im4w4qdV1cNJbqSb9vZ6uuTRG9oAxQOtlmvJfCYpVjDeYZukeCcpVhifeNfqtWSUVuO1ZJxjg/GOb5xjg/GOb5xjg/GOb7ljW9S1pKrG+nHKKafU/vjwhz+8X/sNyzjFM06xVBnPvhjP/MYpnoXGAtxUY3B+XekHXcuiw+eU3Qkc2ZaPBO5syxcCF/Zs937g+9s2n+opfzHwx/t67dVyLZnPJMVaZbzDNknxTlKsVeMT71q9lozysRqvJeMcW9V4xzfOsVWNd3zjHFvVeMe33LEt5lpy4DIlrCRJ2pcCPtBmIfnjqtoKTFXVLoCq2pXkiLbt0TxyevWdreyhtjy3fC9JNgGbAKamppiZmVl0wLt3796v/UZhkmIF4x22SYp3kmKFyYtXkqTlYPJIkrRSnllV97YE0TVJPjXPtulTVvOU713YJae2AmzYsKGmp6cXGS7MzMywP/uNwiTFCsY7bJMU7yTFCpMXryRJy8HZ1iRJK6Kq7m0/7wfeQzee0X1JjgRoP+9vm+8EjunZfR1wbytf16dckiRJ0pCYPJIkDV2Sxyd54uwy8KPAJ4ErgXPaZucAV7TlK4EzkxyU5FjgOODG1sXtgSSnJQlwds8+kiRJkobAbmuSpJUwBbyny/dwIPD2qvrrJB8FtiU5D7gbeCFAVd2WZBtwO7AHOL+qHm7HejlwKXAwcHV7SJIkSRoSk0eSpKGrqs8AT+9T/gXg9AH7XARc1Kf8JuDE5Y5RkiRJUn92W5MkSZIkSdJAJo8kSZIkSZI0kMkjSZIkSZIkDeSYR8D6LVf1Ld9x8XNXOBJJ0qTyWiJJWiivGZImjS2PJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSNBGS/HyS25J8Msk7kjw2yWFJrkny6fbz0J7tL0yyPcmdSZ7dU35KklvbujckyWhqJEmTYUHJoyQ72sn1liQ3tTJP0pIkSZJWRJKjgVcAG6rqROAA4ExgC3BtVR0HXNuek+T4tv4EYCPwxiQHtMNdAmwCjmuPjStYFUmaOItpefRDVXVyVW1ozz1JS5IkSVpJBwIHJzkQeBxwL3AGcFlbfxnwgrZ8BnB5VT1YVXcB24FTkxwJHFJV11VVAW/t2UeS1MeBS9j3DGC6LV8GzACvouckDdyVZPYkvYN2kgZIMnuSvnoJMUiSJElaA6rqniS/A9wN/Avwgar6QJKpqtrVttmV5Ii2y9HA9T2H2NnKHmrLc8v3kmQT3ZffTE1NMTMzs+i4d+/evdd+m0/a03fb/Tn+UvSLbZyMc3zjHBuMd3zjHBuMd3yjjG2hyaMCPpCkgD+uqq3A0E7SkiRJktSrDZNxBnAs8GXgL5K8ZL5d+pTVPOV7F3b3PVsBNmzYUNPT04uIuDMzM8Pc/c7dclXfbXectfjjL0W/2MbJOMc3zrHBeMc3zrHBeMc3ytgWmjx6ZlXd2xJE1yT51DzbLvkkPawM/yArkfkfp+zlOMUCxrMvxjO/cYpnnGKRJGkV+hHgrqr6HECSdwM/ANyX5Mj2hfaRwP1t+53AMT37r6Pr5razLc8tlyQNsKDkUVXd237en+Q9wKkM8SQ9rAz/ICuR+R+n7OU4xQLGsy/GM79ximecYpEkaRW6GzgtyePouq2dDtwEfA04B7i4/byibX8l8PYkrwOOohtz9caqejjJA0lOA24AzgZ+f0VrIkkTZp8DZid5fJInzi4DPwp8ku5kfE7bbO5J+swkByU5lm+epHcBDyQ5rc2ydnbPPpIkSZI0UFXdAPwl8DHgVrp7ma10SaNnJfk08Kz2nKq6DdgG3A78NXB+VT3cDvdy4E10g2j/A47DKknzWkjLoyngPV2+hwOBt1fVXyf5KLAtyXl03wK8ELqTdJLZk/Qe9j5JXwocTHeC9iQtSZIkaUGq6tXAq+cUP0jXCqnf9hcBF/Upvwk4cdkDlKRVap/Jo6r6DPD0PuVfwJO0JEmSJEnSqrbPbmuSJEmSJElauxY625okSdoP6/tMyrDj4ueOIBJJkiRp/9jySJIkSZIkSQOZPJIkrZgkByT5uyTvbc8PS3JNkk+3n4f2bHthku1J7kzy7J7yU5Lc2ta9oc3gKUmSJGlITB5JklbSBcAdPc+3ANdW1XHAte05SY4HzgROADYCb0xyQNvnEmATcFx7bFyZ0CVJkqS1yeSRJGlFJFkHPBd4U0/xGcBlbfky4AU95ZdX1YNVdRewHTg1yZHAIVV1XVUV8NaefSRJkiQNgQNmS5JWyuuBXwKe2FM2VVW7AKpqV5IjWvnRwPU92+1sZQ+15bnle0myia6FElNTU8zMzCw64N27dy94v80n7Vnwcfcnln1ZTKzjwHiHa5LinaRYYfLilSRpOayp5FG/GW8kScOX5HnA/VV1c5LphezSp6zmKd+7sGorsBVgw4YNNT29kJd9pJmZGRa637mLuMbsOGvxsezLYmIdB8Y7XJMU7yTFCpMXryRJy2FNJY8kSSPzTOD5SZ4DPBY4JMmfA/clObK1OjoSuL9tvxM4pmf/dcC9rXxdn3JJkiRJQ+KYR5KkoauqC6tqXVWtpxsI+0NV9RLgSuCcttk5wBVt+UrgzCQHJTmWbmDsG1sXtweSnNZmWTu7Zx9JkiRJQ2DLI0nSKF0MbEtyHnA38EKAqrotyTbgdmAPcH5VPdz2eTlwKXAwcHV7SJIkSRoSk0eSpBVVVTPATFv+AnD6gO0uAi7qU34TcOLwIpQkSZLUy25rkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgZacPIoyQFJ/i7Je9vzw5Jck+TT7eehPdtemGR7kjuTPLun/JQkt7Z1b0iS5a2OJEmSJEmSltNiWh5dANzR83wLcG1VHQdc256T5HjgTOAEYCPwxiQHtH0uATYBx7XHxiVFL0mSJEmSpKFaUPIoyTrgucCbeorPAC5ry5cBL+gpv7yqHqyqu4DtwKlJjgQOqarrqqqAt/bsI0mSJEmSpDF04AK3ez3wS8ATe8qmqmoXQFXtSnJEKz8auL5nu52t7KG2PLd8L0k20bVQYmpqipmZmQWG+U27d+/ea7/NJ+1Z1DH253UXE8+ojFMsYDz7YjzzG6d4xikWSZIkSVou+0weJXkecH9V3ZxkegHH7DeOUc1Tvndh1VZgK8CGDRtqenohL/tIMzMzzN3v3C1XLeoYO85a/OsuJp5RGadYwHj2xXjmN07xjFMskiRJkrRcFtLy6JnA85M8B3gscEiSPwfuS3Jka3V0JHB/234ncEzP/uuAe1v5uj7lkiRJkiRJGlP7HPOoqi6sqnVVtZ5uIOwPVdVLgCuBc9pm5wBXtOUrgTOTHJTkWLqBsW9sXdweSHJam2Xt7J59JEmSJEmSNIYWOuZRPxcD25KcB9wNvBCgqm5Lsg24HdgDnF9VD7d9Xg5cChwMXN0ekiRJkiRJGlOLSh5V1Qww05a/AJw+YLuLgIv6lN8EnLjYICVJkiQpyZPpZoA+kW781JcBdwLvBNYDO4AXVdWX2vYXAucBDwOvqKr3t/JT+OaX2u8DLmgzQkuS+thntzVJkiRJGhO/B/x1Vf1b4OnAHcAW4NqqOg64tj0nyfF0w26cAGwE3pjkgHacS+hmdz6uPTauZCUkadKYPJIkSZI09pIcAvwg8GaAqvrXqvoycAZwWdvsMuAFbfkM4PKqerCq7gK2A6e2yX4OqarrWmujt/bsI0nqYyljHkmSJEnSSvl24HPAnyZ5OnAzcAEw1Sbnoc0EfUTb/mjg+p79d7ayh9ry3PK9JNlE10KJqakpZmZmFh307t2799pv80l7+m67P8dfin6xjZNxjm+cY4Pxjm+cY4Pxjm+UsZk8kiRJkjQJDgS+B/i5qrohye/RuqgNkD5lNU/53oVVW4GtABs2bKjp6elFBQxdQmjufuduuarvtjvOWvzxl6JfbONknOMb59hgvOMb59hgvOMbZWx2W5MkSZI0CXYCO6vqhvb8L+mSSfe1rmi0n/f3bH9Mz/7rgHtb+bo+5ZKkAUweSZIkSRp7VfVPwGeTfGcrOh24HbgSOKeVnQNc0ZavBM5MclCSY+kGxr6xdXF7IMlpSQKc3bOPJKkPu63NY32f5qQ7Ln7uCCKRJEmSBPwc8LYkjwE+A/wk3Rfi25KcB9wNvBCgqm5Lso0uwbQHOL+qHm7HeTlwKXAwcHV7SJIGMHkkSZIkaSJU1S3Ahj6rTh+w/UXARX3KbwJOXNbgJGkVs9uaJEmSJEmSBjJ5JEkauiSPTXJjko8nuS3Jr7Xyw5Jck+TT7eehPftcmGR7kjuTPLun/JQkt7Z1b2jjVUiSJEkaEpNHkqSV8CDww1X1dOBkYGOS0+imWL62qo4Drm3PSXI8cCZwArAReGOSA9qxLgE20Q18elxbL0mSJGlITB5JkoauOrvb00e3RwFnAJe18suAF7TlM4DLq+rBqroL2A6c2qZgPqSqrquqAt7as48kSZKkIXDAbEnSimgth24Gngr8YVXdkGSqTZlMVe1KckTb/Gjg+p7dd7ayh9ry3PJ+r7eJroUSU1NTzMzMLDrm3bt3L3i/zSftWfBx9yeWfVlMrOPAeIdrkuKdpFhh8uKVJGk5mDySJK2INj3yyUmeDLwnyXyz3PQbx6jmKe/3eluBrQAbNmyo6enpRcULXZJnofudu+WqBR93x1mLj2VfFhPrODDe4ZqkeCcpVpi8eCVJWg52W5Mkraiq+jIwQzdW0X2tKxrt5/1ts53AMT27rQPubeXr+pRLkiRJGhKTR5KkoUvylNbiiCQHAz8CfAq4EjinbXYOcEVbvhI4M8lBSY6lGxj7xtbF7YEkp7VZ1s7u2UeSJEnSENhtTZK0Eo4ELmvjHj0K2FZV701yHbAtyXnA3cALAarqtiTbgNuBPcD5rdsbwMuBS4GDgavbQ5IkSdKQmDySJA1dVX0CeEaf8i8Apw/Y5yLgoj7lNwHzjZckSZIkaRnZbU2SJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA3kgNmSJC3C+i1XjToESZIkaUXZ8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA3kgNmSJK2wQYNu77j4uSsciSRJkrRvtjySJEmSJEnSQPtMHiV5bJIbk3w8yW1Jfq2VH5bkmiSfbj8P7dnnwiTbk9yZ5Nk95ackubWte0OSDKdakiRJkiRJWg4LaXn0IPDDVfV04GRgY5LTgC3AtVV1HHBte06S44EzgROAjcAbkxzQjnUJsAk4rj02Ll9VJEmSJEmStNz2mTyqzu729NHtUcAZwGWt/DLgBW35DODyqnqwqu4CtgOnJjkSOKSqrquqAt7as48kSZIkSZLG0IIGzG4th24Gngr8YVXdkGSqqnYBVNWuJEe0zY8Gru/ZfWcre6gtzy3v93qb6FooMTU1xczMzIIrNGv37t177bf5pD2LPs5c+xPLoHhGZZxiAePZF+OZ3zjFM06xSJIkSdJyWVDyqKoeBk5O8mTgPUlOnGfzfuMY1Tzl/V5vK7AVYMOGDTU9Pb2QMB9hZmaGufudO2B2m8XYcdbiYxkUz6iMUyxgPPtiPPMbp3jGKRZJkiRJWi6Lmm2tqr4MzNCNVXRf64pG+3l/22wncEzPbuuAe1v5uj7lkiRJkiRJGlMLmW3tKa3FEUkOBn4E+BRwJXBO2+wc4Iq2fCVwZpKDkhxLNzD2ja2L2wNJTmuzrJ3ds48kSZIkSZLG0EK6rR0JXNbGPXoUsK2q3pvkOmBbkvOAu4EXAlTVbUm2AbcDe4DzW7c3gJcDlwIHA1e3hyRJkiRJksbUPpNHVfUJ4Bl9yr8AnD5gn4uAi/qU3wTMN16SJEmSJEmSxsiixjySJEmSJEnS2mLySJIkSZIkSQOZPJIkSZIkSdJAJo8kSZIkSZI00EJmW5MkSZIkDdn6LVf1Ld9x8XNXOBJJeiRbHkmSJEmSJGkgk0eSJEmSJEkayG5rkiRJkjQkt97zFc4d0B1NkiaFLY8kSZIkSZI0kMkjSZIkSRMjyQFJ/i7Je9vzw5Jck+TT7eehPdtemGR7kjuTPLun/JQkt7Z1b0iSUdRFkiaFySNJkiRJk+QC4I6e51uAa6vqOODa9pwkxwNnAicAG4E3Jjmg7XMJsAk4rj02rkzokjSZTB5JkiRJmghJ1gHPBd7UU3wGcFlbvgx4QU/55VX1YFXdBWwHTk1yJHBIVV1XVQW8tWcfSVIfDpgtSZIkaVK8Hvgl4Ik9ZVNVtQugqnYlOaKVHw1c37Pdzlb2UFueW76XJJvoWigxNTXFzMzMogOeOhg2n7Rn0fv12p/XXYjdu3cP7djLYZzjG+fYYLzjG+fYYLzjG2VsJo8kSZIkjb0kzwPur6qbk0wvZJc+ZTVP+d6FVVuBrQAbNmyo6emFvOwj/f7bruC1ty7ttmvHWYt/3YWYmZlhf+q0UsY5vnGODcY7vnGODcY7vlHGZvJIkiRJ0iR4JvD8JM8BHgsckuTPgfuSHNlaHR0J3N+23wkc07P/OuDeVr6uT7kkaQDHPJIkSZI09qrqwqpaV1Xr6QbC/lBVvQS4EjinbXYOcEVbvhI4M8lBSY6lGxj7xtbF7YEkp7VZ1s7u2UeS1IfJI0nS0CU5JsmHk9yR5LYkF7Ryp1eWJC3VxcCzknwaeFZ7TlXdBmwDbgf+Gji/qh5u+7ycbtDt7cA/AFevdNCSNEnstiZJWgl7gM1V9bEkTwRuTnINcC7d9MoXJ9lCN73yq+ZMr3wU8MEkT2sf+menV74eeB/d9Mp+6JekNaSqZoCZtvwF4PQB210EXNSn/CbgxOFFKEmriy2PJElDV1W7qupjbfkB4A66mW2cXlmSJEkac7Y8kiStqCTrgWcANzDm0yv3mw51qdMtz2cpU6+O87Sy/RjvcE1SvJMUK0xevJIkLQeTR5KkFZPkCcC7gFdW1VfnGa5oLKZX7jcd6rlbrlr0cRZqKVMxj/O0sv0Y73BNUryTFCtMXrySJC0Hu61JklZEkkfTJY7eVlXvbsX3ta5oOL2yJEmSNJ5MHkmShq7NiPZm4I6qel3PKqdXliRJksac3dYkSSvhmcBLgVuT3NLKfpluOuVtSc4D7gZeCN30yklmp1few97TK18KHEw3y5ozrUmSJElDZPJIkjR0VfUR+o9XBE6vLEmSJI01u61JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSB9pk8SnJMkg8nuSPJbUkuaOWHJbkmyafbz0N79rkwyfYkdyZ5dk/5KUlubeve0KZZliRJkiRJ0phayGxre4DNVfWxJE8Ebk5yDXAucG1VXZxkC7AFeFWS44EzgROAo4APJnlam2L5EmATcD3wPmAjEzbF8votV/Ut33Hxc1c4EkmSJEmSpOHbZ8ujqtpVVR9ryw8AdwBHA2cAl7XNLgNe0JbPAC6vqger6i5gO3BqkiOBQ6rquqoq4K09+0iSJEmSJGkMLaTl0TckWQ88A7gBmKqqXdAlmJIc0TY7mq5l0aydreyhtjy3vN/rbKJrocTU1BQzMzOLCROA3bt377Xf5pP2LPo4C7WvGPvFMyrjFAsYz74Yz/zGKZ5xikWTydatkiRJGkcLTh4leQLwLuCVVfXVeYYr6rei5infu7BqK7AVYMOGDTU9Pb3QML9hZmaGufudO+BD+XLYcdb0vOv7xTMq4xQLGM++GM/8ximecYpFkiRJkpbLgmZbS/JousTR26rq3a34vtYVjfbz/la+EzimZ/d1wL2tfF2fckmSJEmSJI2phcy2FuDNwB1V9bqeVVcC57Tlc4AresrPTHJQkmOB44AbWxe3B5Kc1o55ds8+kiRJkiRJGkML6bb2TOClwK1JbmllvwxcDGxLch5wN/BCgKq6Lck24Ha6mdrObzOtAbwcuBQ4mG6WtYmaaU2SJEmSJGmt2WfyqKo+Qv/xigBOH7DPRcBFfcpvAk5cTICSJEmSJEkanQWNeSRJkiRJkqS1yeSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjpw1AEMy633fIVzt1w16jAkSZIkSZImmi2PJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJI29JMck+XCSO5LcluSCVn5YkmuSfLr9PLRnnwuTbE9yZ5Jn95SfkuTWtu4NSTKKOknSpDB5JEmSJGkS7AE2V9V3AacB5yc5HtgCXFtVxwHXtue0dWcCJwAbgTcmOaAd6xJgE3Bce2xcyYpI0qQxeSRJkiRp7FXVrqr6WFt+ALgDOBo4A7isbXYZ8IK2fAZweVU9WFV3AduBU5McCRxSVddVVQFv7dlHktTHgaMOQJK0+iV5C/A84P6qOrGVHQa8E1gP7ABeVFVfausuBM4DHgZeUVXvb+WnAJcCBwPvAy5oH/wlSWtIkvXAM4AbgKmq2gVdginJEW2zo4Hre3bb2coeastzy/u9zia6FkpMTU0xMzOz6FinDobNJ+1Z9H699ud1F2L37t1DO/ZyGOf4xjk2GO/4xjk2GO/4RhmbySNJ0kq4FPgDum93Z812M7g4yZb2/FVzuhkcBXwwydOq6mG+2c3gerrk0Ubg6hWrxYis33LVXmU7Ln7uCCKRpNFL8gTgXcArq+qr8wxX1G9FzVO+d2HVVmArwIYNG2p6enrR8f7+267gtbcu7bZrx1mLf92FmJmZYX/qtFLGOb5xjg3GO75xjg3GO75Rxma3NUnS0FXV3wJfnFNsNwNJ0qIkeTRd4uhtVfXuVnxfu0bQft7fyncCx/Tsvg64t5Wv61MuSRrA5JEkaVQe0c0A6O1m8Nme7Wa7ExzNArsZSJJWnzYj2puBO6rqdT2rrgTOacvnAFf0lJ+Z5KAkx9INjH1ju+Y8kOS0dsyze/aRJPVhtzVJ0rhZcjcDWJ5xKvr1K1/quBXLZW5c49w/vx/jHa5JineSYoXJi3eVeSbwUuDWJLe0sl8GLga2JTkPuBt4IUBV3ZZkG3A73Uxt57cu0AAv55tj6F3NGugCLUlLYfJIkjQq9yU5sg1uuuzdDJZjnIp+/crP7TP+0CjMHf9inPvn92O8wzVJ8U5SrDB58a4mVfUR+n+RAHD6gH0uAi7qU34TcOLyRSdJq5vJo2XSbzBTcEBTSZrHbDeDi9m7m8Hbk7yObsDs2W4GDyd5IMlpdLPrnA38/sqHLUmSJK0t+0weOb2yJGmpkrwDmAYOT7ITeDUT0M3g1nu+MjYtjSRJkqRRWUjLo0txemVJ0hJU1YsHrLKbgSRJkjTm9jnbmtMrS5IkSZIkrV37O+bRI6ZXTtI7vfL1PdvNTqP8EIuYXnk5ZsiZOng8ZsSZjX2cZuYYp1jAePbFeOY3TvGMUyySJGn16De+qmOrSlpJyz1g9rJMr7wcM+T8/tuu4LW3jn488NnZcMZpZo5xigWMZ1+MZ37jFM84xSJJkiRJy2Wf3dYGuK91RWMY0ytLkiRJkiRpPOxv8mh2emXYe3rlM5MclORYvjm98i7ggSSnJQnd9MpXzD2oJEmSJEmSxss++3VN6vTKkiRJkiRJWrp9Jo+cXlmSJEmSJGnt2t9ua5IkSZIkSVoDTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgba54DZkiRp/KzfctUjnm8+aQ/nbrmKHRc/d0QRSZIkabWy5ZEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGcra1IZudDWd2FhzAmXAkSZIkSdLEsOWRJEmSJEmSBjJ5JEmSJEmSpIHstiZJ0ioy2116LrtMS5IkaX/Z8kiSJEmSJEkD2fJIkiRJkiaMLU0lrSRbHkmSJEmSJGkgWx6NgN8SSJIkSZKkSWHySJKkNaDfFxd+aSFJkqSFsNuaJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgxzwaIw6kLUlaSV53JEmStBC2PJIkSZIkSdJAtjySJEmSpFXCVqWShsHkkSRJeoR+Nx7edEiSJK1dJo8mgB/iJUmSJEnSqJg8kiRJ+2Q3CEmSpLXL5NGE8kO8JGkceD2SpMnQ73x96cbHjyASSZNoxZNHSTYCvwccALypqi5e6RhWs0Ef4nttPmkP5265yg/2kiaW15LxZ5drSePOa4kkLdyKJo+SHAD8IfAsYCfw0SRXVtXtKxmHOn5bLGkSeS2ZXL3XndkvMsDrjqSV57Wkc+s9X/nGubiX52VJc610y6NTge1V9RmAJJcDZwBr6iQ97hbSemlfvOBIGiKvJavMclx3FsNrlCS8lszL+wFJc6108uho4LM9z3cC3zd3oySbgE3t6e4kd+7Hax0OfH4/9huKV4xRPCsRS35rUZuPzXvTGM/8jGewhcbybcMOZJVbs9eS+YzTdWYhRhnvIq9Rsybq/WWy4p2kWGF84vVasjReSxjuuXg/z7Vzje17x3jHBuMd3zjHBuMd33LHtuBryUonj9KnrPYqqNoKbF3SCyU3VdWGpRxjOY1TPOMUCxjPvhjP/MYpnnGKZZVbs9eS+UxSrGC8wzZJ8U5SrDB58WogryWMd2ww3vGNc2ww3vGNc2ww3vGNMrZHrfDr7QSO6Xm+Drh3hWOQJE02ryWSpKXyWiJJi7DSyaOPAsclOTbJY4AzgStXOAZJ0mTzWiJJWiqvJZK0CCvaba2q9iT5WeD9dFNivqWqbhvSyy2peekQjFM84xQLGM++GM/8ximecYpl1Vrj15L5TFKsYLzDNknxTlKsMHnxqg+vJd8wzrHBeMc3zrHBeMc3zrHBeMc3sthStVfXXkmSJEmSJAlY+W5rkiRJkiRJmiAmjyRJkiRJkjTQqkseJdmY5M4k25NsGfJr7Uhya5JbktzUyg5Lck2ST7efh/Zsf2GL684kz+4pP6UdZ3uSNyTpN3Vov9d/S5L7k3yyp2zZXj/JQUne2cpvSLJ+kbG8Jsk97f25JclzViKWtv0xST6c5I4ktyW5YMTvz6B4RvIeJXlskhuTfLzF82ujen/miWVkfz9tnwOS/F2S947qvdHoZAWvJfuIY6jn+WWOdejn3WWOd+jnwSHEPLTz0hBiHelnpP2I98lJ/jLJp9rf8PePc7yaDKO6lozb/1/G6J5lgbGN9DPonPjG6p5mgbGNxfuXMbrfWURsY/HeDVRVq+ZBN9jdPwDfDjwG+Dhw/BBfbwdw+Jyy3wa2tOUtwG+15eNbPAcBx7Y4D2jrbgS+HwhwNfBjC3z9HwS+B/jkMF4f+H+BP2rLZwLvXGQsrwF+oc+2Q42lbXMk8D1t+YnA37fXHdX7MyiekbxHbd8ntOVHAzcAp43i/ZknlpH9/bTt/jPwduC9o/zf8rHyD1b4WrKPWIZ6nl/mWId+3l3meId+HhxCzEM7Lw0h1h2M8DPSfsR7GfBTbfkxwJPHOV4f4/9ghNeScfv/Y4zuWRYY22sY4WfQOa85Vvc0C4xtLN4/xuh+ZxGxjcV7NzDupR5gnB7tTXt/z/MLgQuH+Ho72PvEfCdwZFs+ErizXyx0Mzt8f9vmUz3lLwb+eBExrOeRJ7tle/3ZbdrygcDnaYOsLzCWQX/8Q4+lz2teATxrlO/PgHhG/h4BjwM+BnzfqN+fObGM7L0B1gHXAj/MN2/SxuJvx8fwH6zwtWQB8axnSOf5Ice97OfdIcY6lPPgMsc41PPSEOLdwYg/Iy0i1kOAu+aeh8c1Xh+T8WCE15Jx/P9jjO5ZFhDbaxiTe5g+MYzVPc2A2Mbu/WOM7nf2EdvYvXe9j9XWbe1o4LM9z3e2smEp4ANJbk6yqZVNVdUugPbziH3EdnRbXq6Yl/P1v7FPVe0BvgJ8yyLj+dkkn2hNQmebBK5oLK2J3jPoMrojf3/mxAMjeo/SdX+4BbgfuKaqRvb+DIgFRvf383rgl4Cv95SN/G9HK2alryWLNerrzD4N8by73HEO+zy4nF7PcM9Ly20cPyMN8u3A54A/Tdct8E1JHj/G8WoyjPJaMgn/f+P+uWrk9zBzjds9zTyxwZi8f+N0v7PA2GBM3rt+VlvyqF8/3Bri6z2zqr4H+DHg/CQ/OM+2g2JbqZj35/WXGtslwHcAJwO7gNeudCxJngC8C3hlVX11vk1XIqY+8YzsPaqqh6vqZLpvs09NcuJ8oQ8zngGxjOS9SfI84P6qurnf+n67DDMejcSk/n5GfZ3pghjueXdZrcB5cFms0HlpuU3SZ6QD6bqtXFJVzwC+RteVYZBRx6vJMMq/h0n6/5trHD5XjfweZq5xu6fZR2xj8/6N0/3OAmMbm/eun9WWPNoJHNPzfB1w77BerKrubT/vB94DnArcl+RIgPbz/n3EtrMtL1fMy/n639gnyYHAk4AvLjSQqrqv/VN8HfgTuvdnxWJJ8mi6E9nbqurdrXhk70+/eEb9HrUYvgzMABsZ8d9PbywjfG+eCTw/yQ7gcuCHk/w5Y/S/paFb0WvJfhj1dWagFTjvDsUQz4PLZSXOS8tqTD8jDbIT2Nnzre9f0iWTxjVeTYaRXUsm5P9vbD9XjcPn817jdk+zr9jG7f1rMX2ZMbnfmS+2cXzveq225NFHgeOSHJvkMXQDQ105jBdK8vgkT5xdBn4U+GR7vXPaZufQ9f2klZ/ZRj0/FjgOuLE1lXsgyWltZPSze/bZH8v5+r3H+g/Ah6p1mlyI2X/K5ifo3p8ViaXt/2bgjqp6Xc+qkbw/g+IZ1XuU5ClJntyWDwZ+BPjUKN6fQbGM6r2pqgural1Vrac7h3yoql4yivdGI7Ni15L9NOrrTF8rdN5dznhX4jy4LFbovLRsxvgzUl9V9U/AZ5N8Zys6Hbh9XOPVxBjJtWSC/v/G9nPVKO9h+sQyVvc0C4ltXN6/cbrfWWhs4/LeDVRLGDBpHB/Ac+hGev8H4FeG+DrfTjfi+ceB22Zfi64f4bXAp9vPw3r2+ZUW1530zFYAbGh/GP8A/AEseNDld9A1Z3uILrN43nK+PvBY4C+A7XSjuH/7ImP5M+BW4BN0f7xHrkQsbft/R9cs7xPALe3xnBG+P4PiGcl7BHw38HftdT8J/Opy//0uNJ55YhnZ30/P8ab55sC0I/nb8TGaByt0LVlAHEM9zy9zrEM/7y5zvEM/Dw7pb2Io56VljnHkn5H2I+aTgZva38P/BA4d53h9TMaDEVxLxvH/jzG6Z1lgbCP/DNpz3LG6p1lgbGPx/jFG9zuLiG0s3rtBj9kDS5IkSZIkSXtZbd3WJEmSJEmStIxMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5pLGQZEeSHxl1HABJLk3yG/u570ySnxqwbn2SSnLg0iKUJEmSJGnlmDySJElrQpLXJPnztvytSXYnOWCIr/dHSf7rsI4vSdo/I7ge7PeX05Nivi/RtTrYAkKSJK05VXU38IQhv8Z/GubxJUlLtxLXA2k1sOWRxsnJST6R5CtJ3pnksQBJfjrJ9iRfTHJlkqNa+V7dwHoz3kmemuRv2vE+n+SdPdv92yTXtGPemeRFc2I5NMlVSR5IckOS7+jZ9weSfLQd96NJfqBfZZIckOR32mt/BnjunPXnJvlMe427kpy11DdQkiRJkqTlZvJI4+RFwEbgWOC7gXOT/DDwm23dkcA/Apcv8Hi/DnwAOBRYB/w+QJLHA9cAbweOAF4MvDHJCT37vhj4tbbvduCitu9hwFXAG4BvAV4HXJXkW/q8/k8DzwOeAWwA/sPsihbDG4Afq6onAj8A3LLAeknSqtbGwfvF9oXC15K8OclUkqtbwv2DSQ5t256W5P9L8uUkH08y3XOcY9uXCA8kuQY4vGfdI76ASPKTSe5o234myc/0bDudZGeSzUnuT7IryU8uoB7f6Kawr2MkOTjJa5P8Y/ty4iNJDm7rnp/ktlbHmSTftT/v1b7eL0kaN6vletAcmv34cjpzxobNI7vcPTbJnyf5Qqv3R5NMtXVPau/XriT3JPmNzNM1L8lB7Rgn9pQ9Jcm/JDkiyaFJ3pvkc0m+1JbXDTjWN2Ic8B4vKjaNB5NHGidvqKp7q+qLwF8BJwNnAW+pqo9V1YPAhcD3J1m/gOM9BHwbcFRV/Z+q+kgrfx6wo6r+tKr2VNXHgHfRk9wB3l1VN1bVHuBtLRboWg99uqr+rO37DuBTwI/3ef0XAa+vqs+2Ov3mnPVfB05M8v9v7/6j7azrA9+/P/wQA0qBQY4hiRNsg71A6g9OKa0z3tNSJRXGsNYdbCxIUHqzxqGinXglqavj9c7krnRarD9a8GaUEioIqT+GXBER0+56u4YfgqIhIEOUiIFIFFE51Imc+Ll/PN8Tdk72Pjn75Oy9n33O+7XWXvt5vs+P/fnuX8/zfJ/vj3mZuSszt00hT5I0V/xvwOuBU6n+Y28D/oTqhP8w4IqIWEBVoP+fgROA9wCfiYiXlH3cCNxXtvlPwMpJXm831fHhWOBtwF9GxGualr8U+CVgAXAZ8NfNhTJTNNk+/gI4k+pmwgnAe4FfRMSpwKeAdwMvAb4A/L8R8YKm/R70vQKYwvslSXU0W44HM3FzeqKVJZZFZdt/B/ysLNsIjAG/QnUz+w1A2z6JyrXWZ0uc494M/GNm7qZ6r/+G6vrqZeV1/moKMbbSUWyqBwuPVCffb5r+Z6q2xydT1TYCIDNHgaeo/qwP5r1AAPeUO7ZvL+n/EviNUrL+44j4MVUh1UsPEgsT4ym+2yaek4HvTVhvPB/PAr9P9Qe/q9yF+NUp5EmS5oqPZuaTmfk48P8Bd2fm18vJ7eeoTjYvBr6QmV/IzF9k5h3AvcAbI+JlwK8Df5qZezLzK1Q3JlrKzFsz89tZ+Ueqmqv/ummV54D/KzOfy8wvAKPAKzrMU8t9RMRhwNuBd2Xm45m5NzP/e8nr7wO3ZuYdmfkcVSHTPKpCpk7eKyZ7vzrMhyT10mw5HszEzemJnqMqNPqVcuy4LzN/Wmof/R7w7sx8thT+/CWw4iD7u5H9C4/+oKSRmU9l5mcy858z8xmqwq//dQox7ucQYlOf2WG26u4JqsIeYF9zr38BPA48W5KPBn5apvcVAGXm96majhER/wr4ckR8hapA5x8z8/WHGk/xMuCLLdbdRXUXoHm9fTLzduD2qJol/Gfgv7L/gUmS5rInm6Z/1mL+RVT/xxdGRPMJ9pHAP1AV4D9dCuvHfZf9/5f3iYjfA95PdWf7MKpjy9amVZ4qJ/zjmm8sTFW7fZwIvBD4dottJt5E+UVEfI/9b1pM5b2Cyd8vSaqr2XI8mImb0xP9LVU+boqI44BPAu+jej+OpLpJPb7uYex/Y7uVvwfmRcRvlHhfRVVAR0QcTVXIs4yq9hTAiyPi8MzcO4VYx003NvWZNY9UdzcCb4uIV0XEUcD/TXW3YUdm/oCqEOniqDqnfjvQ3Hb4wqZ2uE8DCewFPg+cGhFvjYgjy+PXo6kPiUl8oWz7BxFxRET8PnBa2edEm6iq0S4sVVnXNMU2FFUfFscAe6juWHTypytJqk40/zYzj2t6HJOZ66kK8I8v/7PjXtZqJ+X48hmqWj1DmXkc1f99tFq/C34I/E+ajmFNJt5ECaoLhcen8TqTvV+SNMgG+XjQ7ub0+P/8s1QFWOOab5Y/l5kfyMzTqGqkng9cQvV+7AFObHo/js3M5j5eD5CZv6C6hnkLVa2jz5daRgCrqWpY/UZmHgu8rqS3em/axjzd2NR/Fh6p1jJzC/CnVH/iu6hOrJurNP7vwP9B1ZTtdOC/Ny37deDuiBgFNlM1B3i0/AG+oeznCapS9T8DjppCPE9R/SmvLq/5XuD8zPxhi9X/K3A78A3ga1RtiMcdVvbxBPAjqiqf//5gry9J2s8ngX8TEeeWmwgvjKoz04WZ+V2qJgsfiIgXlBqo7ZoAvIDqGPADYKzcdX5DT3LAvpP1a4EPRsTJJS+/WS5iNgHnRcQ5EXEk1bFjD/sf76aq7fs1Y5mRpP4Y5OPBwW5O3w+sKDe8Jw7C89sRsbR0Nv1TqmZsezNzF1Vzu6si4tiIOCwifjkiptLM7EaqJtMXlelxL6aq6fXj0k/T+yfZx/3A6yLiZRHxS1T91gJwiLGpj2y2plrIzMUT5v/PpumPAR9rs91tVKOztVr2XqrCnVbLHqZqX9xq2aUT5htUo7WNz/8TVaemrbYdaZoeA/64PMb9dXnexTTaCEuSnpeZ34uI5cB/oepUei9wD/COssofUHXK+SPgTuB64LgW+3kmIq6gKqg5iqovjM3djn+C91ANrPBVqqYM3wDOzcyHI+JiqhFDF1CdkP+bzPx5py8whfdLkgbSIB8PMvOpiDgf+DBwDVVn2s03p/+UKk9PA/9IVaBzQln2UqrrpIVULRlupipIg6oG0nrgQaqCn+9Q3TA/WDx3R8SzVM3pbmta9KHy2j+kugF+FXBBm33cERE3A98s6/8Z8KamVaYVm/orMrPfMUiSJEmSJKmmbLYmSZIkSZKktiw8kiRJmqaI2BYRoy0eF/U7NklS7wzK8SAiPtYmzpbdhEjjbLYmSZIkSZKktmrfYfaJJ56Yixcv7ni7Z599lmOOOebgK9aMcffeoMZu3L03U7Hfd999P8zMl8xASJqiiceSOn8PjW16jK1zdY0LjG0qPJb0Xqvrkrp8H7rNfM4ucyWfMHfyOt18dnIsqX3h0eLFi7n33ns73q7RaDAyMjLzAXWZcffeoMZu3L03U7FHxHcPPRp1YuKxpM7fQ2ObHmPrXF3jAmObCo8lvdfquqQu34duM5+zy1zJJ8ydvE43n50cS6bU51FE7IiIrRFxf0TcW9JOiIg7IuKR8nx80/prI2J7RDwcEec2pZ9Z9rM9Ij4SEdFJxiRJkiRJktRbnXSY/duZ+arMHC7za4AtmbkE2FLmiYjTgBXA6cAy4OqIOLxscw2wClhSHssOPQuSJEmSJEnqlkMZbW05sLFMbwQuaEq/KTP3ZOajwHbgrIiYDxybmXdm1Uv39U3bSJIkSZIkqYam2udRAl+KiAT+n8zcAAxl5i6AzNwVESeVdRcAdzVtu7OkPVemJ6YfICJWUdVQYmhoiEajMcUwnzc6Ojqt7frNuHtvUGM37t4b5NglSZIkabqmWnj02sx8ohQQ3RER35pk3Vb9GOUk6QcmVoVTGwCGh4dzOh0/DWrHWMbde4Mau3H33iDHLkmSJEnTNaVma5n5RHneDXwOOAt4sjRFozzvLqvvBBY1bb4QeKKkL2yRLkmSJEmSpJo6aOFRRBwTES8enwbeADwAbAZWltVWAreU6c3Aiog4KiJOoeoY+57SxO2ZiDi7jLJ2SdM2kiRJkiRJqqGpNFsbAj5XlfdwBHBjZn4xIr4KbIqIy4DHgAsBMnNbRGwCHgTGgMszc2/Z1zuA64B5wG3lIUmSJEmSpJo6aOFRZn4HeGWL9KeAc9pssw5Y1yL9XuCMzsOUJA2yiLgWOB/YnZlnNKW/E/gjqpsNt2bme0v6WuAyYC9wRWbeXtLP5PmbEF8A3lVG8JQkSZLUJVPtMHtWW7zm1pbpO9af1+NIJGnWug74K+D68YSI+G1gOfBrmblnfNTOiDgNWAGcDpwMfDkiTi21WK+hGo3zLqrCo2VYi3VKWh3rPM5JkiQNjnZlF9ctO6brrz2lDrMlSToUmfkV4EcTkt8BrM/MPWWd8YEXlgM3ZeaezHwU2A6cVQZnODYz7yy1ja4HLuhJBiRJkqQ5zJpHkqR+ORX41xGxDvifwHsy86vAAqqaReN2lrTnyvTE9JYiYhVVLSWGhoZoNBr7lo2Oju43Xyfdim310rED0jp9nbn4vs2EusZW17jA2CRJqhsLjyRJ/XIEcDxwNvDrVIMwvByIFuvmJOktZeYGYAPA8PBwjoyM7FvWaDRonq+TqcbWaZPrS1s1W7vo4K/TbDa8b/1Q19jqGhcYm1pr1X9eRJwA3AwsBnYAb87Mp8sy+8+TpBliszVJUr/sBD6blXuAXwAnlvRFTestBJ4o6QtbpEuS5obrqPq6a7YG2JKZS4AtZX5i/3nLgKsj4vCyzXj/eUvKY+I+JUkTWHgkSeqX/wb8DkBEnAq8APghsBlYERFHRcQpVCf292TmLuCZiDg7IgK4BLilL5FLknquTf95y4GNZXojz/eFZ/95kjSDbLYmSeq6iPgUMAKcGBE7gfcD1wLXRsQDwM+BleVEfltEbAIeBMaAy8tIa1B1sn0dVVOD23CkNUma64bKzQUyc9f4yJ30oP88mDt9YJnP2WWu5BNmX15b9WEJvcmnhUeSpK7LzLe0WXRxm/XXAetapN8LnDGDoUmSZqeu958Hc6cPLPM5u8yVfMLsy2urPiwBrlt2TNfzabM1SZIkSYPqydIUjfK8u6Tbf54kzSALjyRJkiQNqs3AyjK9kuf7wrP/PEmaQTZbkyRJklR7bfrPWw9siojLgMeACwEy0/7zJGkGWXgkSZIkqfYm6T/vnDbr23+eJM0QC48kSZpFFrfpSFGSJEmaLvs8kiRJkiRJUlvWPJIkaY5qV0tpx/rzehyJJEmS6szCI0mSJEnSQPDGh9QfNluTJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqy8IjSZIkSZIktWXhkSRJkiRJktqy8EiSJEmSJEltHdHvACRJkiRJmmjxmlv7HYKkwppHkiRJkiRJasvCI0mSJEmSJLVl4ZEkqesi4tqI2B0RD7RY9p6IyIg4sSltbURsj4iHI+LcpvQzI2JrWfaRiIhe5UGSJEmaqyw8kiT1wnXAsomJEbEIeD3wWFPaacAK4PSyzdURcXhZfA2wClhSHgfsU5IkSdLMsvBIktR1mfkV4EctFv0l8F4gm9KWAzdl5p7MfBTYDpwVEfOBYzPzzsxM4Hrggu5GLkmSJMnR1ibRqnf/HevP60MkkjT7RMSbgMcz8xsTWp8tAO5qmt9Z0p4r0xPT2+1/FVUtJYaGhmg0GvuWjY6O7jdfJ1ONbfXSsa7F0Or1tz7+E4bmwUdvuGW/9KULfqlrcXRiNnymvVbXuMDYJEmqGwuPJEk9FxFHA+8D3tBqcYu0nCS9pczcAGwAGB4ezpGRkX3LGo0GzfN1MtXYLu3i8MU7Ljrw9S9dcyurl45x1dYjDrpuP8yGz7TX6hoXGJskSXVj4ZEkqR9+GTgFGK91tBD4WkScRVWjaFHTuguBJ0r6whbpkiRpBk1sgbF66RiXrrnVVhjSHGafR5KknsvMrZl5UmYuzszFVAVDr8nM7wObgRURcVREnELVMfY9mbkLeCYizi6jrF0C3NLuNSRJkiTNDAuPJEldFxGfAu4EXhEROyPisnbrZuY2YBPwIPBF4PLM3FsWvwP4OFUn2t8Gbutq4JIkSZJstiZJ6r7MfMtBli+eML8OWNdivXuBM2Y0uAHQagAHSZIkqVemXPMoIg6PiK9HxOfL/AkRcUdEPFKej29ad21EbI+IhyPi3Kb0MyNia1n2kZgwvI4kSZIkSZLqpZNma+8CHmqaXwNsycwlwJYyT0ScBqwATgeWAVdHxOFlm2uohk1eUh7LDil6SZIkSZIkddWUmq1FxELgPKomBP+hJC8HRsr0RqABXFnSb8rMPcCjEbEdOCsidgDHZuadZZ/XAxdgfxWSJAFV87TxEW0kSZKkuphqn0cfAt4LvLgpbaiMfENm7oqIk0r6AuCupvV2lrTnyvTE9ANExCqqGkoMDQ3RaDSmGObzRkdHp7zd6qVjU97vdGLpRCdx18mgxg2DG7tx994gxy5JkiRJ03XQwqOIOB/YnZn3RcTIFPbZqh+jnCT9wMTMDcAGgOHh4RwZmcrL7q/RaDDV7Tq5w7vjos5j6UQncdfJoMYNgxu7cffeIMcuSZIkSdM1lZpHrwXeFBFvBF4IHBsRnwSejIj5pdbRfGB3WX8nsKhp+4XAEyV9YYt0SZIkSZIk1dRBC48ycy2wFqDUPHpPZl4cEX8OrATWl+dbyiabgRsj4oPAyVQdY9+TmXsj4pmIOBu4G7gE+OjMZkeSJEmSVEeL27T42LH+vB5HIqlTU+3zqJX1wKaIuAx4DLgQIDO3RcQm4EFgDLg8M/eWbd4BXAfMo+oo286yJUmSJEmSaqyjwqPMbFCNqkZmPgWc02a9dVQjs01Mvxc4o9MgJUmSJEmS1B+H9TsASZIkSZIk1dehNFuTJEmSpL6LiD8G/pBqNOetwNuAo4GbgcXADuDNmfl0WX8tcBmwF7giM2/vfdQa164vpG7t2z6WpM5ZeCRJkiRpYEXEAuAK4LTM/Fnpf3UFcBqwJTPXR8QaYA1wZUScVpafTjXAz5cj4tSmflrVATvBluYGm61JkiRJGnRHAPMi4giqGkdPAMuBjWX5RuCCMr0cuCkz92Tmo8B24KzehitJg8WaR5IkaT/dbD4gSTMtMx+PiL+gGgH6Z8CXMvNLETGUmbvKOrsi4qSyyQLgrqZd7CxpB4iIVcAqgKGhIRqNxn7LR0dHD0gbJFsf/0nL9NVL958fmgerl461zOvqpWMt99HJujOh3efQ6jXbrTvon+dUzZV8wuzLa7vfUC/yaeGRJEmSpIEVEcdT1SY6Bfgx8HcRcfFkm7RIy1YrZuYGYAPA8PBwjoyM7Le80WgwMW2QXDrFmwWrl45x1dYj2HHRyJT30cm6M6HV67V7zXbrDvrnOVVzJZ8w+/La7jd03bJjup5Pm61JkiRJGmS/CzyamT/IzOeAzwK/BTwZEfMByvPusv5OYFHT9gupmrlJktqw5pEkSZKkQfYYcHZEHE3VbO0c4F7gWWAlsL4831LW3wzcGBEfpOowewlwT6+D1syyybXUXdY8kiR1XURcGxG7I+KBprQ/j4hvRcQ3I+JzEXFc07K1EbE9Ih6OiHOb0s+MiK1l2UciolXTA0nSHJKZdwOfBr4GbKW6xtlAVWj0+oh4BHh9mScztwGbgAeBLwKXO9KaJE3OwiNJUi9cByybkHYHcEZm/hrwP4C1ABOGUF4GXB0Rh5dtrqHquHRJeUzcpyRpDsrM92fmr2bmGZn51jKS2lOZeU5mLinPP2paf11m/nJmviIzb+tn7JI0CCw8kiR1XWZ+BfjRhLQvZeb4kBF3UfU5AW2GUC79VRybmXdmZgLX8/ywy5IkSZK6xMIjSVIdvB0Yv/O7APhe07LxIZQXlOmJ6ZIkSZK6yA6zJUl9FRHvA8aAG8aTWqyWk6S32+8qqiZuDA0N0Wg09i0bHR3db74uVi8dY2he9VxHrWKry/tY188U6htbXeMCY5MkqW4sPJIk9U1ErATOB84pTdGg/RDKO3m+aVtzekuZuYGqw1SGh4dzZGRk37JGo0HzfF1cuuZWVi8d46qt9Tw8t4ptx0Uj/Qlmgrp+plDf2OoaFxibJEl1Y7M1SVJfRMQy4ErgTZn5z02LNgMrIuKoiDiFMoRyZu4CnomIs8soa5fw/LDLkiRJkrqknrc2JUmzSkR8ChgBToyIncD7qUZXOwq4oyoL4q7M/HeZuS0ixodQHmP/IZTfQTVy2zyqPpIcIUeSJEnqMguPJEldl5lvaZH8iUnWXwesa5F+L3DGDIYmSZIk6SBstiZJkiRJkqS2LDySJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqy9HWJEmSJEkzavGaW/sdgqQZZM0jSZIkSZIktWXNI0mSJEnSQVmbSJq7LDySJEmSJM0Z7QrBrlt2TI8jkQaHzdYkSZIkSZLUloVHkiRJkiRJasvCI0mSJEmSJLVl4ZEkSZIkSZLassNsSZIkSZol2nUGvWP9eT2ORNJsYs0jSZIkSZIktWXhkSRJkiRJktqy2ZokSZIkSR2weaDmGmseSZIkSZIkqa2DFh5FxAsj4p6I+EZEbIuID5T0EyLijoh4pDwf37TN2ojYHhEPR8S5TelnRsTWsuwjERHdyZYkqU4i4tqI2B0RDzSleRyRJEmSBsBUah7tAX4nM18JvApYFhFnA2uALZm5BNhS5omI04AVwOnAMuDqiDi87OsaYBWwpDyWzVxWJEk1dh0H/ud7HJEkSZIGwEH7PMrMBEbL7JHlkcByYKSkbwQawJUl/abM3AM8GhHbgbMiYgdwbGbeCRAR1wMXALfNTFYkSXWVmV+JiMUTkufscaRdPwmSJElSHU2pw+xyx/c+4FeAv87MuyNiKDN3AWTmrog4qay+ALirafOdJe25Mj0xXZI0N3X1OBIRq6hqKTE0NESj0di3bHR0dL/5Xlu9dKztsqF5ky/vp1ax9fN9bNbvz3QydY2trnGBsUmSVDdTKjzKzL3AqyLiOOBzEXHGJKu36n8iJ0k/cAeTnPBPVScH9k5O0rt9sjCoJySDGjcMbuzG3XuDHPuAOeTjCEBmbgA2AAwPD+fIyMi+ZY1Gg+b5Xrt0kppHq5eOcdXWeg6G2iq2HReN9CeYCfr9mU6mrrHVNS4wNkmS6qajs9PM/HFENKj6mHgyIuaXu8Xzgd1ltZ3AoqbNFgJPlPSFLdJbvU7bE/6p6uTAPtlJ/ETdPkke1BOSQY0bBjd24+69QY69prp2HFHvOFSxJEnS7DeV0dZeUmocERHzgN8FvgVsBlaW1VYCt5TpzcCKiDgqIk6h6tD0ntI04ZmIOLuMjnNJ0zaSpLnH44gkSZI0AKZS82g+sLH0e3QYsCkzPx8RdwKbIuIy4DHgQoDM3BYRm4AHgTHg8tLsDeAdVCPuzKPq4HTgOjmVJHUuIj5F1Tn2iRGxE3g/sB6PI5IkqeYc6EKa2mhr3wRe3SL9KeCcNtusA9a1SL8XmKy/JEnSLJSZb2mzyOOIJOmQlZYSH6c6RiTwduBh4GZgMbADeHNmPl3WXwtcBuwFrsjM23setCQNkIM2W5MkSZKkmvsw8MXM/FXglcBDwBpgS2YuAbaUeSLiNGAFcDpVX65Xl1YWkqQ2LDySJEmSNLAi4ljgdcAnADLz55n5Y2A5sLGsthG4oEwvB27KzD2Z+SiwHTirlzFL0qCp51jAkiRJkjQ1Lwd+APxNRLwSuA94FzBUBlugjOx5Ull/AXBX0/Y7S9oBImIVsApgaGiIRqOx3/LR0dED0vpt9dKxlumt4my37kRD86a+7iBr93l2kve6fR9aqeP3tltmW17bfRd7kU8LjyRJ0oxr1bnojvXn9SESSXPAEcBrgHdm5t0R8WFKE7U2okVatloxMzcAGwCGh4dzZGRkv+WNRoOJaf12aZvOnXdcNDLldSdavXSMq7bO/kvH65Yd0/LznOr7BK3f57qp4/e2W2ZbXtt9F9t9d2fS7P8HaGIv+ZIkSdKssxPYmZl3l/lPUxUePRkR80uto/nA7qb1FzVtvxB4omfRStIAss8jSZIkSQMrM78PfC8iXlGSzgEeBDYDK0vaSuCWMr0ZWBERR0XEKcAS4J4ehixJA2dO1TyaCe1qL1kVX5IkSeqbdwI3RMQLgO8Ab6O6Ub4pIi4DHgMuBMjMbRGxiaqAaQy4PDP39idsSRoMFh5JkiRJGmiZeT8w3GLROW3WXwes62ZMmpvs80+zlc3WJEmSJEmS1JaFR5IkSZIkSWrLwiNJkiRJkiS1ZeGRJEmSJEmS2rLwSJIkSZIkSW052pokSZIkzXKtRgGTpKmy5pEkSZIkSZLasuaRJEmSJA0YaxJJ6iVrHkmSJEmSJKktC48kSZIkSZLUloVHkqS+iog/johtEfFARHwqIl4YESdExB0R8Uh5Pr5p/bURsT0iHo6Ic/sZuyRJkjQX2OeRJKlvImIBcAVwWmb+LCI2ASuA04Atmbk+ItYAa4ArI+K0svx04GTgyxFxambu7VMW1IF2/XPsWH9ejyORJElSJ6x5JEnqtyOAeRFxBHA08ASwHNhYlm8ELijTy4GbMnNPZj4KbAfO6m24kiRJ0txizSNJUt9k5uMR8RfAY8DPgC9l5pciYigzd5V1dkXESWWTBcBdTbvYWdIOEBGrgFUAQ0NDNBqNfctGR0f3m++11UvH2i4bmjf58n7qVmwz8Vn0+zOdTF1jq2tcYGySZhdr3mo2sPBIktQ3pS+j5cApwI+Bv4uIiyfbpEVatloxMzcAGwCGh4dzZGRk37JGo0HzfK9dOsnwyquXjnHV1noenrsV246LRg55H/3+TCdT19jqGhcYm6T+2Pr4TyY9Rktzmc3WJEn99LvAo5n5g8x8Dvgs8FvAkxExH6A87y7r7wQWNW2/kKqZmyRJkqQusfBIktRPjwFnR8TRERHAOcBDwGZgZVlnJXBLmd4MrIiIoyLiFGAJcE+PY5YkSZLmlHrWi5ckzQmZeXdEfBr4GjAGfJ2qqdmLgE0RcRlVAdOFZf1tZUS2B8v6lzvSmiRJktRdFh5JkvoqM98PvH9C8h6qWkit1l8HrOt2XJIkSZIqNluTJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqyw6zJUmSJEmqscVrbm2ZvmP9eT2ORHOVhUeSJKmvPCGWJEmqN5utSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElq66B9HkXEIuB64KXAL4ANmfnhiDgBuBlYDOwA3pyZT5dt1gKXAXuBKzLz9pJ+JnAdMA/4AvCuzMyZzZIkSZIkSfVmn38aJFPpMHsMWJ2ZX4uIFwP3RcQdwKXAlsxcHxFrgDXAlRFxGrACOB04GfhyRJyamXuBa4BVwF1UhUfLgNtmOlOSJEmSJA2idoVKUj8dtPAoM3cBu8r0MxHxELAAWA6MlNU2Ag3gypJ+U2buAR6NiO3AWRGxAzg2M+8EiIjrgQuw8EiSNIt5AihJkqRBN5WaR/tExGLg1cDdwFApWCIzd0XESWW1BVQ1i8btLGnPlemJ6a1eZxVVDSWGhoZoNBqdhAnA6OjoAdutXjrW8X6majoxttIq7kEwqHHD4MZu3L03yLFLkiRJ0nRNufAoIl4EfAZ4d2b+NCLartoiLSdJPzAxcwOwAWB4eDhHRkamGuY+jUaDidtd2sW7vzsuGjnoOlPRKu5BMKhxw+DGbty9N8ixS5IkSdJ0TWm0tYg4kqrg6IbM/GxJfjIi5pfl84HdJX0nsKhp84XAEyV9YYt0SZIkSTokEXF4RHw9Ij5f5k+IiDsi4pHyfHzTumsjYntEPBwR5/YvakkaDActPIqqitEngIcy84NNizYDK8v0SuCWpvQVEXFURJwCLAHuKU3cnomIs8s+L2naRpIkSZIOxbuAh5rm11AN8LME2FLmmTDAzzLg6og4vMexStJAmUrNo9cCbwV+JyLuL483AuuB10fEI8DryzyZuQ3YBDwIfBG4vIy0BvAO4OPAduDb2Fm2JEmSpEMUEQuB86iuNcYtpxrYh/J8QVP6TZm5JzMfpbo2OatHoUrSQJrKaGv/ROv+igDOabPNOmBdi/R7gTM6CVCSJEmSDuJDwHuBFzeldTrAzwEONpBPPwfT6OZgQBMNzevt6/XLIOZzpgaXmq1mW17bfT97kc+ORluTJGmmRcRxVHeKz6AaSOHtwMPAzcBiYAfw5sx8uqy/FrgM2AtckZm39zxoSVJtRMT5wO7MvC8iRqaySYu0aQ3k08/BNLo5GNBEq5eOcdXW2X/pOIj5nM7ATXNpEJjZltd2v/vrlh3T9XxOqcNsSZK66MPAFzPzV4FXUvVXYT8VkqSpei3wpojYAdxE1d3GJ+l8gB9JUhsWHkmS+iYijgVeRzUwA5n588z8MfZTIUmaosxcm5kLM3Mx1Q2Gv8/Mi+lwgJ8ehy1JA2Ww6uRJkmablwM/AP4mIl4J3Ec1Wk5X+6noZfv3TvtOqHN/C72OrZPPqM59GtQ1trrGBcamGbMe2BQRlwGPARdCNcBPRIwP8DPG/gP8SJJasPBIktRPRwCvAd6ZmXdHxIcpTdTamJF+KnrZ/r3TPinq3N9Cr2PrpB+HOvdpUNfY6hoXGJumLzMbQKNMP0WHA/xIklqr59mpJGmu2AnszMy7y/ynqQqPnoyI+aXWkf1UzFGLWxS87Vh/Xh8ikSRJmtssPJohrU5wwZNcSZpMZn4/Ir4XEa/IzIep7hA/WB4rqZocTOyn4saI+CBwMvZTIUmSJHWdhUeSpH57J3BDRLwA+A7wNqoBHeynQpIkSaoBC48kSX2VmfcDwy0W2U+FJEmSVAOH9TsASZIkSZIk1ZeFR5IkSZIkSWrLwiNJkiRJkiS1ZeGRJEmSJEmS2rLDbEmSJEmSBtDiNbe2TN+x/rweR6LZzppHkiRJkiRJasvCI0mSJEmSJLVlszVJkiRJkmaRVs3ZbMqmQ2HNI0mSJEmSJLVlzSNJkjTwtj7+Ey71LqskSVJXWPNIkiRJkiRJbVl4JEmSJEmSpLYsPJIkSZIkSVJbFh5JkiRJkiSpLTvMliRJkiRJM2pxi4EswMEsBpU1jyRJkiRJktSWhUeSJEmSJElqy8IjSVLfRcThEfH1iPh8mT8hIu6IiEfK8/FN666NiO0R8XBEnNu/qCVJkqS5wcIjSVIdvAt4qGl+DbAlM5cAW8o8EXEasAI4HVgGXB0Rh/c4VkmSJGlOsfBIktRXEbEQOA/4eFPycmBjmd4IXNCUflNm7snMR4HtwFk9ClWSJEmakxxtTZLUbx8C3gu8uCltKDN3AWTmrog4qaQvAO5qWm9nSTtARKwCVgEMDQ3RaDT2LRsdHd1vvptWLx3raP2heZ1v0yt1iK3d59Yutl59zpPp5fetE3WNC4xNmqjdqFWS1CsWHkmS+iYizgd2Z+Z9ETEylU1apGWrFTNzA7ABYHh4OEdGnt99o9Ggeb6bLu3whH/10jGu2lrPw3MtYtv6bMvk1UtpGduOi0a6HNDB9fL71om6xgXGJkmDxgLO2a+eZ6eSpLnitcCbIuKNwAuBYyPik8CTETG/1DqaD+wu6+8EFjVtvxB4oqcRS5IkzVEWEs1d9nkkSeqbzFybmQszczFVR9h/n5kXA5uBlWW1lcAtZXozsCIijoqIU4AlwD09DluSJEmaU6x5JEmqo/XApoi4DHgMuBAgM7dFxCbgQWAMuDwz9/YvTEmSJGn2s/BIklQLmdkAGmX6KeCcNuutA9b1LDBJkqQ5yCZqanbQZmsRcW1E7I6IB5rSToiIOyLikfJ8fNOytRGxPSIejohzm9LPjIitZdlHIqJVp6eSJEmSJEmqkanUPLoO+Cvg+qa0NcCWzFwfEWvK/JURcRpVnxWnAycDX46IU0uTgmuohky+C/gCsAy4baYyUletSmt3rD+vD5FIkrrJu3OSJGkQeQ6jqThozaPM/ArwownJy4GNZXojcEFT+k2ZuSczHwW2A2eVkXKOzcw7MzOpCqIuQJIkSZIkSbU23T6PhjJzF0AZRvmkkr6AqmbRuJ0l7bkyPTG9pYhYRVVLiaGhIRqNRscB7v7RT/joDbfsl7Z6ace76YrJ8jM6Ojqt/PbboMYNgxu7cffeIMcuSdJsFRGLqG5OvxT4BbAhMz8cEScANwOLgR3AmzPz6bLNWuAyYC9wRWbe3ofQpZ4ar2G0eukYl1rbSB2a6Q6zW/VjlJOkt5SZG4ANAMPDwzkyMtJxIB+94Rau2lrP/sB3XDTSdlmj0WA6+e23QY0bBjd24+69QY5dkqRZbAxYnZlfi4gXA/dFxB3ApXTe1YYkqYWDNltr48nSFI3yvLuk7wQWNa23EHiipC9skS5JkiRJ05aZuzLza2X6GeAhqlYOHXW10dOgJWnATLdqzmZgJbC+PN/SlH5jRHyQqhR/CXBPZu6NiGci4mzgbuAS4KOHFLkkSdJBOHCFNLdExGLg1VTXHJ12tdFqf5N2p9GrJu2rl451/TUmMzSv/zH0gvnsjV52AzHbup1o97n1Ip8HLTyKiE8BI8CJEbETeD9VodGmiLgMeAy4ECAzt0XEJuBBquqjlzdV/3wH1cht86hGWZv1I61JkiRJ6o2IeBHwGeDdmfnTiFY9Z1Srtkhr2aXGwbrT6FWT9n73T7N66VhtuwSZSeazR7Y+2zK5Gzd3Zlu3E+3+C65bdkzX83nQb0xmvqXNonParL8OWNci/V7gjI6ikyRJkqSDiIgjqQqObsjMz5bkJyNifql1NJWuNiRJbUy3zyNJkiRJ6ruoqhh9AngoMz/YtGi8qw04sKuNFRFxVEScQulqo1fxStIgmv118iRJkpq06gcJ7AtJGmCvBd4KbI2I+0vanzC9rjYkSS1YeCRJkiRpYGXmP9G6HyPosKsNSVJrNluTJEmSJElSWxYeSZIkSZIkqS2brUmSJEmSpL5q1Seh/RHWhzWPJEmSJEmS1JaFR5IkSZIkSWrLZmuSJEmSJKl2WjVlA5uz9YM1jyRJfRMRiyLiHyLioYjYFhHvKuknRMQdEfFIeT6+aZu1EbE9Ih6OiHP7F70kSZI0N1h4JEnqpzFgdWb+L8DZwOURcRqwBtiSmUuALWWesmwFcDqwDLg6Ig7vS+SSJEnSHGGzNUlS32TmLmBXmX4mIh4CFgDLgZGy2kagAVxZ0m/KzD3AoxGxHTgLuLO3kUuSJKluJjZzW710bN8JpQ6NhUd9YLtNSTpQRCwGXg3cDQyVgiUyc1dEnFRWWwDc1bTZzpLWan+rgFUAQ0NDNBqNfctGR0f3m58Jq5eOzch+hubN3L5m2myPbaa/E+O68X2bCXWNC4xNkqS6sfBIktR3EfEi4DPAuzPzpxHRdtUWadlqxczcAGwAGB4ezpGRkX3LGo0GzfMz4dI2NwY6tXrpGFdtrefhebbHtuOikZkJZoJufN9mQl3jAmOTJKlu6nkGKEmaMyLiSKqCoxsy87Ml+cmImF9qHc0Hdpf0ncCips0XAk/0LlpJkiT1W7vWPOoeO8yWJPVNVFWMPgE8lJkfbFq0GVhZplcCtzSlr4iIoyLiFGAJcE+v4pUkSZLmImseSZL66bXAW4GtEXF/SfsTYD2wKSIuAx4DLgTIzG0RsQl4kGqktsszc2/Po9as1Ooupv0RSpIkWXgkSeqjzPwnWvdjBHBOm23WAeu6FtRBWE1akiRpcHRzwKq5dOPJwiNJkqQOOXKqJEmDzWN5Zyw8qpHFa25l9dKxA0bs8csrSVJ/WNNMkiTJwiNJkiRJkqS2vJlk4ZEkSdKMaXVyed2yY/oQiSRJ0syx8EiSJEmSJAlrGbVzWL8DkCRJkiRJUn1Z80iSJKmLtj7+kwMGwwAHxJAkSYPDwiNJkiRJkqQZ0K7Z26DfNLLwaAC0+vIN+hdPkiRJ0v7sa0VSXVl4JEmS1Aez9c6kJEmafSw8kiRJkiRJ6oNBaWlk4dGA8m6lJEmSJEmDoZNmqXVswnpYvwOQJEmSJElSfVnzSJIkqUZ6XX19UKrLS5Kk/rHwSJKkNupYZVjSYLLLAUnSILPwaJbp5ELHkxVJkiRJknQwFh5JkiTV3EzUWpm4j9VLx7i0zX47rXXnDSlJkmY3C4/mMKtPS5I02OpyLLffJEmSZreeFx5FxDLgw8DhwMczc32vY9DkPAGUVHceS6TJ2V+XdHD9PJb4G5U0aHpaeBQRhwN/Dbwe2Al8NSI2Z+aDvYxDnev0zqYFUJK6xWOJNBhmoulbN5vPea4yt/XyWGJBkaTZoNc1j84CtmfmdwAi4iZgOeAJ/4AaPxhO1m/CxHX77VBPLDvdh6QZ57FEmoWaj7lTOa842D4ONYZm3iyblTyWSFIHIjN792IR/xZYlpl/WObfCvxGZv7RhPVWAavK7CuAh6fxcicCPzyEcPvFuHtvUGM37t6bqdj/ZWa+ZAb2MyfN0LGkzt9DY5seY+tcXeMCY5sKjyWHYAavS+ryfeg28zm7zJV8wtzJ63TzOeVjSa9rHkWLtANKrzJzA7DhkF4o4t7MHD6UffSDcffeoMZu3L03yLHPMod8LKnzZ2ls02NsnatrXGBs6okZuS6ZK98H8zm7zJV8wtzJay/yeVg3d97CTmBR0/xC4IkexyBJGmweSyRJh8pjiSR1oNeFR18FlkTEKRHxAmAFsLnHMUiSBpvHEknSofJYIkkd6Gmztcwci4g/Am6nGhLz2szc1qWXO6Rmb31k3L03qLEbd+8NcuyzxgwdS+r8WRrb9Bhb5+oaFxibumwGr0vmyvfBfM4ucyWfMHfy2vV89rTDbEmSJEmSJA2WXjdbkyRJkiRJ0gCx8EiSJEmSJEltzbrCo4hYFhEPR8T2iFhTg3iujYjdEfFAU9oJEXFHRDxSno9vWra2xP5wRJzblH5mRGwtyz4SEa2GF53JuBdFxD9ExEMRsS0i3jVAsb8wIu6JiG+U2D8wKLGX1zw8Ir4eEZ8fsLh3lNe8PyLuHZTYI+K4iPh0RHyrfN9/cxDi1vTV7Tgxrt3/bl1M/G+qi1a/4X7HNC4i/rh8lg9ExKci4oV9jKWj85EaxPbn5TP9ZkR8LiKOq0tsTcveExEZESf2Izb1X12PJ806/e13ep4TEUdFxM0l/e6IWNy0zcryGo9ExMou57Pr1y51yGv04DqnDvlser2uXRfVJZ/R5WuoGc1nZs6aB1Vnd98GXg68APgGcFqfY3od8Brggaa0/wKsKdNrgD8r06eVmI8CTil5Obwsuwf4TSCA24Df63Lc84HXlOkXA/+jxDcIsQfwojJ9JHA3cPYgxF5e8z8ANwKfH5TvS3nNHcCJE9JqHzuwEfjDMv0C4LhBiNvHtD/v2h0nmmJr+b/b77ia4tvvv6kuj1a/4X7HVGJZADwKzCvzm4BL+xjPlM9HahLbG4AjyvSf1Sm2kr6IqqPl70489vmYG486H08mxNnVaxHg3wMfK9MrgJvL9AnAd8rz8WX6+C7ms+vXLnXIKz24zqlDPpvy27Xrorrkky5fQ81kPmdbzaOzgO2Z+Z3M/DlwE7C8nwFl5leAH01IXk51skt5vqAp/abM3JOZjwLbgbMiYj5wbGbemdUnfX3TNt2Ke1dmfq1MPwM8RHUiPAixZ2aOltkjyyMHIfaIWAicB3y8Kbn2cU+i1rFHxLFUJ1WfAMjMn2fmj+setw5J7Y4T4yb53+27Nv9NfTfJb7gujgDmRcQRwNHAE/0KpMPzkZ5qFVtmfikzx8rsXcDCngdG2/cN4C+B91KdX2huqu3xpFkPrkWa9/Vp4JxS4+Fc4I7M/FFmPg3cASyb6fyN69G1S9/z2qPrnL7nE3pyXVSLfLZRy3zOtsKjBcD3muZ3UpMT7wmGMnMXVH90wEklvV38C8r0xPSeKFXbXk1Vsj0QsZcqjvcDu6l+FIMS+4eoTkZ/0ZQ2CHFDdeD6UkTcFxGrSlrdY3858APgb0qV2I9HxDEDELembyCOExP+d+vgQxz431QH7X7DfZeZjwN/ATwG7AJ+kplf6m9UB2j3X1c3b6e6i1oLEfEm4PHM/Ea/Y1FfDcTxpI2ZPM/Zt00p8P0J8C8m2VfXdfHapRZ57cF1Ti3ySfevi+qSz25fQ81YPmdb4VGrPkYG6Y5Qu/j7lq+IeBHwGeDdmfnTyVZtkda32DNzb2a+iupO5VkRccYkq9ci9og4H9idmfdNdZMWaf38vrw2M18D/B5weUS8bpJ16xL7EVRVua/JzFcDz1JVDW2nLnFr+mr/WXXwv9ureDr9b+qlTn/DPVP6J1hOVa38ZOCYiLi4v1ENnoh4HzAG3NDvWAAi4mjgfcB/7Hcs6rvaH0+mYTrnObU6N+rytUst8tqD65y+57NH10V9z2fR7WuoGcvnbCs82knVBn3cQvpYRXwST5aqZZTn3SW9Xfw72b+6dk/yFRFHUv353pCZny3JAxH7uNJ8oUFVBa/usb8WeFNE7KCq+vw7EfHJAYgbgMx8ojzvBj5HVZ277rHvBHaWOzZQVeV8zQDEremr9XGizf9uv7X7b6qDdr/hOvhd4NHM/EFmPgd8FvitPsc0Ubv/uloonXeeD1xUquHXwS9TFQh+o/wmFgJfi4iX9jUq9UOtjycHMZPnOfu2KU10f4mqmVzP358eXLvUJq/Q1eucOuSzF9dFdchnL66hZiyfs63w6KvAkog4JSJeQNUh1OY+x9TKZmBlmV4J3NKUvqL0iH4KsAS4p1RVeyYizi7tEy9p2qYryut8AngoMz84YLG/JMqoLBExj+oE/lt1jz0z12bmwsxcTPXd/fvMvLjucQNExDER8eLxaaqOTh+oe+yZ+X3gexHxipJ0DvBg3ePWIantcWKS/92+muS/qe8m+Q3XwWPA2RFxdPlsz6Hqg6NO2v3X9V1ELAOuBN6Umf/c73jGZebWzDwpMxeX38ROqk56v9/n0NR7tT2eTMFMnuc07+vfUh0jkqpD+TdExPGlJuYbSlpX9Ojape957dF1Tt/z2aPror7ns0fXUDOXz+xi7+j9eABvpOpd/9vA+2oQz6eo+jp4juoE4zKqNoZbgEfK8wlN67+vxP4wTaM1AcPli/Rt4K+A6HLc/4qq2to3gfvL440DEvuvAV8vsT8A/MeSXvvYm153hOdHFah93FT9jnyjPLaN//YGJPZXAfeW78t/oxptoPZx+zikz7xWx4mmuFr+7/Y7rgkx7vtvqsuj1W+43zE1xfYBqpP6B4C/BY7qYywdnY/UILbtVH0xjP8WPlaX2CYs34Gjrc3ZR12PJxNi7Oq1CPBC4O/Kb/Ye4OVN27y9pG8H3tblfHb92qUOeaUH1zl1yOeEPI/QheuiOuSTHlxDzWQ+x3coSZIkSZIkHWC2NVuTJEmSJEnSDLLwSJIkSZIkSW1ZeCRJkiRJkqS2LDySJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElq6/8H8rDXmo2k27QAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1440x1080 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "housing.hist(bins = 50, figsize=(20,15))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "50002d08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "train_set , test_set = train_test_split(housing, test_size=0.2,random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "1763b83e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>14196</th>\n",
-       "      <td>-117.03</td>\n",
-       "      <td>32.71</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>3126.0</td>\n",
-       "      <td>627.0</td>\n",
-       "      <td>2300.0</td>\n",
-       "      <td>623.0</td>\n",
-       "      <td>3.2596</td>\n",
-       "      <td>103000.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8267</th>\n",
-       "      <td>-118.16</td>\n",
-       "      <td>33.77</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>3382.0</td>\n",
-       "      <td>787.0</td>\n",
-       "      <td>1314.0</td>\n",
-       "      <td>756.0</td>\n",
-       "      <td>3.8125</td>\n",
-       "      <td>382100.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17445</th>\n",
-       "      <td>-120.48</td>\n",
-       "      <td>34.66</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1897.0</td>\n",
-       "      <td>331.0</td>\n",
-       "      <td>915.0</td>\n",
-       "      <td>336.0</td>\n",
-       "      <td>4.1563</td>\n",
-       "      <td>172600.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14265</th>\n",
-       "      <td>-117.11</td>\n",
-       "      <td>32.69</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>1421.0</td>\n",
-       "      <td>367.0</td>\n",
-       "      <td>1418.0</td>\n",
-       "      <td>355.0</td>\n",
-       "      <td>1.9425</td>\n",
-       "      <td>93400.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2271</th>\n",
-       "      <td>-119.80</td>\n",
-       "      <td>36.78</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>2382.0</td>\n",
-       "      <td>431.0</td>\n",
-       "      <td>874.0</td>\n",
-       "      <td>380.0</td>\n",
-       "      <td>3.5542</td>\n",
-       "      <td>96500.0</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17848</th>\n",
-       "      <td>-121.86</td>\n",
-       "      <td>37.42</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>5032.0</td>\n",
-       "      <td>808.0</td>\n",
-       "      <td>2695.0</td>\n",
-       "      <td>801.0</td>\n",
-       "      <td>6.6227</td>\n",
-       "      <td>264800.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6252</th>\n",
-       "      <td>-117.97</td>\n",
-       "      <td>34.04</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>1686.0</td>\n",
-       "      <td>417.0</td>\n",
-       "      <td>1355.0</td>\n",
-       "      <td>388.0</td>\n",
-       "      <td>2.5192</td>\n",
-       "      <td>157300.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9389</th>\n",
-       "      <td>-122.53</td>\n",
-       "      <td>37.91</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>2524.0</td>\n",
-       "      <td>398.0</td>\n",
-       "      <td>999.0</td>\n",
-       "      <td>417.0</td>\n",
-       "      <td>7.9892</td>\n",
-       "      <td>500001.0</td>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6113</th>\n",
-       "      <td>-117.90</td>\n",
-       "      <td>34.13</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1126.0</td>\n",
-       "      <td>316.0</td>\n",
-       "      <td>819.0</td>\n",
-       "      <td>311.0</td>\n",
-       "      <td>1.5000</td>\n",
-       "      <td>139800.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6061</th>\n",
-       "      <td>-117.79</td>\n",
-       "      <td>34.02</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>18690.0</td>\n",
-       "      <td>2862.0</td>\n",
-       "      <td>9427.0</td>\n",
-       "      <td>2777.0</td>\n",
-       "      <td>6.4266</td>\n",
-       "      <td>315600.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
-       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
-       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
-       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
-       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
-       "17848    -121.86     37.42                20.0       5032.0           808.0   \n",
-       "6252     -117.97     34.04                28.0       1686.0           417.0   \n",
-       "9389     -122.53     37.91                37.0       2524.0           398.0   \n",
-       "6113     -117.90     34.13                 5.0       1126.0           316.0   \n",
-       "6061     -117.79     34.02                 5.0      18690.0          2862.0   \n",
-       "\n",
-       "       population  households  median_income  median_house_value  \\\n",
-       "14196      2300.0       623.0         3.2596            103000.0   \n",
-       "8267       1314.0       756.0         3.8125            382100.0   \n",
-       "17445       915.0       336.0         4.1563            172600.0   \n",
-       "14265      1418.0       355.0         1.9425             93400.0   \n",
-       "2271        874.0       380.0         3.5542             96500.0   \n",
-       "17848      2695.0       801.0         6.6227            264800.0   \n",
-       "6252       1355.0       388.0         2.5192            157300.0   \n",
-       "9389        999.0       417.0         7.9892            500001.0   \n",
-       "6113        819.0       311.0         1.5000            139800.0   \n",
-       "6061       9427.0      2777.0         6.4266            315600.0   \n",
-       "\n",
-       "      ocean_proximity  \n",
-       "14196      NEAR OCEAN  \n",
-       "8267       NEAR OCEAN  \n",
-       "17445      NEAR OCEAN  \n",
-       "14265      NEAR OCEAN  \n",
-       "2271           INLAND  \n",
-       "17848       <1H OCEAN  \n",
-       "6252        <1H OCEAN  \n",
-       "9389         NEAR BAY  \n",
-       "6113        <1H OCEAN  \n",
-       "6061        <1H OCEAN  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_set.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "273c7d7b",
-   "metadata": {},
-   "source": [
-    "this introduces a bias. so we use stratified shuffle split on the basis on income categories. so we first create income category"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "fd85640a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "housing['income_cat'] = pd.cut(housing['median_income'],bins=[0.,1.5,3.,4.5,6.,np.inf],labels=[1,2,3,4,5])\n",
-    "housing['income_cat'].hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "f1f3587a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import StratifiedShuffleSplit\n",
-    "split = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n",
-    "for train_index, test_index in split.split(housing,housing['income_cat']):\n",
-    "    strat_train_set = housing.loc[train_index]\n",
-    "    strat_test_set = housing.loc[test_index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "751b9019",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "      <th>income_cat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12655</th>\n",
-       "      <td>-121.46</td>\n",
-       "      <td>38.52</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>3873.0</td>\n",
-       "      <td>797.0</td>\n",
-       "      <td>2237.0</td>\n",
-       "      <td>706.0</td>\n",
-       "      <td>2.1736</td>\n",
-       "      <td>72100.0</td>\n",
-       "      <td>INLAND</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15502</th>\n",
-       "      <td>-117.23</td>\n",
-       "      <td>33.09</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>5320.0</td>\n",
-       "      <td>855.0</td>\n",
-       "      <td>2015.0</td>\n",
-       "      <td>768.0</td>\n",
-       "      <td>6.3373</td>\n",
-       "      <td>279600.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2908</th>\n",
-       "      <td>-119.04</td>\n",
-       "      <td>35.37</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1618.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>667.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>2.8750</td>\n",
-       "      <td>82700.0</td>\n",
-       "      <td>INLAND</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14053</th>\n",
-       "      <td>-117.13</td>\n",
-       "      <td>32.75</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>1877.0</td>\n",
-       "      <td>519.0</td>\n",
-       "      <td>898.0</td>\n",
-       "      <td>483.0</td>\n",
-       "      <td>2.2264</td>\n",
-       "      <td>112500.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20496</th>\n",
-       "      <td>-118.70</td>\n",
-       "      <td>34.28</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>3536.0</td>\n",
-       "      <td>646.0</td>\n",
-       "      <td>1837.0</td>\n",
-       "      <td>580.0</td>\n",
-       "      <td>4.4964</td>\n",
-       "      <td>238300.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
-       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
-       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
-       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
-       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
-       "\n",
-       "       population  households  median_income  median_house_value  \\\n",
-       "12655      2237.0       706.0         2.1736             72100.0   \n",
-       "15502      2015.0       768.0         6.3373            279600.0   \n",
-       "2908        667.0       300.0         2.8750             82700.0   \n",
-       "14053       898.0       483.0         2.2264            112500.0   \n",
-       "20496      1837.0       580.0         4.4964            238300.0   \n",
-       "\n",
-       "      ocean_proximity income_cat  \n",
-       "12655          INLAND          2  \n",
-       "15502      NEAR OCEAN          5  \n",
-       "2908           INLAND          2  \n",
-       "14053      NEAR OCEAN          2  \n",
-       "20496       <1H OCEAN          3  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "strat_train_set.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "540ec57f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3    0.350594\n",
-       "2    0.318859\n",
-       "4    0.176296\n",
-       "5    0.114462\n",
-       "1    0.039789\n",
-       "Name: income_cat, dtype: float64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "strat_train_set['income_cat'].value_counts()/len(strat_train_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "c2532201",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3    0.350581\n",
-       "2    0.318847\n",
-       "4    0.176308\n",
-       "5    0.114438\n",
-       "1    0.039826\n",
-       "Name: income_cat, dtype: float64"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing['income_cat'].value_counts()/len(housing)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "857e4eb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>housing_median_age</th>\n",
-       "      <th>total_rooms</th>\n",
-       "      <th>total_bedrooms</th>\n",
-       "      <th>population</th>\n",
-       "      <th>households</th>\n",
-       "      <th>median_income</th>\n",
-       "      <th>median_house_value</th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12655</th>\n",
-       "      <td>-121.46</td>\n",
-       "      <td>38.52</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>3873.0</td>\n",
-       "      <td>797.0</td>\n",
-       "      <td>2237.0</td>\n",
-       "      <td>706.0</td>\n",
-       "      <td>2.1736</td>\n",
-       "      <td>72100.0</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15502</th>\n",
-       "      <td>-117.23</td>\n",
-       "      <td>33.09</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>5320.0</td>\n",
-       "      <td>855.0</td>\n",
-       "      <td>2015.0</td>\n",
-       "      <td>768.0</td>\n",
-       "      <td>6.3373</td>\n",
-       "      <td>279600.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2908</th>\n",
-       "      <td>-119.04</td>\n",
-       "      <td>35.37</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>1618.0</td>\n",
-       "      <td>310.0</td>\n",
-       "      <td>667.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>2.8750</td>\n",
-       "      <td>82700.0</td>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14053</th>\n",
-       "      <td>-117.13</td>\n",
-       "      <td>32.75</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>1877.0</td>\n",
-       "      <td>519.0</td>\n",
-       "      <td>898.0</td>\n",
-       "      <td>483.0</td>\n",
-       "      <td>2.2264</td>\n",
-       "      <td>112500.0</td>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20496</th>\n",
-       "      <td>-118.70</td>\n",
-       "      <td>34.28</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>3536.0</td>\n",
-       "      <td>646.0</td>\n",
-       "      <td>1837.0</td>\n",
-       "      <td>580.0</td>\n",
-       "      <td>4.4964</td>\n",
-       "      <td>238300.0</td>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
-       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
-       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
-       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
-       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
-       "\n",
-       "       population  households  median_income  median_house_value  \\\n",
-       "12655      2237.0       706.0         2.1736             72100.0   \n",
-       "15502      2015.0       768.0         6.3373            279600.0   \n",
-       "2908        667.0       300.0         2.8750             82700.0   \n",
-       "14053       898.0       483.0         2.2264            112500.0   \n",
-       "20496      1837.0       580.0         4.4964            238300.0   \n",
-       "\n",
-       "      ocean_proximity  \n",
-       "12655          INLAND  \n",
-       "15502      NEAR OCEAN  \n",
-       "2908           INLAND  \n",
-       "14053      NEAR OCEAN  \n",
-       "20496       <1H OCEAN  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "for set_ in (strat_train_set, strat_test_set):\n",
-    "    set_.drop(\"income_cat\", axis=1, inplace=True)\n",
-    "strat_train_set.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "1d1e94e4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_copy = strat_train_set.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "6e41ff89",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing_copy.plot(kind = \"scatter\",x='longitude',y='latitude', alpha = 0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "d950de6f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fc11be6e20>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x504 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing_copy.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.7,\n",
-    "             s=housing_copy[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
-    "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
-    "             sharex=False)\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "aa02b0e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "latitude             -0.142673\n",
-       "longitude            -0.047466\n",
-       "population           -0.026882\n",
-       "total_bedrooms        0.047781\n",
-       "households            0.064590\n",
-       "housing_median_age    0.114146\n",
-       "total_rooms           0.135140\n",
-       "median_income         0.687151\n",
-       "median_house_value    1.000000\n",
-       "Name: median_house_value, dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix = housing_copy.corr()\n",
-    "corr_matrix[\"median_house_value\"].sort_values(ascending=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "2da9ddc2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,\n",
-       "        <AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,\n",
-       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,\n",
-       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],\n",
-       "       [<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,\n",
-       "        <AxesSubplot:xlabel='median_income', ylabel='median_income'>,\n",
-       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,\n",
-       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],\n",
-       "       [<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,\n",
-       "        <AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,\n",
-       "        <AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,\n",
-       "        <AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],\n",
-       "       [<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,\n",
-       "        <AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,\n",
-       "        <AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,\n",
-       "        <AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pandas.plotting import scatter_matrix\n",
-    "Attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
-    "scatter_matrix(housing[Attributes],figsize=(12, 8))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "ccc18dfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='median_income', ylabel='median_house_value'>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "housing.plot(kind = \"scatter\", x = \"median_income\", y = \"median_house_value\", alpha = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "7a1a77d4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# adding new attributes\n",
-    "housing[\"rooms_per_households\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
-    "housing[\"bedrooms_per_rooms\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
-    "housing[\"population_per_households\"] = housing[\"population\"]/housing[\"households\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "bedrooms_per_rooms          -0.255880\n",
-       "latitude                    -0.144160\n",
-       "longitude                   -0.045967\n",
-       "population                  -0.024650\n",
-       "population_per_households   -0.023737\n",
-       "total_bedrooms               0.049686\n",
-       "households                   0.065843\n",
-       "housing_median_age           0.105623\n",
-       "total_rooms                  0.134153\n",
-       "rooms_per_households         0.151948\n",
-       "median_income                0.688075\n",
-       "median_house_value           1.000000\n",
-       "Name: median_house_value, dtype: float64"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "corr_matrix = housing.corr()\n",
-    "corr_matrix[\"median_house_value\"].sort_values(ascending=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "a6ae7a33",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
-    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "789d9159",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.impute import SimpleImputer\n",
-    "imputer = SimpleImputer(strategy=\"median\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "713cf6a8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-118.51   ,   34.26   ,   29.     , 2119.     ,  433.     ,\n",
-       "       1164.     ,  408.     ,    3.54155])"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_num = housing.drop(\"ocean_proximity\", axis = 1)\n",
-    "imputer.fit(housing_num)\n",
-    "imputer.statistics_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "3f9aa2d2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = imputer.transform(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "79511e00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "housing_tr = pd.DataFrame(X,columns=housing_num.columns,index = housing_num.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "1b0dede6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ocean_proximity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>12655</th>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15502</th>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2908</th>\n",
-       "      <td>INLAND</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14053</th>\n",
-       "      <td>NEAR OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20496</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1481</th>\n",
-       "      <td>NEAR BAY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18125</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5830</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17989</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4861</th>\n",
-       "      <td>&lt;1H OCEAN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      ocean_proximity\n",
-       "12655          INLAND\n",
-       "15502      NEAR OCEAN\n",
-       "2908           INLAND\n",
-       "14053      NEAR OCEAN\n",
-       "20496       <1H OCEAN\n",
-       "1481         NEAR BAY\n",
-       "18125       <1H OCEAN\n",
-       "5830        <1H OCEAN\n",
-       "17989       <1H OCEAN\n",
-       "4861        <1H OCEAN"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_ohe = housing[[\"ocean_proximity\"]]\n",
-    "housing_ohe.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "41d74b38",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0., 1., 0., 0., 0.],\n",
-       "       [0., 0., 0., 0., 1.],\n",
-       "       [0., 1., 0., 0., 0.],\n",
-       "       ...,\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [1., 0., 0., 0., 0.],\n",
-       "       [0., 1., 0., 0., 0.]])"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import OneHotEncoder\n",
-    "ohe = OneHotEncoder()\n",
-    "housing_ohedone = ohe.fit_transform(housing_ohe)\n",
-    "housing_ohedone.toarray()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "23ba8875",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
-       "       dtype=object)]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ohe.categories_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "44eef0df",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#using pipelines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "9f611bdb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.base import BaseEstimator, TransformerMixin\n",
-    "\n",
-    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
-    "\n",
-    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
-    "    def __init__(self, add_bedrooms_per_room=True):\n",
-    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
-    "    def fit(self, X, y=None):\n",
-    "        return self \n",
-    "    def transform(self, X):\n",
-    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
-    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
-    "        if self.add_bedrooms_per_room:\n",
-    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
-    "            return np.c_[X, rooms_per_household, population_per_household,\n",
-    "                         bedrooms_per_room]\n",
-    "        else:\n",
-    "            return np.c_[X, rooms_per_household, population_per_household]\n",
-    "\n",
-    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
-    "housing_extra_attribs = attr_adder.transform(housing.values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "8dab5cb0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "num_pipeline = Pipeline([\n",
-    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
-    "    ('Attribs_adder',CombinedAttributesAdder()),\n",
-    "    ('Std_scaler', StandardScaler()),\n",
-    "])\n",
-    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "6c57d77c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.compose import ColumnTransformer \n",
-    "num_attribs = list(housing_num)\n",
-    "cat_attribs = [\"ocean_proximity\"]\n",
-    "\n",
-    "pipeline = ColumnTransformer([\n",
-    "    (\"num\", num_pipeline, num_attribs),\n",
-    "    (\"cat\", OneHotEncoder(),cat_attribs)\n",
-    "])\n",
-    "housing_prepared = pipeline.fit_transform(housing)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc02d665",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "6c685f40f4e372158c8452f24cd9ee0bbc1030a9f2f196d3595227ed632ab81f"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.9.12 ('bcs_project')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "894e2229",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tarfile\n",
+    "import urllib.request\n",
+    "\n",
+    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n",
+    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
+    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
+    "\n",
+    "def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):\n",
+    "    if not os.path.isdir(housing_path):\n",
+    "        os.makedirs(housing_path)\n",
+    "    tgz_path = os.path.join(housing_path, \"housing.tgz\")\n",
+    "    urllib.request.urlretrieve(housing_url, tgz_path)\n",
+    "    housing_tgz = tarfile.open(tgz_path)\n",
+    "    housing_tgz.extractall(path=housing_path)\n",
+    "    housing_tgz.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3e47d403",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ntpath' from 'C:\\\\Users\\\\beserkarray\\\\anaconda3\\\\envs\\\\spooder01\\\\lib\\\\ntpath.py'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5e1817e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fetch_housing_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b983bd98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def load_housing_data(housing_path=HOUSING_PATH):\n",
+    "    csv_path = os.path.join(housing_path, \"housing.csv\")\n",
+    "    return pd.read_csv(csv_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "82ead2e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing = load_housing_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4c1a60e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-122.23</td>\n",
+       "      <td>37.88</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>322.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>8.3252</td>\n",
+       "      <td>452600.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-122.22</td>\n",
+       "      <td>37.86</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7099.0</td>\n",
+       "      <td>1106.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>1138.0</td>\n",
+       "      <td>8.3014</td>\n",
+       "      <td>358500.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-122.24</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1467.0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>496.0</td>\n",
+       "      <td>177.0</td>\n",
+       "      <td>7.2574</td>\n",
+       "      <td>352100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>235.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>5.6431</td>\n",
+       "      <td>341300.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>1627.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>565.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>3.8462</td>\n",
+       "      <td>342200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.85</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>919.0</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>413.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>4.0368</td>\n",
+       "      <td>269700.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>2535.0</td>\n",
+       "      <td>489.0</td>\n",
+       "      <td>1094.0</td>\n",
+       "      <td>514.0</td>\n",
+       "      <td>3.6591</td>\n",
+       "      <td>299200.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>3104.0</td>\n",
+       "      <td>687.0</td>\n",
+       "      <td>1157.0</td>\n",
+       "      <td>647.0</td>\n",
+       "      <td>3.1200</td>\n",
+       "      <td>241400.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-122.26</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>2555.0</td>\n",
+       "      <td>665.0</td>\n",
+       "      <td>1206.0</td>\n",
+       "      <td>595.0</td>\n",
+       "      <td>2.0804</td>\n",
+       "      <td>226700.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-122.25</td>\n",
+       "      <td>37.84</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>3549.0</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>1551.0</td>\n",
+       "      <td>714.0</td>\n",
+       "      <td>3.6912</td>\n",
+       "      <td>261100.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -122.23     37.88                41.0        880.0           129.0   \n",
+       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+       "5    -122.25     37.85                52.0        919.0           213.0   \n",
+       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
+       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
+       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
+       "9    -122.25     37.84                52.0       3549.0           707.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value ocean_proximity  \n",
+       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
+       "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
+       "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
+       "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
+       "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
+       "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f669df7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 20640 entries, 0 to 20639\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   longitude           20640 non-null  float64\n",
+      " 1   latitude            20640 non-null  float64\n",
+      " 2   housing_median_age  20640 non-null  float64\n",
+      " 3   total_rooms         20640 non-null  float64\n",
+      " 4   total_bedrooms      20433 non-null  float64\n",
+      " 5   population          20640 non-null  float64\n",
+      " 6   households          20640 non-null  float64\n",
+      " 7   median_income       20640 non-null  float64\n",
+      " 8   median_house_value  20640 non-null  float64\n",
+      " 9   ocean_proximity     20640 non-null  object \n",
+      "dtypes: float64(9), object(1)\n",
+      "memory usage: 1.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "housing.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "abd50e81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1H OCEAN     9136\n",
+       "INLAND        6551\n",
+       "NEAR OCEAN    2658\n",
+       "NEAR BAY      2290\n",
+       "ISLAND           5\n",
+       "Name: ocean_proximity, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing[\"ocean_proximity\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0803d1d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20433.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "      <td>20640.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>-119.569704</td>\n",
+       "      <td>35.631861</td>\n",
+       "      <td>28.639486</td>\n",
+       "      <td>2635.763081</td>\n",
+       "      <td>537.870553</td>\n",
+       "      <td>1425.476744</td>\n",
+       "      <td>499.539680</td>\n",
+       "      <td>3.870671</td>\n",
+       "      <td>206855.816909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.003532</td>\n",
+       "      <td>2.135952</td>\n",
+       "      <td>12.585558</td>\n",
+       "      <td>2181.615252</td>\n",
+       "      <td>421.385070</td>\n",
+       "      <td>1132.462122</td>\n",
+       "      <td>382.329753</td>\n",
+       "      <td>1.899822</td>\n",
+       "      <td>115395.615874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-124.350000</td>\n",
+       "      <td>32.540000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.499900</td>\n",
+       "      <td>14999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-121.800000</td>\n",
+       "      <td>33.930000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>1447.750000</td>\n",
+       "      <td>296.000000</td>\n",
+       "      <td>787.000000</td>\n",
+       "      <td>280.000000</td>\n",
+       "      <td>2.563400</td>\n",
+       "      <td>119600.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>-118.490000</td>\n",
+       "      <td>34.260000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>2127.000000</td>\n",
+       "      <td>435.000000</td>\n",
+       "      <td>1166.000000</td>\n",
+       "      <td>409.000000</td>\n",
+       "      <td>3.534800</td>\n",
+       "      <td>179700.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>-118.010000</td>\n",
+       "      <td>37.710000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>3148.000000</td>\n",
+       "      <td>647.000000</td>\n",
+       "      <td>1725.000000</td>\n",
+       "      <td>605.000000</td>\n",
+       "      <td>4.743250</td>\n",
+       "      <td>264725.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>-114.310000</td>\n",
+       "      <td>41.950000</td>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>39320.000000</td>\n",
+       "      <td>6445.000000</td>\n",
+       "      <td>35682.000000</td>\n",
+       "      <td>6082.000000</td>\n",
+       "      <td>15.000100</td>\n",
+       "      <td>500001.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+       "std        2.003532      2.135952           12.585558   2181.615252   \n",
+       "min     -124.350000     32.540000            1.000000      2.000000   \n",
+       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+       "\n",
+       "       total_bedrooms    population    households  median_income  \\\n",
+       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+       "std        421.385070   1132.462122    382.329753       1.899822   \n",
+       "min          1.000000      3.000000      1.000000       0.499900   \n",
+       "25%        296.000000    787.000000    280.000000       2.563400   \n",
+       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+       "\n",
+       "       median_house_value  \n",
+       "count        20640.000000  \n",
+       "mean        206855.816909  \n",
+       "std         115395.615874  \n",
+       "min          14999.000000  \n",
+       "25%         119600.000000  \n",
+       "50%         179700.000000  \n",
+       "75%         264725.000000  \n",
+       "max         500001.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e7a2ccfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kSZIkSdJAJo8kSZIkSZI0kMkjSZIkSZIkDWTySJIkSZIkSQOZPJIkSZI09pK8Jcn9ST7ZU/Y/knwqySeSvCfJk3vWXZhke5I7kzy7p/yUJLe2dW9IkhWuiiRNHJNHkiRJkibBpcDGOWXXACdW1XcDfw9cCJDkeOBM4IS2zxuTHND2uQTYBBzXHnOPKUmaw+SRJEmSpLFXVX8LfHFO2Qeqak97ej2wri2fAVxeVQ9W1V3AduDUJEcCh1TVdVVVwFuBF6xIBSRpgh046gAkSZIkaRm8DHhnWz6aLpk0a2cre6gtzy3vK8kmulZKTE1NMTMzM/DFd+/ePe/6Sbea62fdJtNqrhv0r9/mk/b03XYl3geTR5IkSZImWpJfAfYAb5st6rNZzVPeV1VtBbYCbNiwoaanpwfGMDMzw3zrJ91qrp91m0yruW7Qv37nbrmq77Y7zpruW76cTB5JkiRJmlhJzgGeB5zeuqJB16LomJ7N1gH3tvJ1fcolSfNwzCNJkiRJEynJRuBVwPOr6p97Vl0JnJnkoCTH0g2MfWNV7QIeSHJam2XtbOCKFQ9ckiaMLY8kSZIkjb0k7wCmgcOT7AReTTe72kHANV0uiOur6j9V1W1JtgG303VnO7+qHm6HejndzG0HA1e3hyRpHiaPJEmSJI29qnpxn+I3z7P9RcBFfcpvAk5cxtAkadWz25okSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkaeiSHJPkw0nuSHJbkgta+WuS3JPklvZ4Ts8+FybZnuTOJM/uKT8lya1t3RuSZBR1kiRJktaKA0cdgCRpTdgDbK6qjyV5InBzkmvaut+tqt/p3TjJ8cCZwAnAUcAHkzytqh4GLgE2AdcD7wM2AlevUD0kSZKkNceWR5KkoauqXVX1sbb8AHAHcPQ8u5wBXF5VD1bVXcB24NQkRwKHVNV1VVXAW4EXDDd6SZIkaW3bZ8ujJMfQfTj/N8DXga1V9XtJDgPeCawHdgAvqqovtX0uBM4DHgZeUVXvb+WnAJcCB9N9W3xB+/AvSVojkqwHngHcADwT+NkkZwM30bVO+hJdYun6nt12trKH2vLc8n6vs4muhRJTU1PMzMwsaz1GbfNJe/Yqmzq4fzmw6uq/e/fuVVen+Vjf1W2t1VeSNHkW0m1tUFeDc4Frq+riJFuALcCr7GogSRokyROAdwGvrKqvJrkE+HWg2s/XAi8D+o1jVPOU711YtRXYCrBhw4aanp5ecvzj5NwtV+1VtvmkPbz21v6X9h1nTQ85opU1MzPDavudzsf6rm5rrb6SpMmzz25r83Q1OAO4rG12Gd/sNmBXA0nSXpI8mi5x9LaqejdAVd1XVQ9X1deBPwFObZvvBI7p2X0dcG8rX9enXJIkSdKQLGrA7DldDaaqahd0CaYkR7TNJqqrwWpvJmz9xtdCupZMcv0WwvqtHW1GtDcDd1TV63rKj5y9lgA/AXyyLV8JvD3J6+hasR4H3FhVDyd5IMlpdNeis4HfX6l6SJIkSWvRgpNHfboaDNy0T9nYdjVY7c2Erd/46tflBB7ZtWSS67cQ1m9NeSbwUuDWJLe0sl8GXpzkZLrrwQ7gZwCq6rYk24Db6bpPn9+6PwO8nG+On3c1dn+WJEmShmpByaN+XQ2A+2a/MW5d0u5v5XY1kCQ9QlV9hP5fIrxvnn0uAi7qU34TcOLyRSdJkiRpPvsc82hQVwO6LgXntOVzgCt6ys9MclCSY/lmV4NdwANJTmvHPLtnH0mSJEmSJI2hhbQ8GtTV4GJgW5LzgLuBF4JdDSRJkiRJklaTfSaP5ulqAHD6gH3saiBJkiRp2SR5C/A84P6qOrGVHQa8E1hPN3bei6rqS23dhcB5wMPAK6rq/a38FL75hfb7gAvabNCSpAH22W1NkiRJksbApcDGOWVbgGur6jjg2vacJMcDZwIntH3emOSAts8ldDM7H9cec48pSZrD5JEkSZKksVdVfwt8cU7xGcBlbfky4AU95ZdX1YNVdRewHTi1TfRzSFVd11obvbVnH0nSACaPJEmSJE2qqTYxD+3nEa38aOCzPdvtbGVHt+W55ZKkeSxkwGxJkiRJmiT9xmytecr7HyTZRNfFjampKWZmZga+4O7du+ddP+lWc/2s22RazXWD/vXbfNKevtuuxPtg8kiSJEnSpLovyZFVtat1Sbu/le8EjunZbh1wbytf16e8r6raCmwF2LBhQ01PTw8MZGZmhvnWT7rVXD/rNplWc92gf/3O3XJV3213nDXdt3w52W1NkiRJ0qS6EjinLZ8DXNFTfmaSg5IcSzcw9o2ta9sDSU5LEuDsnn0kSQPY8kiSJEnS2EvyDmAaODzJTuDVwMXAtiTnAXcDLwSoqtuSbANuB/YA51fVw+1QL6ebue1g4Or2kCTNw+SRJEmSpLFXVS8esOr0AdtfBFzUp/wm4MRlDE2SVj27rUmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjpw1AFIq936LVeNOgRJkiRJkvabLY8kSZIkSZI0kMkjSZIkSZIkDWS3NWnM9HZz23zSHs7dchU7Ln7uCCOSJEmSJK1ltjySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJA1dkmOSfDjJHUluS3JBKz8syTVJPt1+Htqzz4VJtie5M8mze8pPSXJrW/eGJBlFnSRJkqS1wuSRJGkl7AE2V9V3AacB5yc5HtgCXFtVxwHXtue0dWcCJwAbgTcmOaAd6xJgE3Bce2xcyYpIkiRJa82Bow5AkrT6VdUuYFdbfiDJHcDRwBnAdNvsMmAGeFUrv7yqHgTuSrIdODXJDuCQqroOIMlbgRcAV69UXSRJkrRv67dc1bd8x8XPXeFItBxMHkmSVlSS9cAzgBuAqZZYoqp2JTmibXY0cH3Pbjtb2UNteW55v9fZRNdCiampKWZmZpavEmNg80l79iqbOrh/ObDq6r979+5VV6f5WN/Vba3VV5I0eUweSZJWTJInAO8CXllVX51nuKJ+K2qe8r0Lq7YCWwE2bNhQ09PTi453nJ3b59u8zSft4bW39r+07zhresgRrayZmRlW2+90PtZ3dVtr9ZUkTR7HPJIkrYgkj6ZLHL2tqt7diu9LcmRbfyRwfyvfCRzTs/s64N5Wvq5PuSRJkqQhMXkkSRq6NiPam4E7qup1PauuBM5py+cAV/SUn5nkoCTH0g2MfWPr4vZAktPaMc/u2UeSJEnSENhtTZK0Ep4JvBS4NcktreyXgYuBbUnOA+4GXghQVbcl2QbcTjdT2/lV9XDb7+XApcDBdANlO1i2JEmSNEQmjyRJQ1dVH6H/eEUApw/Y5yLgoj7lNwEnLl90kiRJWopBM6tp9bDbmiRJkqSJluTnk9yW5JNJ3pHksUkOS3JNkk+3n4f2bH9hku1J7kzy7FHGLkmTwOSRJEmSpImV5GjgFcCGqjoROAA4E9gCXFtVxwHXtuckOb6tPwHYCLwxyQGjiF2SJsU+k0dJ3pLk/iSf7Cl7TZJ7ktzSHs/pWdc3i5/klCS3tnVvyDzzM0uSJEnSIhwIHJzkQOBxdDNxngFc1tZfBrygLZ8BXF5VD1bVXcB24NSVDVeSJstCxjy6FPgD4K1zyn+3qn6nt2BOFv8o4INJntYGOb0E2ARcD7yPLsvvIKeSJEmS9ltV3ZPkd+gmXvgX4ANV9YEkU22WTqpqV5Ij2i5H092TzNrZyvaSZBPdPQxTU1PMzMwMjGP37t3zrp90q7l+1m3pNp+0Z8HbLlc8q/n3Bv3rN+h9Xon3YZ/Jo6r62yTrF3i8b2TxgbuSbAdOTbIDOKSqrgNI8la6zL/JI0mSJEn7rY1ldAZwLPBl4C+SvGS+XfqUVb8Nq2orsBVgw4YNNT09PfCgMzMzzLd+0q3m+lm3pTt3EQNm7zhrelleczX/3qB//Qa9z8v1ns5nKbOt/WySs4GbgM1V9SUGZ/Efastzy/taTIZ/qdZitnI1mYT6LSYLP9fUwd3+417H/TUJv7+lWO31kyRpTPwIcFdVfQ4gybuBHwDuS3Jka3V0JHB/234ncEzP/uvourlJkgbY3+TRJcCv02Xofx14LfAyBmfxF5zdh8Vl+JdqLWYrV5NJqN9isvBzbT5pD6+99cAVySSPwiT8/pZitddPkqQxcTdwWpLH0XVbO53uC+6vAecAF7efV7TtrwTenuR1dENtHAfcuNJBS9Ik2a/kUVXdN7uc5E+A97ang7L4O9vy3HJJkiRJ2m9VdUOSvwQ+BuwB/o7ui+gnANuSnEeXYHph2/62JNuA29v257cxWiVJA+xX8mi2+Wd7+hPA7ExsfbP4VfVwkgeSnAbcAJwN/P7SQpckSZIkqKpXA6+eU/wgXSukfttfBFw07Li0NOv7tODfcfFzRxCJpH0mj5K8A5gGDk+yk+6kPJ3kZLquZzuAn4F9ZvFfTjdz28F0A2U7WLYkSZIkSdKYW8hsay/uU/zmebbvm8WvqpuAExcVnSRJ6vvNqyRJkrRSHjXqACRJkiRJkjS+TB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBDhx1AJIkSZIkLcT6LVf1Ld9x8XNXOBJpbTF5tMrMPZluPmkP06MJRZIkSZK0igxK3mn1M3k0ofynlSRJkiRJK8ExjyRJkiRJkjSQLY8kSZJGwHE7JEnSpLDlkSRJkiRJkgYyeSRJGrokb0lyf5JP9pS9Jsk9SW5pj+f0rLswyfYkdyZ5dk/5KUlubevekCQrXRdJkiRprTF5JElaCZcCG/uU/25Vndwe7wNIcjxwJnBC2+eNSQ5o218CbAKOa49+x5QkSZK0jEweSZKGrqr+FvjiAjc/A7i8qh6sqruA7cCpSY4EDqmq66qqgLcCLxhKwJIkSZK+wQGzJUmj9LNJzgZuAjZX1ZeAo4Hre7bZ2coeastzy/tKsomulRJTU1PMzMwsb+QraPNJexa03dTBg7ed5Pr3s3v37omv02J+V6uhvothfSVJGi8mjyRJo3IJ8OtAtZ+vBV4G9BvHqOYp76uqtgJbATZs2FDT09NLDHd0zh0wK9dcm0/aw2tv7X9p33HW9DJGNHozMzNM8u8UBv9e+/2uVkN9F8P6SpI0Xuy2Jkkaiaq6r6oerqqvA38CnNpW7QSO6dl0HXBvK1/Xp1ySJEnSEJk8kiSNRBvDaNZPALMzsV0JnJnkoCTH0g2MfWNV7QIeSHJam2XtbOCKFQ1akiRJWoPstiZJGrok7wCmgcOT7AReDUwnOZmu69kO4GcAquq2JNuA24E9wPlV9XA71MvpZm47GLi6PSRJa1ySJwNvAk6ku668DLgTeCewnu4686I2th5JLgTOAx4GXlFV71/xoLXmrR/Uffni565wJP0Nik9rk8kjSdLQVdWL+xS/eZ7tLwIu6lN+E92NgSRJvX4P+Ouq+g9JHgM8Dvhl4NqqujjJFmAL8KokxwNnAicARwEfTPK0ni8qJElz2G1NkiRJ0sRKcgjwg7QvJarqX6vqy8AZwGVts8uAF7TlM4DLq+rBqroL2M43x92TJPVhyyNJkiRJk+zbgc8Bf5rk6cDNwAXAVBsvj6raleSItv3RwPU9++9sZXtJsgnYBDA1NcXMzMzAIHbv3j3v+kk3ivptPmnPgrddSmyj+t0Nqt9yxrKUui3m/V+M5arfWvyfW4m/mUFMHkmSJEmaZAcC3wP8XFXdkOT36LqoDZI+ZdVvw6raCmwF2LBhQ01PTw886MzMDPOtn3SjqN+5ixhzZ8dZ0/v9OqP63Q2q31LqMtdS6raY938xlqt+a/F/biX+Zgax25okSZKkSbYT2FlVN7Tnf0mXTLpvdmbP9vP+nu2P6dl/HXDvCsUqSRPJ5JEkSZKkiVVV/wR8Nsl3tqLT6WbsvBI4p5WdA1zRlq8EzkxyUJJjgeOAG1cwZEmaOHZbk9aIcZ8KVJIkaQl+Dnhbm2ntM8BP0n1Rvi3JecDdwAsBquq2JNvoEkx7gPOdaU2S5mfySJIkSdJEq6pbgA19Vp0+YPuLgIuGGZNW1jh/UTooNmmSmDySJEmSJGmNMrmlhXDMI0mSJEmSJA1k8kiSJEmSJEkD7bPbWpK3AM8D7q+qE1vZYcA7gfXADuBFVfWltu5C4DzgYeAVVfX+Vn4KcClwMPA+4IKqquWtjrQ6jXMfbkmSJEnS6raQMY8uBf4AeGtP2Rbg2qq6OMmW9vxVSY4HzgROAI4CPpjkaW32gkuATcD1dMmjjcDVy1WR1cw+qJIkSZKk1cAvxifTPrutVdXfAl+cU3wGcFlbvgx4QU/55VX1YFXdBWwHTk1yJHBIVV3XWhu9tWcfSZIkSZIkjan9nW1tqqp2AVTVriRHtPKj6VoWzdrZyh5qy3PL+0qyia6VElNTU8zMzOxnmPu2e/fuoR5/OWw+ac9+7zt1MGNfv6VYC7+/+fZfTN0HHWeU798k/P6WYrXXT5IkSdLasL/Jo0HSp6zmKe+rqrYCWwE2bNhQ09PTyxJcPzMzMwzz+Mvh3CV0W9t80h5eNOb1W4q18Pt77a2D/013nDW95DgWc4zlNgm/v6VY7fWTJEmStDbsb/LoviRHtlZHRwL3t/KdwDE9260D7m3l6/qUawXYp1SSJEnSuHKMV2n87XPMowGuBM5py+cAV/SUn5nkoCTHAscBN7Yubg8kOS1JgLN79pEkSZIkSdKY2mfLoyTvAKaBw5PsBF4NXAxsS3IecDfwQoCqui3JNuB2YA9wfptpDeDldDO3HUw3y5ozrUmSJEmShqZfqyZ7YEiLt8/kUVW9eMCq0wdsfxFwUZ/ym4ATFxWdJEmSJEmSRmq5B8yWJEmSJEn7ydZSGkcmj6Q1zouTJEmSJGk+Jo8kSZIkSVoG4zJz3Gwcm0/aw7lt2S+ItRQmjyRJkiRJGmODklImhLRSTB5JkiRJktaMuYmY2dY5JmKkwUweSZIkSZI0gRbTTW5cutRpMpk8kiRJGiP9PtxfuvHxI4hEkiSpY/JIkjR0Sd4CPA+4v6pObGWHAe8E1gM7gBdV1ZfauguB84CHgVdU1ftb+SnApcDBwPuAC6qqVrIukiRpdVrMuEK24tFa86hRByBJWhMuBTbOKdsCXFtVxwHXtuckOR44Ezih7fPGJAe0fS4BNgHHtcfcY0qSJElaZiaPJElDV1V/C3xxTvEZwGVt+TLgBT3ll1fVg1V1F7AdODXJkcAhVXVda2301p59JEmSJA2J3dYkSaMyVVW7AKpqV5IjWvnRwPU92+1sZQ+15bnlfSXZRNdKiampKWZmZpYv8hW2+aQ9C9pu6uDB205y/fvZvXv3WNbp1nu+slfZSUc/qe+2C/29wvjWd1isryRJ48XkkSRp3KRPWc1T3ldVbQW2AmzYsKGmp6eXJbhROHeB4ypsPmkPr721/6V9x1nTyxjR6M3MzDCOv9N+v6tB7/1Cf6/QDZg9jvUdlnH9/Q7LWqvvsLQuzjcB91TV8/ZnbD1JUn92W5Mkjcp9rSsa7ef9rXwncEzPduuAe1v5uj7lkiQBXADc0fN8f8bWkyT1YcsjSdKoXAmcA1zcfl7RU/72JK8DjqIbGPvGqno4yQNJTgNuAM4Gfn/lw5YkjZsk64DnAhcB/7kVnwFMt+XLgBngVfSMrQfclWQ7cCpw3QqGvCY5Q5k0uWx5JEkauiTvoPtQ/p1JdiY5jy5p9Kwknwae1Z5TVbcB24Dbgb8Gzq+qh9uhXg68iW4Q7X8Arl7RikiSxtXrgV8Cvt5T9oix9YDesfU+27PdvGPoSZJseSRJWgFV9eIBq04fsP1FdN8ezy2/CThxGUOTJE24JM8D7q+qm5NML2SXPmV9x9BbzOQLq33g8+Wo32ImClhJ8004AfD7b7tir7LNJw0zouWzr7qNk8X+fa3F/7lRToxi8kiSJEnSJHsm8PwkzwEeCxyS5M9pY+u1GT0XMrbeXhYz+cJqH/h8Oeq3mIkCVtJ8E05Muomq261f26tox8XPHbj5WvyfG/Q/tBITo9htTZIkSdLEqqoLq2pdVa2nGwj7Q1X1Er45th7sPbbemUkOSnIsbWy9FQ5bkibKhKQgJUmSJGlRLga2tXH27gZeCN3Yeklmx9bbwyPH1pMk9WHySJIkSdKqUFUzdLOqUVVfYJFj60mS+jN5JEmSJEmSxs76QWP8zDMW0jCOIcc8kiRJkiRJ0jxseSRJkiRJktaUQS2S+rGVkskjSZIWxabPkiRJWmvstiZJkiRJkqSBbHmkvdh8T5IkSZIkzbLlkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsgBsyVJkiRJ0sRYv+UqNp+0h3PnTPbkhE7DY/JIkiRJkrSsFjODs6TxZ/JIkiRJkiRNvGElLQcddy21dDJ5JE2wfiextXQCkyRJkiQN35IGzE6yI8mtSW5JclMrOyzJNUk+3X4e2rP9hUm2J7kzybOXGrwkSZIkSZKGazlmW/uhqjq5qja051uAa6vqOODa9pwkxwNnAicAG4E3JjlgGV5fkiRJkiRJQzKMbmtnANNt+TJgBnhVK7+8qh4E7kqyHTgVuG4IMUiSJI0FB42VJEmTbqnJowI+kKSAP66qrcBUVe0CqKpdSY5o2x4NXN+z785Wtpckm4BNAFNTU8zMzCwxzMF279491OMvh80n7dnvfacOHrz/oHov5vVG/d6t5d/fIJP0e52E399SrPb6abI4RpokaaH6XTM2n7TnGy0EJK09S00ePbOq7m0JomuSfGqebdOnrPpt2JJQWwE2bNhQ09PTSwxzsJmZGYZ5/OVw7hK+sdx80h5ee2v/X/OOs6aX/HqDjrFS1vLvb5BJ+r1Owu9vKVZ7/SRJ0triFxHS2rWk5FFV3dt+3p/kPXTd0O5LcmRrdXQkcH/bfCdwTM/u64B7l/L6kiRJWhinGZYkSftrv5NHSR4PPKqqHmjLPwr8N+BK4Bzg4vbzirbLlcDbk7wOOAo4DrhxCbFLkiRJ0qpnix9pPK2l/82ltDyaAt6TZPY4b6+qv07yUWBbkvOAu4EXAlTVbUm2AbcDe4Dzq+rhJUUvSZIkSRoZJwWQ1ob9Th5V1WeAp/cp/wJw+oB9LgIu2t/XlCStPkl2AA8ADwN7qmpDksOAdwLrgR3Ai6rqS237C4Hz2vavqKr3jyBsSZIkac141KgDkCQJ+KGqOrmqNrTnW4Brq+o44Nr2nCTHA2cCJwAbgTcmOWAUAUuSJElrhckjSdI4OgO4rC1fBrygp/zyqnqwqu4CttNN1iBJWqOSHJPkw0nuSHJbkgta+WFJrkny6fbz0J59LkyyPcmdSZ49uuglaTIsabY1SZKWQQEfSFLAH1fVVmCqqnYBtNk7j2jbHg1c37Pvzla2lySbgE0AU1NTzMzMLEuwm0/a07d8uY6/mNeca+rghW8Lw4152Hbv3j2W8S/m/V+Mxdb31nu+slfZ5pP6bzuO7+O4/n6HZa3Vdwj2AJur6mNJngjcnOQa4Fy6VqwXJ9lC14r1VXNasR4FfDDJ0xyPVZIGM3kkrTIOWqgJ9MyqurcliK5J8ql5tk2fsuq3YUtCbQXYsGFDTU9PLzlQgHMHTXd+1vIcfzGvOdfmk/bw2lsXfmkfZszDNjMzw3L9TpfTQn9Xi3Xpxscvqr6LiWMc/w7G9fc7LGutvsutfdkw+4XDA0nuoPti4Qxgum12GTADvIqeVqzAXUlmW7Fet7KRryw/I0paCpNHWhXW0hSJ0mpTVfe2n/cneQ/dB/j7khzZWh0dCdzfNt8JHNOz+zrg3hUNWJI0tpKsB54B3MAKt2IdZguyfq0aF/taS20ZudjWrZPEuk2mca3bcp0H+p1TRtECfpbJI2keg76hMTElLY8kjwce1b4pfjzwo8B/A64EzgEubj+vaLtcCbw9yevouhocB9y44oFrVRrnLyJuvecrfVsTjUt80jhI8gTgXcArq+qrSb/Gqt2mfcqW3Ip1mC3I+v7/L7LV4FJbRi62desksW6TaWzrduvX+hYv9prd75wyihbws8bwndYkMbkiaYmmgPe0D/gHAm+vqr9O8lFgW5LzgLuBFwJU1W1JtgG3041xcf5qGqPCLgWStH+SPJoucfS2qnp3K564VqxeBySNK5NHkqSRqarPAE/vU/4F4PQB+1wEXDTk0CRJEyLdNxBvBu6oqtf1rLIVqyQtE5NHa5jfbDyS74ckSdJEeibwUuDWJLe0sl+mSxrZilXSWJj0XjsmjyRJGsAP4JI0/qrqI/QfxwhsxSpJy8Lk0RjxJmV5TXpmV5IkSZKkcWDySJIkSZImjF88S1pJjxp1AJIkSZIkSRpftjySlpHfAEmSVpLXHUmStBJseSRJkiRJkqSBbHmkkXNga0mSJEnSWjTofnjzSXs4d4xaGNvySJIkSZIkSQOZPJIkSZIkSdJAdlvTUPRremc3NEmSJEmSJo/JI60YZ4SRtBaNczLdMef2zfdIkiTJ5JEkSVpj/DJDkiRpcUweSZK0DExISJIkabVywGxJkiRJkiQNZMsjaT/YwkCStBY5BpQkSWuTySNNlLkfWjeftIdzTeRI0kQw8SBJkjSZTB5pzbHVkCQN32o/11o/SZK0lpg8GgE/kGnc2TpA0mrhNVeSJGnpTB5pbPmBX5Kk4fN6Kw2X/2OSVgOTR5Ik6REWc6MzLi0SvTmTJEkaHpNHkiStMBMdWsuG2TW637HHJcEpSdIkM3kkSZKkJTFpI0nS6ramkkf9PthsPmkP0ysfirSqDOumwYG7JUlL5bVEkqSlW7XJI7sESKPlh3VpbVjp6+3s620+aQ/neq2XJElaEas2ebTSvFHWWmBSVtIweA1dnbxmSJK0eqx48ijJRuD3gAOAN1XVxSsdw0ryg5MkLb+1di2R1qphfo5ynCZ5LZGkhVvR5FGSA4A/BJ4F7AQ+muTKqrp9JeOYazHfeJoMkpam93+ot9vJYj6wO8bS2jau1xItP6+5a8ug64M0DF5LJGlxVrrl0anA9qr6DECSy4EzgLE8SfuhVVo5/r9pESbqWiJpcoziWuQXFCPjtUSSFiFVtXIvlvwHYGNV/VR7/lLg+6rqZ+dstwnY1J5+J3DnEMM6HPj8EI8/atZvslm/yTaoft9WVU9Z6WBWizG9loyL1f4/1Wst1RWs72q3P/X1WrIEQ7qWrPa/29VcP+s2mVZz3WBl6rfga8lKtzxKn7K9sldVtRXYOvxwIMlNVbVhJV5rFKzfZLN+k22112+Exu5aMi7W0t/cWqorWN/Vbq3Vd0ws+7Vktf8eV3P9rNtkWs11g/Gr36NW+PV2Asf0PF8H3LvCMUiSJpvXEknSUnktkaRFWOnk0UeB45Icm+QxwJnAlSscgyRpsnktkSQtldcSSVqEFe22VlV7kvws8H66KTHfUlW3rWQMfaz2Lg3Wb7JZv8m22us3EmN6LRkXa+lvbi3VFazvarfW6jtyQ7qWrPbf42qun3WbTKu5bjBm9VvRAbMlSZIkSZI0WVa625okSZIkSZImiMkjSZIkSZIkDbRmkkdJXpjktiRfT7Khp/xZSW5Ocmv7+cN99r0yySdXNuLFWWz9kjwuyVVJPtX2u3h00e/b/vz+kpzSyrcneUOSflOyjoV56vctST6cZHeSP5izz4tb/T6R5K+THL7ykS/cftbxMUm2Jvn79rf671c+8oXZn/r1bDP25xiNlySPTXJjko+3v7tfm7P+F5LUuJ8XFmq++ib5uSR3tvLfHmWcy2VQfZOcnOT6JLckuSnJqaOOdbkkOSDJ3yV5b3t+WJJrkny6/Tx01DEupz71/R/tOveJJO9J8uQRh6hFSrKxnYu2J9ky6niWIslbktzf+9lktfxPJjmmfS67o51fL2jlE1+/ea4dE1+3Wav5WpFkR7u3uyXJTa1srOq3ZpJHwCeB/x/wt3PKPw/8eFWdBJwD/FnvyiT/P2D3ikS4NPtTv9+pqn8LPAN4ZpIfW5FI98/+1O8SYBNwXHtsXIE499eg+v0f4L8Cv9BbmORA4PeAH6qq7wY+AfzsCsS5FIuqY/MrwP1V9TTgeOBvhhrh0uxP/SbpHKPx8iDww1X1dOBkYGOS06D7YAw8C7h7dOEtu771TfJDwBnAd1fVCcDvjDDG5TTo9/vbwK9V1cnAr7bnq8UFwB09z7cA11bVccC17flqMre+1wAntmv63wMXjiQq7ZckBwB/CPwY3eeVFyc5frRRLcml7P25ebX8T+4BNlfVdwGnAee339VqqN+ga8dqqNus1X6t+KGqOrmqZr+IHqv6rZnkUVXdUVV39in/u6q6tz29DXhskoMAkjwB+M/Ab6xcpPtnsfWrqn+uqg+3bf4V+BiwbuUiXpzF1i/JkcAhVXVddaPCvxV4wcpFvDjz1O9rVfURugREr7TH45MEOAS4d+7+42Q/6gjwMuA323Zfr6rPDznM/bY/9Zukc4zGS3Vmk46Pbo/ZGTB+F/ilnucTb576vhy4uKoebNvdP6IQl9U89S268z3Akxjz8/5CJVkHPBd4U0/xGcBlbfkyxvgavlj96ltVH6iqPe3p9YzxZzL1dSqwvao+0z5XX073NzyRqupvgS/OKV4V/5NVtauqPtaWH6BLRBzNKqjfPNeOia8brL1rRTNW9VszyaMF+vfA381+CAV+HXgt8M+jC2lZza0fAK1p9I/TZTMnWW/9jgZ29qzb2cpWhap6iO6m6Va6m4fjgTePNKhl1tNk/9eTfCzJXySZGmVMQ7DazjFaQa3p9i3A/cA1VXVDkucD91TVx0cb3fLrV1/gacD/leSGJH+T5HtHGuQyGlDfVwL/I8ln6VpZrZbWKa+nS3h+vadsqqp2QXezBxwxgriG5fXsXd9eLwOuXrFotByOBj7b83xVfe5sVt3/ZJL1dD0wbmCV1G/AtWNV1I3Vf60o4APphmLZ1MrGqn6rKnmU5INJPtnnsc/Mf5ITgN8CfqY9Pxl4alW9Z7hRL9xy1q+n/EDgHcAbquozw4l8YZa5fv3GNxrpt/BLqV+fYz2aLnn0DOAoum5rI7+JWM46AgfSffP6v6vqe4DrGHGXlGX+HZ7MmJ1jNFmq6uHWfWkdcGqS76br6vmrIw1sSPrU90S688ShdF0PfhHY1lpjTrwB9X058PNVdQzw86yCLw2SPI+ue/LNo45lJeyrvkl+ha5bzdtWNDAt1dh97tT8WuvvdwGvrKqvjjqe5TLg2jHx1si14pntnufH6LpT/uCoA5rrwFEHsJyq6kf2Z7/WBO49wNlV9Q+t+PuBU5LsoHufjkgyU1XTyxHr/ljm+s3aCny6ql6/xPCWbJnrt5NHNvlex4ib9+9v/QY4uR3zHwCSbGMM+vgucx2/QNciZza58hfAect4/EVb5vqN3TlGk6mqvpxkhq5p87HAx1v+ZB3wsSSnVtU/jTDEZdVT34105/p3t+7JNyb5OnA48LkRhris5tT3HLrxHqA7J75p0H4T5JnA85M8B3gscEiSPwfuS3JkVe1qXdFXRZdEBtS3ql6S5BzgecDp7W9ak2MncEzP85F/7hyCVfM/2b6EfRfwtqp6dyteNfWDva4dq6Fuq/5aMTsUS1Xdn+Q9dN1hx6p+q6rl0f5oXWOuAi6sqv89W15Vl1TVUVW1Hvh3wN9P4k3doPq1db9BN2bCK1c+suUxz+9vF/BAugFVA5wNXDGaKIfiHuD4JE9pz5/FIwePm3jtg/NfAdOt6HTg9pEFtMxWyzlGo5HkKbNdO5McDPwIXbfdI6pqffu72gl8z2pIHA2o76eA/wnMziL6NOAxdBMpTLR56nsv8H+3zX4Y+PRIAlxGVXVhVa1rf7NnAh+qqpcAV9Ily2g/V8U1fFB9k2wEXgU8v6rsyjx5Pgocl+TYJI+h+91eOeKYltuq+J9s9wVvBu6oqtf1rJr4+s1z7Zj4uq32a0WSxyd54uwy8KN0k/GMV/2qak08gJ+g+yD9IHAf8P5W/l+ArwG39DyOmLPveuCTo67DctaP7huRoks4zJb/1KjrsZy/P2AD3T/dPwB/AGTU9Vhs/dq6HXSDFu5u2xzfyv9T+/19gi7J8i2jrscQ6vhtdLOXfYJuTK5vHXU9lrN+PevH/hzjY7wewHcDf9f+Nz4J/GqfbXYAh4861mHWly5Z9Oet7GN0s8yMPN4h1vffATcDH6cbo+OUUce6zPWeBt7blr+lnfc/3X4eNur4hlzf7XRj5sx+lvmjUcfnY9G/z+fQzZT3D8CvjDqeJdblHcAu4KH2ueW81fI/2c6j1c6vs/9vz1kN9Zvn2jHxdZtTz1V3rQC+vV3bP043CdSvjGP90oKSJEmSJEmS9rLmu61JkiRJkiRpMJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkjRiSWaS/NR+7vutSXYnOWC545LA5JFWuSSXJvmNUcchSRofC7k2JJlOsnMZX7OSPHW5jidJWtuS7EjyI7PPq+ruqnpCVT08yri0epk80sjNPfEt17aSpMnltUGSJGl8mDyS5khy4KhjkCStHXYxkKTx076YuDDJ7Um+lORPkzy2rfvpJNuTfDHJlUmO6tmvkrwiyWeSfD7J/0jyqLbuNUn+vGfb9W37ve4/knxHkg8l+UI7ztuSPLmt+zPgW4G/al3VfmnusZIc1WL7Yov1p3uO/Zok25K8NckDSW5LsmFIb6VWCZNHGqkBJ77ntxPYl1u/3+8atG0r/4sk/5TkK0n+NskJi4xhOsnOJK9K8k/AnyY5KMnrk9zbHq9PclDPPvu6YPy/ST7dTsa/3k7+1yX5ajtRP6Zte3iS97a6fjHJ/5q9uEjSWjUO14aeWH65fWjfkeSsnvKDkvxOkruT3Jfkj5Ic3LP+F5PsateQl8055qVJLknyviRfA34oyXe1en251fP5Pds/qX3A/1ySf0zyX3puRM5N8r+T/G7b9zNJfqCVfzbJ/UnO6TnWc9LdCD2Q5J4kv7A/74skrRFnAc8GvgN4GvBfkvww8JvAi4AjgX8ELp+z308AG4DvAc4AXsbipb3OUcB3AccArwGoqpcCdwM/3rqq/Xaf/d8B7Gz7/wfgvyc5vWf981vcTwauBP5gP2LUGuJNqkZq7okP+J90J7pXAk8B3kd3Q/CYeU6SVwPHAUcAHwPeth+h/BvgMODbgE3ArwCnAScDTwdOBf4LwAIvGBuBU9oxfgnYSnfxOQY4EXhx224z3Un9KcAU8MtA7Uf8krRqjNm14XDgaOAcYGuS72zrfovuRuJk4Kltm18FSLIR+AXgWS2Gfl3q/iNwEfBE4Abgr4APtHh/Dnhbz2v9PvAk4NuB/xs4G/jJnmN9H/AJ4FuAt9Ndk763xfUS4A+SPKFt+2bgZ6rqiXTXow8t+l2RpLXjD6rqs1X1Rbpz9ovpPtO/pao+VlUPAhcC359kfc9+v1VVX6yqu4HX883P/gtWVdur6pqqerCqPge8ju4asE9JjgH+HfCqqvo/VXUL8CbgpT2bfaSq3tfGSPozunseaSCTRxo3/w9wVTtRPgT8DnAw8AODdqiqt1TVA+3k/Rrg6UmetMjX/Trw6nZy/he6i8J/q6r728n61/jmyXahF4yvVtVtwCeBD1TVZ6rqK3Q3NM9o2z1El4D6tqp6qKr+V1WZPJKkRxrVtQHgv7Zrw98AVwEvShLgp4GfbzcHDwD/HTiz7fMi4E+r6pNV9bX2+nNdUVX/u6q+TpeAegJwcVX9a1V9CHgv8OJ0Xdr+H+DCVp8dwGt55A3AXVX1p+0G4J10X1T8txb3B4B/pUskQXfdOT7JIVX1par62H68J5K0Vny2Z/kf6VrxHNWWAaiq3cAX6L5EmG+/RUlyRJLLWyvRrwJ/TveFxkIcBcxen3rj6I3xn3qW/xl4bBy+Q/MweaRxM/dk/HW6k+/R/TZOckCSi5P8Qzup7mirFnpinfW5qvo/g+LgkSf9hVww7utZ/pc+z2e/Af4fwHbgA62rwZZFxi1Ja8Gorg1fasmfWbPXgqcAjwNubl3Fvgz8dSufjXfujcNcveuPAj7b6tW7z9Et5sew9zVpvmsOVTXouvPvgecA/5jkb5J8f5/YJEmdY3qWvxW4tz2+bbYwyePpWn7es4/9AL5Gd/2Y9W/mee3fpOuR8N1VdQhdS9L0rJ/vC+d7gcOSPHFOHPcM2F7aJ5NHGge9J765J+PQnXzv6bMtdM3+z6DrEvAkYP3srkuIYa84eORJfyEXjIW9aPct8uaq+nbgx4H/PKcvsiStVeNwbTi0neNnzV4LPk+XkDmhqp7cHk9qXewAdrH3jcNcc+t3zJwx72Y/5H+errXQ3GvSft0AVNVHq+oMuu5x/xPYtj/HkaQ14vwk65IcRje8xDvpugf/ZJKT042J+t+BG1rL0Fm/mOTQ1n3sgrYfwC3ADyb51tYa9sJ5XvuJwG7gy0mOBn5xzvr76Loz76WqPgv8f8BvJnlsku8GzmP/unBLgMkjjYfeE9824LlJTk/yaLoxgR6kO/nN3Ra6k+qDdC1/Hkd38l4O76AbEO8pSQ6nG8didmaEhVwwFiTJ85I8td0IfRV4uD0kaa0bl2vDryV5TJL/C3ge8BethdCfAL+b5AiAJEcneXZPvOcmOT7J44BX7+M1bqD7NvqXkjw6yTTdFwqXt65o24CLkjwxybcB/5lvXpMWrNXjrCRPat3/Zq87kqT+3k43Ht1n2uM3qupa4L8C76L7suA7+Ga35VlXADfTJYuuohtvjqq6hi6R9Im2/r3zvPav0Q24/ZV2jHfPWf+bdPcrXx4w+cGL6b48uRd4D90QHdfsq8LSICaPNA6+ceKj+7D8ErrBQT/fnv94Vf3r3G3bSfKtdM337wFuB65fpph+A7iJ7sR+K91gq78BsMALxkIdB3yQ7luF64A3VtXMUgKXpFViHK4N/wR8ie6D99uA/1RVn2rrXkXX7fj61jXug8B3AlTV1XQDpH6obTPvoNStHs8HfqzV743A2T2v9XN0yaXPAB+hu5l5y37W6aXAjhbzf6J7XyVJ/X20qo5vLUzPqap/BqiqP6qq76iqw6rqeVW1c85+76uqb6+qb2m9DL6RqK+q89vxnlpVf1JVqao9bd10Vb2pLd9WVadUNxnEyVX12qpa13OcK6rqW9uxfqeqdsw51s4W22Et1j/q2fc1VfWSnueP2FfqJ47NK0mSJEnSNyXZAfxUVX1wkfsVcFxVbR9KYNKI2PJIkiRJkiRJA5k80pqQ5JeT7O7zuHrUsUmSRsNrgyRpkKpav9hWR22/2OpIq5Hd1iRJK6I1/36AboDePVW1oc1e8k66AR13AC+qqi+17S+kmxnkYeAVVfX+Vn4KcClwMPA+4ILyYiZJkiQNzdgnjw4//PBav379ovf72te+xuMf//h9b7hCximecYoFjGdfjGd+4xTPQmO5+eabP19VT1mBkMZKSx5tqKrP95T9NvDFqro4yRbg0Kp6VZLj6WY9PBU4im4w4qdV1cNJbqSb9vZ6uuTRG9oAxQOtlmvJfCYpVjDeYZukeCcpVhifeNfqtWSUVuO1ZJxjg/GOb5xjg/GOb5xjg/GOb7ljW9S1pKrG+nHKKafU/vjwhz+8X/sNyzjFM06xVBnPvhjP/MYpnoXGAtxUY3B+XekHXcuiw+eU3Qkc2ZaPBO5syxcCF/Zs937g+9s2n+opfzHwx/t67dVyLZnPJMVaZbzDNknxTlKsVeMT71q9lozysRqvJeMcW9V4xzfOsVWNd3zjHFvVeMe33LEt5lpy4DIlrCRJ2pcCPtBmIfnjqtoKTFXVLoCq2pXkiLbt0TxyevWdreyhtjy3fC9JNgGbAKamppiZmVl0wLt3796v/UZhkmIF4x22SYp3kmKFyYtXkqTlYPJIkrRSnllV97YE0TVJPjXPtulTVvOU713YJae2AmzYsKGmp6cXGS7MzMywP/uNwiTFCsY7bJMU7yTFCpMXryRJy8HZ1iRJK6Kq7m0/7wfeQzee0X1JjgRoP+9vm+8EjunZfR1wbytf16dckiRJ0pCYPJIkDV2Sxyd54uwy8KPAJ4ErgXPaZucAV7TlK4EzkxyU5FjgOODG1sXtgSSnJQlwds8+kiRJkobAbmuSpJUwBbyny/dwIPD2qvrrJB8FtiU5D7gbeCFAVd2WZBtwO7AHOL+qHm7HejlwKXAwcHV7SJIkSRoSk0eSpKGrqs8AT+9T/gXg9AH7XARc1Kf8JuDE5Y5RkiRJUn92W5MkSZIkSdJAJo8kSZIkSZI0kMkjSZIkSZIkDeSYR8D6LVf1Ld9x8XNXOBJJ0qTyWiJJWiivGZImjS2PJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSNBGS/HyS25J8Msk7kjw2yWFJrkny6fbz0J7tL0yyPcmdSZ7dU35KklvbujckyWhqJEmTYUHJoyQ72sn1liQ3tTJP0pIkSZJWRJKjgVcAG6rqROAA4ExgC3BtVR0HXNuek+T4tv4EYCPwxiQHtMNdAmwCjmuPjStYFUmaOItpefRDVXVyVW1ozz1JS5IkSVpJBwIHJzkQeBxwL3AGcFlbfxnwgrZ8BnB5VT1YVXcB24FTkxwJHFJV11VVAW/t2UeS1MeBS9j3DGC6LV8GzACvouckDdyVZPYkvYN2kgZIMnuSvnoJMUiSJElaA6rqniS/A9wN/Avwgar6QJKpqtrVttmV5Ii2y9HA9T2H2NnKHmrLc8v3kmQT3ZffTE1NMTMzs+i4d+/evdd+m0/a03fb/Tn+UvSLbZyMc3zjHBuMd3zjHBuMd3yjjG2hyaMCPpCkgD+uqq3A0E7SkiRJktSrDZNxBnAs8GXgL5K8ZL5d+pTVPOV7F3b3PVsBNmzYUNPT04uIuDMzM8Pc/c7dclXfbXectfjjL0W/2MbJOMc3zrHBeMc3zrHBeMc3ytgWmjx6ZlXd2xJE1yT51DzbLvkkPawM/yArkfkfp+zlOMUCxrMvxjO/cYpnnGKRJGkV+hHgrqr6HECSdwM/ANyX5Mj2hfaRwP1t+53AMT37r6Pr5razLc8tlyQNsKDkUVXd237en+Q9wKkM8SQ9rAz/ICuR+R+n7OU4xQLGsy/GM79ximecYpEkaRW6GzgtyePouq2dDtwEfA04B7i4/byibX8l8PYkrwOOohtz9caqejjJA0lOA24AzgZ+f0VrIkkTZp8DZid5fJInzi4DPwp8ku5kfE7bbO5J+swkByU5lm+epHcBDyQ5rc2ydnbPPpIkSZI0UFXdAPwl8DHgVrp7ma10SaNnJfk08Kz2nKq6DdgG3A78NXB+VT3cDvdy4E10g2j/A47DKknzWkjLoyngPV2+hwOBt1fVXyf5KLAtyXl03wK8ELqTdJLZk/Qe9j5JXwocTHeC9iQtSZIkaUGq6tXAq+cUP0jXCqnf9hcBF/Upvwk4cdkDlKRVap/Jo6r6DPD0PuVfwJO0JEmSJEnSqrbPbmuSJEmSJElauxY625okSdoP6/tMyrDj4ueOIBJJkiRp/9jySJIkSZIkSQOZPJIkrZgkByT5uyTvbc8PS3JNkk+3n4f2bHthku1J7kzy7J7yU5Lc2ta9oc3gKUmSJGlITB5JklbSBcAdPc+3ANdW1XHAte05SY4HzgROADYCb0xyQNvnEmATcFx7bFyZ0CVJkqS1yeSRJGlFJFkHPBd4U0/xGcBlbfky4AU95ZdX1YNVdRewHTg1yZHAIVV1XVUV8NaefSRJkiQNgQNmS5JWyuuBXwKe2FM2VVW7AKpqV5IjWvnRwPU92+1sZQ+15bnle0myia6FElNTU8zMzCw64N27dy94v80n7Vnwcfcnln1ZTKzjwHiHa5LinaRYYfLilSRpOayp5FG/GW8kScOX5HnA/VV1c5LphezSp6zmKd+7sGorsBVgw4YNNT29kJd9pJmZGRa637mLuMbsOGvxsezLYmIdB8Y7XJMU7yTFCpMXryRJy2FNJY8kSSPzTOD5SZ4DPBY4JMmfA/clObK1OjoSuL9tvxM4pmf/dcC9rXxdn3JJkiRJQ+KYR5KkoauqC6tqXVWtpxsI+0NV9RLgSuCcttk5wBVt+UrgzCQHJTmWbmDsG1sXtweSnNZmWTu7Zx9JkiRJQ2DLI0nSKF0MbEtyHnA38EKAqrotyTbgdmAPcH5VPdz2eTlwKXAwcHV7SJIkSRoSk0eSpBVVVTPATFv+AnD6gO0uAi7qU34TcOLwIpQkSZLUy25rkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSBTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgZacPIoyQFJ/i7Je9vzw5Jck+TT7eehPdtemGR7kjuTPLun/JQkt7Z1b0iS5a2OJEmSJEmSltNiWh5dANzR83wLcG1VHQdc256T5HjgTOAEYCPwxiQHtH0uATYBx7XHxiVFL0mSJEmSpKFaUPIoyTrgucCbeorPAC5ry5cBL+gpv7yqHqyqu4DtwKlJjgQOqarrqqqAt/bsI0mSJEmSpDF04AK3ez3wS8ATe8qmqmoXQFXtSnJEKz8auL5nu52t7KG2PLd8L0k20bVQYmpqipmZmQWG+U27d+/ea7/NJ+1Z1DH253UXE8+ojFMsYDz7YjzzG6d4xikWSZIkSVou+0weJXkecH9V3ZxkegHH7DeOUc1Tvndh1VZgK8CGDRtqenohL/tIMzMzzN3v3C1XLeoYO85a/OsuJp5RGadYwHj2xXjmN07xjFMskiRJkrRcFtLy6JnA85M8B3gscEiSPwfuS3Jka3V0JHB/234ncEzP/uuAe1v5uj7lkiRJkiRJGlP7HPOoqi6sqnVVtZ5uIOwPVdVLgCuBc9pm5wBXtOUrgTOTHJTkWLqBsW9sXdweSHJam2Xt7J59JEmSJEmSNIYWOuZRPxcD25KcB9wNvBCgqm5Lsg24HdgDnF9VD7d9Xg5cChwMXN0ekiRJkiRJGlOLSh5V1Qww05a/AJw+YLuLgIv6lN8EnLjYICVJkiQpyZPpZoA+kW781JcBdwLvBNYDO4AXVdWX2vYXAucBDwOvqKr3t/JT+OaX2u8DLmgzQkuS+thntzVJkiRJGhO/B/x1Vf1b4OnAHcAW4NqqOg64tj0nyfF0w26cAGwE3pjkgHacS+hmdz6uPTauZCUkadKYPJIkSZI09pIcAvwg8GaAqvrXqvoycAZwWdvsMuAFbfkM4PKqerCq7gK2A6e2yX4OqarrWmujt/bsI0nqYyljHkmSJEnSSvl24HPAnyZ5OnAzcAEw1Sbnoc0EfUTb/mjg+p79d7ayh9ry3PK9JNlE10KJqakpZmZmFh307t2799pv80l7+m67P8dfin6xjZNxjm+cY4Pxjm+cY4Pxjm+UsZk8kiRJkjQJDgS+B/i5qrohye/RuqgNkD5lNU/53oVVW4GtABs2bKjp6elFBQxdQmjufuduuarvtjvOWvzxl6JfbONknOMb59hgvOMb59hgvOMbZWx2W5MkSZI0CXYCO6vqhvb8L+mSSfe1rmi0n/f3bH9Mz/7rgHtb+bo+5ZKkAUweSZIkSRp7VfVPwGeTfGcrOh24HbgSOKeVnQNc0ZavBM5MclCSY+kGxr6xdXF7IMlpSQKc3bOPJKkPu63NY32f5qQ7Ln7uCCKRJEmSBPwc8LYkjwE+A/wk3Rfi25KcB9wNvBCgqm5Lso0uwbQHOL+qHm7HeTlwKXAwcHV7SJIGMHkkSZIkaSJU1S3Ahj6rTh+w/UXARX3KbwJOXNbgJGkVs9uaJEmSJEmSBjJ5JEkauiSPTXJjko8nuS3Jr7Xyw5Jck+TT7eehPftcmGR7kjuTPLun/JQkt7Z1b2jjVUiSJEkaEpNHkqSV8CDww1X1dOBkYGOS0+imWL62qo4Drm3PSXI8cCZwArAReGOSA9qxLgE20Q18elxbL0mSJGlITB5JkoauOrvb00e3RwFnAJe18suAF7TlM4DLq+rBqroL2A6c2qZgPqSqrquqAt7as48kSZKkIXDAbEnSimgth24Gngr8YVXdkGSqTZlMVe1KckTb/Gjg+p7dd7ayh9ry3PJ+r7eJroUSU1NTzMzMLDrm3bt3L3i/zSftWfBx9yeWfVlMrOPAeIdrkuKdpFhh8uKVJGk5mDySJK2INj3yyUmeDLwnyXyz3PQbx6jmKe/3eluBrQAbNmyo6enpRcULXZJnofudu+WqBR93x1mLj2VfFhPrODDe4ZqkeCcpVpi8eCVJWg52W5Mkraiq+jIwQzdW0X2tKxrt5/1ts53AMT27rQPubeXr+pRLkiRJGhKTR5KkoUvylNbiiCQHAz8CfAq4EjinbXYOcEVbvhI4M8lBSY6lGxj7xtbF7YEkp7VZ1s7u2UeSJEnSENhtTZK0Eo4ELmvjHj0K2FZV701yHbAtyXnA3cALAarqtiTbgNuBPcD5rdsbwMuBS4GDgavbQ5IkSdKQmDySJA1dVX0CeEaf8i8Apw/Y5yLgoj7lNwHzjZckSZIkaRnZbU2SJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA3kgNmSJC3C+i1XjToESZIkaUXZ8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJEmSNJDJI0mSJEmSJA3kgNmSJK2wQYNu77j4uSsciSRJkrRvtjySJEmSJEnSQPtMHiV5bJIbk3w8yW1Jfq2VH5bkmiSfbj8P7dnnwiTbk9yZ5Nk95ackubWte0OSDKdakiRJkiRJWg4LaXn0IPDDVfV04GRgY5LTgC3AtVV1HHBte06S44EzgROAjcAbkxzQjnUJsAk4rj02Ll9VJEmSJEmStNz2mTyqzu729NHtUcAZwGWt/DLgBW35DODyqnqwqu4CtgOnJjkSOKSqrquqAt7as48kSZIkSZLG0IIGzG4th24Gngr8YVXdkGSqqnYBVNWuJEe0zY8Gru/ZfWcre6gtzy3v93qb6FooMTU1xczMzIIrNGv37t177bf5pD2LPs5c+xPLoHhGZZxiAePZF+OZ3zjFM06xSJIkSdJyWVDyqKoeBk5O8mTgPUlOnGfzfuMY1Tzl/V5vK7AVYMOGDTU9Pb2QMB9hZmaGufudO2B2m8XYcdbiYxkUz6iMUyxgPPtiPPMbp3jGKRZJkiRJWi6Lmm2tqr4MzNCNVXRf64pG+3l/22wncEzPbuuAe1v5uj7lkiRJkiRJGlMLmW3tKa3FEUkOBn4E+BRwJXBO2+wc4Iq2fCVwZpKDkhxLNzD2ja2L2wNJTmuzrJ3ds48kSZIkSZLG0EK6rR0JXNbGPXoUsK2q3pvkOmBbkvOAu4EXAlTVbUm2AbcDe4DzW7c3gJcDlwIHA1e3hyRJkiRJksbUPpNHVfUJ4Bl9yr8AnD5gn4uAi/qU3wTMN16SJEmSJEmSxsiixjySJEmSJEnS2mLySJIkSZIkSQOZPJIkSZIkSdJAJo8kSZIkSZI00EJmW5MkSZIkDdn6LVf1Ld9x8XNXOBJJeiRbHkmSJEmSJGkgk0eSJEmSJEkayG5rkiRJkjQkt97zFc4d0B1NkiaFLY8kSZIkSZI0kMkjSZIkSRMjyQFJ/i7Je9vzw5Jck+TT7eehPdtemGR7kjuTPLun/JQkt7Z1b0iSUdRFkiaFySNJkiRJk+QC4I6e51uAa6vqOODa9pwkxwNnAicAG4E3Jjmg7XMJsAk4rj02rkzokjSZTB5JkiRJmghJ1gHPBd7UU3wGcFlbvgx4QU/55VX1YFXdBWwHTk1yJHBIVV1XVQW8tWcfSVIfDpgtSZIkaVK8Hvgl4Ik9ZVNVtQugqnYlOaKVHw1c37Pdzlb2UFueW76XJJvoWigxNTXFzMzMogOeOhg2n7Rn0fv12p/XXYjdu3cP7djLYZzjG+fYYLzjG+fYYLzjG2VsJo8kSZIkjb0kzwPur6qbk0wvZJc+ZTVP+d6FVVuBrQAbNmyo6emFvOwj/f7bruC1ty7ttmvHWYt/3YWYmZlhf+q0UsY5vnGODcY7vnGODcY7vlHGZvJIkiRJ0iR4JvD8JM8BHgsckuTPgfuSHNlaHR0J3N+23wkc07P/OuDeVr6uT7kkaQDHPJIkSZI09qrqwqpaV1Xr6QbC/lBVvQS4EjinbXYOcEVbvhI4M8lBSY6lGxj7xtbF7YEkp7VZ1s7u2UeS1IfJI0nS0CU5JsmHk9yR5LYkF7Ryp1eWJC3VxcCzknwaeFZ7TlXdBmwDbgf+Gji/qh5u+7ycbtDt7cA/AFevdNCSNEnstiZJWgl7gM1V9bEkTwRuTnINcC7d9MoXJ9lCN73yq+ZMr3wU8MEkT2sf+menV74eeB/d9Mp+6JekNaSqZoCZtvwF4PQB210EXNSn/CbgxOFFKEmriy2PJElDV1W7qupjbfkB4A66mW2cXlmSJEkac7Y8kiStqCTrgWcANzDm0yv3mw51qdMtz2cpU6+O87Sy/RjvcE1SvJMUK0xevJIkLQeTR5KkFZPkCcC7gFdW1VfnGa5oLKZX7jcd6rlbrlr0cRZqKVMxj/O0sv0Y73BNUryTFCtMXrySJC0Hu61JklZEkkfTJY7eVlXvbsX3ta5oOL2yJEmSNJ5MHkmShq7NiPZm4I6qel3PKqdXliRJksac3dYkSSvhmcBLgVuT3NLKfpluOuVtSc4D7gZeCN30yklmp1few97TK18KHEw3y5ozrUmSJElDZPJIkjR0VfUR+o9XBE6vLEmSJI01u61JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgYyeSRJkiRJkqSB9pk8SnJMkg8nuSPJbUkuaOWHJbkmyafbz0N79rkwyfYkdyZ5dk/5KUlubeve0KZZliRJkiRJ0phayGxre4DNVfWxJE8Ebk5yDXAucG1VXZxkC7AFeFWS44EzgROAo4APJnlam2L5EmATcD3wPmAjEzbF8votV/Ut33Hxc1c4EkmSJEmSpOHbZ8ujqtpVVR9ryw8AdwBHA2cAl7XNLgNe0JbPAC6vqger6i5gO3BqkiOBQ6rquqoq4K09+0iSJEmSJGkMLaTl0TckWQ88A7gBmKqqXdAlmJIc0TY7mq5l0aydreyhtjy3vN/rbKJrocTU1BQzMzOLCROA3bt377Xf5pP2LPo4C7WvGPvFMyrjFAsYz74Yz/zGKZ5xikWTydatkiRJGkcLTh4leQLwLuCVVfXVeYYr6rei5infu7BqK7AVYMOGDTU9Pb3QML9hZmaGufudO+BD+XLYcdb0vOv7xTMq4xQLGM++GM/8ximecYpFkiRJkpbLgmZbS/JousTR26rq3a34vtYVjfbz/la+EzimZ/d1wL2tfF2fckmSJEmSJI2phcy2FuDNwB1V9bqeVVcC57Tlc4AresrPTHJQkmOB44AbWxe3B5Kc1o55ds8+kiRJkiRJGkML6bb2TOClwK1JbmllvwxcDGxLch5wN/BCgKq6Lck24Ha6mdrObzOtAbwcuBQ4mG6WtYmaaU2SJEmSJGmt2WfyqKo+Qv/xigBOH7DPRcBFfcpvAk5cTICSJEmSJEkanQWNeSRJkiRJkqS1yeSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjpw1AEMy633fIVzt1w16jAkSZIkSZImmi2PJEmSJEmSNJDJI0mSJEmSJA1k8kiSJEmSJEkDmTySJEmSJEnSQCaPJEmSJI29JMck+XCSO5LcluSCVn5YkmuSfLr9PLRnnwuTbE9yZ5Jn95SfkuTWtu4NSTKKOknSpDB5JEmSJGkS7AE2V9V3AacB5yc5HtgCXFtVxwHXtue0dWcCJwAbgTcmOaAd6xJgE3Bce2xcyYpI0qQxeSRJkiRp7FXVrqr6WFt+ALgDOBo4A7isbXYZ8IK2fAZweVU9WFV3AduBU5McCRxSVddVVQFv7dlHktTHgaMOQJK0+iV5C/A84P6qOrGVHQa8E1gP7ABeVFVfausuBM4DHgZeUVXvb+WnAJcCBwPvAy5oH/wlSWtIkvXAM4AbgKmq2gVdginJEW2zo4Hre3bb2coeastzy/u9zia6FkpMTU0xMzOz6FinDobNJ+1Z9H699ud1F2L37t1DO/ZyGOf4xjk2GO/4xjk2GO/4RhmbySNJ0kq4FPgDum93Z812M7g4yZb2/FVzuhkcBXwwydOq6mG+2c3gerrk0Ubg6hWrxYis33LVXmU7Ln7uCCKRpNFL8gTgXcArq+qr8wxX1G9FzVO+d2HVVmArwIYNG2p6enrR8f7+267gtbcu7bZrx1mLf92FmJmZYX/qtFLGOb5xjg3GO75xjg3GO75Rxma3NUnS0FXV3wJfnFNsNwNJ0qIkeTRd4uhtVfXuVnxfu0bQft7fyncCx/Tsvg64t5Wv61MuSRrA5JEkaVQe0c0A6O1m8Nme7Wa7ExzNArsZSJJWnzYj2puBO6rqdT2rrgTOacvnAFf0lJ+Z5KAkx9INjH1ju+Y8kOS0dsyze/aRJPVhtzVJ0rhZcjcDWJ5xKvr1K1/quBXLZW5c49w/vx/jHa5JineSYoXJi3eVeSbwUuDWJLe0sl8GLga2JTkPuBt4IUBV3ZZkG3A73Uxt57cu0AAv55tj6F3NGugCLUlLYfJIkjQq9yU5sg1uuuzdDJZjnIp+/crP7TP+0CjMHf9inPvn92O8wzVJ8U5SrDB58a4mVfUR+n+RAHD6gH0uAi7qU34TcOLyRSdJq5vJo2XSbzBTcEBTSZrHbDeDi9m7m8Hbk7yObsDs2W4GDyd5IMlpdLPrnA38/sqHLUmSJK0t+0weOb2yJGmpkrwDmAYOT7ITeDUT0M3g1nu+MjYtjSRJkqRRWUjLo0txemVJ0hJU1YsHrLKbgSRJkjTm9jnbmtMrS5IkSZIkrV37O+bRI6ZXTtI7vfL1PdvNTqP8EIuYXnk5ZsiZOng8ZsSZjX2cZuYYp1jAePbFeOY3TvGMUyySJGn16De+qmOrSlpJyz1g9rJMr7wcM+T8/tuu4LW3jn488NnZcMZpZo5xigWMZ1+MZ37jFM84xSJJkiRJy2Wf3dYGuK91RWMY0ytLkiRJkiRpPOxv8mh2emXYe3rlM5MclORYvjm98i7ggSSnJQnd9MpXzD2oJEmSJEmSxss++3VN6vTKkiRJkiRJWrp9Jo+cXlmSJEmSJGnt2t9ua5IkSZIkSVoDTB5JkiRJkiRpIJNHkiRJkiRJGsjkkSRJkiRJkgba54DZkiRp/KzfctUjnm8+aQ/nbrmKHRc/d0QRSZIkabWy5ZEkSZIkSZIGMnkkSZIkSZKkgUweSZIkSZIkaSCTR5IkSZIkSRrI5JEkSZIkSZIGcra1IZudDWd2FhzAmXAkSZIkSdLEsOWRJEmSJEmSBjJ5JEmSJEmSpIHstiZJ0ioy2116LrtMS5IkaX/Z8kiSJEmSJEkD2fJIkiRJkiaMLU0lrSRbHkmSJEmSJGkgWx6NgN8SSJIkSZKkSWHySJKkNaDfFxd+aSFJkqSFsNuaJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgxzwaIw6kLUlaSV53JEmStBC2PJIkSZIkSdJAtjySJEmSpFXCVqWShsHkkSRJeoR+Nx7edEiSJK1dJo8mgB/iJUmSJEnSqJg8kiRJ+2Q3CEmSpLXL5NGE8kO8JGkceD2SpMnQ73x96cbHjyASSZNoxZNHSTYCvwccALypqi5e6RhWs0Ef4nttPmkP5265yg/2kiaW15LxZ5drSePOa4kkLdyKJo+SHAD8IfAsYCfw0SRXVtXtKxmHOn5bLGkSeS2ZXL3XndkvMsDrjqSV57Wkc+s9X/nGubiX52VJc610y6NTge1V9RmAJJcDZwBr6iQ97hbSemlfvOBIGiKvJavMclx3FsNrlCS8lszL+wFJc6108uho4LM9z3cC3zd3oySbgE3t6e4kd+7Hax0OfH4/9huKV4xRPCsRS35rUZuPzXvTGM/8jGewhcbybcMOZJVbs9eS+YzTdWYhRhnvIq9Rsybq/WWy4p2kWGF84vVasjReSxjuuXg/z7Vzje17x3jHBuMd3zjHBuMd33LHtuBryUonj9KnrPYqqNoKbF3SCyU3VdWGpRxjOY1TPOMUCxjPvhjP/MYpnnGKZZVbs9eS+UxSrGC8wzZJ8U5SrDB58WogryWMd2ww3vGNc2ww3vGNc2ww3vGNMrZHrfDr7QSO6Xm+Drh3hWOQJE02ryWSpKXyWiJJi7DSyaOPAsclOTbJY4AzgStXOAZJ0mTzWiJJWiqvJZK0CCvaba2q9iT5WeD9dFNivqWqbhvSyy2peekQjFM84xQLGM++GM/8ximecYpl1Vrj15L5TFKsYLzDNknxTlKsMHnxqg+vJd8wzrHBeMc3zrHBeMc3zrHBeMc3sthStVfXXkmSJEmSJAlY+W5rkiRJkiRJmiAmjyRJkiRJkjTQqkseJdmY5M4k25NsGfJr7Uhya5JbktzUyg5Lck2ST7efh/Zsf2GL684kz+4pP6UdZ3uSNyTpN3Vov9d/S5L7k3yyp2zZXj/JQUne2cpvSLJ+kbG8Jsk97f25JclzViKWtv0xST6c5I4ktyW5YMTvz6B4RvIeJXlskhuTfLzF82ujen/miWVkfz9tnwOS/F2S947qvdHoZAWvJfuIY6jn+WWOdejn3WWOd+jnwSHEPLTz0hBiHelnpP2I98lJ/jLJp9rf8PePc7yaDKO6lozb/1/G6J5lgbGN9DPonPjG6p5mgbGNxfuXMbrfWURsY/HeDVRVq+ZBN9jdPwDfDjwG+Dhw/BBfbwdw+Jyy3wa2tOUtwG+15eNbPAcBx7Y4D2jrbgS+HwhwNfBjC3z9HwS+B/jkMF4f+H+BP2rLZwLvXGQsrwF+oc+2Q42lbXMk8D1t+YnA37fXHdX7MyiekbxHbd8ntOVHAzcAp43i/ZknlpH9/bTt/jPwduC9o/zf8rHyD1b4WrKPWIZ6nl/mWId+3l3meId+HhxCzEM7Lw0h1h2M8DPSfsR7GfBTbfkxwJPHOV4f4/9ghNeScfv/Y4zuWRYY22sY4WfQOa85Vvc0C4xtLN4/xuh+ZxGxjcV7NzDupR5gnB7tTXt/z/MLgQuH+Ho72PvEfCdwZFs+ErizXyx0Mzt8f9vmUz3lLwb+eBExrOeRJ7tle/3ZbdrygcDnaYOsLzCWQX/8Q4+lz2teATxrlO/PgHhG/h4BjwM+BnzfqN+fObGM7L0B1gHXAj/MN2/SxuJvx8fwH6zwtWQB8axnSOf5Ice97OfdIcY6lPPgMsc41PPSEOLdwYg/Iy0i1kOAu+aeh8c1Xh+T8WCE15Jx/P9jjO5ZFhDbaxiTe5g+MYzVPc2A2Mbu/WOM7nf2EdvYvXe9j9XWbe1o4LM9z3e2smEp4ANJbk6yqZVNVdUugPbziH3EdnRbXq6Yl/P1v7FPVe0BvgJ8yyLj+dkkn2hNQmebBK5oLK2J3jPoMrojf3/mxAMjeo/SdX+4BbgfuKaqRvb+DIgFRvf383rgl4Cv95SN/G9HK2alryWLNerrzD4N8by73HEO+zy4nF7PcM9Ly20cPyMN8u3A54A/Tdct8E1JHj/G8WoyjPJaMgn/f+P+uWrk9zBzjds9zTyxwZi8f+N0v7PA2GBM3rt+VlvyqF8/3Bri6z2zqr4H+DHg/CQ/OM+2g2JbqZj35/WXGtslwHcAJwO7gNeudCxJngC8C3hlVX11vk1XIqY+8YzsPaqqh6vqZLpvs09NcuJ8oQ8zngGxjOS9SfI84P6qurnf+n67DDMejcSk/n5GfZ3pghjueXdZrcB5cFms0HlpuU3SZ6QD6bqtXFJVzwC+RteVYZBRx6vJMMq/h0n6/5trHD5XjfweZq5xu6fZR2xj8/6N0/3OAmMbm/eun9WWPNoJHNPzfB1w77BerKrubT/vB94DnArcl+RIgPbz/n3EtrMtL1fMy/n639gnyYHAk4AvLjSQqrqv/VN8HfgTuvdnxWJJ8mi6E9nbqurdrXhk70+/eEb9HrUYvgzMABsZ8d9PbywjfG+eCTw/yQ7gcuCHk/w5Y/S/paFb0WvJfhj1dWagFTjvDsUQz4PLZSXOS8tqTD8jDbIT2Nnzre9f0iWTxjVeTYaRXUsm5P9vbD9XjcPn817jdk+zr9jG7f1rMX2ZMbnfmS+2cXzveq225NFHgeOSHJvkMXQDQ105jBdK8vgkT5xdBn4U+GR7vXPaZufQ9f2klZ/ZRj0/FjgOuLE1lXsgyWltZPSze/bZH8v5+r3H+g/Ah6p1mlyI2X/K5ifo3p8ViaXt/2bgjqp6Xc+qkbw/g+IZ1XuU5ClJntyWDwZ+BPjUKN6fQbGM6r2pqgural1Vrac7h3yoql4yivdGI7Ni15L9NOrrTF8rdN5dznhX4jy4LFbovLRsxvgzUl9V9U/AZ5N8Zys6Hbh9XOPVxBjJtWSC/v/G9nPVKO9h+sQyVvc0C4ltXN6/cbrfWWhs4/LeDVRLGDBpHB/Ac+hGev8H4FeG+DrfTjfi+ceB22Zfi64f4bXAp9vPw3r2+ZUW1530zFYAbGh/GP8A/AEseNDld9A1Z3uILrN43nK+PvBY4C+A7XSjuH/7ImP5M+BW4BN0f7xHrkQsbft/R9cs7xPALe3xnBG+P4PiGcl7BHw38HftdT8J/Opy//0uNJ55YhnZ30/P8ab55sC0I/nb8TGaByt0LVlAHEM9zy9zrEM/7y5zvEM/Dw7pb2Io56VljnHkn5H2I+aTgZva38P/BA4d53h9TMaDEVxLxvH/jzG6Z1lgbCP/DNpz3LG6p1lgbGPx/jFG9zuLiG0s3rtBj9kDS5IkSZIkSXtZbd3WJEmSJEmStIxMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5JEmSJEmSpIFMHkmSJEmSJGkgk0eSJEmSJEkayOSRJEmSJEmSBjJ5pLGQZEeSHxl1HABJLk3yG/u570ySnxqwbn2SSnLg0iKUJEmSJGnlmDySJElrQpLXJPnztvytSXYnOWCIr/dHSf7rsI4vSdo/I7ge7PeX05Nivi/RtTrYAkKSJK05VXU38IQhv8Z/GubxJUlLtxLXA2k1sOWRxsnJST6R5CtJ3pnksQBJfjrJ9iRfTHJlkqNa+V7dwHoz3kmemuRv2vE+n+SdPdv92yTXtGPemeRFc2I5NMlVSR5IckOS7+jZ9weSfLQd96NJfqBfZZIckOR32mt/BnjunPXnJvlMe427kpy11DdQkiRJkqTlZvJI4+RFwEbgWOC7gXOT/DDwm23dkcA/Apcv8Hi/DnwAOBRYB/w+QJLHA9cAbweOAF4MvDHJCT37vhj4tbbvduCitu9hwFXAG4BvAV4HXJXkW/q8/k8DzwOeAWwA/sPsihbDG4Afq6onAj8A3LLAeknSqtbGwfvF9oXC15K8OclUkqtbwv2DSQ5t256W5P9L8uUkH08y3XOcY9uXCA8kuQY4vGfdI76ASPKTSe5o234myc/0bDudZGeSzUnuT7IryU8uoB7f6Kawr2MkOTjJa5P8Y/ty4iNJDm7rnp/ktlbHmSTftT/v1b7eL0kaN6vletAcmv34cjpzxobNI7vcPTbJnyf5Qqv3R5NMtXVPau/XriT3JPmNzNM1L8lB7Rgn9pQ9Jcm/JDkiyaFJ3pvkc0m+1JbXDTjWN2Ic8B4vKjaNB5NHGidvqKp7q+qLwF8BJwNnAW+pqo9V1YPAhcD3J1m/gOM9BHwbcFRV/Z+q+kgrfx6wo6r+tKr2VNXHgHfRk9wB3l1VN1bVHuBtLRboWg99uqr+rO37DuBTwI/3ef0XAa+vqs+2Ov3mnPVfB05M8v9v7/6j7azrA9+/P/wQA0qBQY4hiRNsg71A6g9OKa0z3tNSJRXGsNYdbCxIUHqzxqGinXglqavj9c7krnRarD9a8GaUEioIqT+GXBER0+56u4YfgqIhIEOUiIFIFFE51Imc+Ll/PN8Tdk72Pjn75Oy9n33O+7XWXvt5vs+P/fnuX8/zfJ/vj3mZuSszt00hT5I0V/xvwOuBU6n+Y28D/oTqhP8w4IqIWEBVoP+fgROA9wCfiYiXlH3cCNxXtvlPwMpJXm831fHhWOBtwF9GxGualr8U+CVgAXAZ8NfNhTJTNNk+/gI4k+pmwgnAe4FfRMSpwKeAdwMvAb4A/L8R8YKm/R70vQKYwvslSXU0W44HM3FzeqKVJZZFZdt/B/ysLNsIjAG/QnUz+w1A2z6JyrXWZ0uc494M/GNm7qZ6r/+G6vrqZeV1/moKMbbSUWyqBwuPVCffb5r+Z6q2xydT1TYCIDNHgaeo/qwP5r1AAPeUO7ZvL+n/EviNUrL+44j4MVUh1UsPEgsT4ym+2yaek4HvTVhvPB/PAr9P9Qe/q9yF+NUp5EmS5oqPZuaTmfk48P8Bd2fm18vJ7eeoTjYvBr6QmV/IzF9k5h3AvcAbI+JlwK8Df5qZezLzK1Q3JlrKzFsz89tZ+Ueqmqv/ummV54D/KzOfy8wvAKPAKzrMU8t9RMRhwNuBd2Xm45m5NzP/e8nr7wO3ZuYdmfkcVSHTPKpCpk7eKyZ7vzrMhyT10mw5HszEzemJnqMqNPqVcuy4LzN/Wmof/R7w7sx8thT+/CWw4iD7u5H9C4/+oKSRmU9l5mcy858z8xmqwq//dQox7ucQYlOf2WG26u4JqsIeYF9zr38BPA48W5KPBn5apvcVAGXm96majhER/wr4ckR8hapA5x8z8/WHGk/xMuCLLdbdRXUXoHm9fTLzduD2qJol/Gfgv7L/gUmS5rInm6Z/1mL+RVT/xxdGRPMJ9pHAP1AV4D9dCuvHfZf9/5f3iYjfA95PdWf7MKpjy9amVZ4qJ/zjmm8sTFW7fZwIvBD4dottJt5E+UVEfI/9b1pM5b2Cyd8vSaqr2XI8mImb0xP9LVU+boqI44BPAu+jej+OpLpJPb7uYex/Y7uVvwfmRcRvlHhfRVVAR0QcTVXIs4yq9hTAiyPi8MzcO4VYx003NvWZNY9UdzcCb4uIV0XEUcD/TXW3YUdm/oCqEOniqDqnfjvQ3Hb4wqZ2uE8DCewFPg+cGhFvjYgjy+PXo6kPiUl8oWz7BxFxRET8PnBa2edEm6iq0S4sVVnXNMU2FFUfFscAe6juWHTypytJqk40/zYzj2t6HJOZ66kK8I8v/7PjXtZqJ+X48hmqWj1DmXkc1f99tFq/C34I/E+ajmFNJt5ECaoLhcen8TqTvV+SNMgG+XjQ7ub0+P/8s1QFWOOab5Y/l5kfyMzTqGqkng9cQvV+7AFObHo/js3M5j5eD5CZv6C6hnkLVa2jz5daRgCrqWpY/UZmHgu8rqS3em/axjzd2NR/Fh6p1jJzC/CnVH/iu6hOrJurNP7vwP9B1ZTtdOC/Ny37deDuiBgFNlM1B3i0/AG+oeznCapS9T8DjppCPE9R/SmvLq/5XuD8zPxhi9X/K3A78A3ga1RtiMcdVvbxBPAjqiqf//5gry9J2s8ngX8TEeeWmwgvjKoz04WZ+V2qJgsfiIgXlBqo7ZoAvIDqGPADYKzcdX5DT3LAvpP1a4EPRsTJJS+/WS5iNgHnRcQ5EXEk1bFjD/sf76aq7fs1Y5mRpP4Y5OPBwW5O3w+sKDe8Jw7C89sRsbR0Nv1TqmZsezNzF1Vzu6si4tiIOCwifjkiptLM7EaqJtMXlelxL6aq6fXj0k/T+yfZx/3A6yLiZRHxS1T91gJwiLGpj2y2plrIzMUT5v/PpumPAR9rs91tVKOztVr2XqrCnVbLHqZqX9xq2aUT5htUo7WNz/8TVaemrbYdaZoeA/64PMb9dXnexTTaCEuSnpeZ34uI5cB/oepUei9wD/COssofUHXK+SPgTuB64LgW+3kmIq6gKqg5iqovjM3djn+C91ANrPBVqqYM3wDOzcyHI+JiqhFDF1CdkP+bzPx5py8whfdLkgbSIB8PMvOpiDgf+DBwDVVn2s03p/+UKk9PA/9IVaBzQln2UqrrpIVULRlupipIg6oG0nrgQaqCn+9Q3TA/WDx3R8SzVM3pbmta9KHy2j+kugF+FXBBm33cERE3A98s6/8Z8KamVaYVm/orMrPfMUiSJEmSJKmmbLYmSZIkSZKktiw8kiRJmqaI2BYRoy0eF/U7NklS7wzK8SAiPtYmzpbdhEjjbLYmSZIkSZKktmrfYfaJJ56Yixcv7ni7Z599lmOOOebgK9aMcffeoMZu3L03U7Hfd999P8zMl8xASJqiiceSOn8PjW16jK1zdY0LjG0qPJb0Xqvrkrp8H7rNfM4ucyWfMHfyOt18dnIsqX3h0eLFi7n33ns73q7RaDAyMjLzAXWZcffeoMZu3L03U7FHxHcPPRp1YuKxpM7fQ2ObHmPrXF3jAmObCo8lvdfquqQu34duM5+zy1zJJ8ydvE43n50cS6bU51FE7IiIrRFxf0TcW9JOiIg7IuKR8nx80/prI2J7RDwcEec2pZ9Z9rM9Ij4SEdFJxiRJkiRJktRbnXSY/duZ+arMHC7za4AtmbkE2FLmiYjTgBXA6cAy4OqIOLxscw2wClhSHssOPQuSJEmSJEnqlkMZbW05sLFMbwQuaEq/KTP3ZOajwHbgrIiYDxybmXdm1Uv39U3bSJIkSZIkqYam2udRAl+KiAT+n8zcAAxl5i6AzNwVESeVdRcAdzVtu7OkPVemJ6YfICJWUdVQYmhoiEajMcUwnzc6Ojqt7frNuHtvUGM37t4b5NglSZIkabqmWnj02sx8ohQQ3RER35pk3Vb9GOUk6QcmVoVTGwCGh4dzOh0/DWrHWMbde4Mau3H33iDHLkmSJEnTNaVma5n5RHneDXwOOAt4sjRFozzvLqvvBBY1bb4QeKKkL2yRLkmSJEmSpJo6aOFRRBwTES8enwbeADwAbAZWltVWAreU6c3Aiog4KiJOoeoY+57SxO2ZiDi7jLJ2SdM2kiRJkiRJqqGpNFsbAj5XlfdwBHBjZn4xIr4KbIqIy4DHgAsBMnNbRGwCHgTGgMszc2/Z1zuA64B5wG3lIUmSJEmSpJo6aOFRZn4HeGWL9KeAc9pssw5Y1yL9XuCMzsOUJA2yiLgWOB/YnZlnNKW/E/gjqpsNt2bme0v6WuAyYC9wRWbeXtLP5PmbEF8A3lVG8JQkSZLUJVPtMHtWW7zm1pbpO9af1+NIJGnWug74K+D68YSI+G1gOfBrmblnfNTOiDgNWAGcDpwMfDkiTi21WK+hGo3zLqrCo2VYi3VKWh3rPM5JkiQNjnZlF9ctO6brrz2lDrMlSToUmfkV4EcTkt8BrM/MPWWd8YEXlgM3ZeaezHwU2A6cVQZnODYz7yy1ja4HLuhJBiRJkqQ5zJpHkqR+ORX41xGxDvifwHsy86vAAqqaReN2lrTnyvTE9JYiYhVVLSWGhoZoNBr7lo2Oju43Xyfdim310rED0jp9nbn4vs2EusZW17jA2CRJqhsLjyRJ/XIEcDxwNvDrVIMwvByIFuvmJOktZeYGYAPA8PBwjoyM7FvWaDRonq+TqcbWaZPrS1s1W7vo4K/TbDa8b/1Q19jqGhcYm1pr1X9eRJwA3AwsBnYAb87Mp8sy+8+TpBliszVJUr/sBD6blXuAXwAnlvRFTestBJ4o6QtbpEuS5obrqPq6a7YG2JKZS4AtZX5i/3nLgKsj4vCyzXj/eUvKY+I+JUkTWHgkSeqX/wb8DkBEnAq8APghsBlYERFHRcQpVCf292TmLuCZiDg7IgK4BLilL5FLknquTf95y4GNZXojz/eFZ/95kjSDbLYmSeq6iPgUMAKcGBE7gfcD1wLXRsQDwM+BleVEfltEbAIeBMaAy8tIa1B1sn0dVVOD23CkNUma64bKzQUyc9f4yJ30oP88mDt9YJnP2WWu5BNmX15b9WEJvcmnhUeSpK7LzLe0WXRxm/XXAetapN8LnDGDoUmSZqeu958Hc6cPLPM5u8yVfMLsy2urPiwBrlt2TNfzabM1SZIkSYPqydIUjfK8u6Tbf54kzSALjyRJkiQNqs3AyjK9kuf7wrP/PEmaQTZbkyRJklR7bfrPWw9siojLgMeACwEy0/7zJGkGWXgkSZIkqfYm6T/vnDbr23+eJM0QC48kSZpFFrfpSFGSJEmaLvs8kiRJkiRJUlvWPJIkaY5qV0tpx/rzehyJJEmS6szCI0mSJEnSQPDGh9QfNluTJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqy8IjSZIkSZIktWXhkSRJkiRJktqy8EiSJEmSJEltHdHvACRJkiRJmmjxmlv7HYKkwppHkiRJkiRJasvCI0mSJEmSJLVl4ZEkqesi4tqI2B0RD7RY9p6IyIg4sSltbURsj4iHI+LcpvQzI2JrWfaRiIhe5UGSJEmaqyw8kiT1wnXAsomJEbEIeD3wWFPaacAK4PSyzdURcXhZfA2wClhSHgfsU5IkSdLMsvBIktR1mfkV4EctFv0l8F4gm9KWAzdl5p7MfBTYDpwVEfOBYzPzzsxM4Hrggu5GLkmSJMnR1ibRqnf/HevP60MkkjT7RMSbgMcz8xsTWp8tAO5qmt9Z0p4r0xPT2+1/FVUtJYaGhmg0GvuWjY6O7jdfJ1ONbfXSsa7F0Or1tz7+E4bmwUdvuGW/9KULfqlrcXRiNnymvVbXuMDYJEmqGwuPJEk9FxFHA+8D3tBqcYu0nCS9pczcAGwAGB4ezpGRkX3LGo0GzfN1MtXYLu3i8MU7Ljrw9S9dcyurl45x1dYjDrpuP8yGz7TX6hoXGJskSXVj4ZEkqR9+GTgFGK91tBD4WkScRVWjaFHTuguBJ0r6whbpkiRpBk1sgbF66RiXrrnVVhjSHGafR5KknsvMrZl5UmYuzszFVAVDr8nM7wObgRURcVREnELVMfY9mbkLeCYizi6jrF0C3NLuNSRJkiTNDAuPJEldFxGfAu4EXhEROyPisnbrZuY2YBPwIPBF4PLM3FsWvwP4OFUn2t8Gbutq4JIkSZJstiZJ6r7MfMtBli+eML8OWNdivXuBM2Y0uAHQagAHSZIkqVemXPMoIg6PiK9HxOfL/AkRcUdEPFKej29ad21EbI+IhyPi3Kb0MyNia1n2kZgwvI4kSZIkSZLqpZNma+8CHmqaXwNsycwlwJYyT0ScBqwATgeWAVdHxOFlm2uohk1eUh7LDil6SZIkSZIkddWUmq1FxELgPKomBP+hJC8HRsr0RqABXFnSb8rMPcCjEbEdOCsidgDHZuadZZ/XAxdgfxWSJAFV87TxEW0kSZKkuphqn0cfAt4LvLgpbaiMfENm7oqIk0r6AuCupvV2lrTnyvTE9ANExCqqGkoMDQ3RaDSmGObzRkdHp7zd6qVjU97vdGLpRCdx18mgxg2DG7tx994gxy5JkiRJ03XQwqOIOB/YnZn3RcTIFPbZqh+jnCT9wMTMDcAGgOHh4RwZmcrL7q/RaDDV7Tq5w7vjos5j6UQncdfJoMYNgxu7cffeIMcuSZIkSdM1lZpHrwXeFBFvBF4IHBsRnwSejIj5pdbRfGB3WX8nsKhp+4XAEyV9YYt0SZIkSZIk1dRBC48ycy2wFqDUPHpPZl4cEX8OrATWl+dbyiabgRsj4oPAyVQdY9+TmXsj4pmIOBu4G7gE+OjMZkeSJEmSVEeL27T42LH+vB5HIqlTU+3zqJX1wKaIuAx4DLgQIDO3RcQm4EFgDLg8M/eWbd4BXAfMo+oo286yJUmSJEmSaqyjwqPMbFCNqkZmPgWc02a9dVQjs01Mvxc4o9MgJUmSJEmS1B+H9TsASZIkSZIk1dehNFuTJEmSpL6LiD8G/pBqNOetwNuAo4GbgcXADuDNmfl0WX8tcBmwF7giM2/vfdQa164vpG7t2z6WpM5ZeCRJkiRpYEXEAuAK4LTM/Fnpf3UFcBqwJTPXR8QaYA1wZUScVpafTjXAz5cj4tSmflrVATvBluYGm61JkiRJGnRHAPMi4giqGkdPAMuBjWX5RuCCMr0cuCkz92Tmo8B24KzehitJg8WaR5IkaT/dbD4gSTMtMx+PiL+gGgH6Z8CXMvNLETGUmbvKOrsi4qSyyQLgrqZd7CxpB4iIVcAqgKGhIRqNxn7LR0dHD0gbJFsf/0nL9NVL958fmgerl461zOvqpWMt99HJujOh3efQ6jXbrTvon+dUzZV8wuzLa7vfUC/yaeGRJEmSpIEVEcdT1SY6Bfgx8HcRcfFkm7RIy1YrZuYGYAPA8PBwjoyM7Le80WgwMW2QXDrFmwWrl45x1dYj2HHRyJT30cm6M6HV67V7zXbrDvrnOVVzJZ8w+/La7jd03bJjup5Pm61JkiRJGmS/CzyamT/IzOeAzwK/BTwZEfMByvPusv5OYFHT9gupmrlJktqw5pEkSZKkQfYYcHZEHE3VbO0c4F7gWWAlsL4831LW3wzcGBEfpOowewlwT6+D1syyybXUXdY8kiR1XURcGxG7I+KBprQ/j4hvRcQ3I+JzEXFc07K1EbE9Ih6OiHOb0s+MiK1l2UciolXTA0nSHJKZdwOfBr4GbKW6xtlAVWj0+oh4BHh9mScztwGbgAeBLwKXO9KaJE3OwiNJUi9cByybkHYHcEZm/hrwP4C1ABOGUF4GXB0Rh5dtrqHquHRJeUzcpyRpDsrM92fmr2bmGZn51jKS2lOZeU5mLinPP2paf11m/nJmviIzb+tn7JI0CCw8kiR1XWZ+BfjRhLQvZeb4kBF3UfU5AW2GUC79VRybmXdmZgLX8/ywy5IkSZK6xMIjSVIdvB0Yv/O7APhe07LxIZQXlOmJ6ZIkSZK6yA6zJUl9FRHvA8aAG8aTWqyWk6S32+8qqiZuDA0N0Wg09i0bHR3db74uVi8dY2he9VxHrWKry/tY188U6htbXeMCY5MkqW4sPJIk9U1ErATOB84pTdGg/RDKO3m+aVtzekuZuYGqw1SGh4dzZGRk37JGo0HzfF1cuuZWVi8d46qt9Tw8t4ptx0Uj/Qlmgrp+plDf2OoaFxibJEl1Y7M1SVJfRMQy4ErgTZn5z02LNgMrIuKoiDiFMoRyZu4CnomIs8soa5fw/LDLkiRJkrqknrc2JUmzSkR8ChgBToyIncD7qUZXOwq4oyoL4q7M/HeZuS0ixodQHmP/IZTfQTVy2zyqPpIcIUeSJEnqMguPJEldl5lvaZH8iUnWXwesa5F+L3DGDIYmSZIk6SBstiZJkiRJkqS2LDySJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqy9HWJEmSJEkzavGaW/sdgqQZZM0jSZIkSZIktWXNI0mSJEnSQVmbSJq7LDySJEmSJM0Z7QrBrlt2TI8jkQaHzdYkSZIkSZLUloVHkiRJkiRJasvCI0mSJEmSJLVl4ZEkSZIkSZLassNsSZIkSZol2nUGvWP9eT2ORNJsYs0jSZIkSZIktWXhkSRJkiRJktqy2ZokSZIkSR2weaDmGmseSZIkSZIkqa2DFh5FxAsj4p6I+EZEbIuID5T0EyLijoh4pDwf37TN2ojYHhEPR8S5TelnRsTWsuwjERHdyZYkqU4i4tqI2B0RDzSleRyRJEmSBsBUah7tAX4nM18JvApYFhFnA2uALZm5BNhS5omI04AVwOnAMuDqiDi87OsaYBWwpDyWzVxWJEk1dh0H/ud7HJEkSZIGwEH7PMrMBEbL7JHlkcByYKSkbwQawJUl/abM3AM8GhHbgbMiYgdwbGbeCRAR1wMXALfNTFYkSXWVmV+JiMUTkufscaRdPwmSJElSHU2pw+xyx/c+4FeAv87MuyNiKDN3AWTmrog4qay+ALirafOdJe25Mj0xXZI0N3X1OBIRq6hqKTE0NESj0di3bHR0dL/5Xlu9dKztsqF5ky/vp1ax9fN9bNbvz3QydY2trnGBsUmSVDdTKjzKzL3AqyLiOOBzEXHGJKu36n8iJ0k/cAeTnPBPVScH9k5O0rt9sjCoJySDGjcMbuzG3XuDHPuAOeTjCEBmbgA2AAwPD+fIyMi+ZY1Gg+b5Xrt0kppHq5eOcdXWeg6G2iq2HReN9CeYCfr9mU6mrrHVNS4wNkmS6qajs9PM/HFENKj6mHgyIuaXu8Xzgd1ltZ3AoqbNFgJPlPSFLdJbvU7bE/6p6uTAPtlJ/ETdPkke1BOSQY0bBjd24+69QY69prp2HFHvOFSxJEnS7DeV0dZeUmocERHzgN8FvgVsBlaW1VYCt5TpzcCKiDgqIk6h6tD0ntI04ZmIOLuMjnNJ0zaSpLnH44gkSZI0AKZS82g+sLH0e3QYsCkzPx8RdwKbIuIy4DHgQoDM3BYRm4AHgTHg8tLsDeAdVCPuzKPq4HTgOjmVJHUuIj5F1Tn2iRGxE3g/sB6PI5IkqeYc6EKa2mhr3wRe3SL9KeCcNtusA9a1SL8XmKy/JEnSLJSZb2mzyOOIJOmQlZYSH6c6RiTwduBh4GZgMbADeHNmPl3WXwtcBuwFrsjM23setCQNkIM2W5MkSZKkmvsw8MXM/FXglcBDwBpgS2YuAbaUeSLiNGAFcDpVX65Xl1YWkqQ2LDySJEmSNLAi4ljgdcAnADLz55n5Y2A5sLGsthG4oEwvB27KzD2Z+SiwHTirlzFL0qCp51jAkiRJkjQ1Lwd+APxNRLwSuA94FzBUBlugjOx5Ull/AXBX0/Y7S9oBImIVsApgaGiIRqOx3/LR0dED0vpt9dKxlumt4my37kRD86a+7iBr93l2kve6fR9aqeP3tltmW17bfRd7kU8LjyRJ0oxr1bnojvXn9SESSXPAEcBrgHdm5t0R8WFKE7U2okVatloxMzcAGwCGh4dzZGRkv+WNRoOJaf12aZvOnXdcNDLldSdavXSMq7bO/kvH65Yd0/LznOr7BK3f57qp4/e2W2ZbXtt9F9t9d2fS7P8HaGIv+ZIkSdKssxPYmZl3l/lPUxUePRkR80uto/nA7qb1FzVtvxB4omfRStIAss8jSZIkSQMrM78PfC8iXlGSzgEeBDYDK0vaSuCWMr0ZWBERR0XEKcAS4J4ehixJA2dO1TyaCe1qL1kVX5IkSeqbdwI3RMQLgO8Ab6O6Ub4pIi4DHgMuBMjMbRGxiaqAaQy4PDP39idsSRoMFh5JkiRJGmiZeT8w3GLROW3WXwes62ZMmpvs80+zlc3WJEmSJEmS1JaFR5IkSZIkSWrLwiNJkiRJkiS1ZeGRJEmSJEmS2rLwSJIkSZIkSW052pokSZIkzXKtRgGTpKmy5pEkSZIkSZLasuaRJEmSJA0YaxJJ6iVrHkmSJEmSJKktC48kSZIkSZLUloVHkqS+iog/johtEfFARHwqIl4YESdExB0R8Uh5Pr5p/bURsT0iHo6Ic/sZuyRJkjQX2OeRJKlvImIBcAVwWmb+LCI2ASuA04Atmbk+ItYAa4ArI+K0svx04GTgyxFxambu7VMW1IF2/XPsWH9ejyORJElSJ6x5JEnqtyOAeRFxBHA08ASwHNhYlm8ELijTy4GbMnNPZj4KbAfO6m24kiRJ0txizSNJUt9k5uMR8RfAY8DPgC9l5pciYigzd5V1dkXESWWTBcBdTbvYWdIOEBGrgFUAQ0NDNBqNfctGR0f3m++11UvH2i4bmjf58n7qVmwz8Vn0+zOdTF1jq2tcYGySZhdr3mo2sPBIktQ3pS+j5cApwI+Bv4uIiyfbpEVatloxMzcAGwCGh4dzZGRk37JGo0HzfK9dOsnwyquXjnHV1noenrsV246LRg55H/3+TCdT19jqGhcYm6T+2Pr4TyY9Rktzmc3WJEn99LvAo5n5g8x8Dvgs8FvAkxExH6A87y7r7wQWNW2/kKqZmyRJkqQusfBIktRPjwFnR8TRERHAOcBDwGZgZVlnJXBLmd4MrIiIoyLiFGAJcE+PY5YkSZLmlHrWi5ckzQmZeXdEfBr4GjAGfJ2qqdmLgE0RcRlVAdOFZf1tZUS2B8v6lzvSmiRJktRdFh5JkvoqM98PvH9C8h6qWkit1l8HrOt2XJIkSZIqNluTJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElqyw6zJUmSJEmqscVrbm2ZvmP9eT2ORHOVhUeSJKmvPCGWJEmqN5utSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElq66B9HkXEIuB64KXAL4ANmfnhiDgBuBlYDOwA3pyZT5dt1gKXAXuBKzLz9pJ+JnAdMA/4AvCuzMyZzZIkSZIkSfVmn38aJFPpMHsMWJ2ZX4uIFwP3RcQdwKXAlsxcHxFrgDXAlRFxGrACOB04GfhyRJyamXuBa4BVwF1UhUfLgNtmOlOSJEmSJA2idoVKUj8dtPAoM3cBu8r0MxHxELAAWA6MlNU2Ag3gypJ+U2buAR6NiO3AWRGxAzg2M+8EiIjrgQuw8EiSNIt5AihJkqRBN5WaR/tExGLg1cDdwFApWCIzd0XESWW1BVQ1i8btLGnPlemJ6a1eZxVVDSWGhoZoNBqdhAnA6OjoAdutXjrW8X6majoxttIq7kEwqHHD4MZu3L03yLFLkiRJ0nRNufAoIl4EfAZ4d2b+NCLartoiLSdJPzAxcwOwAWB4eDhHRkamGuY+jUaDidtd2sW7vzsuGjnoOlPRKu5BMKhxw+DGbty9N8ixS5IkSdJ0TWm0tYg4kqrg6IbM/GxJfjIi5pfl84HdJX0nsKhp84XAEyV9YYt0SZIkSTokEXF4RHw9Ij5f5k+IiDsi4pHyfHzTumsjYntEPBwR5/YvakkaDActPIqqitEngIcy84NNizYDK8v0SuCWpvQVEXFURJwCLAHuKU3cnomIs8s+L2naRpIkSZIOxbuAh5rm11AN8LME2FLmmTDAzzLg6og4vMexStJAmUrNo9cCbwV+JyLuL483AuuB10fEI8DryzyZuQ3YBDwIfBG4vIy0BvAO4OPAduDb2Fm2JEmSpEMUEQuB86iuNcYtpxrYh/J8QVP6TZm5JzMfpbo2OatHoUrSQJrKaGv/ROv+igDOabPNOmBdi/R7gTM6CVCSJEmSDuJDwHuBFzeldTrAzwEONpBPPwfT6OZgQBMNzevt6/XLIOZzpgaXmq1mW17bfT97kc+ORluTJGmmRcRxVHeKz6AaSOHtwMPAzcBiYAfw5sx8uqy/FrgM2AtckZm39zxoSVJtRMT5wO7MvC8iRqaySYu0aQ3k08/BNLo5GNBEq5eOcdXW2X/pOIj5nM7ATXNpEJjZltd2v/vrlh3T9XxOqcNsSZK66MPAFzPzV4FXUvVXYT8VkqSpei3wpojYAdxE1d3GJ+l8gB9JUhsWHkmS+iYijgVeRzUwA5n588z8MfZTIUmaosxcm5kLM3Mx1Q2Gv8/Mi+lwgJ8ehy1JA2Ww6uRJkmablwM/AP4mIl4J3Ec1Wk5X+6noZfv3TvtOqHN/C72OrZPPqM59GtQ1trrGBcamGbMe2BQRlwGPARdCNcBPRIwP8DPG/gP8SJJasPBIktRPRwCvAd6ZmXdHxIcpTdTamJF+KnrZ/r3TPinq3N9Cr2PrpB+HOvdpUNfY6hoXGJumLzMbQKNMP0WHA/xIklqr59mpJGmu2AnszMy7y/ynqQqPnoyI+aXWkf1UzFGLWxS87Vh/Xh8ikSRJmtssPJohrU5wwZNcSZpMZn4/Ir4XEa/IzIep7hA/WB4rqZocTOyn4saI+CBwMvZTIUmSJHWdhUeSpH57J3BDRLwA+A7wNqoBHeynQpIkSaoBC48kSX2VmfcDwy0W2U+FJEmSVAOH9TsASZIkSZIk1ZeFR5IkSZIkSWrLwiNJkiRJkiS1ZeGRJEmSJEmS2rLDbEmSJEmSBtDiNbe2TN+x/rweR6LZzppHkiRJkiRJasvCI0mSJEmSJLVlszVJkiRJkmaRVs3ZbMqmQ2HNI0mSJEmSJLVlzSNJkjTwtj7+Ey71LqskSVJXWPNIkiRJkiRJbVl4JEmSJEmSpLYsPJIkSZIkSVJbFh5JkiRJkiSpLTvMliRJkiRJM2pxi4EswMEsBpU1jyRJkiRJktSWhUeSJEmSJElqy8IjSVLfRcThEfH1iPh8mT8hIu6IiEfK8/FN666NiO0R8XBEnNu/qCVJkqS5wcIjSVIdvAt4qGl+DbAlM5cAW8o8EXEasAI4HVgGXB0Rh/c4VkmSJGlOsfBIktRXEbEQOA/4eFPycmBjmd4IXNCUflNm7snMR4HtwFk9ClWSJEmakxxtTZLUbx8C3gu8uCltKDN3AWTmrog4qaQvAO5qWm9nSTtARKwCVgEMDQ3RaDT2LRsdHd1vvptWLx3raP2heZ1v0yt1iK3d59Yutl59zpPp5fetE3WNC4xNmqjdqFWS1CsWHkmS+iYizgd2Z+Z9ETEylU1apGWrFTNzA7ABYHh4OEdGnt99o9Ggeb6bLu3whH/10jGu2lrPw3MtYtv6bMvk1UtpGduOi0a6HNDB9fL71om6xgXGJkmDxgLO2a+eZ6eSpLnitcCbIuKNwAuBYyPik8CTETG/1DqaD+wu6+8EFjVtvxB4oqcRS5IkzVEWEs1d9nkkSeqbzFybmQszczFVR9h/n5kXA5uBlWW1lcAtZXozsCIijoqIU4AlwD09DluSJEmaU6x5JEmqo/XApoi4DHgMuBAgM7dFxCbgQWAMuDwz9/YvTEmSJGn2s/BIklQLmdkAGmX6KeCcNuutA9b1LDBJkqQ5yCZqanbQZmsRcW1E7I6IB5rSToiIOyLikfJ8fNOytRGxPSIejohzm9LPjIitZdlHIqJVp6eSJEmSJEmqkanUPLoO+Cvg+qa0NcCWzFwfEWvK/JURcRpVnxWnAycDX46IU0uTgmuohky+C/gCsAy4baYyUletSmt3rD+vD5FIkrrJu3OSJGkQeQ6jqThozaPM/ArwownJy4GNZXojcEFT+k2ZuSczHwW2A2eVkXKOzcw7MzOpCqIuQJIkSZIkSbU23T6PhjJzF0AZRvmkkr6AqmbRuJ0l7bkyPTG9pYhYRVVLiaGhIRqNRscB7v7RT/joDbfsl7Z6ace76YrJ8jM6Ojqt/PbboMYNgxu7cffeIMcuSdJsFRGLqG5OvxT4BbAhMz8cEScANwOLgR3AmzPz6bLNWuAyYC9wRWbe3ofQpZ4ar2G0eukYl1rbSB2a6Q6zW/VjlJOkt5SZG4ANAMPDwzkyMtJxIB+94Rau2lrP/sB3XDTSdlmj0WA6+e23QY0bBjd24+69QY5dkqRZbAxYnZlfi4gXA/dFxB3ApXTe1YYkqYWDNltr48nSFI3yvLuk7wQWNa23EHiipC9skS5JkiRJ05aZuzLza2X6GeAhqlYOHXW10dOgJWnATLdqzmZgJbC+PN/SlH5jRHyQqhR/CXBPZu6NiGci4mzgbuAS4KOHFLkkSdJBOHCFNLdExGLg1VTXHJ12tdFqf5N2p9GrJu2rl451/TUmMzSv/zH0gvnsjV52AzHbup1o97n1Ip8HLTyKiE8BI8CJEbETeD9VodGmiLgMeAy4ECAzt0XEJuBBquqjlzdV/3wH1cht86hGWZv1I61JkiRJ6o2IeBHwGeDdmfnTiFY9Z1Srtkhr2aXGwbrT6FWT9n73T7N66VhtuwSZSeazR7Y+2zK5Gzd3Zlu3E+3+C65bdkzX83nQb0xmvqXNonParL8OWNci/V7gjI6ikyRJkqSDiIgjqQqObsjMz5bkJyNifql1NJWuNiRJbUy3zyNJkiRJ6ruoqhh9AngoMz/YtGi8qw04sKuNFRFxVEScQulqo1fxStIgmv118iRJkpq06gcJ7AtJGmCvBd4KbI2I+0vanzC9rjYkSS1YeCRJkiRpYGXmP9G6HyPosKsNSVJrNluTJEmSJElSWxYeSZIkSZIkqS2brUmSJEmSpL5q1Seh/RHWhzWPJEmSJEmS1JaFR5IkSZIkSWrLZmuSJEmSJKl2WjVlA5uz9YM1jyRJfRMRiyLiHyLioYjYFhHvKuknRMQdEfFIeT6+aZu1EbE9Ih6OiHP7F70kSZI0N1h4JEnqpzFgdWb+L8DZwOURcRqwBtiSmUuALWWesmwFcDqwDLg6Ig7vS+SSJEnSHGGzNUlS32TmLmBXmX4mIh4CFgDLgZGy2kagAVxZ0m/KzD3AoxGxHTgLuLO3kUuSJKluJjZzW710bN8JpQ6NhUd9YLtNSTpQRCwGXg3cDQyVgiUyc1dEnFRWWwDc1bTZzpLWan+rgFUAQ0NDNBqNfctGR0f3m58Jq5eOzch+hubN3L5m2myPbaa/E+O68X2bCXWNC4xNkqS6sfBIktR3EfEi4DPAuzPzpxHRdtUWadlqxczcAGwAGB4ezpGRkX3LGo0GzfMz4dI2NwY6tXrpGFdtrefhebbHtuOikZkJZoJufN9mQl3jAmOTJKlu6nkGKEmaMyLiSKqCoxsy87Ml+cmImF9qHc0Hdpf0ncCips0XAk/0LlpJkiT1W7vWPOoeO8yWJPVNVFWMPgE8lJkfbFq0GVhZplcCtzSlr4iIoyLiFGAJcE+v4pUkSZLmImseSZL66bXAW4GtEXF/SfsTYD2wKSIuAx4DLgTIzG0RsQl4kGqktsszc2/Po9as1Ooupv0RSpIkWXgkSeqjzPwnWvdjBHBOm23WAeu6FtRBWE1akiRpcHRzwKq5dOPJwiNJkqQOOXKqJEmDzWN5Zyw8qpHFa25l9dKxA0bs8csrSVJ/WNNMkiTJwiNJkiRJkqS2vJlk4ZEkSdKMaXVyed2yY/oQiSRJ0syx8EiSJEmSJAlrGbVzWL8DkCRJkiRJUn1Z80iSJKmLtj7+kwMGwwAHxJAkSYPDwiNJkiRJkqQZ0K7Z26DfNLLwaAC0+vIN+hdPkiRJ0v7sa0VSXVl4JEmS1Aez9c6kJEmafSw8kiRJkiRJ6oNBaWlk4dGA8m6lJEmSJEmDoZNmqXVswnpYvwOQJEmSJElSfVnzSJIkqUZ6XX19UKrLS5Kk/rHwSJKkNupYZVjSYLLLAUnSILPwaJbp5ELHkxVJkiRJknQwFh5JkiTV3EzUWpm4j9VLx7i0zX47rXXnDSlJkmY3C4/mMKtPS5I02OpyLLffJEmSZreeFx5FxDLgw8DhwMczc32vY9DkPAGUVHceS6TJ2V+XdHD9PJb4G5U0aHpaeBQRhwN/Dbwe2Al8NSI2Z+aDvYxDnev0zqYFUJK6xWOJNBhmoulbN5vPea4yt/XyWGJBkaTZoNc1j84CtmfmdwAi4iZgOeAJ/4AaPxhO1m/CxHX77VBPLDvdh6QZ57FEmoWaj7lTOa842D4ONYZm3iyblTyWSFIHIjN792IR/xZYlpl/WObfCvxGZv7RhPVWAavK7CuAh6fxcicCPzyEcPvFuHtvUGM37t6bqdj/ZWa+ZAb2MyfN0LGkzt9DY5seY+tcXeMCY5sKjyWHYAavS+ryfeg28zm7zJV8wtzJ63TzOeVjSa9rHkWLtANKrzJzA7DhkF4o4t7MHD6UffSDcffeoMZu3L03yLHPMod8LKnzZ2ls02NsnatrXGBs6okZuS6ZK98H8zm7zJV8wtzJay/yeVg3d97CTmBR0/xC4IkexyBJGmweSyRJh8pjiSR1oNeFR18FlkTEKRHxAmAFsLnHMUiSBpvHEknSofJYIkkd6Gmztcwci4g/Am6nGhLz2szc1qWXO6Rmb31k3L03qLEbd+8NcuyzxgwdS+r8WRrb9Bhb5+oaFxibumwGr0vmyvfBfM4ucyWfMHfy2vV89rTDbEmSJEmSJA2WXjdbkyRJkiRJ0gCx8EiSJEmSJEltzbrCo4hYFhEPR8T2iFhTg3iujYjdEfFAU9oJEXFHRDxSno9vWra2xP5wRJzblH5mRGwtyz4SEa2GF53JuBdFxD9ExEMRsS0i3jVAsb8wIu6JiG+U2D8wKLGX1zw8Ir4eEZ8fsLh3lNe8PyLuHZTYI+K4iPh0RHyrfN9/cxDi1vTV7Tgxrt3/bl1M/G+qi1a/4X7HNC4i/rh8lg9ExKci4oV9jKWj85EaxPbn5TP9ZkR8LiKOq0tsTcveExEZESf2Izb1X12PJ806/e13ep4TEUdFxM0l/e6IWNy0zcryGo9ExMou57Pr1y51yGv04DqnDvlser2uXRfVJZ/R5WuoGc1nZs6aB1Vnd98GXg68APgGcFqfY3od8Brggaa0/wKsKdNrgD8r06eVmI8CTil5Obwsuwf4TSCA24Df63Lc84HXlOkXA/+jxDcIsQfwojJ9JHA3cPYgxF5e8z8ANwKfH5TvS3nNHcCJE9JqHzuwEfjDMv0C4LhBiNvHtD/v2h0nmmJr+b/b77ia4tvvv6kuj1a/4X7HVGJZADwKzCvzm4BL+xjPlM9HahLbG4AjyvSf1Sm2kr6IqqPl70489vmYG486H08mxNnVaxHg3wMfK9MrgJvL9AnAd8rz8WX6+C7ms+vXLnXIKz24zqlDPpvy27Xrorrkky5fQ81kPmdbzaOzgO2Z+Z3M/DlwE7C8nwFl5leAH01IXk51skt5vqAp/abM3JOZjwLbgbMiYj5wbGbemdUnfX3TNt2Ke1dmfq1MPwM8RHUiPAixZ2aOltkjyyMHIfaIWAicB3y8Kbn2cU+i1rFHxLFUJ1WfAMjMn2fmj+setw5J7Y4T4yb53+27Nv9NfTfJb7gujgDmRcQRwNHAE/0KpMPzkZ5qFVtmfikzx8rsXcDCngdG2/cN4C+B91KdX2huqu3xpFkPrkWa9/Vp4JxS4+Fc4I7M/FFmPg3cASyb6fyN69G1S9/z2qPrnL7nE3pyXVSLfLZRy3zOtsKjBcD3muZ3UpMT7wmGMnMXVH90wEklvV38C8r0xPSeKFXbXk1Vsj0QsZcqjvcDu6l+FIMS+4eoTkZ/0ZQ2CHFDdeD6UkTcFxGrSlrdY3858APgb0qV2I9HxDEDELembyCOExP+d+vgQxz431QH7X7DfZeZjwN/ATwG7AJ+kplf6m9UB2j3X1c3b6e6i1oLEfEm4PHM/Ea/Y1FfDcTxpI2ZPM/Zt00p8P0J8C8m2VfXdfHapRZ57cF1Ti3ySfevi+qSz25fQ81YPmdb4VGrPkYG6Y5Qu/j7lq+IeBHwGeDdmfnTyVZtkda32DNzb2a+iupO5VkRccYkq9ci9og4H9idmfdNdZMWaf38vrw2M18D/B5weUS8bpJ16xL7EVRVua/JzFcDz1JVDW2nLnFr+mr/WXXwv9ureDr9b+qlTn/DPVP6J1hOVa38ZOCYiLi4v1ENnoh4HzAG3NDvWAAi4mjgfcB/7Hcs6rvaH0+mYTrnObU6N+rytUst8tqD65y+57NH10V9z2fR7WuoGcvnbCs82knVBn3cQvpYRXwST5aqZZTn3SW9Xfw72b+6dk/yFRFHUv353pCZny3JAxH7uNJ8oUFVBa/usb8WeFNE7KCq+vw7EfHJAYgbgMx8ojzvBj5HVZ277rHvBHaWOzZQVeV8zQDEremr9XGizf9uv7X7b6qDdr/hOvhd4NHM/EFmPgd8FvitPsc0Ubv/uloonXeeD1xUquHXwS9TFQh+o/wmFgJfi4iX9jUq9UOtjycHMZPnOfu2KU10f4mqmVzP358eXLvUJq/Q1eucOuSzF9dFdchnL66hZiyfs63w6KvAkog4JSJeQNUh1OY+x9TKZmBlmV4J3NKUvqL0iH4KsAS4p1RVeyYizi7tEy9p2qYryut8AngoMz84YLG/JMqoLBExj+oE/lt1jz0z12bmwsxcTPXd/fvMvLjucQNExDER8eLxaaqOTh+oe+yZ+X3gexHxipJ0DvBg3ePWIantcWKS/92+muS/qe8m+Q3XwWPA2RFxdPlsz6Hqg6NO2v3X9V1ELAOuBN6Umf/c73jGZebWzDwpMxeX38ROqk56v9/n0NR7tT2eTMFMnuc07+vfUh0jkqpD+TdExPGlJuYbSlpX9Ojape957dF1Tt/z2aPror7ns0fXUDOXz+xi7+j9eABvpOpd/9vA+2oQz6eo+jp4juoE4zKqNoZbgEfK8wlN67+vxP4wTaM1AcPli/Rt4K+A6HLc/4qq2to3gfvL440DEvuvAV8vsT8A/MeSXvvYm153hOdHFah93FT9jnyjPLaN//YGJPZXAfeW78t/oxptoPZx+zikz7xWx4mmuFr+7/Y7rgkx7vtvqsuj1W+43zE1xfYBqpP6B4C/BY7qYywdnY/UILbtVH0xjP8WPlaX2CYs34Gjrc3ZR12PJxNi7Oq1CPBC4O/Kb/Ye4OVN27y9pG8H3tblfHb92qUOeaUH1zl1yOeEPI/QheuiOuSTHlxDzWQ+x3coSZIkSZIkHWC2NVuTJEmSJEnSDLLwSJIkSZIkSW1ZeCRJkiRJkqS2LDySJEmSJElSWxYeSZIkSZIkqS0LjyRJkiRJktSWhUeSJEmSJElq6/8H8rDXmo2k27QAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1440x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "housing.hist(bins = 50, figsize=(20,15))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "50002d08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "train_set , test_set = train_test_split(housing, test_size=0.2,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1763b83e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14196</th>\n",
+       "      <td>-117.03</td>\n",
+       "      <td>32.71</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>3126.0</td>\n",
+       "      <td>627.0</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>623.0</td>\n",
+       "      <td>3.2596</td>\n",
+       "      <td>103000.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8267</th>\n",
+       "      <td>-118.16</td>\n",
+       "      <td>33.77</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>3382.0</td>\n",
+       "      <td>787.0</td>\n",
+       "      <td>1314.0</td>\n",
+       "      <td>756.0</td>\n",
+       "      <td>3.8125</td>\n",
+       "      <td>382100.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17445</th>\n",
+       "      <td>-120.48</td>\n",
+       "      <td>34.66</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1897.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>915.0</td>\n",
+       "      <td>336.0</td>\n",
+       "      <td>4.1563</td>\n",
+       "      <td>172600.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14265</th>\n",
+       "      <td>-117.11</td>\n",
+       "      <td>32.69</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>1421.0</td>\n",
+       "      <td>367.0</td>\n",
+       "      <td>1418.0</td>\n",
+       "      <td>355.0</td>\n",
+       "      <td>1.9425</td>\n",
+       "      <td>93400.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2271</th>\n",
+       "      <td>-119.80</td>\n",
+       "      <td>36.78</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>2382.0</td>\n",
+       "      <td>431.0</td>\n",
+       "      <td>874.0</td>\n",
+       "      <td>380.0</td>\n",
+       "      <td>3.5542</td>\n",
+       "      <td>96500.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17848</th>\n",
+       "      <td>-121.86</td>\n",
+       "      <td>37.42</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5032.0</td>\n",
+       "      <td>808.0</td>\n",
+       "      <td>2695.0</td>\n",
+       "      <td>801.0</td>\n",
+       "      <td>6.6227</td>\n",
+       "      <td>264800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6252</th>\n",
+       "      <td>-117.97</td>\n",
+       "      <td>34.04</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1686.0</td>\n",
+       "      <td>417.0</td>\n",
+       "      <td>1355.0</td>\n",
+       "      <td>388.0</td>\n",
+       "      <td>2.5192</td>\n",
+       "      <td>157300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9389</th>\n",
+       "      <td>-122.53</td>\n",
+       "      <td>37.91</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>2524.0</td>\n",
+       "      <td>398.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>417.0</td>\n",
+       "      <td>7.9892</td>\n",
+       "      <td>500001.0</td>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6113</th>\n",
+       "      <td>-117.90</td>\n",
+       "      <td>34.13</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1126.0</td>\n",
+       "      <td>316.0</td>\n",
+       "      <td>819.0</td>\n",
+       "      <td>311.0</td>\n",
+       "      <td>1.5000</td>\n",
+       "      <td>139800.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6061</th>\n",
+       "      <td>-117.79</td>\n",
+       "      <td>34.02</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18690.0</td>\n",
+       "      <td>2862.0</td>\n",
+       "      <td>9427.0</td>\n",
+       "      <td>2777.0</td>\n",
+       "      <td>6.4266</td>\n",
+       "      <td>315600.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "14196    -117.03     32.71                33.0       3126.0           627.0   \n",
+       "8267     -118.16     33.77                49.0       3382.0           787.0   \n",
+       "17445    -120.48     34.66                 4.0       1897.0           331.0   \n",
+       "14265    -117.11     32.69                36.0       1421.0           367.0   \n",
+       "2271     -119.80     36.78                43.0       2382.0           431.0   \n",
+       "17848    -121.86     37.42                20.0       5032.0           808.0   \n",
+       "6252     -117.97     34.04                28.0       1686.0           417.0   \n",
+       "9389     -122.53     37.91                37.0       2524.0           398.0   \n",
+       "6113     -117.90     34.13                 5.0       1126.0           316.0   \n",
+       "6061     -117.79     34.02                 5.0      18690.0          2862.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "14196      2300.0       623.0         3.2596            103000.0   \n",
+       "8267       1314.0       756.0         3.8125            382100.0   \n",
+       "17445       915.0       336.0         4.1563            172600.0   \n",
+       "14265      1418.0       355.0         1.9425             93400.0   \n",
+       "2271        874.0       380.0         3.5542             96500.0   \n",
+       "17848      2695.0       801.0         6.6227            264800.0   \n",
+       "6252       1355.0       388.0         2.5192            157300.0   \n",
+       "9389        999.0       417.0         7.9892            500001.0   \n",
+       "6113        819.0       311.0         1.5000            139800.0   \n",
+       "6061       9427.0      2777.0         6.4266            315600.0   \n",
+       "\n",
+       "      ocean_proximity  \n",
+       "14196      NEAR OCEAN  \n",
+       "8267       NEAR OCEAN  \n",
+       "17445      NEAR OCEAN  \n",
+       "14265      NEAR OCEAN  \n",
+       "2271           INLAND  \n",
+       "17848       <1H OCEAN  \n",
+       "6252        <1H OCEAN  \n",
+       "9389         NEAR BAY  \n",
+       "6113        <1H OCEAN  \n",
+       "6061        <1H OCEAN  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_set.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273c7d7b",
+   "metadata": {},
+   "source": [
+    "this introduces a bias. so we use stratified shuffle split on the basis on income categories. so we first create income category"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fd85640a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "housing['income_cat'] = pd.cut(housing['median_income'],bins=[0.,1.5,3.,4.5,6.,np.inf],labels=[1,2,3,4,5])\n",
+    "housing['income_cat'].hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f1f3587a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import StratifiedShuffleSplit\n",
+    "split = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n",
+    "for train_index, test_index in split.split(housing,housing['income_cat']):\n",
+    "    strat_train_set = housing.loc[train_index]\n",
+    "    strat_test_set = housing.loc[test_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "751b9019",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "      <th>income_cat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12655</th>\n",
+       "      <td>-121.46</td>\n",
+       "      <td>38.52</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>3873.0</td>\n",
+       "      <td>797.0</td>\n",
+       "      <td>2237.0</td>\n",
+       "      <td>706.0</td>\n",
+       "      <td>2.1736</td>\n",
+       "      <td>72100.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15502</th>\n",
+       "      <td>-117.23</td>\n",
+       "      <td>33.09</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5320.0</td>\n",
+       "      <td>855.0</td>\n",
+       "      <td>2015.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>6.3373</td>\n",
+       "      <td>279600.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2908</th>\n",
+       "      <td>-119.04</td>\n",
+       "      <td>35.37</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1618.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>667.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>2.8750</td>\n",
+       "      <td>82700.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14053</th>\n",
+       "      <td>-117.13</td>\n",
+       "      <td>32.75</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1877.0</td>\n",
+       "      <td>519.0</td>\n",
+       "      <td>898.0</td>\n",
+       "      <td>483.0</td>\n",
+       "      <td>2.2264</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20496</th>\n",
+       "      <td>-118.70</td>\n",
+       "      <td>34.28</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>3536.0</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>1837.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>4.4964</td>\n",
+       "      <td>238300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
+       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
+       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
+       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
+       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "12655      2237.0       706.0         2.1736             72100.0   \n",
+       "15502      2015.0       768.0         6.3373            279600.0   \n",
+       "2908        667.0       300.0         2.8750             82700.0   \n",
+       "14053       898.0       483.0         2.2264            112500.0   \n",
+       "20496      1837.0       580.0         4.4964            238300.0   \n",
+       "\n",
+       "      ocean_proximity income_cat  \n",
+       "12655          INLAND          2  \n",
+       "15502      NEAR OCEAN          5  \n",
+       "2908           INLAND          2  \n",
+       "14053      NEAR OCEAN          2  \n",
+       "20496       <1H OCEAN          3  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_train_set.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "540ec57f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    0.350594\n",
+       "2    0.318859\n",
+       "4    0.176296\n",
+       "5    0.114462\n",
+       "1    0.039789\n",
+       "Name: income_cat, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_train_set['income_cat'].value_counts()/len(strat_train_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c2532201",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    0.350581\n",
+       "2    0.318847\n",
+       "4    0.176308\n",
+       "5    0.114438\n",
+       "1    0.039826\n",
+       "Name: income_cat, dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing['income_cat'].value_counts()/len(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "857e4eb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12655</th>\n",
+       "      <td>-121.46</td>\n",
+       "      <td>38.52</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>3873.0</td>\n",
+       "      <td>797.0</td>\n",
+       "      <td>2237.0</td>\n",
+       "      <td>706.0</td>\n",
+       "      <td>2.1736</td>\n",
+       "      <td>72100.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15502</th>\n",
+       "      <td>-117.23</td>\n",
+       "      <td>33.09</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>5320.0</td>\n",
+       "      <td>855.0</td>\n",
+       "      <td>2015.0</td>\n",
+       "      <td>768.0</td>\n",
+       "      <td>6.3373</td>\n",
+       "      <td>279600.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2908</th>\n",
+       "      <td>-119.04</td>\n",
+       "      <td>35.37</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>1618.0</td>\n",
+       "      <td>310.0</td>\n",
+       "      <td>667.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>2.8750</td>\n",
+       "      <td>82700.0</td>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14053</th>\n",
+       "      <td>-117.13</td>\n",
+       "      <td>32.75</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1877.0</td>\n",
+       "      <td>519.0</td>\n",
+       "      <td>898.0</td>\n",
+       "      <td>483.0</td>\n",
+       "      <td>2.2264</td>\n",
+       "      <td>112500.0</td>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20496</th>\n",
+       "      <td>-118.70</td>\n",
+       "      <td>34.28</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>3536.0</td>\n",
+       "      <td>646.0</td>\n",
+       "      <td>1837.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>4.4964</td>\n",
+       "      <td>238300.0</td>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "12655    -121.46     38.52                29.0       3873.0           797.0   \n",
+       "15502    -117.23     33.09                 7.0       5320.0           855.0   \n",
+       "2908     -119.04     35.37                44.0       1618.0           310.0   \n",
+       "14053    -117.13     32.75                24.0       1877.0           519.0   \n",
+       "20496    -118.70     34.28                27.0       3536.0           646.0   \n",
+       "\n",
+       "       population  households  median_income  median_house_value  \\\n",
+       "12655      2237.0       706.0         2.1736             72100.0   \n",
+       "15502      2015.0       768.0         6.3373            279600.0   \n",
+       "2908        667.0       300.0         2.8750             82700.0   \n",
+       "14053       898.0       483.0         2.2264            112500.0   \n",
+       "20496      1837.0       580.0         4.4964            238300.0   \n",
+       "\n",
+       "      ocean_proximity  \n",
+       "12655          INLAND  \n",
+       "15502      NEAR OCEAN  \n",
+       "2908           INLAND  \n",
+       "14053      NEAR OCEAN  \n",
+       "20496       <1H OCEAN  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for set_ in (strat_train_set, strat_test_set):\n",
+    "    set_.drop(\"income_cat\", axis=1, inplace=True)\n",
+    "strat_train_set.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d1e94e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_copy = strat_train_set.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6e41ff89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing_copy.plot(kind = \"scatter\",x='longitude',y='latitude', alpha = 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d950de6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1fc11be6e20>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing_copy.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.7,\n",
+    "             s=housing_copy[\"population\"]/100, label=\"population\", figsize=(10,7),\n",
+    "             c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
+    "             sharex=False)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "aa02b0e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "latitude             -0.142673\n",
+       "longitude            -0.047466\n",
+       "population           -0.026882\n",
+       "total_bedrooms        0.047781\n",
+       "households            0.064590\n",
+       "housing_median_age    0.114146\n",
+       "total_rooms           0.135140\n",
+       "median_income         0.687151\n",
+       "median_house_value    1.000000\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix = housing_copy.corr()\n",
+    "corr_matrix[\"median_house_value\"].sort_values(ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2da9ddc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:xlabel='median_house_value', ylabel='median_house_value'>,\n",
+       "        <AxesSubplot:xlabel='median_income', ylabel='median_house_value'>,\n",
+       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_house_value'>,\n",
+       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_house_value'>],\n",
+       "       [<AxesSubplot:xlabel='median_house_value', ylabel='median_income'>,\n",
+       "        <AxesSubplot:xlabel='median_income', ylabel='median_income'>,\n",
+       "        <AxesSubplot:xlabel='total_rooms', ylabel='median_income'>,\n",
+       "        <AxesSubplot:xlabel='housing_median_age', ylabel='median_income'>],\n",
+       "       [<AxesSubplot:xlabel='median_house_value', ylabel='total_rooms'>,\n",
+       "        <AxesSubplot:xlabel='median_income', ylabel='total_rooms'>,\n",
+       "        <AxesSubplot:xlabel='total_rooms', ylabel='total_rooms'>,\n",
+       "        <AxesSubplot:xlabel='housing_median_age', ylabel='total_rooms'>],\n",
+       "       [<AxesSubplot:xlabel='median_house_value', ylabel='housing_median_age'>,\n",
+       "        <AxesSubplot:xlabel='median_income', ylabel='housing_median_age'>,\n",
+       "        <AxesSubplot:xlabel='total_rooms', ylabel='housing_median_age'>,\n",
+       "        <AxesSubplot:xlabel='housing_median_age', ylabel='housing_median_age'>]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas.plotting import scatter_matrix\n",
+    "Attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\n",
+    "scatter_matrix(housing[Attributes],figsize=(12, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ccc18dfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='median_income', ylabel='median_house_value'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9NKHheTyzNsPBIOHK3oilVgCVZJjnXNnp0xsnjHNJ5EGmJXVfoI3gJ850cB2HubrP5d0hgStZ78YsNAICF2ZrNdYHYzqhy/W9CUpJ4rRgkmmSomS+5aOkpOEpZuseZ2cbdIcJSjncGYzZ2B2TVPDQUoNG6FAWgnPzEZ4j7I5cwrm5GnFScG1vzJvbAza7Y5q+T24qXCVohA5K2J1s6LrM1UKiQPHkqTaBEozz8nPfXXZle4AjBHOtCCkMazM16oG6b3dZmcNLG12yvGQmCkiKCqM1s42Qx0/VCV3nc9ldJoF+liO0QCnzI9Vd9kGF/0/byZzHRi9gNxr/wBjzq0KIWeDXgDXgDvArR8ZfCPG/Bf4itq73vzTGfG16/AW+18L8NeDfmbYwB8DfB57DRjB/zhhzY7rmLwL/m+n7/6ox5u980PX+IE7m84ay0nzr5iGBo/Adm8dNy4ovn5slrzTfvdllpuYfv747yfjiuRmiqYO8e22cl7yy3uOpUy1uHEyYZAXXdsdIJTAG/vAjizy8UOefvbHFdj9hmJYsN0KUEiw2fR5eavATZ2fueTj6cc63bx5ybW/MOCnZHiY8utSiFijm6z5vbw+5uPi9Lpqar46vKS80b+8OSYqKp1ZaPLPaoRW5x+d85XaXt7ZGLLVDHl6oc262zjt7Q87P1rjdi7k436QROmhjGCY5tw5ivnXjkLQsuXkQ0wwcAlfxlYfmmKn7PLrUoKqMdWJKcHVnSGnAFYKHFxvWCPoO46zk9kHMuCjpjjPANn+UxrDU9FluhXhKcmlrSN1XTApNb5wjhODMbEReaYZJwR96aI6a797zdwH47s0uGsPvXN4ndBX7wxTHkVRas9oJ+cOPLXJhrsYoq7iyNwLDezqKju6N9zM6H/ce+7jn+aA1R7+T8L6G9Oh17+wM+LvfvIPENrZ86cwszZrDl8/N/kCf6dPC3V2i9/ubPOj4ICfzqWY6psb+mfscP8RGM/db86vAr97n+IvAk/c5ngK/8j7n+tvA3/54V/2jgeOmgWn+wncUk6w8fsCPaj1HDugoV3u/tVIIilLjOZL5msfvvbPH7iBjuR3w3GqH7WFCzVP8T764xr+8us83bhxSGMNyI+SJlRbdScofXD/EkQKlBI8tNbm0OWCznzBX9+mEPsO0QBvNarvOjcMJzvQaH1tqHj+MT620eXWjx6WNIYEv+cLqDK4jeGOrzwtrHV5f73OnG1NU0Io8JlnBnd4Eow2PLzV5bKVJXmm2BgllT+NISc1XJHnJXN1jmAmWmj5FBcvtgKzUXN0ZUWnD6XbEc2ttfv/aHi+v99gbZLiOYHeU8fzZDo8sN1BS8NMPzzHKKl5Z73Fpc8j2MGGlGbLSjnCkREroJwXdSU5eaQqt8ZUiKUqqyrA3zHhja8CTy21cx9bIyrv+ZoGUXFioUZaatKg4N19jnOb4juIPrh7SHefkWjNb84//tm9s9Y+N7ydl7L6f8xytyQuNxvDcaofZ+vc2Oo6SjLPyA8/bj3Ne2+hzaWPIaidECIGjBHf6E37lwurn0sHc3SXqB+/9m/yo44Tq/0cUH+RIjvKxb2z1mWTl8cN8dMO/e602BteR5KVmc5AyV/OQQnBhrs4wLZl3PZKyohG6/OLTKzRDF0dKIs9GC+v9hOdW20TT7pdX1/u2QwaBpxQoON2JSIqSV9f7NEOX59dmkNLw6nqfnzjbQSrrEJ5bbZMWFYuNACXt9U4ym3LsJ7k1TlIwX/e5tDlglJZM0oqfeXSBduiyPUyp+w6t0GOSF6z3Yk7NROyMMioNjlLUPEl/UmBMjBKSzV5MnJc8vlzn968eEucVZtrp04sLhIF//MomFxcaeK79br90bgYlBY8VDVxX8sRyi3d2R2wNEs7O1tgdZuSTjMVawIXFGtv9lMh3+GOPL7Hem/DyepdzsxFKSV6500cpwZlOjdu9CbORx9W9ETVPIQQErkMzdEnyigrD9d0Jiw+FwL2bC+A9xu7Vjd70b/PeNO374fsxmmWleW2jT5KV7A5z8qri2t6YX37ulK0xTtd90HmP3lcJQeQrQs8jKSoeXWwwyStCT933vX/Y+KAN34mTOcEDiw9zJK3IPU6dvTstcb+1v/DkClf2RoySAtd1WGraNZO8JKsqPGmNQOQ5PLrY5KuXtilKjQFWmiG+o0intSOwqSaNIa8qMALflZyda2IMrLQj4rzkyt6Y7V7Cpa0BD803aEYOjy01qfkOpTYoaetTgyTnjc0Bt7sJm70YtGGUV8w1PGYbPqfbIbuDlOVWwFqnxmGcMUhylBKcm62jJJyZjXhspUFvnLPZT7m5P0IbWGz6eEpSFoZv3zpkkBQ0ApfDUUFRVfSTgsWGTytyaQQuQsCrGz2E4Z5o4vrBGK01aV4xVw/wHVszWGmHPLpc57I7YqUdWQfZ8Nnuxyglmal5x+e43Ztwdi6kN844M1Njo58wG3lsD1O0NjhS0gk9jDCMs4JW6N2zuXi3scsLzaWNIWlRUfOdjxzVfD9Gc2+U8sbGgJ1BiiMF5+fr9OKMX3txg8eXm/iu5MJ8/QPPe/S+dX/alaklGNt67Lvy2FF93vBhmYMfdZw4mQcQHzUXfrcjOcpzl9Md7dH6yLv3Fjg6d81X73FC8w0Pg0EaybWDETf2x+SV5pHFOrnWvHKnDwJGScn52Yha4CKA79zq0kvtAJnGpp6ePzuDud3j6t7omAnhhTMzvLk1YJBk3DyMkUCSVyy2Ag7jjHbk2NrQSour+2N2Bwl3DhPisqQTeZxuhawfxtw6nBBnFQ8tNljtRDy50rbODEthM1t3kdLOTmSFZm025O2dIbcOJ7hSMV93GaU+z56ewXUkrhLsjVKKwiAF9OICz7FGTxrYGsbM1DtU2hC4iiyvMAI605rXkbF8eLHO5Z0Re8OEmu+y1Ay43Z3gSMGdXkzNc5mpe5Ra47oSgcCRkrSoMMZwc2/E71zaIS40rqPwJPzmW0McqYg8xU9emKXUmocXGlTGsD9Kj9NSR/fJkbFzpOTtXZt2XGwElNp85BTOxzWaZaW5tjsmL0sOxhmeUuwM92hHPmfnajRDB4Hg6s4IBO97Xk9J0qnDzgvDrYMhnchDV+a4I/PziA/b8P2o48TJPGD4uLnwd+e5k6K0nFiOfF+aifc7d+A5fPHMLG9s9ZmvewwTj5VWyOEkZ6kZMVPzeGenzz99fZvT7QhfSb58fpZKG6rK4ErAgMFQ8xRfPj/DC2fbx6m1cVaSFZq3tgZs9GLOztVZaAY0A4/tfszlnSHjpCQvK55ZbfN2oXniVJMbBxN8R/H6Rp+nT7dZaQeMU7uT/8JaZ8paIIg85/hhrwpNOp3Cu7EfA7BUD2lEDpOspNSG37++jxSCRuCy0groxgWRp+j27G48K22qJi81wyTn7Z0h2hiWWgF117nHWCZFyZ3DhHOzNW51Jyz4ivV+zKNLTWZrPq4jubwz4GHTwHclz612+O6tQ16+06U/yXllvcfuIKXQmocWmiw2PN7YHuO7ikeX7N/gty7v8vBCg1965hShJ/nu7UPS3PDK7S5PnG4zX/ePP3+cZiRFxRdWZ1BSoiTviUbebzPzcY2mrT0ZIs9GeghDoSHNNYGrcKVESckkK7m40OD6wfiDz2sg9BRnZiIWmj5fOjdz3FX5ecUHZQ5+1PH5/suc4B58v7nwozWOJ7m+PwYBX1jt3LN7hQ/Ohx+hFbk8NFfjlTs9GoFHUtgBtTv9CaEneX1zhK8UzchFaPjnV/ZYaYR88ezsMWfUdj/md97eI3DVcQ0D4PX1PpGv+Mnzs7y8IVEIfMdhmObsjTMWmz69pOBON+Ha3oRzszVWZyKUEiRFwTAr2BlMuLI3oem7XNmbMFP3ODNXOzZWrUjy5XOzxHnJi7d79oGXkkbg4jqSh+ZrXNkZ4ytFURoMhvVuzJmZkC+sdRASBnHBXD3gwmKd83M13t4ZUQumj5IAR0qeONXi8s6QSVaCACkkke/QqfnM1D32hilnZ2rMTqOd2ZrPhbk6FxfrzNd9HCWRQlKWmje3hkyyish3ScqSjf6EfpyjjSF0FcO0YFKUNH1rxN/cGjDOcl5bH5AWml6cc+F2j688NMezpzvHnz9wFa5jJ2LeHTV82IbjyGh+rzX//eshR+dUUvBTF+a4vjei7jvkZcVy09bWjt5/vuEz3/Dva4zzShM4ii+szVBUGldJBkl+3Dr8eYfzY+ZcjnDiZB4gfD+58LvXpIWlowAotCZwnHuKwh907rvbSy9tjwgcxVw9IC0qNroxvpIM0py8qDg9E2EqQ2kMSV6x0PEZpjmbvZSDccrL05TXQjPkzGzEG1t9zsxEvLE1oOY5KCV4eK7JO3tD5kPFei8hdBx2hxlnZ+q0Ipe9YWIjgqZPw1P8k8u77AxSXr5d8cxqi6WWTy1wOBjn/MlnVqiH3zOQR9Hd21sjIl8hhAAjjmtI3XHGxiBhuRUgjcAgOJjkIAxfPDtDO3CRCmaiANcRPLrUZKUdUGg7QDdICkJP8cJah35SUGrNOztjfEcxSgtuHEw4HGVIKQg8xWzN52Cccv1gjJSC292YMzMRGMPZ+YhL20NcZRmpqwoOJhkjtyRwHVZnQgZpiSMk9cAh8h3eXO+xNUhZbAXEeU7oKa7vjfjyuZnjjUMz9HhmtXPfaOTDNjNH90Kcl7y9M/rQqNpRkmdX21zdG0074uostQJ0ZZ2k5e+7N2q53/18lKYrtY2AHqTaxifVNv4g4sTJPED4fgqI76am0cYO07l37R6Pd5r3ObcEtvox13bHGKxzSrKKwFPkVUXgKjp1j6zS5IU+ngkJXcWV3RGuIwkcxWsbfUJHsdGPkQhuHcSkhSYvKs7M1Xhre0jo2rmcUmtudsc8vtTk+bMdJPAvru1zp5vQilzyUlPzXVZCh+4k4xs3Djndjlhth/zLa/u8tWWL2U+fmqGoKjvUdtd3clQjCHxpO5KMYJyWCGGYpCU7w5RG4BC6DroyJFlJWlS8tT2iN8m4vDPikYUG3bDgX31imZ1RSqntMO3R9xZnJS/d6XFtb4wxUGrN06fa3OnFaG3o1HxW2xFvbQ041Qq5eTjh6VMd5hvW4fz3r20zyQu645zDScbeKKPpu+istIbKETyx0sARkjizdQ5tDHcOJuwOUyoMaVGxN0wQQjBKS5KiAgT9JKcdeu+bwvmgzcxR6jUrNFf3Rsfpvg+LqmfrPr/y/CqvrPeQ2Fb2i6cadCL3A2di7saDWtv4UZ+R+TCcOJkHCN/PQ3b3mqoqOdUJEQgGSfGe9UdzKN1xhu8pzs3W+Pat7vFcymOLLQJXcW1vzMWFBpuDhGFii+B/5JEFbvdivnimw0t3+sR5Rd1X/LFHF6mFDuu9hIcW69zuTmhHdtLadSQ3DycstQJqvsPpdsQ3rx+Qa0NRan764XmaoQfAT5yb4053nYNxSug6nJ4JcZTg7FzIy7d7GCG5czjBVQolLPHhy+uHzEYB1buYG+O8JCkrLi40uHk4sb8Xhl965hSBJzmcZPzW5T1uHYxRQtKueZaksKi4ujdhuRUAglbg8ltv7/LLz57i6v736gjvngPCCA7GGd+5ecj+OCPyXNY6EaXWjJKcV0cZ/Tin5jl4juRON6bSGl/Z2tlM6NOPLQW95wq+8vA8c3WXubrPKC3p1Fx6SYkSAiUkFxcbXD+YsNFLqYxAoJlreFzbHeE6CgP40zRl7d18Qbx3MxPnpSV7vTvCcSRKCDa6Ce3Q+0hR9Wzd52cvLrA3Srm2O+bK7ggBPLzUYP6ueZkPwoNW2/hxn5GBEyfzwOGDHrL3C8nfvQa47+sMBmHACNCV4fLOkJrnTGcSFNcORjy21GCh4VNpzVLDp9CaJ5abXN2f0PBdFhshoav4rcs7nJttsjfJOBc4SGFJQLOqInQUg6wgnRqvp063uN21XWEPLzbJq4o4L9nqJVyYtzFI6Cl++blTvLE5QGIH8J5aaWNMRS8uUEIwzgp8R7LZT5it+wSuw0+cn+Hq/pjZaZ3jaJjv+u6EwJdcXGjA9PMutQLKSjPKKr58do6dcWIpfErDT12YQynDi3d6pLlhEMcstYLjFNvTp1qApdSJ85J+kmMMdg4ICD1JUkjWZiPm6wECwW+8tUNvkrHaiUhLzc1Dy323NUwQgELyyFKTpWbBY8sNxkVJqCTNyEMKyfn5GnFRcmGhxq+/soPnSnxHcXGxwVzD57u3u9R8xSAteGShyeYg5eceXqQZuGhj+OaNfSLXwXAvM8DdG5PdQcJ6P2GtU+Nbt7rkhaE141FpTeDZQdai0pSaj5y6unEwIfId8tIyN7y5OeSJ002ePd053uF/UHrpQapt/LjPyMCJk3kgcb+H7MNC8nevuR+lx5tbg+Pi9CDNuXRryNOrbYrK4FUwTgpevN1DV+DOWgbZQmv+yatbZKVhuRVydrZGLy6pBx5RYNmkr+yNqPuK33xrh6s7YzJteHihTjN0+Ylzs5zuRPiO4s3NAWlesTfOWGqEXN4esdj0uN2LSXNDO3R56lTLGm9HUFQVr270qfsOL97uMckKPNdhqREw3/B5YqXF+fk6h5OcfpJT95zjXeVjK02u7494baN/TE0Dlu9ppRUyTEpW3QiDoR24uI5gq5cSuBKEwXMktw7HNEOXy1tD5JTNYK0TWVG5bsJWPyZraVxHkFUa35E8t9bh5uGESVqw3U9YnYmYrQf4ruL6/phhnBO4ikeXmuyMU67vDzk332C5FfD2zpDTnYidUcpax0dj+OKZWXxHcHG5jj/VHSq15vRMRM2VrA9SPCnJtaZeOOyNM3bHKUIIDkcZX7owQyvw3rPDbkUuL6x1+IPrh8eDtHFe8spej9m6a6Oxdo3LuwNGaXHcwPFhm54jo+t4gssHY2qeg8QSuB69/4dN/T9I+HGfkYETJ/MjgU8iJH/3jstow5X9Ibf7ExTS1jYKzaMrDZ4+1ebNrQFb/QRhBFJBWRkWmz7v7A4pK83ZuRpaQ1JVjJOCOK/o1AKePWuJJLcGMU3f4ZeeOUVeaTqRy+NLTd7ZG/HIYgMpBXvDlP/4d68xyapjCpjfveLRmqbQKqN5aL5OWmgEglboMdfw6Mclh5OC5VbA4STj6t4YAWgMvXFOVho7iyIVsy2PZ9faaGP41s1D8sLSzhzxmw2Tgnd2R7RCl2v7Y87M1LhxMKEdukyykseWmzRCd5pWKvjqm9s8t9rh4kKDS5t9/uBal5qnODsX8ZUL8wSu4qmVFnujdDq171BUliY+8uxw4ZnZOpv9hFbosZnEKAzX9sastAJ8V/DzTyzSDF0wMEwybh7ElBXcOBiy1oloRi5PrrT49s0Sd2rgRWXpbM4IQyvw6MUZ26OE0Hn/HXZeaUqt8R37fUeew1onYpzZe0E5gl95fpVwKi189732fpseCZTaMEyscizSzgKFrsPhOGWY5rw1bSz5UUgvPah1pE8SJ07mRwCfREh+947LkZJ3dkYErqLuO2Sl5vZhTKkNrYEDRrDei/Edm7aqBy57w5QkrxilJY4SPLc6Qz1w6MUZ48jh2u7YapynFedma/TjnE7d5atvbvPkSgvPlZxqR7y1Y4kvdWXrMpu9lHOzNVxHcm1vzGsbfZ5fbTPMKsZJyRvrfWZqPnmpMcJqhLyw1uKt7RFvbg45jDO+fH6WmZpPnJf81p1dHllsEE71NXZHGRJ4/UiBNPCQUvDW1oC1Tsjl7SGOUsRS4DsS31X8hZ88yzApGKY5zamDAZDSdqiVleHGwYTKGC4u1VnthFRasztKWWkHtqvMsQScm72ErX6K1hol7dT7QtNnruEzSHL+6COLxHlJWmr2xzmvrse8fKdH6DoUpWZnmHK6E/IT5+d4rt5mnFU8e6rFVj/l8tYQ35HoSnN2vs7+OCcrNYMkR0rBcjMkmUr6jvPSUuff1cJ8lFbc7Cc8ttjCdQTNaYTzYQSW99v0PLrU4O2dEVlRceUgng7R2uHc1zZ6JEWFEFbgqzXjfd/38ucND1od6ZPGiZP5jPBuKYCP8vp+UuA5gmbgHbeOvlsy4EhKVcDUQYhjpbzeJCMtSpqBfWDffZMfnS/NK7QwPDxf5+3dEcM4Z5gWnJ9t0Kk7vL0zoh546GmL7u4gYW90ZPwVZanR2jDKc7JKs9issdkbszcqEMJwuhNSVpr+JGeSV7iOpNKGcVpR9wxSgisF26OEtU7I5iAFA+vdGK3tfMUkK9gdWmN883CCpxSH45xSazzX4excje4kpTDQTUq+eG6Wc3MRv38t59U7A0ZpxdpMROQqru9bmV1XCZ5caZFWtmPOaBAIPCnRBnaGGa9s9DkzUyMvfZabEdf2R0RKcr07oRN6HE56/MzDi5ydr6G1jZBeW+9zfX/E4ThnpRPRDFzivAIMj51q0Aw8PCXZ6iVs9bdoBgopHc7O1lmbqXHtwLYFV8Zwfr7Ob1zaphG47I0T6r7D1b0xrVDRjUuGccFGL2FvlPOvPbNCWlR8/e0dfufyPjf3J3YGKFR880aXmchjvuZxZiEi8hSDpGSYFryy3kNXhoeXGkyyippvZ5aUEjx9usWVvREvr3eP04ofdv/eb9MzSApeXe/brrYZK0W+N0wJPMXVnfEx4amUhlfW+8zW/fcIbT3IeJDqSJ80TpzMZ4DbB2O+dmmHorRqd7/w5Aqrs9EHvv4fv7rJetcSNH75wgwvnJnhre0BN/ZijDA8vNDg0aUGd3oxVWVIy4qDccb2MKU3ydgeJPzT1xW+K3nmVIv5ZkDgqONw3WD41o0DXrrd452dMe3QYbkdcHGhSeQrfKmojJVqLSur8ieEYGeQkpdW+7wZuASuncG404250x2T5IZXVI9xpvnCmQ6OIxnEBW9tD4mzgp1hiu8oFpq27rM9SNkf5azNRSw3g+MdcqUNjhJ40nDrYMwgyUkLK+yUFQaMoeY7DJKc/WHGo0sNmqHDICmYizyeW53h+sGIeuCgtRVfu7o7ZJzbFFfoWcmAg0nG3jDht9/eRWtLG9MMPSJPcq0bk2Sa290Jag6u7qWM05LdQcpcw0cKq4b637y6zp96ZoVO3efsTJ2DOMNVklIb4rQiLzR5WdGOPOqulVJIi5I3t/oEjsRrBGAMG92YTs3l4nydwhh0ZXAcuN2L8ZTkYJSx1AwoKs0wgWQ6oKm1YW+Y8vKtLkpJjDFkpaY+lR/YH9vBy6dPt5AK3toe8tRKi+fPdPjWjQNcKXFdyc4g5Vs3D3h8uXnPzNLFhSZxXvDsWtum6T4E96tDAEjEcdQXeZbU85GlBtqYewhP1zo1RllBWlQ/lumlHzWcOJlPGWle8rVLO9R9h1rDZZIX/PqbW/yFnzx73x1hmpd89fUd+nHOSjuiLDUv3eqycRgTBY6lRheG290J13fHfOFsh1bgEOcF1/fGXFyo83tXUowRCAw1V/E7b+/z5fMdvnRunlJrXt2wO9fbhzHrhwnNwMEguLEfM0pL/vQXThO5Lq9sdOmObUR0MM5ZbllH1QgdlpshT55uUZaacVIwySt6cU4tgK1+Ql5UfPd2ly+ebfPOfkwjdGn4DrXAGr6a77DZT2lFHu3IYbufkOQVjy83mVnwubI3YqHu8/rGEE1FoWGpGdDwHXoTm/YJPUnNUxxOUm7sG1xXUXMV47zk+v6IrNScbtW41R1TVJpJXvHMqQ5aGCaZNXzzdY/furzPcjPk0naf7X5KPyl5+lSDZuSzOhOxNYh5a2uEMXB2LmJ7kPLaRp/Alcw1AjqhiwGeWmnyxuaQTt1lf5gSuYpr+yNGWcHzax1+5uIcv3d1n3e2xmRVyc4wm6YVFRu9mBfv9LhzOMH3FT95bo6ffmSeN7cGzNddru7EHE4yDkYZy62QUV4gpaDhK25PpRHe2R3y9Ok2lRaAwHMUnalo2VzDpxG4PLbcojvOePJ0EzBc2x2z0AytCmSpubI7ptJ2SDJ0HRC2NvfQXP09PHfvh/vVIZ5dbXN5Z/ieAng7dO8hPM3KimbkfGhK7gQPDk6czKeMo9RVrWF3gDXPpTfJGeflfZ3MOC+tOp+ynE6uJ+nGOcOsIJjOUQCgC+KyPJ7gl1JQaTtoWUyjjsNJQV7B4SRjlNpW08BVdMcZaVkxSgsO44ya55LkJd409ZUUFTN1n6dOtXlkucYfXPFxnS5poZkUFXO+x8XFBj9xbobtQcKrG11GaUF3YtN7IK1OuTbsjXKyQjNfF4xSjcGmouxrFd2JpYzxlGKlbXf0V/dHOFJyEBestANOtUNKNEYL0rKkFblsD2JmawGeo3BGMXGucSpDmlW8tjlEsUszdPmpC7P85IVZtIGFpkfNddGY4/mYW4cxeaEBgUTQCF3GecWdbsxyCSudgKzyGUxyAs/hkeUGr2/2SQpbu+pPcuKsREnIteUw++aNQ7qTgtm6z+lOxHIr4ImVFtf3x/zLKwcMU8sCsDuw9aB25PHGZp/KGJ48tYByBJv9hKQo6Y5zenEFU7ngrDTM1j1UDAeTgv1xxqmZkKbnIh14a2uI7ylagUM/zii0wYBlZ9aGy9sDklxTmj10CXd6MaOs5NxcHUcJqkojDDy2ZK+31Jo00zy81PhYxv5+dYj7FcCDu/jk3n38g/DjPEH/oOHEyXzKqHsOrmOlcWuejWRcRx5LpN7v9ZHjMEoTCm2NviOh6btICXmpQRiQEDlWrwVAa3M8Xe9Jyf4gxXElnhS4SrI/zpFHDLeeQgrB4Ti3gmRVxSgryCYpUloyy6ys8F1J5DhEnmJtJqI7ylBKHhNLxlnJb1/eY5DY/x9mBcXY7lAXGz6Fxqa2BNQDh71RhtGGsqpASEZpRamtXsvpTkTdd3n0VJPv3Drk+t6E/XGB7wrySrPQDPiXVw8IHEGn5vHYchshBJGnaEcOV3aHaA17owQlDI6QCCH47cu73OklFFrz6GKTi4t1NvoJNc+h5rmcm6vzm5e2EQL6SYmnBHM1b0rjnzNT87k43+COmrDWiagqgycFQlh6EyklxlS8sTnAVQ5v746Z5BUCgZJyOmdk5ahv7I4ZpgXtyCPNS27ktoi/NhNRVIaVtm1Pnq15XD8Y8Y3rB3zj6gGeo3hiuWl12gvNC+c6PLXS4ts3u/zB9UPqvkNaVASOC7qkKg2DLKfShjgtaQSWPqiXFDRxkELwjeuHOEKy2o7opznv7IxYnQ25uNQg8h1cR/DkqRbjrKAy5iMPS96Nd9ch3q8A/nEL4z/uE/QPGk6czKeMwHP4hSdX+PU3t+hN8uOazPvt1ALP4RefWeG/fWWDO93JcU3mi2dmeWtnwPXdyXFN5rGlJrd7k2Pup194coXbvQlrcxEvrndRpSQpKp471aEeOBxMMmq+w7OnO8RFyesbA+Ks4tK2zb+3my6PLbW4NM3Zn52r8a2bXX7v6j67/QzPlczVPRwpQDA1rJLHl5scjnLmIpe9saEdOCip+MKZFpO85KfnZ7m0M6QVuKSlZpyX+I7DJKsoK81mP2O25pMWFa407A5Tljo+7cgjzkuu7o3ZGaQsNnxeOGtZld/ZHXG+E9GKfN7Y6tGbFAySlEkGSoLvKgK3stTxecVjp5poDPvDHKkFT51qE7gKJQWPLzf5rcu7bPZihBCstAIybciFIXChwvDsWps3N4cM04JxXvHQfIjjOHRHKVuDjPVuQjMY4ypBzVeErk05bfYThllJnPmklabShrKCflpQDyRGKxabAZNiTOgIXt8YkJWW2n+p7oOwpKZKCtqRTyu0MyqnZ2ostUI6NY9SG75x9ZC6Z7sBV1o+L97q8ui5FjN1DyUFo7QgK6zz2x+ljNMKoytmIp+VZsAgqzg7E/Glc3MAxwwRSgmePWVniOK8fE/jyMeNJt6vAP5RC+MnE/QPHk6czGeA1dmIv/CTZz9yd9nqbMS/9dPn39NdttwO+Mnz93aXLbeD92i+xFnJn3xqBSkVStlJ/gtzdZ4/2zleV6sUz5/r8MSpJg8t1glcy0b89Ok2h9Oc/ZsbQ3aGKRfm6uyNMlsLKUq+fH7ONgNIQzBl3z0zZ2nXm4Fj24PzkgtLdR5farExSFjpRLy1PaDle3z98hbDvCQrSjzlEnqKWqB46fYhb2332eqniAEsN0OkEszUrHLiM6c79CYllTGUpaEROQzinDgtkEJTVhJjtGXlNYbDuKAdOgSeQglJqTVCQiVsFxhYcsa01PzZL67xD19aZxgX3O4lhK4i8qAbF3TqPkJKHl6oc6sb8/hSk/V+gjYFcaqp+Q7awKXNAaEnkQi0gEubPTSCx5ddSm3YHaRTPrEB47yk4bu06i4PLTRYaAT8/o19MALXsbWm6/sxyzMBStbJC03dV8f1jSOj/BPn5vjOjQOSsiTQknNzdTQGKQShp4h8B0faJoRJXnK7O2GhHtBPCoZJzivrXb5wuo3vOzy9+r2I4O7IYpyVfOvm4T2Rg8H8UKKJkwn6Bw8nTuYzQvARW5fvfv3Su17vKHnM5XX3sbsfLo198J5ZmznOqceZ5tGV5j1rHSV59nSHVzd6KCFRSvLoYgsD0yKwNUpZoTmc5Azjkkme0whq7AxT8krz3GqHU62QG/tjxlmBEpLnz86idcU7exMi1+F3r+5zfq6OFFDoil976Y4VACs1eQWjrGQ28tjpp2z1E55abds0T15RVAmPLtWp1X1qgcv2MCF0HYQwNEOHU62QlbMh4yxnb5iTFBMCT9KPS7JK4wFL7dA6ju4EoyFw7GBkd5Lx+vqApCin5JaWNfmNzT7jrGC2Zp3fMC3Y7CUooJ8VBI7kS+fmmFzZJc0qIhc8Rx03Imz3E07P1LgwG3IwzlhphzxzqsONgxGh73Bhvs4r6z0M8MSpFqfbEZv9hHqgOD9TZ7Hlc2lzSG9aNxPScDDJWagHZGXFLz912jZ/TFHz7WZhklfsTzJL8VJp5loeW4OEw0mOUrbu89Bcnf1xlzi3mjyXd8aUlWacVfzys6d5Y3NAK3AJphuRuxmZ3emckNaG19Z7GAE1z/nMo4nP0wT9SV3oo+HEyfyI4egh/Cg59Vbk8pULc1xcbHB1Z0ReVQynXVdvbQ25sT/hVtdqs3hSUCjbxeaqGhjBYtPjX1w7JMkqPCXxlOLSZp/tYcpPP7TAbMPjtfUev3azS6ENoyQnLa0WezWVFyhKqIzhYFQwyqwmymIrYLNvW7HXu5InTrdpBg4v3ulaXRxlU45CStZ7Cc3Iw3EE2kBRahqBQ+AKljoRxhjmmh79uCAvS7pxyiS3Ms/n5+pI5XBlZ8g/fHFEO3QZJSVJVrFR2NZw33EYpxV5qRllBaHnsNSMODNTI87tnEkz8FjvxfTGFZUxnOqEnJtvsDPKrSy1tnNEoevwzFqbyLM1lFrg4CrJQtPnjz22xN//5g1+860d0sIwSDIi12HPz3hkpQFa8IW1NpsDS0VzxMP25taAvLARmtVmESAM7chld5SxO8hIyoq67/KHHpknDByu7Y548XaPZuAwV/epeYp/8tomX7kwi0DwwpmZ46gkrzSDuKAb58dRS81zqHuKmehe5c+PEk18VMP8SQmmfVo4qQt9dJw4mR8xvJt1+Sin/n4PoaMky62Q+bqdiH9lvW93qI6i0JbOfaRLUq1ZaQfM1X3OzNUYJQWb/ZSH5uvc3I/ZHiakhebp1RaBp9ibpASu4Or+BKYF+nFSMskqXCVwXIVrKrSBtKjIqwKB4E43JnAdzs5EXK80Xzo/S81zyCvNmdka52frhL7kMM4IfduB9+hSi//h0g6eY7VcEIasFDy25LDQCJipe7wy7tGLCwSGf/72gZ3dEHs8v9q2MynGzgIdjlMMhkpbHZlRXiKGCfNND4Gtt2wNY5bbIcYYNnsJ++OMoqo4MxcRui6zNY+DUcZiw2dvmJGXFUVlWGoHtEMPz5V4nuTpUy3SUk/JOT0O4py8NASupNAOygjiPOdgmNOKXK4fxCw3w2P9n5fv9BglOYfjksrYzrbzC3UCpdgdZTxzuo2zZrsPJ3lFI3D5yoV54qwkdATNwKcRumwPUgaJHXYVwGvrPZ6dOkMJrPdi6r5DK/SY5AU7g5THlhofO5q42ykeyULP1v33OJSPKpj2w4oiTupCHw8nTuZzih8kFL/7IZTYFFr5IbvMo9+lRUV9utOeq/lcmK+z1PSpMPTjjP1RzlIz51QnpNSwPYgZZRmDuMAY+NaNLvM1j9uHMYejlElaEvpqSu0PtcChKCuqUiMFuAq8qYF6fKVGVlZsDhIktvX20taIVuhwbqbOmZkaUgnKCpLCztTc6k7ojjIiR3AwpfRv+jatWBnDpe0hjy41qRD4rmKrl9COXKpK4zhWtuBUJyTwHFZnIkqjuXMQsz/KEAICJawzU4pfenqJrUHKOC1Ym4lwlWS5FfDWZh9PCVqhz0IjIM41cV5wbq7GhYU6jhCsdEIcaesby82AQhsGSWmHZVc7DNMcV1iWZgA1VozinMNxxXxTE5SaPK+4sjvkhbMt9kYZX3tjm+4kx3dsvepwUlAbZnzxzAzbw5Qre6NjBVTfNXhKEnmSP/rYAt+4cYAjBHd6MXFe4ivJw4sNru6NKEpNWlREgcOFuTprnYjupGCQ5DhScnY24tGVJtf375VJhvc2B9x9P7+5NaAoNVv9lKQoubY35o8/vsjWID12KI8tNbm8M/xQA/7DnKA/qQt9PJw4mc8hPolQ/Ej98aOe54ir6s31Af204FQrwnMFy62QnUHKqXZA6CoCV7DVTzkzW+O7tw54Z3vIpDAkecnabIQScLM7wZGC/bEhLjS+J3nyVIvNXkJRGXqTHIHAmbYCG6MpKstZ1R0XxFlJ4EhmGwGONAgBt3pjzs7UeHKlSaHNMTV/6Cn+i5u3meSayFdI5dvGgMqgpGK2Likqu3PWxkojSyWOBdl6cU7gOrRrBs+VNAKHmq84mFjDWGoQScnV3RE//8QST55qMcoKHl2oc3nK79YKPQLfYaVlW5Gv7Y2YqXm0Q4+nV9vHBJIA+6OMK3uWNkZjeGypSc1XZJUg9ByiQrM/zhDGUFQVnZpLkpXM1zy+c7tHzZX8p79/g91+jusKar6DkoJv3zxkoeVZYToMjy21eOVOj91RSs137k0pCcnzq7N87dI2/XGB7yh+9uI8s/WAl+90WZuNWGiGlFpPGbTtELAUwurqaM183We+7n9gc8Dd91o+FbXb6qe4SlLzA3ZHCb/+xjZfOjtLK7AUMq+s9yzZafD55S77PNWFHgScOJnPGT6JUPyIk+y1jf5HKs7e/Z7N0GWUFWwOYlY7IZOsJCsrAk+yNhOSV4b5hmCm5rHdT9kZZCgpUEqy2U1IM01eVSy1As7M1jg3V+dbNw64ujOiHTo8edrWQ65uDWlELjM1n61+YtM1g5yaLxmlxjIHa9gf5RyOM7QRSGEVJh9ZbvLlc3M4yipbPrxYJy7mePHWIVv9FCngdCdioelRaVibCXAdycEwZX8aXblT9l8NrM1GTLKS/nTgMtPWORpjqAcKiW0B/vXXN/n5p1Z4YqXJP3tzByUEaVmx3ktxlGCUjMgrjVKCL6zN0AgdLu8M+fK5WcDu8q9MmZePjNN3bx0SuXbi/bGVBv/yygH9OAcNrZpH5NnfdeOCvKpwlUtvUnL9YMzFpQbDNCfJNUleUfetlk1VGaQUPLbc4LGVJnVPTSNAm2Z7c2vAI8sN1mYD/uDaAa6yUtq7IztP9ORyy9Z2EKRFxRPLTW5143scyN0yyR/lnvWmzi8pSmp+MP0skiz/niS479jv2sDn2oB/XupCDwpOnMznDD9oKH4wSnltY0BeVtzcj3lurYPvfO88cV7iKHl/uV1l22efPNVhszcBA9+6eUB/UnCqE1BzXSZFxUNzNV5b77PVz2jX7I5TYpmZF1oek7hib5hTVgOeXW3zzGqb/iRnf5Qy2wh4ZLHJfD3g2ze7PN2JyEvDIC0pTWVZDaY1mExXzNcC1nsTlBTs9DMqDafa0bGio6ckUWAHRmcil+1+yigtuXMYc36uzh99Yp6XbvVIc01l4EtnZrixPyHyFKO85OnlNo3Q4WcuzvO77+yz0vE51QkYxDmH45QsByUNcw2fCri00beGT9jW8jtbA0ZZyUzdZbbucfNgzFMLLTo1b0rsWbLenXB5e4Q2lmHgiZUWTmRbxm/sxZxfiDgYFRQVtGoOPzc3T1KU3N5PMNrO6RyOU9JCc3Y2YpSWjNOSl+8ccqpZoyw17chjpe1za3+CrgxGwKl2yDeu7bMzzDg7W6cZ2fRXVRlyNLe6Ce0o4MbBiDOzEcvtOufn6gSeYpQWvL07JM0sS8STK637Uvrf7551pCROM+K8PO5odJTkudUO1/bGHIwtMea52RpX9kbHA8VZWeG58jhl9nky4O9OX/+w60IPEk6czOcMP0gofjBO+Ucvb6CEfb0Rhss7A76wNkOpNUlR8uqd/nuUEI/es5gOC46ygsNJgatyLm+NkMKSNJ6bidAIVto+ncjHdQWOEDR8yxDsSEF3XCAlrHcndOOMXpLRCVxGaUmlQYiMK2LE40tNFuo+p9uWbv76/hghBEVlqLkOlTakuSYPNONc8/hycyrK5fLtW4f83KNWmjmvNMtNn/9ic8CrG0OUFJyZqbHQCjiYJPz3r23hu5KHFussNjw2egln5moErkQgmGl47Pcz/odLO2z1YyZFyW4/ZpIVUz0cjTF6ytosGUwKbh/GXFxqstlL2OxP2Okl3Dywksaltg5pZxDTDF32RjG//saW3aELQ1ZotvoJjyw2WWr5VEazO8wIHIdW6BI4inbokeYG31XsDjIWGj5padOBNw5ilJS4SpAVgn6Sc3om4sJ8nfm6T6fm88hig9c2+7x6p48QwjZaKMkzdcuorLXh0t5U2iB0ubhYp+a7vHCmg0FwaXPAG1sDQlfx3FoH1xHHEdn9jKkECq2Po6i3d61cQ+Aqnln9ntrlbN3nV55f5dX1vr3XXXk8QHw0UHx0T36eDPj7pa9/nJmVPw5OnMznDB8Uin9QM0BZaV5d76OEYK5u0xF5pZnkJdv9GKUExggi/3upmje2+sdEhCvNgN+8vMsoKVjvTsgKzWY/ZZwVtAIPXyl2xxlnOhFGgwGW6wEbw4TxIMUYzWLdQziKTuQjhGB7mGAqwzAuOT8fcWV3QmmsBkorsHQwV3fH3OxO0AZmI4+i0hSm4uJCg7MzEaUxHEwyAsdS4QSOoq9zDscFb+2MyAvNd28esj+2zMeeI6e8YCmV0Sw2Qjo1n/1RxuHEshg7SuJIQV4Y7hxM+BdXDphkOQiJkpKDSU6aVziOxOSarIIkKzmc5LhKsjFIEEJQDxxLFZMVVnvHC1FK861r+7y+0ed0K8TzHDxpiTSv7Y8YZyWhqxhnBdf3C07PhByMcpqBJC0sf5xAcLoZsD2IWZ0NOTtTp5ekXNocAhWRB83ApT3j0Q4dnlht2WaIvKIZejY1OCqQwjqXuu+w3k14+nQLDHRqLlffHOM6lnbo6dNtbu5PkBJqvsP5+RppUbHQDAHLhZcX+r7R9FHknGQVV3bGpHlFu+7yhdUZXEe8J202W/f5mYvz99zH7x4oPnoOPg8G/KST7AfHiZP5HOJ+O7kPawawA3Yaz5HTnwooON0KEQImWcVGP6Edesfps61+zO+8vYejJDcOxlxcaBB5ildvO7y+1QesTk0vyWlHHghbx3hkpUEj8MhLTbvnkuQaBIyzgsNxziDObH1DSXJjkGXJ1b0JjUCxP8lIkpyvxTmrMxGdyMMRgp95eJ6DScbpTsQwzXl8uYmSimt7I5ZbAeOsYK7pM8pKTrUibh9OCHyF5wj2RinDuMBzrJNJipI40yy1fOqBzfLvDxM8R9HyHTxHcXOY0p+kHIxzWoFLklcEnmJnmJJmBWkJ86FLJirKtMQI2yjgKknkOewMEvxYYhBErkNlDLujlFGSEXgenQq2EfTjkvPzNSpj6E8K4qJkoe5PKe7h3FzEb13a42Ccoo2hHXjcmsoz7/RTfM+hrAxPLDcJztjiuKckg7Sk5kuWWxFJVjHOSiLX4fRMiBKCymjC6UxRaTRCGCaZJWsdJwXn5yJcxzIhvHj7kFOdiNmaBwhu7E/wPUUvztjs2U6wyhieWW3fw8R8d+QceIqzszWu7A155lR7ev9x31Tv3Wnao///vBrsk06yHxwnTuZzirsfvA/bTR11ht3at1ozeaGnbMyGymi2hgUY2B0kvHKnx089NMfhJOUb1w85N1vDcSRFYbjTizk7E+E4ksBVgDXaVWWQQMt3uTBf59nTM1zeGjDJS87P11mdiai04Tcu7eC1BHe6lsL/cJIRSKiEoDtK2OyDJ6HQILOKnWGGMbDZT9kYWMGyUx2fp+otHlmoc2l7xJOnmrQjh1fW+4yTgsBT/NzFeS7tDI+li9O8QhuDlJLepMCgLQ1+6FFUsDOMGcYFSlrernroMlvzGSS2tjLXChhkts7hStCOQghDPylwBASuQEhpVRsLjTulz1fSZb7hExcF+6OUqqrIShCyYm9U4XsOSVXiOtZwl1qjBFxcaPD2zhCFtOm9+Yis0Gz0E5qhwwWnzno3xiBYnYkYZyUHk5RKw2rHDmI204K3d0coIdgcpJydqeEqSW9sZ2oWGwGOEihhZ498JXn5Vo/KGLZ6KZ4jUY6gLDWb/YTFZsDlnREPzTUwwPm5Gr/+xvaxA1lr1+5Jmd0vct4bpzhCkBTVlAXh/qneB2mQ8aST7AfHiZN5APBBuymw3UI1z+G5tQ6XdwZMspJHFxs8vNTg19/YZq7u4ynFQwuCt7YHrHUDbnZjVtohC82QpLAtugwgzSve3hlwMMq5MF/j9c0S4xgCX/L8mQ7PnukQeJbhWGvDIM4ptVVxXGr4zNZ9BH26cYE2hlboMZikpFmF1hBELnVXUmhNb5xSVVD3rXLilbwABP/2zz7Eld0xvisJHIf1fsKTKy0uLjZRUvDGZh9XghISoeBwkqOFQCGo+ZKa7zJfD2iFHpuDhMNxRlVpfubRed7aGtKPc4qqYq7ucqeX0PAd6oGiP84IPEm7GeI40vKaFSVaSyLPRRhL7uk6AiWsBENWaCtQVsI4AykhchWlNmwPEjqhy+F0WPP0TI3zszWEFNzZS/gjjy2w0LB0MfujlNPtkNAT/It3DhmlVsYhLzUH45RW6PJT5+cZpgWTvCRyFb/yhVW+ef2AhxfqOMIye9/an/Cv/+QaT6w0eX2jzyAuKErNMC3oJwVn52rc6SYUeclKFDBGE3kOM7UAVwku7w54aK5OK3R5eMEKwbnSphG7k+x4B29nsASeIxnnOaFj5aDXZm3UdneN5e4d/4OWfjrpJPvBceJkHgB80G7qbgfkO/DM6Q5b/QnPn+vgSIkwAoxtEQ1dh7WZGk+cbuG7DrvDbKpyqeywpmN31kbDMC0IPEu46DiCh+ZrfPHMLE+vtnnxdpfNfkLoObx6p0/kSVo1n/m6R1JofvqRBdDw1UvboA07paETefSz0l6TEAigMpJenNKdWB0XR9l23X/08h08x2G+bmdeitJ2ONV8e7uO04Ka77LRG5GVFVlpWGz47A9TxnlJWlS0w4CDSc6pVognBYOkZL1nCSrHWcXBSKJUStN3OJykxFmJEdCJfCJfMVvzGacFWjuMy4JOXVEUElcp6qFLI3QZxAW3DifoqkRgh00lglxbTR5XCs7OtWmHLjcPJ5yZiXjuzAxFVdlaV+hSaRt5Xt+dkOtqKr8AwnYQ059kjJKCSsNrG31WOxFLTZ9G6BG5DoU21KRVJ711KBhnJTMNl4VGQGkMm72Uh5fqbPQSdgYpu8OUuZrPwSSzBKxK8dSpiLy0M0FH2jGR5+BPmyOUlO/ZwXvKbhTGWcnOIJvWvwK+8tAcrdC9SyZc3XMvP4jpp89bI8KDhs/EyQghFPAisGmM+UUhxAzwD4GzwC3gzxhjetPX/g3gLwEV8O8aY35jevx54D8DQuCrwF81xhghhA/8PeB54BD4s8aYW9M1fx7496eX8R8aY/7up/5hPyY+ymT/h+2mjhxQXmje2hkwSkvqvsvTq20uLNbY7CUkhR2ke3ixznIzZLOfcnomZKObcDDOkVLw808sUVWGa3tjHl9u4TsKIQXjLOeXnj7NQtNjlOVc2hyw0AhwAslSy2e9F/PIYoP21DC7StCIXB5aqPPGRp/ZyGWn0tRdRak1wbQJwVWGQlt1x8CR1HyHvNRs9y0R5nLLR1SC692Y8/M1pBAMkpyDSc58I+DiYoOd/oRLU70ZR1mp5yQr6E1SECCNYZRV5FrTn2T044JxWtKKPDypiPOKYVLy1KkWq52I9V7MlZ0h24PkeKDVdxSHo4KnTze5uT8hckIqIXAcq9VSVoZTMx7bg4xJUZIVFY4AYwz7I6sqqpTg1fU+692EL1/oAILXNgYoYdkLaqHDo+0Gv/POHnFWcjDK2B1ljJIcieDhpSajrOBOL6Y0Ib5SvLMzYrMXc21viBKSlVaEEILXNocEzpi1TsTuJMXvSlxHopTldpNSMF/3WO6E1jkqyTOn21avpqzohO5H3sGHruLsbERRadZmI1qh+4FDwO/eMMV5QaltOvbzjM9z3eiTwKdJ9vlZRTJ/FbgMNKf//9eB3zbG/E0hxF+f/v9fE0I8Dvw54AlgBfgtIcRFY0wF/H+Avwx8C+tkfh74GtYh9YwxDwkh/hzwfwX+7NSR/e+BF7DNUC8JIf7pkTP7PODj5Kbfbzd1ZAxe3ejx4s0uvTjndKfGze6EQhteODND4A7IcitW9uzpzj1qhCvtgPmmx7n5GsbAjYMxu1Pp4C+db6Ek7Awkb+0M+PU3J6x3YzYOJ7QiS6Gy1Y/ZGcS8KAQCwULDZ6UdcnYuZGsQsdlLWO/FrM3USMuKnX5CkpXM1QMCzw5DTlJNUmrmpWBnkKKNJvIUaalphh5xXoEx/P7VPUpjCJTDG5sDBklBWVkBt1FaWvmByp5LI0mrkn5cErkS11Gs92LitMJ3FQsNn9C3Yl6Bp3hovsFBnOM6iqw0SCHRlW3/dR1Jw3dJC6s4emV3TOAp2pGHKyXaaIRSrM5E3DyYkBcVpRaEnuRwnLPQ9FFKcHGxTi/O2e6nXJiv8/J6n3FasjdO+Z9+8SxzjYCHFhq8sdGl5iseCWtc3TdkpWajF1MaTc0reXixzndud3lzY0hclAwmGe1awEJD8+RKk5fXe5xuR9R8F09KbnXH/NzFRQZxzv5UvjlwFWvT+o7BsDOIWe8nrHVqvHin96GtxHmlCRzFF9ZmKCqbMhwkOXFevm867Gjd0RzM7jC190Ynuuc9T/DZ4tOukX3qTkYIcRr4BeBXgf/V9PCfBH52+u+/C/wu8Nemx/8rY0wG3BRCXAO+JIS4BTSNMd+cnvPvAX8K62T+JPB/mJ7rHwH/sRBCAH8C+Loxpjtd83WsY/ovP51P+vHw/eSm32831Ypcnlpp8srtHk+stAhch7yquLo34svnZ/jKhbkPVSMcJAX/9UvrACy3AwSCG/tjTncChBBsHMYM4oK5msf1/THrvQn9OGeSlRyMc0otkMp2uYW+LTY70rb5nmqFIARrQYDAECjYn5SkhUYJwaPLdW53Y3pJjuMIJA79OKcCHp5vkvgFb24MWJuLCB2HzTyhP8lZmQnRVcXBVOtGAIM0pyg1vpJEjmKcFuzGOb6ncIRATFNRlTFkWcnhKGOSFfz227s0QpfdQcpmPyZwFLXAI3BtvcgYA6aiUwtAQHec0YttOm53nBKnlrut0prQlTYKFJJhaqn25xoB24PMthkHBVuDhIcW6kgEr2/0uXU4ZqkV0A4dtocZk2mrs9ZQakBqytKQUHH7IObW/ohG4BC6lp1OCcPTq208V1JVkBeaO70JRVVxez/mD9QBSVay0PR5ZLHOTz40RyOwM1JlpfmD64c8t9om8tz33Iv3u+eOIpJS21TmUToNuG86bH+Ucf1gfGzIHl6o88bmgOdWrcbR570286OKz6JG9llEMv8v4N8DGncdWzTGbAMYY7aFEAvT46ewkcoRNqbHium/3338aM369FylEGIAzN59/D5rjiGE+MvYCIm1tbWP/+m+T3zSuWk7xa+QYrrWCFuP4aOpEYaeOi70PrnS4p3dEQej1LIO91NuHUwY5xVzDddyS0UegaOIixIhJXFRUqSaQWJTLl+54LLcjnCUYHuYEjiSsrRqmFtJwSgpyLWmrGB/kvHMqRa9pKQVOUhh51gmeck3b+wDhl5sFSlnIo8b+yN6k4LD2O6c24GHoxyb9hN2ULEyNpWDEJRVSpJXSCWnjsCwfphQCxTLrYBm6PLaen/apmyoBR4YQ3+SIaWkqjRFUbCV5gSuy1zDp7PgcWN/TKfukBYu+6OMrNKkpablK0oErrB8aGVlMMbgCKgFine2RwziktmGx0LD5+xcjaLSbPVjtvopz5xuc2ljwKSsGCQ5WtsJfm1shLXY8FjvpTQCF985EmQzdMcpSgrqgWRzkBIlOXMNj6VWwDgueGoqdnYwyXl7Z8RXLswdF/EdKYg89yPfi++XTos89Z76oYDv0ekcGbLNAQJx3BL9WdRmTvRf3ovPokb2qToZIcQvAnvGmJeEED/7UZbc55j5gOPf75rvHTDmbwF/C+CFF154z+8/LXzSrZGR57yn/nJhsXbPXMOHXc9RoVcKgdEGV0kOhjnjrKAeOpRGczDMiIuC5VaD2ZpHP87Ji4qqkghpyHLDKC7YG6WM0oK673JursbOIGGU2drEOCsZ5vr4vQ9HBVf3h1yYb1LzHE53Im4cTIi7FZO8RAo7aHg4zhhlOTuDDClhoe6z0asYZSXPn2mz3A7Z6SVEkaQ/stLLWudUxkYuvjA0Gz55XoHQtMOArNJoY6gFDmVhqPkSx3XoTVKEAE+BH3lTcTOY5AVRJtkdlIzTgu/e6iGEVeRs+C6uMEwKQ8NXpIWl17eKpT6jvCDLNQbNdj/mVndC3XP4wpk2a7MR8w2Xl+6kDOKK/tQRVxocJaxEgKPQRrM3ykhyzShNbVrDGGZqHn/86SV2+ylPnGrzjesHiCnnWmUMh6OM5FaXxXqIxtAOXfpJTt1zLKeZ+Ph8YUfR8N1F/vs5n4eXGlzZGeE73zNknzVH2YPUNv1Z4rNo0f60I5mvAL8khPgfAQHQFEL858CuEGJ5GsUsA3vT128Aq3etPw1sTY+fvs/xu9dsCCEcoAV0p8d/9l1rfveT+2g/GD7p1khHSb58bo5X3d499Zf7ne9+O7qj63ltvfc9SpHVGd7aGYAQBI5kkpUMYjtJ7irBfDPA2x5QakNpKqhAChgkBYeTnCyvyCqNQqAcAUh8BeO0QvK9nYAGjAEpJNf3J/Tignbg4ruCUguUtMqOB+Ochq/wPUkn9NkfZ1RG4CjIioqtfsJGP2Gu9OglJbM1j4bvMAldhkmJNrbI7ruCuuNgjHVeQ2XrNntZhqtdfCz/VlkVOFLguZLFZjitOWhuHk6otMFTClcqxlmFrwRKCtq1gGyU2BZfoZmpezyy3GCuFhA4ipfv9HAdwTgtKbKS7iTjO7e6vLHZJ84147RgqR2yMhMxyXLKfU3oOlRGk+YlypEgIfIVcW7JS0NXEOclv/HaLo5rOcIuzNdJS813b8ZU2nAQ57RqHoO0wHfgO7e6KAG7Y5vyM9jByZrnfKx78f2K/HenYgGuq/E9huyz5Ch70NqmP0t8Fi3an6qTMcb8DeBvAEwjmf+1MebfEEL834E/D/zN6c9/Ml3yT4F/IIT4f2IL/w8D3zHGVEKIkRDiy8C3gX8T+I/uWvPngW8C/2Pgd6ZdZ78B/J+FEJ3p6/740bV8XvBJt0YeKV1+0Pk+aEfXilyeXWvfQykiERyMMpSExUbAhfkGM3WXQClWWgGzzYBWP2GYVzCl0ncUjKdaKQ3XRWB343ujHGG0dSpYB6OmNRKtbRvzzy7WAEGmKwpdMZhUDJKU3XFGmlcI4eNIgRJwYb7BKCt4Z2fE7YMx1w5jJIK9YUY7cOlPUiLXxVOCmZpLVmqKyqA1tOs+AjgY5+wMMxqeoiwNE1OQl5qap1hqR+SVNfxZXhL5LoXWFBp8KcmqCoygqCpq/pRm3xgcIWlHLnU/InAV3XFB3XWRQtp5oihgq58w37B0N64U3O4l1F2XojL0JzlgiTHXZmoMk4LtQYYBQi0JZESn5jPfCMjLyg5nBi51z+G1zQGHk5S1do1rB2O0gdMzEQY4HGcEbsFMzedMp0Y3KVAIXrrTY7kVoCv4xeeWWW6GH+le/DDjffc57mfIvh/do+8HD2Lb9GeJT7tF+4c1J/M3gV8TQvwl4A7wKwDGmEtCiF8D3gJK4K9MO8sA/m2+18L8tel/AP8/4O9PmwS62O40jDFdIcT/Cfju9HX/wVETwOcJn3Rr5Aed726j4HiScVbw6kbvODcPNu0WBQ6l1pYK38DMNC2WlRXxuGKh7rE9SJipeZxqR9w+iKlMCkaQVRWTwv4stKHm2c6vi4sNepOMvWF+PPWvgcqAJ6zTuXU4odQV52brlNrKHV8/nBDnVj7AaMutdaodsj/KWe/G1HyH2brLwbhAIYl8O/OzM0pxpCAr4cJCHW0M+8OMg0lKUQkOJlZOuBM5mNgQuA5SQeAoS+QpBQ/P1DmMc3aASZKzP87wlaIZKuq+y+4wo113ySvDOCkoK81KO2SlE+AohdEwKUrOzIZs9q2qZc2VXNkdsDPIGCclnisZUeEKQSNSFOMKIQTLTZ9+UtIMHDZ7CY6jcAR4StFL7IzNbN21tC95jkHx1t6QTs1DGitBUJWGZ1fbzNUD6p7L3jBGCIGUgoM4QzkQFxopBK3QY5Rah7083WB8GD6O8W5FLi+sdRjnJXXPIZim6fKpLMXbO6NPLZV1MrX/4fg0W7Q/MydjjPldpukqY8wh8Efe53W/iu1Ee/fxF4En73M8Zeqk7vO7vw387e/3mh8UfNSC5pFRyI3m7YMRpdbEWcXFxQbLLWtY7m6JHsY5Gs0vPLPMy7d6vLMzRAJXD8Y0A49r+2OGSU5RaQSSuCwxGBqeZLsfA4Km7xL5LmWlCVyHTh0agcvhOGWYVBhhWKj7tGsBDU9x6yBmGJe0Q4etbnrMyRW4VnwsKyrakUelDbM1O5Ox2AjJpvov3UlOMJ24r4e2eyrJKhxlv6dm4OEpxSQvGSUZSaHs4KEHonTwHMEgLogLw9t7Q053aoRKYTyJkJKVdkhnSpkTupI4LZmJXMy0LjKMC6QSDNKU0HUAwzfGlljzIM4ZxAVlaTgzE1EZiLOSXpwzW/doBx5Foa1T1ZqLS3WMgYeKiqTQlFVFM7Q8axcWamAMr90ZMExzeklFO3RYbtd46lQTz5HI6RxkkpeEnqSXljy90mKYlSw3Qtb7Vs6hEXoYY4d1JeIj7/A/jvF+dwS91o6404/JCivx/ehSk9ma/6G6R9/Pbvtkav+Hi5OJ/wccH6eg6SkJAt7eHRI4VlHRcwVXd0bM1/3jh85gEAaUkhSl5q3NITv9jO6ksESZ2jBMCnzl4UhFWmh8V5FXBte1E+95aRAYKlPQilx2himuIxhMCpSSuMqhXVN0ag5fuTBP5Ln83rU9homlUymqyvJ1GUFlrHSwQeK7CrRhsRmSFAU3DibkhSHTFdIIsilvW6lhvhaglMCRMMoqkqK0uvXS1nAKI1gMPdqhy8YgBeBwUlFWBikhLwx5NcJXknroUxe2Y01IaAYOK62QRuiggI1uzJ1BijCGurTvMYgzQs8hKTR1X7EobapvUpYstGx0OEoNRWmjtFFWsNDwyUrN6ZkaEtgZpGSFZpJZJoODcc5iw+dfe/oU/+3Lt5nkOZO8YpRV7A0zUJKnaVEPXB5dbJJXmmFccGGxxtm5Gqc61iHfPJxQTxwOxhkLnqTShtMzIc7H2OF/VON9FEG7UuA79p766qVtnltt44cOSgg2ukfkrfePhn7Qwv3J1P4PDydO5hPCJ90eeff5gPueO81LXrzdpeG7x/K1r270pvMOznuuw1GS83M1/uDqPnGmkQpWmiGH44xhmk+L3Zo3tobUfIfIV7yzC7f2JxSlphkqWpHCU4rL2yPirGCQlMw1PDwlcB3FJM1BKkJXA4K5ukeF4ImVBsO04Ob+hHFmNWci36UeuByMMhAZaIMrBUVR8vZhRlnZduOMijwv8VybYtkaJHRqAXFRIVAUusARylLuO9DwHGqBQy9Op2zAhtOdCIVho59QlBnaaFwhQBtGaUVWWJLNOK9wpCSQgtCVtHxJUVnDO9cIcB07PPrMqQ5r8xG/f/WA292YqqoQUy6vbpqTZIa01HiOoZp26t3cn1ALXApt6E9K0sKwOhMwU2tTacNc3cWVEs9VzNZ94qxgZ5jgOYJkWFAZy432+HKbm4cT3t6dkBVYqWIJk6zgcJjRjTMrZVBpDsY5o6zg7EJE3Xds+3nocX4OZmsuT5xqcnV3gkTgKMFjS8172JE/DB/FeOeVZhAXdGOboqyMYZSUSClwpSTwFEleUlSW2ubd0dAnVbj/UZ/a/7ziYzkZIcQfAh42xvwdIcQ8UDfG3Px0Lu3BwSfdHnn3+dLSlqQCR91z7n6c89LtHpe3hnQinwvzdYwxXNoYkhbVsa773dfRj3Ou7Y7pTXJ8V7HUDNjox7y13ef67hikQGOQCJ5ebbPei7m2PeLm4YSstIJmO4McRxq2Bgk1z2WSaxqBwyOLDWbqcOfAsDfKKO0gPqO0JKwMrSmFvRSgjcCXkkBJWqHL/qTg5sEIJSTt0KGsYJSUuA74DqS5wQBzbYdHFlvM1F2u706ohYqZmkdalKRFQegKHMfDFYI4K9kf5/jqqBvLIck1eVEyyW0NSElDieHRxTrNmsO13SGeI2y3XGXoFRkI37aBS9sRtjYTstxy+JUvrbLUDAAo395lq58R5znGkThIEAWutDLVu6OMQZLjOw6N6VDo/jAlrQyN0KHmOXQil1bkoKRlUn5tvce1vQnjLKcfF7RDl07o0a55DLKcQWyHTh1HIJSgqmx3nu8qsqLk7b0hddejGTn4nmWnDj2HSV7eM2m/3k15brVD6Nlutcs7w499H3+Y8ZbAei+m7ju0Qo9hmnM4sbx5QeBYhufdAaO0wHPle6Khk8L9g42P7GSEEEcULY8Afwdwgf8c26b8Y4tPuj3y3uK84Pr6GAzH6pZHQmNvbg2o+2oqf2y4sjeiKCsCX7LYCCi1uec6jlMWjuTsfJ07hxO+fmmX2bpPXhmEicmNrY+sdxN2RgkXZmtc258wTHPbUaUkcZ5RlhWN0CNwrRTAOCsZZQXzdY8zczVKDEaDknYmZpJX3DocsTXIiHwXKSqUgn5aMpdWFFrT8Bw8zxa3u5N02hBgqeolGk/Bz15cwGjD1b0x3ThjUjpIAwZNWhiyskIDvgIlJUKD64OUgr1BjktJUloiy8CRVFrTHWfsDRMwAm0kwmgcYZ1MpiEtSh5eaBC4lj1gqRWgpOSd3RFK2utLC4NA0I48a/xKjYNAOYq9UTad1xFEnqJT9/nZ0x2+cX0XgeD8bJ2kqvjOzRFzDR9hDOv9Cd2kJFIKwXSA1MB8w8pC3zmMSfOSduRxuxujs5IKEMag0BxMCjYOE7JK86VzMzx5qk1RaZQUPLXS5OX1/j2T9pd3hryw1uHtndGn0uargbVORHdSMEhsxPzCmQ5poSmrDOUIfuX51feVdz4p3D/Y+DiRzC8DzwEvAxhjtoQQjQ9e8qOPT3qXdff50rJEIqbaMJa+Y5KVjKcyt63A46G5BtcORhyOU4QQfPncHEpKlLxXMOrovFIIdoYZB8Mcg2ChGbAzSNgaJiw2QxqRy3xZcacbk+f2YZZSURYljrTzLNpoWydxHKTWtISg5isWmiGr7Yia5zBISnaHCaGrMMDrm0PySrDQtJrvO4OU0JMYbNSSV+BUhkpCL7ZRjJSSvLKOIwgUw6TgYFgwzipaoUer5tEdpfQmVirZc6wzTQp7jQJQpXUogyyn1Jrp3CFFBSDxHUnoK7QRdGoew8RS5ZgKHAG+q9gaJES+VazMShu53dqf8Pp6n1v7E/JSgzSMs4Kq1LjC4IeK0hiarkNcGrQ2nJ2r8ehiw0Y82Jbtb98+pCg02sDTp5sYIfjWrUOSrERHHs3QI3QdPCm4fTghLUvywkabq52Q7iTl+v4EJRWRJ3GUSy/OadVcJrmt4RgMGnMsYuZK+Z5J+6N7yg8UlTYYw/uqYX5ceErSjFxm6z5S2EHhQutjVdYPSzGfFO4fbHwcJ5NP508MgBCi9ild0wOFT3qXdff5XCnRGDCWKffo3PXpwFxWVtQDh/NzNdqhQ+g4uI4lOjh6rcTyiUmscb26P2K1HbLbT3AU7A6tUNbB2E64o6HuOTQDl6wqcZUkzQtcJVHCUGDIClBORQczdTqGuufiOYLv3j7g+l5MM3QYZgW6MpTaELqKYuroVtoBlbFT8RjDILX5+GFqWGkFuBKkUORlhesIKm3ACN7ZGeE5VjzMcyTrhzFxVuB7grywDrqsAGN3zw62plNWJXFhqHvTtmkNpdZTOntBkmvOL9Z5aL7OV9/cxlMSqQx5Zdgd5uSRYZxLIlfSneTkpU0n3d4f009y4qKiN85xlCQzFdoI+sMCMOzrgrm6y3wjoB9nXNm1n+GZ0216k5yxX3JrbGtev/fOPkoKHCFwlURKST/JSUqNkgYp7ZxPWlZc3h1RVrDS8nhiqcF80+fWYUxWaUI3oOYqkiKlP8nY6sY8e2aGZ1Y799C+ONK2sSM4vqcOxukHqmF+P7jbSdydigs+xnlPCvcPLj7O3fNrQoj/BGgLIf5nwF8E/r+fzmU9OPg0JvfvfiBPtyMMhkGS3/NwHr1md5hy+zBmuemTS013kuM71lEdsdsePdiLrYA3N4cEnmSx6TNXc3lze0Q/LhjEJVmrYpgVNDzbeRbnIDRM8oqqrDBAI1B0Gr5t102sgZIItgcxtw7HTDKbljkcp+wPMvIKap6kqAyR7zDJCqQAVwl6k4ztwvKdVQaMhlIbHClwhB12NEbS9AWBq+hNMtqhRxQ4eErRTwqqsmKc2kJ1ZWDqk20UIwEMSWF1I5CKpivpTgryCnRecrrdoBF6PDrfYmsYEziCYQqBNHaWR0MvLo4ZCiRdnlqdoTvOubY3wpFWOVJI29mmhJ38D11BVhhK4DAuOBgXtCIHIQQrrYgsL7m0PWScZAySgshz2Rwm+ErAlH+uH9t27Lmaa0XURhk133LHldpQVJrDccooN6SVoReXVJVmw0An8igrje/Z9OjzZzrH9ZWnVtp888Y+V/cmVJXm4lKDrDQ8ttTkv35p/X3VMH8QfBJO4qRw/2DiIzsZY8z/Qwjxx4Ahti7zvzPGfP1Tu7IHCJ/G5P67aTnux6L8wlqH33hrG08JeklBPso41Qn58vkZPCV58U7vnhz77iDl0ZUGeVExjku+dmmbrKo4M1NjbSYiLyvOz9XYH1gq+obn8t+8fAtd2QhASqba8ZK5qRFbavjcOJiwM0iZ5BWR55DnJUlRYYw19I6jKHWFqSpSU6GmnFrbw5wSUNNzO9JGRfOtgN1RhhKghMD3FHNNH11pBIJb+zGlniAELLcCcpMwjq0TNIArQCkIHZBSUWmNzgy+A7qyEsiVgU5g25F7ccpXL22QVVCUcGYmZJgU9NMcg418bK8crPcSdoY7zNZcJnnJYt2fDq4K+mmG7zqM8hKMPRcwrU/ZeptB0E9zrh+MyYuC7qQAbRVJ06qiUNAOfJxAUZWwOhMx3wh4Z29EaWyNKHCd44aQvXFJK3IZxiUCM1UThd4kZ6bu8q88vMg4L3l9vc/zZ20dpuYry0ygIHQcbncnAPyhh+Y+UA3zB8WJk/jxxMeKg6dO5cSx3Aef9uT++7WGbnYzQs8WmLOi5Mb+hGfX2szW/HtqRY6UpEXFw0t1/rtXNrl5MCYuNPMNn5rvUAsUV27bmZDdsY0YxllFNy6RCkJH4kg7EGmwOXXXUeyMrHMpqopSG5K0JLU0ZlYQS0KaFwgBpZEYo1ieCdlfz+5hK52Ot5AXFaO8st1OWUlRVfTiCk/Z6/eVYJwXFCU4CvaHCa60szBHKTIElCX0S4h8je8ofFfTHVdM7T6uBN91ORznRJ7Cd+S0RVgwyS1Vi5g6o3v+LhqUsDWpJNfsTzLKSlOWmlyDKW0950hS4Ch1Z6f/NWhDVUFWFqSFTck5UuA44GiBr6wOjTaCWBfUA8VWPyEvKhSGtIS8Kpir25mS/VFO4Cgyowkc2xJc8x0Cx/Bzjyyy2Ay50x3znUFKWlY0QpezMzVuHsRErmJvZJkcbu5PeHy5+YFqmB+GE5bjE9wPH6e7bMT3WIw9bHfZxBjTfP9VJ/i0UaHZ7GeEroMTuKSV5u3NIf/KxXmUssSJk7TkxsHYMg5rwyi1PFy+A3lpKVjWe5amvp+WZIXmdhLTCV2SosRocByJJy3lSlpoTrdraDQv37ZMPQZBVRoyY53FkYkRwhpuzxGW4RjF7jBhUpTHN9MRb5Ax0I6s0uQkLzEGPGXIK9jsJgSexK85eEpQlvZ4P6loBA4VlpwTAXn1vXPqyjCuvmf45fRNJRC4knFaMEg1s5GHUraFu6ZsEX3rPvJ2pbbDqgjJfMOlO84ZTVusJaANeHbQH2GsRoUL+J5Ea83V/THnOgGuclC+ISkrilKjK3CV7UILPIUUgsARvLVlaziOkjSjiN4kJyvLqWKn5PRMyHzD452dMWWlERLGqY0kv3Vzn6bvsdFL6dRt+/Bjyy3e2rZtyluTlMBVOJ5DWlRc2R3z/JnO90VaecJyfIL3w8dJl93TSSaE+FPAlz7pCzrBR0fkOZyZqfHN/oHt2sFwbqaGUlZxcq0d8c/e2ObG/pjQU/z0w/9/9v47SLIsO+8Ef/c+6do9dGpZWbKrq6urdROSC8AIgiAxBAnuzBDkYBc7XBjJmeXSAMyOGbgkwQVndjk2A+6S1kbCCGoAHAqQQBNoyIZoVd1d3SWyqjKzUoeOcP30vXf/uO6RkVGRIlJUpfDPLDMiXvhzv+7hfs8753zn++ZwJJxdG/DkXI3jMzUubgzZGCQYDcfnqrjSQciClW6EMCGudMDVZJmmkBrXEVR8yfHZCkmq+L18DdexvYvCAQqoeMDYEyZVSNeWvfqJop9kGG2DwDjzKLCBSRkbrHwJFU+SpIpkpOycGkhTTS/NCEbzM7my7Ytca0Jpg0s2ashI7GPExei+sWW5sW6aMnC1E1HxXYwQNEKPqNAYYLmfEjp6KxPZjsLY+xjEKUpL4tzgjG7muba0luVQD2z/ajNSOAJbfvJsmaqdFcxWQtYHKUlaUBjbeBdGUi5ZC2qtBVmhmK2FTFUD4qxAGwNoZioB9dClUfFHlPMErWwG6CMIA4/ZasCl9Zg46zPfKnGkVWEjSjm/OWChFjBV9VjsxgghMGiOzlRsL8l39lz6nagcT3Az3DFtxBjz70fWyROw+4T+WFlWMmY+2dr29mn8G032g2WFjYUqfcfWxzeGOdM1l7mqtc791MkZrm7GZFrhScHBKSvbrpXm7ZU+czWPvCjRLPtcbkccapbwHcFaPyHw7PYbpTlpYTi/3scYSdmXFFrgOpK5eoDWmsudhFA4BK7LQqvCF8+vMUgVcaZIc/u8xj0IrUC61rZ4xrNBqZ8ZHCkwRpHoUR8GbAQw9o2ogc2hbbI72KzBFzZQjGGApLDn+q69+neEwTjWcCwtuC4wXHOtGdkKGEtNFg6EDpQ8Qa0U0I4tIcEVgjRXSBw8R6GMDSzX/a1z6Beaqapry1qZ2WLmFaMHHBZQdQ1lz8FzoVHyaJZ9q/OmFO2hdckUjiAwEimsy2RnmDFVCZiqBCSFINWaVtmlF2es9RME1nYgygqiVPHWYMBMzSHT0Cp5DFJF2ZdEmdU5A3ARrI76dev9hH5ScKhVQhtD4Ammq2WOTVVx3Gt04tsNDoXSdOKMLNdWeYDbo/FPSmsPFu7n32Mv5bIf2PajxA5mvmcmXw8ydpvQLwrN5U7MdNnnSiciyhXdKGehHvLsgQYfPzaDwWydF+eFndh2JXFuN5DzG0OWewnNwGN9mPDGYo84N1QClx94cT9/5qUjTFcDPnGixT//0iWiTPPli22+48k5/vVaj996ax0BrPYTjs9UiXON0YrTS/0tC2JlJYfxHMXqwG7wncj+Yd9Y7IOwcvyOsQZeroQr631W+xmh75AUljacqGvbeaLBLRRZoTjY9Fkf2u+1sRkPbCtnjd5B+ehnpezjxKMbjL/uCgNZYQONLyVK7X5jD5stFTAa3oTAgOu41AOPWslhvZczLBTtYWaHLEc9rJ09Gcb3A/TiDKUhHzEOxs/BHf2+VfIBG9CXugn9VHGgEdLODJ6UZCpH4pCqggOtElGu6EUp68OUzWGKQLC/VUYLwzP76nxTWf21K+2Iiu+QaTs4enYlY1+thHShFw+5vBlT8qztQDqar3prpY8UgsAXfM8z+9jXLFP2HV672mW+FuK47JkVOX7fj0UupRRbIpc36+VMSmsPFu7332Mvmcz3bfu+AC4A33/PVvKQYrcJfa0MnutQ9hxeXexiDKwPUg5PlYkzzeXNCM/ZxBWCcuDi+pJzawMQ8MEDTc6s9nlnbUjJc5gqWbXjNxZ7eK5grhaSFopf+cYyh6YqfPrENF+80ObZAw2utGOEMby1POBKZ0iea8qBw3Q54NUrHZplh06kmKr4pIWmE6cMM0OrLAk8j3yQbF3pG2FnRdLckI56HCpRDFNl2VYG6mUHnSh2bu2ugJlaALpgqhoSq5hhquxmfBvI1FaCc0NIRj0YLIVaGfOujGMMAYSuJU9k+bVgGGcF5zcLjogKUV4QZYrAtQOggywnVbuvYUyRHha726+WfHt0sZNQ9l1qgUecFZQ9gTaM5m0SHGyAlMKhG2d4jkOUaLRg5IhpWO2lnJytoD3NgWaZy5sRVzoxaxiEEHzkyBS5TlmPM+qhS8lzMeSkhbY+OiUf6QiKrCAtCg62aiz2ErJCsx5l1gXVWPryXjaW7e/7RujjCsnp5S6n5mq7SsPsdt5upbVJhvPe4r0ode6lJ/MX78kjPmLYbUK/MIY0t+WKLFd4roNB4DkOmVFoDcMkx/ccWpWAJFdIYTemKC/QGrQ2aMBzrcqxNgZPOjhSELgOSaHYGCS045y80DTLHo4QVEOf1UGfooDpWkAnttfXhQHP9TBofM+l5NuBzTHzy4wm+OPc9gj0aDoermUdxYjKm2PLWdlorTuhRudLaQUZvZEvzb1GocabtLZzL6PjAhvo1IiEYIQt4QW+RBrNILPPKVU2+FzdjKiELhgrajmejxkv2Rvd1/j+DZakMP7+Xc9fG2tNUNjM1nEk/qiZb+edJIHnE3iGqCjIc8V6XzFX85GjhQtps65cWWr22eUhx+YryI5BSkOSa2qBy5XNCCElC/UA35VobSgHkumqT5xayl7Zd4gDl6cX6vi+Fer8/c4aB5plDjbLTFeDPc/D7FS6mKr6PGFqPH+oQbPk3/B+bqaQcSOXzQnuH94LXbhbBhkhxM9yk4tKY8xfuScreUix24S+FDY4ZIXC9xyMAYGxG8ZISbcSerb+X6gtR0UElD0rEy+lndbPC0Xo2Zp9rq1IZVooXCGZroa0Sh6ea6/QpSPopxm1wKMX5whgruZzeTOi7FsxyY1BSi/OaZatWGOcWZvgtABjbGklcA3DTJMoG0zGGNOSwW64G/EuEWZ0u16cUwsE5zcUafHubOdWGLO1xk37nShGN8oKKBtGG6wmGQUHZWymEzr251zDRlRs3ff4a1yAcQzpMKdQ4Lo2wMht9OXdMrDi3Ye2EBc2yI7TsSgrqAUurmPnhWZrHkmmWO9lo96SiyM0qTaowuA4NpPJNQhtWO+ldDPFmcU+G8McT7ok5EjpYIRl5PXTgqpx0MCLh1pM1XxevdIjHREGjBBsRDlzMrAWDNpaMYCxFy2x2tPGspvSReDJmwaYG503VqaYkAfee7wXunC3k8m8fM8e7RHEjSb0lTJcakc8v7/JlW5EyXdoRxkL9ZBDU2U+fHgKYHSerckLrKDkoaky0+WA8xtDlnoxJ2aqtEo+r17tsNpPqQQuf+LFBT5xfIbQd/j2J+f4rTdWcRB0koKXjrR4en+Vr1/ssNqLWezG7GuUaA8zjs5UuLQxJMkUzWrIBw5NERcFSVrQTQrKruRyJyFKM0CzM4yMWVpCXuuv7BZqAhc6sUGb4rbLZIzue6tfc5PbSWyGIYXt26TKapX50pbyxKjsV/IcBoWyw5DaHst2rCdRbJE0XGOzo3yvUXEbrO7SONgZpDJ04wKlFYHrUeRWMXmYKwql8T2Hfc0ymVb0o9wG/PHzLwwX20Ma5YBgpJxQKMNMxccZzfLkSvFHTswwUwsZJjkX2xGHpyz7762VHrnCGrkVhuVeyjP7a+TacHy2SlJounFqs609yubfidLFjc7TMFFafh/wXujCCWP2sAM84njppZfMyy/fWUx9r9hla72USsXlYL1MUuit8oIyhqNTFVtyGW0Y64OIz3z+PIO0oBZ6YKCXFHz0aIvnDjaYrvrUA58oL4gzxVfeafMvv3yRotD0soKlzYholM1sv3JvhlZ5OHQFQgoGsd5qeo8zn/GmfqQVstxLthr440zIAGXX3i4t7FV71bebezK6bcVha7BzfO72d2tZ2ixDjZv5jt1MG2UXow2DpACtiUb3r7gWTG6EsitohA5Lg5vlKrdG6NhynO9YNerAExitOTVfJysEzbLLG0tdHGkFK6UjGKS29AmQFtZbxZeCfY0Q33eZKnkYDO+sD1mohggpmap4uI5gemSF3Sj7NEouz+yv8eZin18/vUKz5LG/VWalH3N2uc/HTsxwoFni5QubJLni+GyV73v+AIemy3t+nnfaQ9l5XqE0Xzy/YTOZ0RV1UqhJJvMe4W57YUKIrxpjXtrtd3thl80CPw48A4Tj48aY79jzih5B3GpCP9x5wi7nFdsYWq4jqZf8625bLjT9PKKzUXBpPWaYFMzVwy3DssVezOFpS21e7yd84VybpXaCEtAe5viOgyPhw0enOD57bewp9F06IqMdpyx1k62NvF7xSXoZoxnHrWAzSKwmV+AK8sIQeJDn17IQCWSjp7LST6zp1i40raIAP7xWGnOlJMquvQaxuj4g7LyHaPTL8foKBYVUKOWOgokgLSzbbYybBRgHyAtDL77zAHNN42xUplO23+VJgSrsHJDWmrVBQi8p8BwbsB0x6iVJweHpEmsDSwtOlCbK7RXmsdkGi52EJ2brCDS5NjRKDq5nfWgWuxGHZ8pUfZer7YRqyaNR8imHDu044/hMFV9KjrTKXGxHHJ2pcGK2Rtl3uNgesq8Z7nmDuVOli90+LxOl5fcP91PyZy/ssn8B/ALwvcB/C/wwsHY/FvU44lY0wu0skMxoTl/tcGa1z/MHm5yaq1MNXau3FVtJ+3//9UXyVLHUS5mrhVRDl3rgMswLqr67lVWN7/sbVzoME8VcPSB0bdC60h7iSZspOFjywPYeST/V1EJbwtn+Rtq+kScFRNtoX56w92NG95PmowY30E6uDwE3CwjbMdYs08Awh6KfkusbZy0CqLgQFdc0ycbDmw7g+5DGN++7bMd4riff9njjzE1jM7q80BQG+kmOMYJOnOFLQVYYS7CQ0Kp4eELQixVRqtBYdet66BKlitV+yiC19tQGax9wsFllqmZ9Zd5ZG/Lbp1f5I0/OEDgu+5slDrVKrPZT4rxgUPJ48cgULxyuo88ZFuolxEjxea89mfuBidLyo4m9BJlpY8w/FkL8VWPM7wK/K4T43fu1sMcJt0MjHLNAXF/y5nqfWujhS4dMac6tDVhoBJxdG5AVijeWerbZrgTV0OFSe8h0JcBpCqbKAV+/0qEyYhuVfIdCadJMEfoOJ2ZrXNockmuD7zrM1mAzzkkyu9YxfTd0xtmDRoyYV9s3dDn6N96o/VH5LBsNXspRsLnpHMwdwBXXdNDyG9zGcE28ckwuGDf6A99aKWAMvaS4LcKC5vosa2dw01haNgLWBxm1UdBwHYcss/I6hYZa4OBKh0wpKoHNwnwPBqnmYKtE6DqUPU03LTg5W2OpE/HOep/9rVmEgGPTVQ62yjRCn9cWuzTLLs8daPL6Uod+4vDkXI0Tc1VOLw14c7nPH57b4NBUmZLncKBVeiBMwCYimo8e9hJkxp/ZJSHE9wKLwMF7v6THDzejEY5/L7He54M0p5/krA9SMq24tBZRDa0fzOFWmUvtiMVOzPmNIc/uq7NQK1HyHGqhx7HpMqXAZb4W0olyfuErlzk2XcZzJKuDxM7qGOv90iy71ANY7CaY4fV9EUfYTVEAwWgif+fGatdrG+mOY2dDhqne6rfcaKblbuFIW6LybzFoY82y7E0KdW0otNAG33PRSYrv2kzsdpY6zoZ2Blu4Fsh8z6pmmhHzDVPgO5ZtWChr1hY4Vhy0nxiEMESpQYqMC+sG1xXkmaHi27ka35Wc3xjiepLQcTg1X2eY5VzciEhzzVcvdTgxU+GJ+Rqn5mq0yh4vX2pTFIZ2lLMxyFgfpHzwYBOx68TPBBPcPfYSZP62EKIB/DXgZ4E68N/fl1U9QridhprE6m/1k3yLzuw4gl6U8fpSH4DAkxxpVTiz1uf0UpdBVrCvEaKNoZtk1HH56uUOoSso+S4Vz2N1kNEMPbSBZskjV4YX5xvWxvlqmyvtmEGSs9ZL2IxTBolmmOZoY5ip+cxUSnQjRbWkSfv5tSv2Udks9AVKWXFIX1ra7XhDLrAbJ0AJyB27uRquaZXdjzgzDmI7GWTb4WKpygKr5DxepytBjDTWNNatckxg2Ilxz2X8MO7odsEoMG3HWKNNYEiygjjLCaVDoiB0HXKtcYSx5nK+wCkkU1WfXlIQpVYF+1jdI3AcVpKIaenTznOe2ldlphqwr1Hi/MaQM6s9tIGSl7K/VebZ/XWSXPOxo1OEvkuUWfHTC5tDSp7k2f0NNoYpgWfnr97vctkEjyb2EmS+ZIzpAl3g2+/Teh4p3I5cw/g27UHGb1xaYbriUyt5fOL4FP/+G4vXGUhdbA853CpRaCgKw9vLA0JXsj7IODZbQRs4PFXG5ArftUKTAM/uayAlnJyrkhaK1xb7/OHZdXqxre97rmBjmHOwGeJIq6b8xtU+njukHLhobbauc33JiDZrm+QjI86t+ZTdEGsoMsV2IuOtAowAQm80VJlBJi0tedzP2e32txu0CizpwMU+D4X93mhwXEOa5zgI0IapikeSF1aWX1/LWFxnVAIbPfa4B5TfoJFTAI4CI+wTkJ6hEfgUyuBqQa3soXOolR2GucYYgyOs/lvJs94uYNhXDWjVAnpJju9IDrcqfPBQk0xpAleyNshwXWn7Tr5LofKtzGpcDisKTeC4pLmiGniMGaYPQrlsgkcPewkyfyiEOI9t/v9bY0z7Pq3pkcDt9FnGt/GkIFGaZ/bVybXmibkanz+zji8lM7WQTCkudYYs1AK+sjkkzgqywl51DjM78NkIPdpxzqXNiJlqwB99Zp6XL25yeLpMPfQ5OGVFFU8v9ljpx5xbH5IWGq1hvhYwTAvOrPXRGnJlTbiU0qz3IkYeXBiuscYk9io/Uzdu0AcjxWON/epz62a6ByCsm2bgSqqBg+PYwda1QYrKrz3+9n7JOMA4QDUQdNNbh5zxQOf4e4mlU3vCkI7StiTXTFV8Ngc5gdGUfJduUmwpAtR9q8DQjq1SwlgdYPQ0ttYlscoDGKiVXEJHMlsLWR0kpLkmyTXPLTQoh4JvXunhS0ngSApV4LkuR2ds76RR9qj6Hmmhma75PLuvSVwoAsfhY8emeGO5S9lzSQvNICuuG6xzHckLh5q8tdzDdaAbK2ZqPgZ44dCEyTXB/cFtv6uMMU8A/yPwLPBVIcR/EkL8V/dtZQ85tvos7rU+i1Jmq8+y/TZS2rJTLfRxhB2w08rgCMkgKXCEJEoLeknOSidloVGy3ihC0E1yZioBgSd58UiLku/QKnm0Kj7f+sQ8Hzk6zXMHGkxXAlwhmG8ErPQSilyRZIo0Vyy2I5TSxJlCa6tNprRgmBZ0U9uc39kAH2/I0tmmqLwDqb4+AN2oEQ+W7TWyYaEwEOWaYaZZHeZ04pxa2ccV4qYqAIyO54UhEKOAtQ23uqLS2CCaaRtAfc8G2tVeihSaauDiug6hZ+VoglF5bZAWuNK+BjNVl2D0YowDzDjYWHMySSP0ybUhKzStSshUJaARuLje2OpAsNRLiPMcIayh2ptLPYSQPLO/yQcPNflTHzrIE/M1NocJb1zpodCcXu5xuFUlzhXDrEAr8y4asCMFR2cqSCmYqfocnS7zgx8+xHQ1uMWrM8EEd4Y9XboYY75sjPm/YX1kNoGfvy+regSwXa4B2FWuYXwbrW05bZjluFLijLxhYqU4s9rni+fWeGu5j1KaxV7MgVaJqWpAxZfUQo8n91WIc8Xbyz0cAyfmKrxwsEmr6iGE3VjSQpFrzdmVPmdXB8SF3ir7aGHNz+qBiyNdpDDkhbppc15zTaDSAW5HYupmucWwuKaUPGawua41FutEOedW+/Qzs+VDczNEauT74l1rujO6b2f070ZtbhcoB86WFH5cGLICQs+lVQ7JckWuhJ1/EZb5JaUNOMFIDmaq4uKM7mv8eJ5r55E8R9BPMsRI1HOq7FEo6KU5lzYikkzheQ776h6B53BqrkLgOuQG3lruEbiSWsljdZjw4UNNQt/lxaNNPnl8FgScWelzdKbCD3zoIJ88ObNVni2UphdnfPNyh7layLc/OceLR1s0Sz4V37GDv+pWr+wEE+wdtx1khBB1IcQPCyE+C/whsMTEtOyGGA+XJYVic5iSFOpdV5Xj2+TaMF0OGKQFjZLVoTrcqlAJHA5PlXBcwfGZGifmGhxslrmwMeBYq0w9dHnxUBNhBHGukVLwxHyNZiXgzNqApxfqW48/zAo8x+HUQs1K/huxVb4RQCP0eGKhxlw9JPRcO4dxg6gwnurXI+2wcQYwxp3ylDxhVQAsEQKSTNNPCqSwGQPcft9FYWdmDLav0wol04G1XR4PPm7HeKBTAr1EcW49piiuBbR2VJBkCVGmyAuDkNa5cjzro7H3G2e2IOi7tqdUCQSBB77jEDjWvMyVEt+1WnRX2jEKzVzNZqervYxQwvH5OtKRXGjHGGPYXw/xXcnvnVmFkWxRMiprln2Paujy4qEWJ+YrvHSkxb5maeu91okyvnh+gy+e2+DVxS55YXCkzaj6acHvn1vnK+c3+eL5DbrRzfLNBweF0pPA+JBgLz2ZbwD/Hvibxpgv3J/lPFq4neGy7bfZHNT45tUuea5Y7MY8va+O61jpFkcIFjtDlnoJl9aGXN20v//AwSbaGELfYboSIARc2YzZ3wyvczkslObrlzpo41D1HdrC2hcrYUtDjhAcaJXQRiAlTJU9Lm9EwPVlrjEjq+JbKZNLm6ndXLfVr3YLBONnXvFgkL/7NoEAzxWEnosjC4aZ2dr4UwXpiMV2RxDWZtp1HaargvVhgd5RbzNARYL0BCq1GdO4f1RgmWMXOgWu5QOg9LX1aKCfX6MwZ7rAEdZPphz69OOUSslDa02r7NGqeGwOM/pJTslz8IRlfvmOoB3nVAKHvFBIBHlRIJ0AjcFzJEWh2RxmlAOXqu+OLLZzpBT2OcrrNci29wardY8rnZjTK11ePNQiLRSX2xEfOtSi7LsPjSjlxI/m4cJe3knHjTH//Y0CzEiteYId2KlVdqPbRFnBf3p1mavthEvtmMubMb/11irvrA1Icmv69ZWLbcqeZLZe4on5GrmBRsnl3NqA9X7G2bUBF9sR7WGGGgUWsDbNZd9FAK9d7uB6gmpgP5S+A7XAxXddLq/HnJitcHymylw9QDgS6Vy7wgebaVQ9wXTFo+x5HJwqEwZyi2V2I0gJJc9S0xxsv2T7K1IYG0h6cU4x6pynyk7lG+zcy/iK6HYzJV+O2Gl61IwXMEiUde7kWilrjFSD0QL/BpdeGkuNNkCz7GyV3fS23zN6XuXAJVWK1V5Mo+RxsFmm5PnMVHxmayX2t8oEjkPJdyn7DoEvqZc8XM8G+EsbEVXfRRlBnKqtjG6ln3FmZcCRVoXQdzncKvP1yx1++801/uM3FmkPU16+1N7KSLb3Bh0pODVXoxfnLHVjBqnicKtMefSEd+sbPmjYHjSnKgGh6/DqYmeS0TzA2IufzK0uJD91l2t5bFEozSuXOzhC0CoHvLXSJfCklaLP9Zbq7pvLfaYqAbPVkKmqz0rf6oyt9zOOzJQZJIphkrOSxByeKvGFdzYouQ4fPNSiUfaYqfl882oXpSAb+Y5IBFXfsrfOrA0pDCSFYhDlBK4NA57UuI6DMQrftT2jpW6GIzO0EWTKIHfSvbbBBcq+fax+orf0zXYOcKoRUUCOGGuBY5WecwOp2RYYRkMq+Q3ozIxuF3oSgSFVBk9YgcqSbxl5YxmY7YFOAMrorSb+jbYtA3SHNlK54t1WAImCQGme3tegM0x5dn+DatlDaUM7znFdST8t2NcoMdcIWOzGBEbyiRMzLDQCvnK+zXqUMlcNODlX4eJmRCfKOT5T4dufmmehEXKxPWS25lul7wMNTi/3ma8HJLl13RxnJNt7g1mheXutj4vEkYLnDtQ5szq4rzLv9xrvhf/JBPcWeymXTXCfYCf6BSXPtb7vSDwH9jVCPnCgwTBTfGB/ndVBSjXwWO2l9NMMz7Fz2vONgGrgUvIcpiseS92YxX5MKXbRWCOub3lihivtmIVGicB3MVqxNswwWiAdSZZbH5yVXsJ6P0YbQbPsEboOCkOcWrO1zlAjR5f/aQEYQ87ub6SxYKYj7VyPI/R1Mv7bTcbktuPjTCXfQY8ef+9gMyNX7+714gAzZWlLXso+r5JvjdP66cgee9t9OoyGSbH2z7e6mhoz4EIB0hfkO+jSBsgKQztKEY6kmxZsxDmV0P49cmXwpaBAkxaKZsnlI0dmOT5foRF6NEOfKMupl/yR0yXkxYBBXLDUTYhyZXt4WWE33MDBEYJ66NONM4yBKCmIsoJ6yecD+5t843KbVxe7lDyHDx+dwnMFZ1Zt3+70cu+hEaV8L/xPJri3uK9/GSFEKIT4shDiG0KI14UQ/8/R8SkhxOeEEGdGX1vbzvlJIcRZIcRbQojv3nb8w0KIV0e/+9/EqBMshAiEEL8wOv4lIcTRbef88Ogxzgghfvh+Pte9IMkK1gcJybhJ7Eh8T3JwyjKaBmlOVqhRT0YSeLaU8l3PLBBnCimgnxScmK2wGaWcnKny1EKdZ/Y1WKgHbAxSar5V4K34LmdW+3Riy1w7Ml1FFZp6KaDkuggBUVoghGGhYX3mayWP0HPwPYlCkxfaSqzkdnO2ysLvdov0Ru8mgc1CGiVL59VYJeKxCdrOcpfBNsqR1+jQN8skMm3XUA0lzVBeV/ICGyw2Y00n0paCncPGUNGNFGl+jcG2/faptlmT60AtdGmEdq7nRqU5NVqHJ68NvW5HXhiWOgklxxrOVQOX0HX49PEZ/tSHDrDQCDHK0B7mgOCrlzb58jub/MG5dbpJzny9zJV2zKtXu+QK6mUP33foRBlGGy61h4S7sBOzXPPaYoczawNeudShG+U0yh4vHG7yxGyVFw9PUQ3drdLYuG/3kWNTfPzY9APf27gdQs0EDxbuZSaz2+cxBb7DGDMQQnjA74/YaT8A/KYx5meEED8B/ATw40KIZ4Afws7i7Ad+QwhxyhijgH8A/CjwReBXge8BPgv8CNA2xpwUQvwQ8HeBPyuEmAJ+CngJu6d8VQjxy+/3EOnF9QGffX2ZvNB4ruR7n9vPoenylsz5oakSzYqL50iUtva9h1tlXr7URinD0ws1jsxUWOvG/NLXrqCUZTo9PV+jlxRcbkes9FPeWhnw1EINgSDPrWWv70memK/yyuU2Jd9hphaQ5oo0L0iV4cJGn6yAWiAxxtCLMrqRgpE52JgAsH0oc9xbUdim/jCzwSdXkKnCzohoe64rbzwrM9YI80aT9DfbMjwBtcBu3HmhGI6Ob6/Wbc9wxuvb7uy5E+PMquoIjkxViJKcDTenHeVbOm3b9dsElgxQaE3ZE2TbsplA2tfLlXbOaWXQpey6ZEZTDR1cx1KGU2WYr3i0o9xeSIQOx2ervL7Y4yNHW0S5YqkT4zhwZNqqNUSZLXvNVwM0bL1vpssBFzZs/65Z8XnxkM1WxmWzsu9SDl0KrXHk9RnAwyZKOVFrfriw5yAjhKgYY4a7/Op/3Xlg1McZjH70Rv8M8P3At42O/zzwO1ivmu8H/rUxJgXOCyHOAh8VQlwA6mPSgRDinwJ/Ehtkvh/4G6P7+jfA3x9lOd8NfM4Yszk653PYwPSv9vqc7xWSrOCzry9TDVwqNY9hlvMrry3yFz5x9F0fHLgmjPnypfZ1ygHn1gb8xusrtCo+tcCnG6d8/twaHzkyxUvHWry1PGC1l5Dk1kOmWfb4xsUux2arnB50OdAMubAxpFX2CVzJxfaQdFigtcBxDMPcIHVBLx1t/MJe4Ut1vV4XQOBd808Bycm5Eku9CK0g12bLLAxsz2U7xhmL69gylefZjVkpvWX2tl3JeYzcgEKQZJpeqm465Ak396TZCYmhE+Ukec4gtTS4cUlt+32M7ydXIKXZGri0is4CbayQpRaGg60S64OMA42QpXZCw3d5Zz1iX6NEreTRS3L6cU4vzjm7NuD8+hAp4aNHW7x8EdJcEfqSU7M1cqXQ2rDYS3jlUocPHmptvW+SrMVXLraZr4UjGRq2+hVl332k/FoetsD4OGMvpmWfBP4RUAUOCyE+CPxfjDH/VwBjzD+5wXkO8FXgJPD/NcZ8SQgxb4xZGp23JISYG938ADZTGePK6Fg++n7n8fE5l0f3VQghusD09uO7nLN9fT+KzZA4fPjwrV+Iu8AgK8gLTaVmSxIV36M9zBhkBeGIgbZzjiYa1923NTq7UUpcKA4E1smw5HvWl8QYKr7PUwt1lOrQTwrmZ0Iaoc/p5R6/+toSM1WftUFCxbc9nGGuyHJD4AkOTddQWvPWco/CCCoBeI7LMM1J8t31wRwpKIwBIci1Ic0VZddF+IZeVOA4djhypxw+XMsexnTi2ZJPlObUSg7GGGaqPlfbyZaA5XZ0E42HLeNtt2y+WwxSGBYRqRqTB0Dl17Kgnc9hrEDtYIdHwfZjAPppDikYI2hHGY3AJUsV7SQkHBnLxVnG+tAKo55e7nNipsLldkQ7SvnmlS7f/uQcm1HOoWaJxW5CpgzlkuQD8413ZSq+I6kELoU2I8fR6/sVd5sB3K174gSPJ/aSyfwv2OzglwGMMd8QQnzLrU4albpeEEI0gX8nhHjuJjffrZphbnL8Ts/Zvr7PAJ8Ba798k7XdNaq+i+dKhllOxbeZjOdK619yA/iOBAHdOKMaeBRa0ygHlFyHfppRC3zizJZbQtcdaZoJDrXKDLOCVtnHlZLlXsFKP6UdZUyVfc6uDVDaMFsLyJWiGxX0YsWBZolG6OF7lg22MrAGYNpcm57f/iKNNcIagaDQmpVugu9BoQWDkVvmrcil498v9jI8IPSsMOWphTrdOCcZql0zkJwbC1LeKbSwXjlj4czhtjRp5xtu+5yMHL0wxSig+sJaKOcKpIholgOGWcHqIGe1n6IN5LqgnxpKvqDqe1A3fOniJtOVgLyAtX7CH5xZ48f/2FO0ylYU89UrXfY1Su/KVMYXKLfKVu40A5jMpkxwp9hTucwYc3k8eT3CbV9AGmM6QojfwZasVoQQ+0ZZzD5gdXSzK8ChbacdxPrWXOF675rx8e3nXBFCuEADK3lzhWslufE5v3O7670fCH2X731uP7/y2iLtYbbVkwlvEmQGqZVnP7PaRxs41Ax56egU87WAf/nlS2wMrK/Ij33rE3SSjHMrQ4wwHJut2I3hag+tDd+83GUjSnClZLYa4EpIc80gTokzRTV0QFgL32Fqhw4LM1IaNteitgaq3rVp+jG6qd5q1g8VOJgtWf+9IAccremnOZ9/a5XBPRxAvxmZYAxl7OT+zW5nuBY8x69BMTpPCNuT8SQ4jqQwmk4M2uRAjnQESljxzziys0OztZCFakA/VuS5oVUOiLOcJHNoD3O+cG6DViXAk5KLmxFlz2OqahlohTbX9a92K7vaQc87zz5uR+x1ggluhL0EmcujkpkRQvjAXwFO3+wEIcQskI8CTAn4o9jG/C9j7Zt/ZvT1P4xO+WXgXwoh/h628f8E8GVjjBJC9IUQHwe+BPx5rKcN2+7rC8CfBn7LGGOEEL8G/J1tzLXvAn5yD8/3vuDQdJm/8ImjDDJrg3yzADP+cE9VfJ4/2ODlCxv8wZkNLrdjnlyo85e/8yRaQ6vkUS35FErziePXGGu//dYaaVFwfj2im2QMk4Jy4HJxc0iUWUqyJ8ELHEyhKbkCoxWB56K0IknNVj9ie28kvYGR13UzL3f4+jjYhno/0ffc2Ox2xvUM0E1vfbudzy+QoIoRe25EkogSbY3bBAgpwBjS3OBJTWKs9pkrBa4wLPVTcqXoRQViscMw05bh50qutGOiTPPi4SmeWhCcXu6yPwlZ7CZbpJDtmcU4W7lX2cdkNmWCu8Fegsx/i23uH8BmCb8O/NgtztkH/PyoLyOBXzTG/CchxBeAXxRC/AhwCfhBAGPM60KIXwTewO5pPzYqtwH8JeCfYD2wPjv6B/CPgX82IglsYtlpGGM2hRB/C/jK6HZ/c0wCeL8R3iK4jHHNclnw9sqATlRQCV2aZZ8rnYjQlXzy5AyuI7e0nMBO+EdZgTGGA40ya/0MVQvoxQVJochybbMSM5Jsiazdb5wpCmUIPG0DjcoZFqNZF66VynabTblXULAVwVz2ngm91xiXEIsRwcFgg80YSo38ZiJF6ILnOiSZwhEaxxGUfZfL7YRK4FD1PQ60Six3IyqBi5SGRslhc5gxVQnIlaZZ8jnQKGEwN5WDuZfZx2Q2ZYK7wV4m/teB/3Ivd26M+SbwoV2ObwDfeYNzfhr46V2Ovwy8q59jjEkYBaldfvdzwM/tZc0PEsYf7uVezLmVAeuDhMB1OT4LEkFcKDKlGaQFX3xnnXdWI4wwzNcCHCk4vdTjrZU+rhQ4RjBd9YjTgjTTePIarTjTjOjSo4xFWVfIePtm+T48/5sFmL2YlN0vBACODdSjb3ddmCusJYKU1oAszjWNik+z5LE+yHCEYK4ecmq+xmInYmPgUGhwlEEIaWnLSpPkilevdujFOUIIaiWPwJW7Zhb3Mvu4nV7P44QJAWJv2Au77H8C/jYQA/8Z+CDw3xlj/vl9WttjD9eRPL1Q5xe/fBktrLTLTNXncnvIfD2k5DpI4BtXOix2EqarAf0k4z+/tkKr7FFos+Xd3qy69NLcUoqNZXSN5V3csSrxqPeSFOAKfVvlpfuB22GLvd8BBuwQmLttof7I0jnaER1zA24Bzark2f1NVnoxB1sl/thz+3nl0iZfvtRlthKS5ZpOVCAkzFZ9BplisRtRCRz21UJevdLF8wRHZyt8+Z0NPvd6wqmFGsemqjju7jYSaaHsvE5q6YF3mn1MZlMsJgSIvWMv75TvMsb0gD+OLZedAv76fVnVQ4gbSY9vn+7f7TY7j41/HsQZ64MEgeHYbJmPH5vl6X01lIGNQUaz5PHsgQYaSEfWxoXWLHaSkYtkQifO8B1B1Xc5OVNltuoxW3Oo+bZUJhjphRmrjOxuk8HPzZ1L9t8txsHvYcD2eJIWtmy2G4SEXAs2hymFMix1E371tUWWBxnfempmyzcnLxRT5YBBphBCUPE9Zqslnj3QYK5mjdu+eqGNIx0KZWgPMl5f6vD0Qn1XG4n1QcIfnl3nG1c6pLlmmN55Tno7Yq+PMibinHeGvfRkxuH6jwH/atTzuA9Levhwo6ub7dP9hdYcna4yVfG3bmMw1513uFnmUidisR3ztUttpis+mVIsdRIrBeK4nJgr042srtXp5R5PL1jL5gvrQwSCC+0+i53YDlr6DnGiuRRHvHq1y2I3JlPbnRpHDWtjRR2Vvn7TfFj/umMttPe6xLfTV2cnqr7g4vqQaujSSyHJFIHv8MxCnQ8eapIWiiubId+82qVe8vCExHclFzYGfONKh5cvtTnUsjbMea5YjjKOzlTQZnfl5ErgUPZcnj/coOrb+ZkJK+zOMSFA3Bn28sr8RyHEm1iZlt8cMceS+7Oshwc3uroZxNnWdP++hg0MX7vU3tKweuVKm29e7myd50nJr76+hFGGN5Z61EI783J+PSLKNLXQRxnDb7+1xlP76uxrlAhdh9cWuxgDzYpLnOVsDjM816FV8xEY3tkcMswK0qKgGE3sj6m3ubZ9l2y0G+/sgbyfJam7uTbcLr//fmKsEtAqORyZKuEISaI0hTZ4UtBJCi5uRHzjyiaHpsq8cLhF4NkCZz/OaccZm1HGbC1guuqzv1lipZfSTwtWBglztYBa6FIOHM4s9991RZ0pjQEaoY8j5UMh5f8g43bcbid4N2771THG/ATwCeAlY0wODLGSLo81tvt1wDVPjnac2+l+3w5Q+q6DNhDlhWXoZIp4xNYBK0WSF5p05ANTC3yi3NKMpSOYrQScmK1SD1ykECitCVyHjX7Ca1d7tIc5udFMlXyM0Vxdj7nYjkGDRuBIyW6J53iS/VGS495NXeC9hpWXsV99RzBTCygFjp3EV5o4VxRKI6TgymbCr7y6yMX1IWXfilmemK1ysFXCGMPRmQq1wKNR8pit+XzkSIt6yQYOZQxPzzesntyO4DHZFO8tJuKcd4a9NP7//Lbvt//qn97LBT1suBG9s1Xytqb77SS+VU8ue5ZyGvgOwrB1njYGz5UEo6bqWj+mPUxZ7cVkyjBX9clyzcYg5cxKn6VewoF6maV2Si/JMWiWuxkXNgaoQlsKLIZBluNkObnhhnMnDzpN+GGEA5Q8saVz1o1y4kwR+i5FYS0PCq2phx5zjRAJXNocsj7IODFboxJ4DNKcpFCcnK2RFpZFeHkzRhvDXC3g2f0N5ushhTYo3h08Jqywe48JAWLvELf2Ihvd8HrnyxBLQf6aMeZP34+FvR946aWXzMsvv7zn87pRzquLnXf1ZC5vRPzKa4vkhUYZw5GpynU9GeC68460KlxsW2vlz76+RDP0WB+kxCPFXoxhf6vE8/tbCMfKyR9qhVxu2+aukIaNYUZnkFJoQ+g55EVOYawWWJTqmwaU8czHg5AJPArwBLSqLpXAZbYc8MS+Gm8t9bi0GZPmmsCTLDRKTFcDjk6VmKkHJLmiExUcbJbIlGa+bk3q3ljuUfIcTs3VQRh6SU7Vt14zt2I5TSi3E9xvCCG+aox5abff7WVO5i/vuNMG8M/ucm2PBG50dbNzut915Ltus/O8fc2QzkJGo+SS5IrPn1nj8qYizwoCxyVKNV+73OH5A3XQ0EtyLm8OyQpNOXQoew6bWhFl1lUTA4Gref5gk0Gec3ltQFzYIcGxOn3FsX4qhYGpwBIButn79Wo+WkiyAk8I3IYddn1yX4OFRonVXspyL0FpTcmVFAZWuhn7myFxpmjHGa6QeI7DsdkKudLM1Us40lYRlDa8cNhmJbcKHhPF4gneT9xNKT7Cyr5MwI0/yDun+7dP59/Iy8OamDlc3IzopwpHCnzHJdeKjaHmUKvMlU6E4wjObyqksGWZKC1Ispx+wpZbpRQQZYZLGwOMkMRK4EiBKzTpSBdszGr1JEzXQ65uPPZ8jj3B4RqhYqyM4Err3mm0IMoNwki6cYHvStLc8NT+OgdaJWZqPm8t9ZmtB5yatyrYa/2UQ60KnSTjKxc2Ob8+4Kl99S0vmLFmme/I21KOmGCC9xN76cn8R65VURzgaeAX78eiHmXspDs/vVCn5Dv4jmSQFlu/GyQF7WFGPfToDDICVxBIj0GcsTFMWeknnJqr4Xkuzx2os9AIeeVKh6WOHbpzsRsdWPbY1W6O70DoOQxThRr9JT1h52QUgIYza5MAsxeMqdLBSBATo8kLK/svkUhH4nuCfprTTxVpoXhuf4N9jRJpXnBspooxsFAvkStNoa20z2aUMlUOEEKwPkhJckWUFqz0Ei63o101yyaY4EHEXi6D/t/bvi+Ai8aYKze68QTvxk49qfVBwi999TJPzNUQAgZJwXw9oBH6hJ7k8qZHreRycqbCyxfbFFqjCo9jMxWiTNtZnI0IrQzVwKMWuPiOg+sq4pzrfFgUlrJcjDIjowyhYxWTJ7hzOIAjwXcFgS+pBQH9KCdRBUIIPGmYrZbY1yhRD12kFCx2E1Z7CUIIzq0NybXho8dmaJV9lrsR3aQgVYqsgKmKhyslJdfhuYN1vnapc1PNsgkmeNCwl57M7woh5oGPjA6duT9LenSxfZhLacOljRilDNpozq0MeXu1zzMLdZ7aV6dR8jk8VaY9zPjm1Q4lX6KNYH+zNGL3CQZpQcV3eWOpyzDNudqJSZTG3GAMYswuy0dpTD4KMLcjgT/B7lBA6AiOz9XYVw850CzxzuaQzUFCXhgKDb24YKqieOFwi2f21fmN00tc3IzxpYOUcLhV4uzqgJNzVS63Yz54sME760PivODKZs7TBxoEvkNWGCRWVBOuHwYEJs39CR5I7KVc9meA/xnrySKAnxVC/HVjzL+5T2t75LCd7tyLc95a6SGEYKWfMlcJ6EcF59YHXOnEvHi4ydXNhEwpjs9WOTVfJysUv/XmCvO1kKvdiEubCY4QTNV8FLDWz2gPij1Puk8CzJ0jdKEaOAhjWO4lXO5EVD2XZsmnmyoCY/AcwYnZCh852mKYKYaJZn+9RFLY0tiFjZgnFmo8va+GKwUlz6UWtLnYjsgyxXTFpVCG1xd7nFntI6VguhJs0eWjtOBrK/2JntYEDyT2Ui77fwAfMcaswpZXzG8AkyBzmxjPLbxypc0bSz2kgCOtMle7Ea8vd5hr+lR8j06U8fm31/iOp+dZ7aWUPJer7RgEHGyVeWetz8WNhHaU4AhJP8mZb5RQI0Gy22SlT3CXCAQEnsswLVjsJISeQyWQhBWHOFfUA5ey71IvufTTgrV+iisE842QCxsDyr5H4ApSlbHSSWiVrCOp5wo+fmKGp+OUKNOErhwpLju4QnJ6ucupuRq+ZwVUTy/3HilDsQnl+tHCXoKMHAeYETZ4eHQMHxg0yh4fOtQkyRUvHGpydm1AVmiiTPPRmTqB69AtpySpYqrsszHMQBjSQpFra2TVjRULzYA4s4q9m8PMilqqfMtvftJquf8IPah6grUM+klOUhSUgjJaG7LcgC6oBC5PzteJU8WHDrdoljziQrPaH4mmGsNsJeDYbBW5bXhSqQLHkbxwqM7bK32rJKE15VByYqbC84caNEv+Q6GntZegMVE5fvSwlyDzn0duk/9q9POfBX713i/p0UfZtwN6oevw3P469dAlP7dBVig8R3JqvsaZ1QFCCE7O1Di90iXLNdIR1HyHbpKTFQ5I0FrTiTPLENOQ7chiwhG/thj9m/Rf7h6hY90vpSNYHeY40qovz1Z8ssJmk6EvkVJwYrqKwfDEvirNkkemNM8frHN5M0IbOzB7fLZK4Nl5l7Iv32WffG59wMYg5XInIskUyhhePDK1tWE/yIZiewkaE5vnRxN7afz/dSHEfwF8CtuT+Ywx5t/dt5U9whiXzb54fp2zqwOEEXzs2BSB61AJXHxP8kefmuf0UheBVWc+fqpK4Ar+3dcXqXoucV4QupL1XoYrHRxptmTmt/tmKWUNs4weKS7rSZC5W0iswvEgVdY+WQoEht4wAyFpllw0htlqwJVuhHTKLNQCfv2NFS5uDnGQzNWtmOr4771d7mXn7NTTC3V+4SuXMVgW4ZHpMqeXe1ub772WjrlX5aq9Bo2HISubYO/Y0ySXMeZ/B/73+7SWxwqVwCH0HD54sEk1sCKaw6zgQ4earPUS/uOri1zeiEgLxYFG2X7oPclMxeep/TW+eHadTpTTjXMaFY9GKWBzmG+5MIrRRL8BAlfgOYZoMsV/T2C0pS3XAodBpogzgzKQCzg6GzJd89noZ8zWS5ycq9CPM/75ly7hOFD1PQ62yvSTgumZgJeOtLY8Wm60uaeFQiuD70mr4OA4ZEptbb7bFSfGmWpxhxvzvSxX7TVoTGyeH03c9l9PCPEDQogzQoiuEKInhOgLIXr3c3GPMnZTb2ZkPPbrr6/SiTL2NSxd+fzmkOVuSuA4fP1ShyRXHJutcnyuQiV0cHDY6MfkI9GxQApC/5pIo+dIksxuPpMs5u4gAeGCFA5GQOAJQs9O92sDw6RgkBQgBOv9hCubMZc2Uyv743qUPJeVXgpAUeitrKUTZXzx/AZfOb/JF89v0I2sHEOhNGdXBpRLDo2yR+g5nF7uvsvl0nUkWaF5+VL7Xfdxu7jXplx7VYGeqBw/mthLJvM/Ad9njDl9vxbzOCFKC95e7eMIS1k9OFXCdQRZYYiKAt+xPRdXSgoBUWalLetll84wJ1OGflLgSJekKOinaiTrD3FuECNnS20Mw0xNlJbvETSgNYS+g0nta1sa9V/KvoPrCoZJQei7lHwHYQTrg4SFRog2GgSkeUFWuAQjpYeblZXGnjBPzzc4u25pynGuODVXu27zvRf9jHtdrrqTUt5E5fjRw16CzMokwNwbFErz5kqfp+cbXOoMibOCN5d7/OCHD1HxHcquSz+J7QakNcpoQtchV5pm4DNV9jm/MeDCuqJZdhkkgkJpYqPxJES53QwF1hJ4wmi+txByFCiUwXcdAs9lvh6w0c+Ic4XvCA5NVyl5Lv3E6ow1Sz6DtOBqOwIDHzrS4oWDLVxHEmUFShlcX5AUBZ6UW+ZiErZYhR/Y32CQFWhlmK0F163pTgLEzvLc/ShX3UnQmAh6Plq4ZZARQvzA6NuXhRC/APx7IB3/3hjzb+/P0h49jD/UxWhDaJQ9jrllisIwTHPaUUp3CC8da/L5twoWezHaaEqe4OLmkG6aMVsNkQIwhl6cEXgOBo3rSnSqGYy4y4Zrjf4JnfneQQKmgE6kqJc8KoEkU4aVXsq+WsjBqTKeJzgxU+WZA3XeuNrj4HSJsucSupKr7ZiPHZ+iEV7rc/iOJCkU5y4PkAg0hoPNMlFa8OZKnzjVvLG4wbHpCq2qzwcPtXYVVd1LgLhR7+V++M/sFjQmszCPD24nk/m+bd9HwHdt+9kAkyBzG9j+oUbAxiDl1asdVnsZ7SjhymaEBjqxYq7q8YH9DT50oMGvv7nMhbWYQivq5YB99YCVfkrVFfTSHBPnxDfIVtJJA+aeQjJSV3Yg9CTV0OXZ/XWU1ix1Eo5OVzg2U+PEXJV31gZ0o5woU9RDHzC0hwWHpsscm6siEFvlrC0YtqiBhda8sdhDGUM3zglcyYXNiI8em9pqxO/cqG8WILbfFrhhae29KFdNZmEeL9wyyBhj/uLt3JEQ4ieNMf+vu1/So4ed9fIoy7mwMWRjkOE6gqVOQicuKJRmqurTTwpevtjmc6eX8V2HUuDQSzQbg4RBUhC4ks1BgSegNymHvWfQQKbBl9AoeVR8l1roMVP1masFNEoBjiO40hlydLbMS4dbXGnHlAKJKyXCiejEOYJ3646FrsOLh6fIlcZzJKu9mEFWsDnI8RzJXK3E+iDhtcUe09XgOsXu7Rv1bgFi56Z+YrZ609La/SxXTWZhHj/cy7/qD97D+3qksJNJJkfGU4daZQ5NlQkcSVJo4kIzTDVRZqX401yR5Ip2lBOnBUlh6Ce27h9lBQo5YYu9hxBYxp6UduPeHCScXxsQp4p9zRJLvZgL6wPeWRuSZprQdzg6XcUAw7RACqs5ZrieaTUudRWj3kuhNYHv4AlBnFsPmkwpQt++f6KsuCELzHXkFiUadmeMnVnuWwLCbbK+7iV2Y1WO+08TPJq4l45H4h7e1yOFnfVyPbJGdl1BlmuWezFxmpMrg8BKklQ9h1RpjJAYDVGmMQZCX5AVOVGut4YvJ3hvYABXWJO3XCukkIDh3NqAd9YM0nGYqviUPIfzG0MKpamXXaarHlIKjs6UObM6oBcXBCPdsXHWsbPU9cLBFoXWnNsYsj5ICH2Hw80KzugTe7tN/hsRAk7N1Ti3PrinvZfbwWQW5vHDvQwyk6rNDbBbvfz7nj/Al86v8UuvXyEuFNpYF8sktUOXudG8eGiKS+0hK70MCZRCB98VJLmV8x9/LCex5r2DtC4LlFwHKQxL3WSLRfbMgQap0tQDl6WeFTTd0iLLNYHn8IMfPkTJd4iygtPLvevKXbuVun7ww4d45XIHAMe191f2ndveqG+0qc/WAmZrwXWP91404++HQsEEDzYmmcx7hJ31crDT3zM1n0Ib+n6OwdAIPQ5Nl7ncjsk0HJyqcHymapvIucJ1JHlRsLgZY6RBGsEwN6T62h9gHO23y8tMcGsIbL/lRoQJASDt10xrSmGAKyXV0GGQFmz0M7Q21AKXfbUSrpTUS+/ukxRK87Xl/nV9iVeutPnQoeZ1pS6A6WrAt56afdfmf7sb9a029Rv1be5nM34yC/N44V4GmV+6h/f1SGJ7QzXKCnJjSHOYKnlsDDPSvKAT5aRK04sLZqp2RiIfuY01ytZXpB0rUm3lTbQypKNI0gwdkkIRjyYvJwFmb5DYfsuNUsOaD57rIAVkhcF1wHUE09WQXNtSZr3ksVAL2NcqbV1M7Gyk7yxhZbnm9Ss9klxRCdx3bfC7NeL3slHf6rbvRzN+Mgvz+GAvpmWzwP8ZOLr9PGPMfzP6+nfu9eIeZUhAGIPvwsWNBE8KBoXBkbA+zJiu+FxsR9QCF1cKaqFLnGkKpTASKoEkLQwFGlmA70KmFHpSO9szPAHVwPZXDjXLnN8Y0t9FkcVzXcqBg9HQKEk81+XETJXcKA40wpHQpR2Y9KTDy5fau2YE20tYrpS8udIjDCTztZBCm9ve4PeyUd/sthNhygnuJ/aSyfwH4PewRmWT+b67wLg0UWjoDHMQglbFJ/QcemmONNBPFAJDoTQVz6Ne8hDGcG4tpZ/ZzanqC3ppZvsEGsxNSj0T3BiuhJInKTR4nsNUNWTYTrZUExzAdaAROjSrAR8/MsOT++q8sz7g/MaAk60qTyzU+fCRJm8uDyh5Es9x0Gb3gLG9hBUlKXGuePHQFI6UOJLrNvj3ok8yacZPcD+xlyBTNsb8+F7uXAhxCPinwAK2CPEZY8z/KoSYAn4BmxVdAP6MMaY9OucngR/BBrK/Yoz5tdHxDwP/BChhfWz+qjHGCCGC0WN8GGuk9meNMRdG5/ww8D+OlvO3jTE/v5f13w9sL00cmCrx0rEpXrvaZa4esDnIyTZtqUzpAqWFleY3oLThcifBcyS+o3CkJMkL8gLKPuTKTvdPsHdkCqJckWnBlW7CgUbIZpyRZ7bRFYycKU/tq3OwWeY7n10gzRWdOEdpzamFOp88OUPJd0iyDkudgkJrXCmZqni7ZgTjElaUFYSeg+fajtr2Df696pNMmvET3E/s5V30n4QQf2yP918Af80Y8zTwceDHhBDPAD8B/KYx5gngN0c/M/rdDwHPAt8D/P+EEM7ovv4B8KPAE6N/3zM6/iNA2xhzEvhfgL87uq8p4KeAjwEfBX5KCNHa4/rvGIXSRFnxLgXb7XMCnpTM1kOeP9jkyFSVUws1JIJa4OA5DtVQ4AhDoRVprmiGHq2Kj0GgtUZpge9A2fcoe4JmxSVwJgyMvcIAhTI2YxHQiXI+sL/BQsOjXnKZqXocna5SD3wC32GQ5ryzPkQZTSX0aJY9Ti/30EpzuRODMDRKPgjDpXZ0ww+Z60jqJSsTs1N5GLinisi3wjjofeTY1Nbk/73EjT4PEzz62Esm81eB/0EIkQI5I/KSMaZ+oxOMMUvA0uj7vhDiNHAA+H7g20Y3+3ngd4AfHx3/18aYFDgvhDgLfFQIcQGoG2O+ACCE+KfAnwQ+Ozrnb4zu698Af18IIYDvBj5njNkcnfM5bGAaO3veN9zsCnRnaeJws8IwLTgxUyVTCk/Ab729hjKwOUjxPFAp1Eoj9pMnWaiV6MQpuSowQtJLcgIp6ScFRk8a/nuFBhwpKfsOs9WAxV6MMD6e47K/4jJbLXFirmqVsI0gygreWe1ypZvSDH2Wugmn5iucnK1yuFVhI0rpxtYSe64WkilNeJPH360xPxbNfC/7JPerGT+RkXm8cdvvKGNMzRgjjTElY0x99PMNA8xOCCGOAh8CvgTMjwLQOBDNjW52ALi87bQro2MHRt/vPH7dOcaYAugC0ze5r/uKW3ly7PTMcFw7C/GpJ2b41IlpLrdjDrVCXCkohw6+dDkxU6EoDIenSwSOpJ9mFIVCaRgmmlwBGMSotDbB3iBH/01XQtJC4wtrq5woQzsqaCcZZ1b6ADy1UOODBxv0Ms3hVonpesBaP+HXX1/llcsdXEdwaq7KkekymdYsdxK+frlzS2+XnZP62y9GlDZsDhPSQt/WB/ZByhrutUfNBA8f9kRhHpWbnoBrF2bGmM/fxnlVrKPmf2eM6dlEY/eb7nLM3OT4nZ6zfW0/ii3Dcfjw4Rut67ZxO0ydG1FKv3p+ndeX+qSFlZKZrnhUA5fQ97jUTnh7uc+R6SoL9YAzqzlSGBQgDfRSW+6ZfHT3Dk+CUYbNKKUb5UxVPIZJQTVwaUc5We5S+C4fOTpFq+oj5YgJpjQX14cErg0QSmm0FESp4o3lHiXP4bnDTTxX7JkSPL4Y+cI7a7y+2GOll7BQDzEYPn5s5oaZwIOWNUyYaxPshcL8f8KWzA4Cr2B7LF8AvuMW53nYAPMvttkCrAgh9hljloQQ+4DV0fErwKFtpx8EFkfHD+5yfPs5V4QQLtAANkfHv23HOb+zc33GmM8AnwF46aWX7joPuF2mzs7SxCDO+A/fXKIWSKarHkWh2RjEOKJC4ElqJQeJoJ9mdOKcwJEMldU4U9joOaH87R2esEG/GrrUAoe4KNgYZvgjWZ9GGGCMoOw5uKOJ+8AV1EourhDkyjIAFzvWBVMZ+D88O0euNHP1Es5Ip+5ONtZK4BA4DmXX4Zn9DQLHWgW84rX51ImZB2Le5VaYMNcm2Mtf+q8CHwEuGmO+HVv6WrvZCaPeyD8GThtj/t62X/0y8MOj738YS48eH/8hIUQghDiGzZq+PCqp9YUQHx/d55/fcc74vv408FvGGAP8GvBdQojWKAP7rtGx+wrXsZpUnThjqRvTHqYcnSpTKM3mMOFqe8jmMGEQZ6wPEpKR42U7zimU4ehMlc4wx3EkWQFKa1b7CUoZWhUfVzrUSz6Z0vQTPZnqvwuMbHkwWlP2LOUYI1EGpBAopRmmGcZoFpoB4Ug4LPRdvve5/eSFYbWf8OrVLs2Kx+ogIdWFzW58K3QJtydAuVuJK1Oadpyz2EtYbMdcbEdkuSHN1K6CkjvFJ10pGKbFlqvq+4GJpfIEeymXJcaYRAiBECIwxrwphHjyFud8CvivgVeFEK+Mjv0PwM8AvyiE+BHgEiMFZ2PM60KIXwTewDLTfswYM75A/0tcozB/dvQPbBD7ZyOSwCaWnYYxZlMI8beAr4xu9zfHJID7iU6UcXq5R5QWvLVs6/jfvNqlH2es9lP6icZ3DWXf4/BUhUrg8p1PzxE4go1hSj8pcAVkRYHEsNaPWOpE5AbeWR8yWw3wHEN7qLYyFztGOAk2e4UBQtcGmvVBSiVwKXuSJB+pJksQQrC/GfKdT+4j9OVWZnBousx//YnD/IdvLPLKlTZl12Gxm9DIfFzR57uf2cfqMLktSvCNSlwSWO4mOMKWmQqtudqNeHKhekudsqzQvLnSI0mtsvMLB1vvW9lsIiPzeGMvQeaKEKKJdcb8nBCizbWS1a4wxvw+N2bUfucNzvlp4Kd3Of4y8NwuxxNuYDNgjPk54OdutsZ7iXG5wpOC9jBnmBUIBFmueH2pj+/CQqPM28s9akFBM/QYpBmf+d1zTFU8WoHP28tDBkmGMoZj0yUurMfkylr+Kg1Xu6lVC9j2uJM+zJ3D9x0qgUucK7RWDFLbixESSp7DMFU8OdegGjqAIEpSoqygXvLRwFTF58m5Olc6MSdmqkSZwnMkS/2Yjx2dQmMvAjRsSfFvx81KXBo4Ol3Gk4LL7RgDtEo+T+2v31Sn7BuX27y62KXkOXzocOuOekJ3gpsNjk5kZB5f3HaQMcb8qdG3f0MI8dvY3sd/vi+rekhxrVwhrUy/ESx1Y5JcMUhy6mUPDAghQQqWegkHp0osdmLeWOySacNU1UUbRZxplnsZhbJ6WsbYjWrce/EEuMamexPcGSTgOQ7TlYCNyPr0hL5DpjQBksCVlByHi+0BFzYqbA5zexvP4ehMhfMbQ86vRaRKkWTKZpQGntvfRBltTc4KfdNG/M0a474jqZc9pqsBHzjYtP4vAmarwQ2fU6Ps8cLhJkmu7rontBc8aISDCR4c3PIdJ4Soj75Ojf8BrwK/D1Tv8/oeKozLFVobPGkDjOcIqqG9Mu4ndkJca0WW2/Cw2ksZpgWFNqS5ohcVqEKjlKWrCgGFBmWuL4flowAzuTa8c2igM8w4t9YnyQoC16EWelQ8h7LvEHqS0HfYjHJ+8eXL9OOcFw9NEXiSX31tidC1mUItdCi0YaYa8p1PLxD69n0gufFA5bgHI2GrxAXX92/GmUmu9dbxFw62bhkoyr5LOXT31BO6G0xoyhPcDLeTyfxL4I8DX+Xd1GADHL8P63oosV2eo1H2KfsOgeuQZIpWyWczSjm70ifwXXxX0omt7liz4lHKHa52ktGGIAmFxHUE9bKhG2nyUYRxuJ5FNvkY3x2UgUxZodJ66PL8wRYvX1xnuZey3ofZRsCBqTLtKGexHZMeLqi4LnmhkcLOMr10ZIbpSkg5cFBGkxSGD+xvotndXGytn3JufbB11X+kVeFie7hr/+ZO+hnvtUzMhKY8wc1wyyBjjPnjo6/H7v9yHn5s16Rqlj2GScYX3mlzwq9yqCjRLPtMlX1Ozld55UqHr19q04ly5mohrbJisWuvbusll9lawHw9JM8LPvfmxoSmfI8hgaovkI7NRAoDymiUsYKZWQ5xZvjapQ4z1YBVN+E33ljlW0/N4rnSstGAQmtm6wEvHW6h4TrfmJ30XQG8vdqn4rsIF9pxxtnVPh852iJRmqrvEvp378DxXjbbJzTlCW6GW76bhRAv3uz3xpiv3bvlPBoYa1I9u7/Ov/jiRQZJQabtBnKpHdMqB1zpxhydrhG4Dm8t9WgPMwxwaq7KMC1oVcKRAKOhGycT5th9gAFyDSUXyqGH0pqzqwNrNhb6xI4m16C1xhhD4LkkecGrV7t83/P7WBmkVrVhlCmMg8O4FLabrfITCzXeXu6zOUj5w3c2yJWhn2Ss9BP2NUrv6mfcTa/jvWq2TwQ2J7gZbueS6f8z+hoCLwHfwJbMnsdKxHz6/izt4UYnynj1apcoK7i8OWS6GqBdWx8/u9rl6QNNhlnOUi8h8CRxlhPnBWW3hJQS33VoBJpcKYbJu8tkE9wZXGnN3hT2Tay1QWmo+w4vHp3m1HyNr1zc4NJGzELL453VPgqB70qOT5XppQqjNG+v9nnhcJPAda7LPnYLCjsdUd/UPX7v7DrVwKXiCdZ6Cd+80uXkXBW4xgQDHrjhyhthQlOe4Ea45TvBGPPto+HLi8CLxpiXjDEfxg5jnr3fC3wYMW6Elj2XJNdMVX3SQtONU3pxTqbg9FKPr1/cZKOfkSqDkRJXumxGGVFasDFMqIUeZ1eHbETpJMDcAwgAbdl6zuhnKSwjLPBcnlyo0ap4HKiX6SU5lzaGOI41jMtyzXqUMVvzGWSKMysDfvY3z/KFsxu8fKlNN8pvqwHuOpIjMxXizGqSpUqzr1lCCohzW25SypCN6MDbhyu3/+5BxE79tQkmgL2Rk54yxrw6/sEY8xrwwj1f0UOKzjDh7eUenWGytTnkSlHybAPfcQTKwKHpEqfma7x0uMXGMEVgEIBAEHiSwBEEnsNmP+FqN2IQpWxGE6LyvYLClsjGUjwAvmMZXlXfRQhBKXA4NVulyAuMNkyXQw40S3SijPV+xqGpMsnoin21n+JJO6S5pZy8LSh0o5zfP7fOV85v8sXzG3SjnP2NkOOzVRYaIU/M1RixjCl51/cztvc64P6zxCaY4H5gLx3G00KIfwT8c+zn878CTt+XVT1k+MO3V/jMH1wgKzS+K/nRTx/n4saQ33lrlY1BTq4KXEeQ5pp31jQrnYT9zQqFEjhSsNiNudqOKbTCcx0ONMv4nuTyxoCNZNKNuVcw2KsqiQ0yrrR0X891iVJFN8koK5e1fkat7DHfLFP2XBbqIb4reGO5T8l10KLMMCkoey5CjCVo7N9JAN0ko+q7pIXicjviQ4dalEc/j8td3/f8fn7ltUWG/ZhG2eNIq8IwVe/qZ0x6HRM87NhLkPmLWGmXvzr6+fNYI7HHGp1hwmf+4AK1wKXe8OklGf/wd85SLbuEnsOBpsPZ1T7duMCRduZluZ+SG/AFXNqMiLMCbQyFBp0rrmwOSHLF8Obq8BPcATT2dVcGPAeqJZdGyUcbuNqJCV0XMEyXfd7SsD5M2BgmFFoghebsimKpl1ILHebqJWaUjza2/5IVmrhQnLs6xAjDoVaZw60y5VG/Zju199B0mb/wiaMMsoLqqMS0Wz9j0uuY4GHHXib+EyHEPwR+1Rjz1n1c00OF1X5GVmjqDR+AeuhztRPj5g7PH2hYuRLgtatdyoFLURg0mn5syyabUQ5CMFv1SQorhpkVdghzIn55fzAONI4UpIWmnxQcaJY42CrjOZKVfsLiaswwzUnzgrQwzNQDan5ANXBYHaR85OgUvSSnnxQkheK5/Q1OL/eYrgTMnywxSHM7JyJuTO0NtwUXYCsY7cREkmWChxl7kfr/E8D/DPjAMSHEC1jRyT9xn9b2UGCu5uO7kl6SUQ9tJlP1HOolh36SEbguUZaTKUVeCLLCSlmWfIeNQUaz7KE1lAPJm0s9QkeghUAVejJoeY8hYEv6JfCs5XKaayoeHJkq0x5kFMbQCH2iLMcRhrTQpIUBZZUbAtdhpgrPH2gwUwtYH6Z86JAtYW0fSGyUfDaHKU8s1Di3Nti13DWRYpngccBeymU/BXyUkSeLMeaVkdvlY41mJeQvfctJ/sHnz1ofElfyY99xCmMM//D3zpHmik6Uc2yqzOogZZjZlnPZt4q/J2ZqXG1HdKICjaBe8oiVIUdAMeGU3Uv4ErKRPbV0JEenSzTDgG6c89VLm3z7UwvkheLzb6+hjaGXFuxrluhFOYVWdOOcZggz1ZCZmjUQqwTuVgay20DibDVgthq8q9z1IHq/TDDB/cBegkxhjOnexNXyscXHTkzz5EKF1X7GXM2nGvp88fwG/82njrHYifjyhU3W+xn7mw6+EzNIFcM0oz2E5V7KXDOkVfbIi4Cpistqv2Ati97vp/VIYfyudbCK1q50qAcBh6crnF3tsz7QvL7UZa7iE+eapCjwHIeNYUY98HCllZ05MV/lyYUagzTfU5N+Z+CYSLFM8LhgL0HmNSHE/xFwhBBPAH8F+MP7s6yHD81KSLNiXanHVNZGyed8EVEPfAaxYrbmsdxLcCR0YkUt8Mi1YXOQkoea73luH1+5sInWCY6UlF1FVkyUlu8GZRcKA0bZLCaQUAtdZuoBS13L7BJC4Dv235sr1pIB4yIca69QDhwOtUosNMq8eKTFi4dblHznrpr0EymWCR4X7OUd/ZeBZ4EUK5rZ5RrTbIJtGG8ggzTHGMOhqTIaw1I3I3QdOxmjBUIKmmUPpSFKFV+52EaOGtKtsotWkyn/u4EnwHcdSq6gWZGEHlRDD99zaZWtSvHGMKNe8phthCht0AbKvsdcwyd0JUWhWO0lzFRCPnpsmnro8dpi94YKw7c7kDhxjJzgccFeMplnRv/c0b/vB/4EVl5mgm0YbyCvXGkTpYowkPyJD+7n82fWSPKCOMsIPEmUZKSZZJgWeK5ktuKzMkxZ6SQk6lqjehJo9oZAWnZeNXDINczXQzpJDloTZTkGwztrER89OsWBqRL10GYzUVZQ8oe0hyn9RFENPeaqIaVAEivF+fUBRaEZpIokV1QC97ab9bsZek3oyRM8DthLkPkXwP8deI2JwvwtUQkcPnSoyZGpEq8tdolSxWw5oOI5uK7g8nrEpc0cz9V4Epqhx5n1LmlqSPWoxPN+P4mHEAJINYQOFEZTKIgLjSPtJH+aa4yB0Heol1z+3EcOcWbN9r88KfiWk7N87s0VvnGpTeg7PLevyWaUIhGEnsNb60NSpfl0aQYpb89x8mYssseVnnwzF80JHi3sJcisGWP+431bySOETpTxzcsd1ocZp5c6XF6P2YyzLWOyrFBsRhlC2F5BqmBtkJErW66ZZC53j0yByQzSwDDOKPkeWaEwBvKioOQFrA5SvnGlxydPzlDyHSTw8qU233Jqlumqj0RgDLiuoBPnrHRjrnZjpkoBr13t8fS++paW2I02ygmL7N2YULcfL+zlXf5TQoh/JIT4c0KIHxj/u28re0hRKM2Xzm/w9kqfly9s8NXzHdaGKb4j8SSsD1L6SYEBXAGpHZshH/uZvN9P4BGBBiqexPclsTIonduAISVISZQpVnsZVzoRX73YxnfklslYPfT5wP4Wge9Y90op+OSJaWqhx4FGiUbFqjmcXu6C4KbN+odN5PJ+Y+Ki+fhhr7IyTwEe18plBvi393pRDzOirODs6oB6ycMREjkiAJR9FykkSoEjDcYIpANoMMLKz1c8GEykZO4Z0sLgSJst9lKDK0AbQ64MvajgyYUaJc/lzGqfjx+fouy7W4yvauhyaq7KoFHiuQN1XrvaI84VB1tljIBMKeJccWquNmGR7QET6vaDiftZvtxLkPmgMeYD9/TRH1EII3CFVVWWwmA0RGlOJ0oZ5jlyJBgzMlakGH2dBJg7w1j00lKPIRqlg8PC4Auohg6h5yCEQADDtEBrzTurA2YqJWaqVhJoN/Otl45M0Sh7NEIPg6EWeASuZJAVaGWYrQW7rmn7h3YicnkNk6D74OF+ly/3EmS+KIR4xhjzxj179EcQZd/lxHyFq+2YmUrIvmYZrSPaw5x+pii5LgZwBFY0E1siG3/1sOnhZDbm1pBYV0vpOEgBWaHQ2pYhzaj06DgwXfGphC4gWR+kOBIcR1INfFb6MTM1b2uTuxHjK/RdPnJkmlcXOySx3Rg/eKi1a7C4lXHZ9nMetwb4xEXzwcJ70TPcS5D5NPDDQojz2FkZARhjzITCvA2uI/n4sRle8dqkmeLJhSqzNZ9/9/WrrPZTljoJUV4QZ4q5qsf6UCGFJlGGTqzYnsxIJjS+3eAJaJYlcabRBiq+g5AG34FupAhcSAtLZQbwXEmhDLVA0ip7yIqP68B0JWCm6nN0pnpbr/PtUI5v9qHdKYD5uDbAJ9TtBwfvRflyL0Hme+7JIz4GaJQ9PnViZutDFGUFjdCzUvK+Q8V3acsMicZzNVkhyNW7c5dJgNkdrgOO47G/5bIxTGmWPY7PVhgkBV++0MZogzPOZjREmWK+VqJV9WgYjyTXHGyWODxTQQIzFX8rk9m+8SPg1FyN2VpwnTzMzT58t/uhfdxZZ48rdftBw3tRvtyL1P/Fe/aojwG2f4jKvsvR2Spn1gYMk4yNYUyUKqbKHrNVn+VeTJq9zwt+iFD2HBZqPsZI5suGTz8xy6efmOVL59Z5Zy0iyQuiXJErQyWQVAKXmbrPH31qnpPzVd5e7rPUS8mV5om52lbZa/vGnxnNmys9Xrva5QP7G3zwUOu2sozb/dBOGuATPAh4L8qXe8lkJrhDuI7kw4eb/Nrri3iuGbX9Nf1MMVcP8VxJJXSIEoUR14gAE7wboQOZVpxd6+MLyf7pChc2IxbW+ix2E2ol26D3PZdulGAQJJlitZfy22+v4jqSmXrIR49P0yh510nAjDd+15e8ud6n4ntIITACvnJxg0+fmCG8gefLGLf7oZ00wCd4UHC/y5eTIHOPkGTFlsvheCMqlCbKCjpxxlfe2WC1E7M0SFF5Qa4EyhSc34wwSqNRlAPBIJtEmJtBAHGGHVp1NcO04Mxqj5mKx6HpEtIRXNyIuLIZUQs8PE+yr1UizRRl32VzmDNXD7m4Gb2rNLVdc67QGs+1QeDC+pBenCO4xja7GW7nQ/u4N8AfdsLDw77+nbif5ctJkLkHuLg+4LOvL5MXGs+VfPczCwSe5JXLbc6tDbm0EbE5SFgd5CSpZpBpisKyyIZxihF209SYPU3HPo4oNFuzL9pAnCpaFY+vnN/gk0/M0hkWHJ+uMFV2SXJNJy4IXEnougSOzRikENcNRG7fLMaac/20sIOYQuBIQbPiUw2c2+6b3M6H9nFtgD/shIeHff3vNR6Pd/V9RJIVfPb1ZaqBy8GWbST/g989yy98+RJ/eHaDwHHwHMnFdkyhDK4rEdpSlK19mS2PjZv8k2b/9XCw2cv4aqgSSFwpcB2rsIwwrA1TVvsZL5/vMMxSzm8M8R0XDBybqXBqoU7Jlyz1YiRWCBMBUVrwxfMbfOX8Jn9wbp2lTkwlcHh2f53DUyWSXHNmtU+cKU7O1Cj73j2f1r9d1eZHBQ/7xP/Dvv73A5NM5i4xyAryQlOpeShtaEd2SI+RzPzG0NZ20txgLOmbfNv7cSIjc3OMXx8hoDzyd6mFLp04Jy0USlk9slrZI1eKy5sprpS8eLjFwaky3SSjH+dUfJdCZ2xECemi4thMha9eajNdCa5r8j+zUCfTmoV6iZlKyNcvCjKlcSR04+yWMjIT3BwPO+HhYV//+4FJkLlLVH0Xz5UMsxxXSoZpTjl0qXgunSgnygpyZYjTgrjICVwXzxmVe5goLYPNVHxxTXl6HIN9xwpdjr+fb5aJkpxCG1olO0A5zBSDrMBoQ1xoDIKpSsBU2We5l3C4VebpfXU0htOLfZ472KARegyygm9e6jJzPOSd9eFWk19jeO1yl1Y1oBdnLHcTOsOMc6sDDrRKfOBAk2GqaJQnG8qd4GEnPDzs638/cF9fGSHEzwkhVoUQr207NiWE+JwQ4szoa2vb735SCHFWCPGWEOK7tx3/sBDi1dHv/jcx8oAWQgRCiF8YHf+SEOLotnN+ePQYZ4QQP3y/nmPou3zvc/vpRwWX2xFprvnA/gZIQT8ueH2xR14UHJwKmSmHFMbgudLOcdyvRT1kqHggHIHvQNkDHxt41DjAYKX5tTZM1wK+9dQcf/FTx/mBDx1ioVFipuKTK0M/LujFGb4rqJV8WlWfwhiUAWMEx2YqTJUDHCmp+i5GGNpRSqE1CFtfrwYuF9tDzq32ObMyoBvnJErz5EKVmWpAo+RNyiN3gYfdrO1hX//7gfudyfwT4O8D/3TbsZ8AftMY8zNCiJ8Y/fzjQohngB/Cum/uB35DCHHKGKOAfwD8KPBF4Fexg6GfBX4EaBtjTgohfgj4u8CfFUJMAT8FvITdy78qhPhlY0z7fjzJWsnlyf01hkmOnq1yfnNIxXd5el+dfa0QX0jWhyluTeA4VkQTo8mzSaABO5FPoYkLkDsyPAfwfUnFd5ECDk6VOTpdYa4ecrk9ZK4REBeKNNekWuO7DsoYvnZpg/3NEv/lx49QC70tGf/xFWihDU/M1ZBSEKUKjeHp+QZJrpBCIqUV03Qcgc6gFvhkSl9HGphsLHeGh53w8LCv/73GfX11jDGfBzZ3HP5+4OdH3/888Ce3Hf/XxpjUGHMeOAt8VAixD6gbY75gjDHYgPUnd7mvfwN85yjL+W7gc8aYzVFg+Rz3SbFg3Aishx5HZ2pM1wNcJM/sq/PMgTpztZB2lHC1nbLcz4gyewUcTQLMFqJMkyur51byJYFrr34cCeXAoexLEqUp+y7P7WvygUNNfE/awJJrlDaUfAdPSsq+Q5RahWSw0v7lEa185xXox45N8y1PzPKnPnyAkzNVMqVIC8XhqTJPztc5MBWyrx7iu5YsIAREeWHLe5ON5a7wsBMeHvb1v5d4P3oy88aYJQBjzJIQYm50/AA2UxnjyuhYPvp+5/HxOZdH91UIIbrA9Pbju5xzHYQQP4rNkjh8+PCen8zORmDVd0m14tURA6UbZby22MMYTZIrtDZE6aTdvx3bX4480/gOCAmehIovqIU+UsAnT07xwcMNfEeSF5KTc1UQVlW5m+TUXYnvSAplS22OEHz9cptvOzWH68gbXoHua5SYrQY2UwFAcKUTMV0OWezGHJ+p0ktySzlPFCfmK5O+zAQT3CYepMa/2OWYucnxOz3n+oPGfAb4DMBLL7205+Ri3AiMsgJjIMpyS7m1bSOUMigN9ZKHMYYoy0km5fwbwmCDjos1dBOpxnUU8/UQKQS/9voKaVHQKvk8e6DBp0/OYgycXupxqR0hhWC6EWCwbqNac11p60bzK9uPf+zYNOFlSVwonlqo8cR8jbdX+/iOpBp4FFo/VjpjE0xwN3g/gsyKEGLfKIvZB6yOjl8BDm273UFgcXT84C7Ht59zRQjhAg1see4K8G07zvmde/s0LFxHcrhZ5he/cokLmxGOFMzWfJ7b3yTOCpbaMUvdIYNYMZjo9982DOBKMEYTeJJTCzVWeynrg4xhXrDsxqz0Yj79xCxV32GuHpArQ15o8kLhllxmaj6OvJ75czuT2o2yxydPXhM4zZTGWRM0StZ3xpET2uoEE9wu3o8g88vADwM/M/r6H7Yd/5dCiL+Hbfw/AXzZGKOEEH0hxMeBLwF/HvjZHff1BeBPA79ljDFCiF8D/s425tp3AT95P55MoTRfurDB68t9pACtDYudiNevdolzwyBO6SeK4STA7AkC259pljxOztXAGNaHGUmuKHseSitOL/U4szqgXvJphB610NoiO1JwsFEm8CQvHLrG/NnLpPbOjGdCW51ggjvDfQ0yQoh/hc0oZoQQV7CMr58BflEI8SPAJeAHAYwxrwshfhF4AzsQ/2MjZhnAX8Iy1UpYVtlnR8f/MfDPhBBnsRnMD43ua1MI8beAr4xu9zeNMTsJCPcEvTjjD89tEHqWFrvUidgYZFQCF4VhpZ9STALMniCxcv6+41APAwZJQW+YIR2JlIJukrLYTghch1MLZQ5PlUgLxYuHWtRKNnuREj50qMV01TpX3o20/uOuMzbBBHeD+xpkjDF/7ga/+s4b3P6ngZ/e5fjLwHO7HE8YBaldfvdzwM/d9mLvEJnW5JmiFxf04px2XKC0oVX2WevHZMqQT2hkt4VA2uwl9F1cCUobemlGtexwaqHG+c2IlV6KKwSeI6gEkrzQ1EOfdpQS+g6fOmmDxs5y2N1Oak9oqxNMcGd4kBr/DyWqnovvO5S1YZgq8kKDgHIgWF9MKdREj+xWkFj6cqPiUS+51IOAdpRQC10OTJV46UiL5W7Kswt1mqFHPynINhS1wJbI4tymirXAuyGt9F5Mak+MtiaYYO+YBJm7hHQknzw+wzevdkhyxb5GgBBwZqXPMNeEviDJzHVyKRNcw3gexh9ZWWa5YUOlTJUDTsxWeWZ/nVY5IM9BGc3JuRqOI/juZxd4+eImq72ETuTwqROzvHhk6oZBYFLymmCC9weTIHOX8B3JQitkvj5PqjSuhG9e6TJT8dEGNocJ6ypHGsi0vWo32KbT4wzJtd7LQiMEbLDZVy/z9L4aruPw4pEWy/2E9UGC5wn+zIcOow2cWe5jgO+t7+fgVInpik+95N8Xaf1HzTdkggnea0yCzF3CdSSHW2V+9bWlrVLZfD2knxQcn65SKMVGlCOEINAG13HIC4XQPBa9GokVt6z6tqyVFnbAsuQ7xFmB6zjsb4aUfY9myePgVJlPHJ9mY5gjJOxvlAB44VBzq4k/Hpy8nY1/Z5DYS8lr4hsywQR3j0mQuUsUSnOpHfGhQy2kEORK8ztvLbM5zNmIMgZJwVTZoxp4dAYpw1whRiZljzoCB2bLHkbATC3k4HSJsudxdqXHci+jXBLMVUOmyyEKw/ow5ch0mdeXexxslvn40SkSpa9zG4Xb743cTZC4GzbaBBNMcA2TIHOXGLOWqr4k1xpXClZ7OZ5rxTAdR1LxJMdmq1x0BYPVIUnx6PdnSi4EriAzhqcX6pQDlx944RC10ONfffECmTbMVEPiTLHSTzDGMN8IWeln7JcOgzTnq5c7YLijLGK3IPGNy21eONy8Lc2p99s3ZFKmm+BRwSTI3CV8R5IUinOXB0gEvSSnMIap0CPKXA5PVeglBefWhvTjjOgxacZoBdWKz3QtwHdd6qHHmbUBJ2crLPUTtGZLaHKpGzNd9Ww5TAAYrnZi5mohjbJ/R1nEziCRFZpXF7t2mDN0bxm03k/fkEmZboJHCZNLpHuFUfkrdCQSQ5QW+I4zMsjK6UUZxhj8x+QVVwYKrRgkBWmRs68RkivDxiBnkCpqgb2+aUcpG4OUNDd4jqQe+ryzMSQvNNXAbqyB6+zZ9nh7kFDaOl+WPIe5eum2LHPfL9+Qib3vBI8aJpnMXSJTmtB1ePHwFLnSeCO13y9d2KQdZThScGSqzJmVAY4A+YjzygSWJSaxw5TKQC/RPDlfpxy6nJytcGa5zzsbQ65sRmhhqJZcTs1XWemlTFcMAjg2U6XQGkfe+UzLdspykmo+dLiFI8Vta4+9HwOY73eZboIJ7jUmQeYusf2KWQr79cBUmb92aoZf/voiv3d2jfVuSpQWaKNQj1jD3wEqgSAvDIErENLBd6DQMF8LSArD8/sbeJ7khYMtKoHDR05M0Sx7NEoupUASZ5p6ycPNNVMVn48en+Jjx6Y5vdy7q5mWcZCIsoLQc/Bcq4y9l6D1Xg9gTux9J3jUMAkyd4mxCvN/enWJJC8IPZfve34/02UfpeETx2a42ol55eImi70E8RBblZVdkFKOSA4Gxxga1YAoVZTqHklWUPYdFPDUfJ2ZWkAtcHjmQJ1Pn5jZYoh9/NgMniNRxlAOXY5NVbmwOWAQK5472ODDh6fuWRbhOpJ6yeeFg62HYhBzMjQ6waOGSZC5SxRKc3qlh+8IXMfa/J5e7hHlIRc2BlQDjyvtCCOgETq2pFZYJ7aHCaELriPYVw/5tqdmiXLF8ekKFzYjunHOVNlnM84YJIpTs1UqoY/rCAyGjx2zAWbMmKoEDt/yxCxP76tvDVaemqvxxEKN2WpwS++XO8HDpD32MK11ggluhUmQuUtEWcHZ1QEz1QDfcciU4q3lHlmuQAhWewmr/ZTVfgrGoM3DN+1f8aFZ8ikMTDdCpJA0QoeoMKS5YaFe4mo7Zn+rRODmVEMPZTTHp2u8eHSK6WqwK2NquyPle7GZPkzaYw/TWieY4GaYBJl7AGEEmJEZpxEoBUYYDrfKfP3SJoUyZLlGG0OhbmDR+YDAE7Z5nxvbvK+Fgn2NMmHoUgscWqWA5w82WemnAKwPMholj9yAQOA7Dh883GSYFHzsxDT1kn/Lwcb7tZlOZk0mmOD9xyTI3CXKvsuJ+QpX2zFxLtDGcGpfFU8KgtEQpgEKpehFOekDSEW1wUSitEYiKLSh4ksC1yFwJJtRzozjUK97zNcD5uolNoY5jhRMV336aYEqFFGuODRVwpWCcuhSHvVg3g/G1GTWZIIJHgxMgsxdwnUkHz82w9fkJv00pxZ4vHhkiiQv+ObVLuv9FKUhyhWZ0e97FuML8BzIlM1WXAFTZZfDM2W0Nqz2U6qeS47h5GwVRwoONMs0Kx6elES5otCaE7NVTi93masHhNLh2f11unHOwWaZXJvrmtXvNWNqIgkzwQQPDiZB5h7AYJCOIHQdpCPoRhmXOhFztYDffnOViidHXvNWHBPz3jb+JTBdcfAcyeGpKtNVj9eudtFK44ceR6ZKrPYy2lFGlhumKi7PzVc50CxjgE+emMZxBJ6ULHYS+mmOJyVHZyp829QcczXfulbC1sBk2Xe2Hv+9ZkxNZk0mmODBwSTI3CXGV80V32WqHBBlBb/6+hIfONAgVZqn99dpDzJmKoqrnSGhC0kBwrw3vZlAwLc+NUPJczmzOqBV9RjmhnLg4bmS5xbqvLMxZJgUzFQCZms+rnRolX1CV1JgUMbgYDORetnlpcMtNqOMsysDLrcjFnsxH9jfxGBuWKJ6LxlTk1mTCSZ4cDAJMneJnVfNUgjywjb5JYKZSkjJcal4krVBwkBpQk+jtS1ZGQOKex9wPAGhJ3AdyWwtpFUOeP5Qk1wbpIHTKz2Uso/tSMGBVonjs1VC1+VqN2IzyqiVPL711AxfeGeTvNB4ruR7n9uP60jeWR9SDtytTfyVK22EwR67QYnqvWJMTWZNJpjgwcEkyNwldl41a2PwXIkUAs+VTFU8hmmBloKpio8nC9JC44+mz/Nck2hNP909zJQcSHexcJbYY61AYITEdeyBdmLLcSXfoRY6YAwCw8m5Kle7MR882CTwJM8caPD1y23210Pm6wG+61ANXK50Imoll331Ev/Fiwc4v3HNxkAbw8X2kEbZfVc5anOQYgS0KsHWsZ0lqveS7TWZNZlgggcDkyBzl9jtqvl7n9vPxfaQ6XLApXTI8wfrXNqMONKs8PvvrFELXJJCkxeaOCuYqngsdlKUtsKarisQBp6Yr1IvB1xY67PazyiAQVTguSCEwHcE+5tljs2WudKOGSaKakmPZFRcpIBq4JEow2wtYDPKeHu1z4uHWmjH8NR8jQ8fbWGM4OWLm5xbGdIMPZ4/0ORTJ2co+Q5nV4c0wmtvk82hpS7vLEcFvoMw3LBE9X6wvSazJhNM8P5jEmTuAXa7at7XDMmURjJLpjSvXLJB6LWlLsOsoBcXHJ4uUfbKHJmq0M9yBHCwUaESOjy5UKc9zBimBa8vljHG8PUrHVa6MY6QzNZ8Vgc5cV7QHuZ85Og08/WAfqT4/NlVHFeiCkM1dGy2JQVPLzT4+qU276wPWe0nHG6VeWOpz9MLdT5xfJqPHG3hSrnlt1IovWtvo+y77wqsLxxsAexaopqwvSaY4PHFJMjcI+y8at7+s6s0TyzUeHOxx3P7Gyx1Y4qmYarkkSpDveTRjgvmah7DQvG9T+5jvlGiUJpOnFEKXCq+g+s6vHa1y9V2BNLh+EzAyfkKx6arfPuTs7y9NmCjn/KNpYDVXkKSKQLX6owV2hB6kqf31ci15kCzRdl3WR8k/NJXL/PEXI3Ak9f1Lm7W22iU5a7lqN2OTdheE0zw+GISZO4z1gcJr1zuIBE4juD7XtjP5Y0h37zawxWAEKz0UkJXsq9RoR2lvLbYY3qk4dUs+QSeRAiYqQUcaoV4juRgM0Q4gplKwL5GyFQ14GlH8ktXL/P8gTqvAY2Sh+9KDk6V+caVDh/Y3+Cp/XXeXu5T9l2yQnFurU9eaBvEHPmuDONmvY3dylG7HZuwvSaY4PHFJMjcR6z3E/7N167gCEHJczk4VWJtkPLcwQZxrjm/NuD8+hBHCj50uIUymnA0XzK+yt+eTcxVQzpRRsnzaCcZ+0olDk9X+OChFq4jKfkOT8zV8F1BLfSohZZ08Oz+Br0427IePucM2BikvHq1w1cvtnEdgedIPniwtWUOdqtgshdM2F4TTPD4YhJk7hMKpfnGlS6OEMxUQ7JCc2UzZr4e8Ppin33NEoemypzsxPzO22sMkpxy6HK4WcFxue4qf3s28R1P2R5PofV1/ROw5wSexJOC0HcotN5ivMlR5uA6kqcX6vzCVy5xtRNT9h0W6iHrw5TXFzucmq/dlwxjwvaaYILHE5Mgc4+wk547nnwPfavM7LsOvSQjUy6htPMlAIemKvyRkwZHCgLPwXG56VW+68gtX5bdcGK2ypnlvmW2tYdMlz2+ebXD/7+9e4+Rqy7DOP59utNtt1tKL0CxNwrStBIKtF0JFSVqgVQk1ESJCCIRI4EoFyMiSOKNFDEQhQQDkooloYJSUYlRCgErxnDbAm25XwRLodpFWkupve7rH+e3ZLo7sztLZ/bMzj6fZNOZs2fOPrPbM++cc37ze6eNa6V93SZmTxpLS3MTh05opTBsGCOHN7Fh83a27NjF1p27mXHwfjUrAN2PiDyBpVnjc5GpglLDc1tHNDFi+DCmjW1l3eZ32fK/XeyJoG36eF7auHWv6xPjRjfTNm0cnVDyBbeS4b/F6yCYM20sJ8yYwKOvbWLahFZGNQ9/b1RX27RxjBpZoNAkRhSamDqhhS3bC8w8KOvnktfvzBNYmjUev33cR8XDc8e3jmBkoYm1b24GsiOSpgJM2j/7NP3p86YycUwLsyeNZfvuPbz97g62797D7EljGdlc2OvUV1/b3100m3P3dVqbC7zy1lY6IZsRuTl78R5RaGLPnqATOGbKOCaPa+E/W3ewadtODjtgNHMPGT8gRxSVPCczaww+ktlHvQ3PLXcdoj/XJyoZ/ltuHej5ocmuUV2jmoexYNZE5h+WrVeqwNWKhzSbDR3eo/dR8fBcoMfw3ELTsJIv4OWW93f7va3T9aHJ7kdNxZ+DGdPSzJiW5gF9ca/kOZlZY/CRzD6q9fDcSrb/fj40mScPaTYbOhSRdxut2pK0ELgBaAKWRMQ15dZta2uL9vb29/Vzaj1SqpLtD7bRWoMtr5mVJmlVRLSV+l5DH8lIagJ+BpwErAcel3RPRDxb7Z9V68kYK9n+YJsQcrDlNbP+a/Q9/Fjg5Yj4R0TsBO4EFuWcycxsyGj0IjMZeL3o/vq07D2SzpPULqm9o6NjQMOZmTW6Ri8yKrFsr4tQEXFLRLRFRNuBBx44QLHMzIaGRi8y64GpRfenAG/mlMXMbMhp9CLzODBD0qGSmoEzgHtyzmRmNmQMhSHMpwDXkw1hvjUiFveybgfwz26LDwDeqlnA6nHO6nLO6nLO6qq3nIdERMnrDQ1fZPaVpPZy47/riXNWl3NWl3NW12DJCY1/uszMzHLkImNmZjXjItO3W/IOUCHnrC7nrC7nrK7BktPXZMzMrHZ8JGNmZjXjImNmZjXjItMLSQslvSDpZUmX552nFElTJf1F0nOSnpF0cd6ZypHUJOlJSX/MO0tvJI2VtFzS8+n3Oj/vTN1J+kb6ez8t6Q5JI/PO1EXSrZI2Snq6aNl4SfdLein9Oy7PjClTqZzXpr/7Gkm/kzQ2x4hdmXrkLPrepZJC0gF5ZKuEi0wZRW0CPgUcAXxB0hH5pippN/DNiPgQcBzwtTrNCXAx8FzeISpwA3BvRMwCjqbOMkuaDFwEtEXEkWQfND4j31R7WQos7LbscuCBiJgBPJDu520pPXPeDxwZEUcBLwJXDHSoEpbSMyeSppK1MVk30IH6w0WmvEHRJiAiNkTEE+n2O2QviJN7f9TAkzQF+DSwJO8svZE0BjgB+AVAROyMiM25hiqtALRIKgCjqKM5+SLiIeDtbosXAbel27cBnxnITKWUyhkR90XE7nT3EbL5DnNV5vcJ8FPgMrpN+ltvXGTK67NNQL2RNB2YAzyac5RSrifbITpzztGXw4AO4Jfp1N4SSa15hyoWEW8A15G9g90A/Dci7ss3VZ8mRsQGyN4YAQflnKcS5wJ/zjtEKZJOA96IiNV5Z+mLi0x5fbYJqCeSRgO/BS6JiC155ykm6VRgY0SsyjtLBQrAXOCmiJgDvEt9nNp5T7qesQg4FJgEtEr6Yr6pGoukK8lORS/LO0t3kkYBVwLfzTtLJVxkyhs0bQIkDScrMMsi4u6885RwPHCapNfITjt+UtLt+UYqaz2wPiK6jgaXkxWdenIi8GpEdETELuBu4CM5Z+rLvyV9ACD9uzHnPGVJOgc4FTgr6vODhB8ke4OxOu1TU4AnJB2ca6oyXGTKGxRtAiSJ7PrBcxHxk7zzlBIRV0TElIiYTvZ7fDAi6vKdd0T8C3hd0sy0aAHwbI6RSlkHHCdpVPr7L6DOBieUcA9wTrp9DvCHHLOUJWkh8G3gtIjYlneeUiJibUQcFBHT0z61Hpib/u/WHReZMtLFv68DK8h24N9ExDP5pirpeOBssqODp9LXKXmHGuQuBJZJWgMcA1ydb5y9paOs5cATwFqy/bhuphmRdAfwMDBT0npJXwGuAU6S9BLZiKhr8swIZXPeCOwH3J/2pZtzDUnZnIOGp5UxM7Oa8ZGMmZnVjIuMmZnVjIuMmZnVjIuMmZnVjIuMmZnVjIuMmZnVjIuMWY1IWimpLd3+UzWnjZd0vqQvVWt7ZrVSyDuA2VAQEVX9gGxE5P4hQbNK+EjGrIik6alp1ZLUEGyZpBMl/T013DpWUmtqJPV4mql5UXpsi6Q7U8OrXwMtRdt9rauxlKTfS1qVmo6dV7TOVkmLJa2W9Iikib3k/L6kS9PtlZJ+LOkxSS9K+lha3iTpOklrU6YL0/IFKffa9DxGFGW8WtLDktolzZW0QtIrks4v+tnfSs99jaQfVPUPYA3HRcasp8PJGpcdBcwCzgQ+ClwKfIdsBtwHI+LDwCeAa1M7gAuAbanh1WJgXpntnxsR84A24CJJE9LyVuCRiDgaeAj4aj8yFyLiWOAS4Htp2XlkEynOSZmWKeuguRT4fETMJjubcUHRdl6PiPnA39J6nyNrhvdDAEknAzPI+i0dA8yTdEI/ctoQ4yJj1tOraRLCTuAZso6OQTZP2HTgZOBySU8BK4GRwDSyZme3A0TEGmBNme1fJGk1WVOsqWQv2gA7ga7W1KvSz6pU1+zbxY87Ebi5qwlXRLwNzEzP78W0zm0pd5euSWDXAo9GxDsR0QFsT9eUTk5fT5LNnTarKL9ZD74mY9bTjqLbnUX3O8n2mT3AZyPiheIHZRMi995zSNLHyV7850fENkkryYoUwK6iqeX30L/9sytj8eNUIk+pPkmltlP8vLvuF9LjfxQRP+9HNhvCfCRj1n8rgAvTNPtImpOWPwSclZYdSXa6rbv9gU2pwMwiOxVVK/cB56cWzUgaDzwPTJd0eFrnbOCv/djmCuDc1CQPSZMlDYYul5YTFxmz/rsKGA6skfR0ug9wEzA6tQi4DHisxGPvBQppnavITpnVyhKy3jNr0um5MyNiO/Bl4C5Ja8mOUCoeqZbaPP8KeDg9fjnZ1PhmJXmqfzMzqxkfyZiZWc34wr9ZHZN0JXB6t8V3RcTiPPKY9ZdPl5mZWc34dJmZmdWMi4yZmdWMi4yZmdWMi4yZmdXM/wERgQOwINqkewAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind = \"scatter\", x = \"median_income\", y = \"median_house_value\", alpha = 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7a1a77d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# adding new attributes\n",
+    "housing[\"rooms_per_households\"] = housing[\"total_rooms\"]/housing[\"households\"]\n",
+    "housing[\"bedrooms_per_rooms\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]\n",
+    "housing[\"population_per_households\"] = housing[\"population\"]/housing[\"households\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bedrooms_per_rooms          -0.255880\n",
+       "latitude                    -0.144160\n",
+       "longitude                   -0.045967\n",
+       "population                  -0.024650\n",
+       "population_per_households   -0.023737\n",
+       "total_bedrooms               0.049686\n",
+       "households                   0.065843\n",
+       "housing_median_age           0.105623\n",
+       "total_rooms                  0.134153\n",
+       "rooms_per_households         0.151948\n",
+       "median_income                0.688075\n",
+       "median_house_value           1.000000\n",
+       "Name: median_house_value, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix = housing.corr()\n",
+    "corr_matrix[\"median_house_value\"].sort_values(ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a6ae7a33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
+    "housing_labels = strat_train_set[\"median_house_value\"].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "789d9159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer = SimpleImputer(strategy=\"median\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "713cf6a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-118.51   ,   34.26   ,   29.     , 2119.     ,  433.     ,\n",
+       "       1164.     ,  408.     ,    3.54155])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_num = housing.drop(\"ocean_proximity\", axis = 1)\n",
+    "imputer.fit(housing_num)\n",
+    "imputer.statistics_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3f9aa2d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = imputer.transform(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "79511e00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_tr = pd.DataFrame(X,columns=housing_num.columns,index = housing_num.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1b0dede6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ocean_proximity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12655</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15502</th>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2908</th>\n",
+       "      <td>INLAND</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14053</th>\n",
+       "      <td>NEAR OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20496</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>NEAR BAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18125</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5830</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17989</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4861</th>\n",
+       "      <td>&lt;1H OCEAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ocean_proximity\n",
+       "12655          INLAND\n",
+       "15502      NEAR OCEAN\n",
+       "2908           INLAND\n",
+       "14053      NEAR OCEAN\n",
+       "20496       <1H OCEAN\n",
+       "1481         NEAR BAY\n",
+       "18125       <1H OCEAN\n",
+       "5830        <1H OCEAN\n",
+       "17989       <1H OCEAN\n",
+       "4861        <1H OCEAN"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_ohe = housing[[\"ocean_proximity\"]]\n",
+    "housing_ohe.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "41d74b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1.],\n",
+       "       [0., 1., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [1., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "ohe = OneHotEncoder()\n",
+    "housing_ohedone = ohe.fit_transform(housing_ohe)\n",
+    "housing_ohedone.toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "23ba8875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
+       "       dtype=object)]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ohe.categories_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "44eef0df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#using pipelines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9f611bdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n",
+    "\n",
+    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
+    "    def __init__(self, add_bedrooms_per_room=True):\n",
+    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
+    "    def fit(self, X, y=None):\n",
+    "        return self \n",
+    "    def transform(self, X):\n",
+    "        rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n",
+    "        population_per_household = X[:, population_ix] / X[:, households_ix]\n",
+    "        if self.add_bedrooms_per_room:\n",
+    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
+    "            return np.c_[X, rooms_per_household, population_per_household,\n",
+    "                         bedrooms_per_room]\n",
+    "        else:\n",
+    "            return np.c_[X, rooms_per_household, population_per_household]\n",
+    "\n",
+    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
+    "housing_extra_attribs = attr_adder.transform(housing.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "8dab5cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "num_pipeline = Pipeline([\n",
+    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
+    "    ('Attribs_adder',CombinedAttributesAdder()),\n",
+    "    ('Std_scaler', StandardScaler()),\n",
+    "])\n",
+    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "6c57d77c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.compose import ColumnTransformer \n",
+    "num_attribs = list(housing_num)\n",
+    "cat_attribs = [\"ocean_proximity\"]\n",
+    "\n",
+    "pipeline = ColumnTransformer([\n",
+    "    (\"num\", num_pipeline, num_attribs),\n",
+    "    (\"cat\", OneHotEncoder(),cat_attribs)\n",
+    "])\n",
+    "housing_prepared = pipeline.fit_transform(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc02d665",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6c685f40f4e372158c8452f24cd9ee0bbc1030a9f2f196d3595227ed632ab81f"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('bcs_project')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 1/Introduction to Jupyter Notebooks.ipynb b/Sakt Launde/Week 1/Introduction to Jupyter Notebooks.ipynb
new file mode 100644
index 0000000..1e109ad
--- /dev/null
+++ b/Sakt Launde/Week 1/Introduction to Jupyter Notebooks.ipynb	
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "217c9e16",
+   "metadata": {},
+   "source": [
+    "# Introduction to Jupyter Notebooks\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf2c2565",
+   "metadata": {},
+   "source": [
+    "Jupyter Notebooks provide environment to create and work on python projects. They are saved as .ipynb files. In a Jupyter Notebook we can have multiple cells for different purposes. For example:- Code, Markdown, Heading, etc. We can also choose to run an individual code cell. Jupyter Notebooks make our work very easy. Here is how to use them:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cd74f0b",
+   "metadata": {},
+   "source": [
+    "Opening Jupyter Notebooks and creating a Notebook file:\n",
+    "\n",
+    "1. Open Anaconda Navigator and select Jupyter Notebooks.\n",
+    "\n",
+    "2. Now to go to the folder where you want to create the notebook.\n",
+    "\n",
+    "3. Now click on \"New\" and select Python 3(ipykernel)\n",
+    "\n",
+    "4. An unnamed Notbook file will be created at the desired location and now, you can work on this notebook in Jupyter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c629acc",
+   "metadata": {},
+   "source": [
+    "Adding a cell to the Notebook:\n",
+    "\n",
+    "1. Click on \"Insert\" and select either \"Insert Cell Above\" or \"Insert Cell Below\" as required.\n",
+    "\n",
+    "2. A blank cell will be created.\n",
+    "\n",
+    "3. Now select the cell type from the dropdown on right of \"Run\" tab.\n",
+    "\n",
+    "4. The cell type will change to the desired type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1628757",
+   "metadata": {},
+   "source": [
+    "Running a code cell:\n",
+    "\n",
+    "1. Select the cell.\n",
+    "\n",
+    "2. Click on \"Run\".\n",
+    "\n",
+    "3. The code written in the cell will be run and output will be printed below the code box in the cell.\n",
+    "\n",
+    "You can try it out with the below cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2c5fa7ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Jupyter Notebooks!!!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello Jupyter Notebooks!!!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 1/Introduction to Matplotlib.ipynb b/Sakt Launde/Week 1/Introduction to Matplotlib.ipynb
new file mode 100644
index 0000000..c72c7bd
--- /dev/null
+++ b/Sakt Launde/Week 1/Introduction to Matplotlib.ipynb	
@@ -0,0 +1,629 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a47576e7",
+   "metadata": {},
+   "source": [
+    "# Introduction to Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad3152cf",
+   "metadata": {},
+   "source": [
+    "Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34d7bd54",
+   "metadata": {},
+   "source": [
+    "Before using Matplotlib we first need to import it as follows:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1984ad40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3041952d",
+   "metadata": {},
+   "source": [
+    "Now, matplotlib, pyplot and numpy have been imported as mpl, plt and np respectively. Let's see how to draw the line joining points (0, 0) and (10,10) using Matlplotlib:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "537e2e2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xPoints=np.array([0,10])      #Define the points\n",
+    "yPoints=np.array([0,10])\n",
+    "\n",
+    "plt.plot(xPoints,yPoints)      #Plot the points\n",
+    "plt.show()                     #Show the Plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a5d325b",
+   "metadata": {},
+   "source": [
+    "Now let's see how to plot multiple points:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "619285d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMpUlEQVR4nO3dcWifd53A8ffnkorpvBGlUWw6LhNG7o6NIxKO6UBk9eg4hwvjPHbHZIrQf+50ilTW+2d/blAR98chlDodOLaTGuo4xChTkfunmDaDbqvFY86uabU/8aIigXX1c3/kV9ekXZvkeZqnn+b9gpJfvvnl93x4aN799fk9T36RmUiS6vmLrgeQJK2PAZekogy4JBVlwCWpKAMuSUUNbuTGtm3blmNjYxu5SUkq78iRI7/JzJGV6xsa8LGxMWZnZzdyk5JUXkT88nLrHkKRpKIMuCQVZcAlqSgDLklFGXBJKuqqZ6FExJPAvcDZzLy9v/Yu4L+AMeBV4J8z8/+u3ZiSVNOhuXn2zZzg9MIi24eH2LNrnKmJ0VYeezXPwL8B3LNi7RHg+cy8DXi+/7kk6SKH5ubZO32M+YVFEphfWGTv9DEOzc238vhXDXhm/gT47Yrl+4Cn+refAqZamUaSbiD7Zk6weO78srXFc+fZN3Oilcdf7zHw92TmGYD+x3e/1R0jYndEzEbEbK/XW+fmJKme0wuLa1pfq2v+ImZm7s/MycycHBm55EpQSbphbR8eWtP6Wq034L+OiPcC9D+ebWUaSbqB7Nk1ztCWgWVrQ1sG2LNrvJXHX2/AnwMe6t9+CPhOK9NI0g1kamKUx+6/g9HhIQIYHR7isfvvaO0slNWcRvgM8GFgW0ScAh4FHge+FRGfBk4CH29lGkm6wUxNjLYW7JWuGvDM/Je3+NLOlmeRJK2BV2JKUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRRlwSSrKgEtSUQZckooy4JJUlAGXpKIMuCQVZcAlqSgDLklFGXBJKsqAS1JRBlySijLgklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRTUKeER8PiJeiogXI+KZiHh7W4NJkq5s3QGPiFHgs8BkZt4ODAAPtDWYJOnKmh5CGQSGImIQ2Aqcbj6SJGk11h3wzJwHvgScBM4Av8vM76+8X0TsjojZiJjt9Xrrn1SStEyTQyjvBO4DbgW2AzdFxIMr75eZ+zNzMjMnR0ZG1j+pJGmZJodQPgL8IjN7mXkOmAY+2M5YkqSraRLwk8CdEbE1IgLYCRxvZyxJ0tU0OQZ+GDgIHAWO9R9rf0tzSZKuYrDJN2fmo8CjLc0iSVoDr8SUpKIMuCQVZcAlqSgDLklFGXBJKsqAS1JRBlySijLgklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRRlwSSrKgEtSUQZckooy4JJUlAGXpKIMuCQVZcAlqSgDLklFGXBJKsqAS1JRjQIeEcMRcTAifhYRxyPiA20NJkm6ssGG3/8E8L3M/KeIeBuwtYWZJEmrsO6AR8TNwIeATwJk5uvA6+2MJUm6miaHUN4H9ICvR8RcRByIiJtW3ikidkfEbETM9nq9BpuTJF2sScAHgfcDX83MCeCPwCMr75SZ+zNzMjMnR0ZGGmxOknSxJgE/BZzKzMP9zw+yFHRJ0gZYd8Az81fAaxEx3l/aCbzcylSSpKtqehbKZ4Cn+2egvAJ8qvlIkqTVaBTwzHwBmGxnFEnSWnglpiQVZcAlqSgDLklFGXBJKsqAS1JRBlySijLgklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRRlwSSrKgEtSUQZckooy4JJUlAGXpKIMuCQVZcAlqSgDLklFGXBJKsqAS1JRBlySijLgklTUYNMHiIgBYBaYz8x7m4+kKzk0N8++mROcXlhk+/AQe3aNMzUx2vVYkjrQOODAw8Bx4OYWHktXcGhunr3Tx1g8dx6A+YVF9k4fAzDi0ibU6BBKROwAPgocaGccXcm+mRN/jvcFi+fOs2/mREcTSepS02PgXwG+CPzpre4QEbsjYjYiZnu9XsPNbW6nFxbXtC7pxrbugEfEvcDZzDxypftl5v7MnMzMyZGRkfVuTsD24aE1rUu6sTV5Bn4X8LGIeBV4Frg7Ir7ZylS6rD27xhnaMrBsbWjLAHt2jXc0kaQurTvgmbk3M3dk5hjwAPDDzHywtcl0iamJUR67/w5Gh4cIYHR4iMfuv8MXMKVNqo2zULSBpiZGDbYkoKWAZ+aPgR+38ViSpNXxSkxJKsqAS1JRBlySijLgklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlEGXJKK8pdZqSzfH1SbnQFXSb4/qOQhFBXl+4NKBlxF+f6gkgFXUb4/qGTAVZTvDyr5IqaKuvBCpWehaDMz4CrL9wfVZuchFEkqyoBLUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRRlwSSrKgEtSUQZckopad8Aj4paI+FFEHI+IlyLi4TYHkyRdWZNfZvUG8IXMPBoRfwkciYgfZObLLc0mSbqCdT8Dz8wzmXm0f/sPwHHAXw0nSRuklWPgETEGTACHL/O13RExGxGzvV6vjc1Jkmgh4BHxDuDbwOcy8/crv56Z+zNzMjMnR0ZGmm5OktTXKOARsYWleD+dmdPtjCRJWo0mZ6EE8DXgeGZ+ub2RJEmr0eQZ+F3AJ4C7I+KF/p9/bGkuSdJVrPs0wsz8HyBanEWStAZeiSlJRRlwSSrKgEtSUQZckooy4JJUlAGXpKIMuCQVZcAlqSgDLklFGXBJKsqAS1JRBlySijLgklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlEGXJKKMuCSVJQBl6SiDLgkFWXAJakoAy5JRRlwSSrKgEtSUQZckooy4JJUlAGXpKIMuCQVNdjkmyPiHuAJYAA4kJmPtzLVRQ7NzbNv5gSnFxbZPjzEnl3jTE2Mtr0ZSSpn3QGPiAHgP4F/AE4BP42I5zLz5baGOzQ3z97pYyyeOw/A/MIie6ePARhxSZtek0Mofw/8b2a+kpmvA88C97Uz1pJ9Myf+HO8LFs+dZ9/MiTY3I0klNQn4KPDaRZ+f6q8tExG7I2I2ImZ7vd6aNnB6YXFN65K0mTQJeFxmLS9ZyNyfmZOZOTkyMrKmDWwfHlrTuiRtJk0Cfgq45aLPdwCnm42z3J5d4wxtGVi2NrRlgD27xtvcjCSV1OQslJ8Ct0XErcA88ADwr61M1XfhhUrPQpGkS6074Jn5RkT8OzDD0mmET2bmS61N1jc1MWqwJekyGp0HnpnfBb7b0iySpDXwSkxJKsqAS1JRBlySijLgklRUZF5y7c2121hED/jlhm3w2tgG/KbrIa4j7o83uS+Wc38s12R//FVmXnIl5IYG/EYQEbOZOdn1HNcL98eb3BfLuT+Wuxb7w0MoklSUAZekogz42u3veoDrjPvjTe6L5dwfy7W+PzwGLklF+Qxckooy4JJUlAFfpYi4JSJ+FBHHI+KliHi465m6FhEDETEXEf/d9Sxdi4jhiDgYET/r/x35QNczdSUiPt//GXkxIp6JiLd3PdNGiognI+JsRLx40dq7IuIHEfHz/sd3trEtA756bwBfyMy/Ae4E/i0i/rbjmbr2MHC86yGuE08A38vMvwb+jk26XyJiFPgsMJmZt7P0q6Yf6HaqDfcN4J4Va48Az2fmbcDz/c8bM+CrlJlnMvNo//YfWPoB3bS/qDwidgAfBQ50PUvXIuJm4EPA1wAy8/XMXOh0qG4NAkMRMQhspeV36rreZeZPgN+uWL4PeKp/+ylgqo1tGfB1iIgxYAI43PEoXfoK8EXgTx3PcT14H9ADvt4/pHQgIm7qeqguZOY88CXgJHAG+F1mfr/bqa4L78nMM7D0ZBB4dxsPasDXKCLeAXwb+Fxm/r7reboQEfcCZzPzSNezXCcGgfcDX83MCeCPtPRf5Gr6x3bvA24FtgM3RcSD3U514zLgaxARW1iK99OZOd31PB26C/hYRLwKPAvcHRHf7HakTp0CTmXmhf+RHWQp6JvRR4BfZGYvM88B08AHO57pevDriHgvQP/j2TYe1ICvUkQES8c4j2fml7uep0uZuTczd2TmGEsvUP0wMzfts6zM/BXwWkSM95d2Ai93OFKXTgJ3RsTW/s/MTjbpC7orPAc81L/9EPCdNh600XtibjJ3AZ8AjkXEC/21/+i/L6j0GeDpiHgb8ArwqY7n6URmHo6Ig8BRls7cmmOTXVIfEc8AHwa2RcQp4FHgceBbEfFplv6R+3gr2/JSekmqyUMoklSUAZekogy4JBVlwCWpKAMuSUUZcEkqyoBLUlH/D3J8D95IktRQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xPoints=np.array([1,3,5,10])      #Define the points\n",
+    "yPoints=np.array([0,4,3,10])\n",
+    "\n",
+    "plt.plot(xPoints,yPoints,'o')      #Plot the points with 'o' marker\n",
+    "plt.show()  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5785545",
+   "metadata": {},
+   "source": [
+    "We can use other types of markers as well:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8e6d5755",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xPoints,yPoints,'*')      #Plot the points with '*' marker\n",
+    "plt.show()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6e854b1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xPoints,yPoints,'x')      #Plot the points with 'x' marker\n",
+    "plt.show()  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4ce274",
+   "metadata": {},
+   "source": [
+    "Similarly, we can also plot different types of lines:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "642dccaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xPoints,yPoints, ':')    #Dotted Line\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d284cac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xPoints,yPoints, '--')    #Dashed Line\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b98b7e29",
+   "metadata": {},
+   "source": [
+    "Similarly we can set marker color and size:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a5493cb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xPoints,yPoints, marker = 'o', ms = 20,mec='g', mfc = 'r')    #mec and mfc stand for marker edge color and marker face color\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3727ef63",
+   "metadata": {},
+   "source": [
+    "We can also add labels to our axes as follows:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c4f4159d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Y-Axis')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
+    "y = np.array([2, 4, 6, 8, 10, 12, 14, 16, 18, 20])\n",
+    "\n",
+    "plt.plot(x,y)\n",
+    "\n",
+    "plt.xlabel(\"X-Axis\")         #Setting up the labels for axes\n",
+    "plt.ylabel(\"Y-Axis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8667d0cf",
+   "metadata": {},
+   "source": [
+    "Let's add a tile to our plot as follows:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cc3fc2da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Graph\", loc = 'center')     #Adding the title\n",
+    "plt.xlabel(\"X-Axis\")         \n",
+    "plt.ylabel(\"Y-Axis\")\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ecb758d",
+   "metadata": {},
+   "source": [
+    "Let's add a grid to our plot using grid() function:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bcc3703a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Graph\", loc = 'center')     #Adding the title\n",
+    "plt.xlabel(\"X-Axis\")         \n",
+    "plt.ylabel(\"Y-Axis\")\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "\n",
+    "plt.grid(color = 'g', linestyle = '--', linewidth = 0.5)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "936b49ac",
+   "metadata": {},
+   "source": [
+    "With the subplot() function you can draw multiple plots in one figure. Let's see how:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "98465d8e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1=np.array([0,10])     \n",
+    "y1=np.array([0,10])\n",
+    "x2 = np.array([0,10])\n",
+    "y2= np.array([0,20])\n",
+    "\n",
+    "plt.subplot(1, 2, 1)      #Plot 1\n",
+    "plt.plot(x1,y1) \n",
+    "plt.title(\"Graph 1\")\n",
+    "\n",
+    "plt.subplot(1, 2, 2)      #Plot 2\n",
+    "plt.plot(x2,y2)\n",
+    "plt.title(\"Graph 2\")\n",
+    "\n",
+    "plt.suptitle(\"Graphs\")    #Adding super title\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ecdcc8",
+   "metadata": {},
+   "source": [
+    "In the subplot function, the arguments are as follows : subplot(now of rows, no of columns, index of current plot)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53e08203",
+   "metadata": {},
+   "source": [
+    "With Pyplot, you can use the scatter() function to draw a scatter plot. Let's create two scatter plots:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b85c2528",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x1=np.random.random((1,10))               #Creating arrays of random numbers\n",
+    "y1=np.random.random((1,10))\n",
+    "x2=np.random.random((1,10))\n",
+    "y2=np.random.random((1,10))\n",
+    "\n",
+    "plt.scatter(x1,y1,c='r',s=10)             #We use \"s\" to specify size and alph to specify transparency \n",
+    "plt.scatter(x2,y2,c='g',s=500,alpha=0.5)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fede9c9",
+   "metadata": {},
+   "source": [
+    "#Graphs:-"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5baa1382",
+   "metadata": {},
+   "source": [
+    "We can represent our data using bar graphs with the help of Matplotlib. Let's see how to create bar graphs:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b14def5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
+    "y = np.array([1, 2, 4, 8])\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.bar(x,y,width=0.5)         #For vertical bars\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.barh(x,y,height=0.6)        #For horizontal bars \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e303820",
+   "metadata": {},
+   "source": [
+    "A histogram is a graph showing frequency distributions. We use hist() function to plot histogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "234fd1dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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45gTqm5Sp6H+SncCfAG+rqp8uWdVO/6tqKj/AB4FH6J8P/iTwQuDl9K9GONZNL5x0nWPs/z8C/0L/9MlVXds523/6f0EtAD+jP8K66Uz9Bd4P/BvwKPCWSdc/pv5f380/A5wAvjxl/X+M/rnuQ93nr1rrv7fSS1KjpvUUiiQ1zwCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjfoffIWM7furSw8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.random.normal(100, 10, 200)      #For creating a normal data distribution\n",
+    "\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60780f0f",
+   "metadata": {},
+   "source": [
+    "We can create Pie Graphs in Matplotlib using pie() function. Here's how :-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8e9e0541",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a = np.array([10, 7, 5, 2])\n",
+    "mylabels = [\"Delhi\", \"Mumbai\", \"Lucknow\", \"Banglore\"]\n",
+    "myexplode = [0.5, 0, 0, 0]\n",
+    "\n",
+    "plt.pie(a, labels = mylabels,startangle=90,explode=myexplode)      #We use \"startangle\" to specify start angle for our graph and  \n",
+    "                                                                 #\"explode\" to make a wedge stand out\n",
+    "\n",
+    "plt.legend(title='Cities')                                       #Adding legend to graph\n",
+    "plt.show() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bb0b14c",
+   "metadata": {},
+   "source": [
+    "We can now see how useful Matplotlib is for visualizing data and interpreting it. More information on Matplotlib is available on its documentation at https://matplotlib.org/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f106e71",
+   "metadata": {},
+   "source": [
+    "Sources:-\n",
+    "1.https://matplotlib.org/\n",
+    "2.https://www.w3schools.com/python/matplotlib_intro.asp\n",
+    "3.https://www.geeksforgeeks.org/python-introduction-matplotlib/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 1/Introduction to Numpy.ipynb b/Sakt Launde/Week 1/Introduction to Numpy.ipynb
new file mode 100644
index 0000000..c6e1774
--- /dev/null
+++ b/Sakt Launde/Week 1/Introduction to Numpy.ipynb	
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8352eee1",
+   "metadata": {},
+   "source": [
+    "# Introduction to Numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "216296dc",
+   "metadata": {},
+   "source": [
+    "Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. To start working with Numpy, We first need to import it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0173f5ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac69dc8c",
+   "metadata": {},
+   "source": [
+    "Numpy is now imported and we can use it by simply writing \"np\". Here is how to create an array and print it and its shape:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1e4b9323",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5,)\n",
+      "[1 2 3 4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,3,4,5])\n",
+    "print(a.shape)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67cb88df",
+   "metadata": {},
+   "source": [
+    "We can also create some special arrays by Numpy functions. For example:- "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a6badc5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0.]\n",
+      " [0. 0.]]\n",
+      "\n",
+      "\n",
+      "[[1. 1. 1.]\n",
+      " [1. 1. 1.]\n",
+      " [1. 1. 1.]]\n",
+      "\n",
+      "\n",
+      "[[7 7]]\n",
+      "\n",
+      "\n",
+      "[[1. 0. 0.]\n",
+      " [0. 1. 0.]\n",
+      " [0. 0. 1.]]\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.zeros((2,2))   # Create an array of all zeros\n",
+    "print(a)   \n",
+    "print(\"\\n\")\n",
+    "\n",
+    "b = np.ones((3,3))    # Create an array of all ones\n",
+    "print(b)             \n",
+    "print(\"\\n\")\n",
+    "\n",
+    "c = np.full((1,2), 7)  # Create an array of all elements as one number\n",
+    "print(c) \n",
+    "print(\"\\n\")\n",
+    "\n",
+    "d = np.eye(3)         # Create a 3x3 identity matrix\n",
+    "print(d)    \n",
+    "print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8789e418",
+   "metadata": {},
+   "source": [
+    "Slicing is the way to access sub-parts of our data structures. Here is how to slice an array:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a69a0d5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2 3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([[1,2,3,4], [5,6,7,8]])\n",
+    "b = a[:1, 1:3]                           #Here we create an array b with 1st row and all columns between 1st and 4th columns of a\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1f1a73b",
+   "metadata": {},
+   "source": [
+    "We can also perform various mathematical operations,which are quite handy, using Numpy. Here is how to do it:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1545afdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 6  8]\n",
+      " [10 12]]\n",
+      "\n",
+      "\n",
+      "[[-4 -4]\n",
+      " [-4 -4]]\n",
+      "\n",
+      "\n",
+      "[[ 5 12]\n",
+      " [21 32]]\n",
+      "\n",
+      "\n",
+      "[[0.2        0.33333333]\n",
+      " [0.42857143 0.5       ]]\n",
+      "\n",
+      "\n",
+      "[[1.         1.41421356]\n",
+      " [1.73205081 2.        ]]\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([[1,2],[3,4]])\n",
+    "y = np.array([[5,6],[7,8]])\n",
+    "          \n",
+    "print(np.add(x, y))          # Elementwise sum;\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.subtract(x, y))     # Elementwise difference\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.multiply(x, y))     # Elementwise product\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.divide(x, y))       # Elementwise division\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.sqrt(x))            # Elementwise square root\n",
+    "print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "215624f0",
+   "metadata": {},
+   "source": [
+    "Various Matrix operations can also be performed through Numpy in the following way:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "51eb1320",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11\n",
+      "\n",
+      "\n",
+      "[[19 22]\n",
+      " [43 50]]\n",
+      "\n",
+      "\n",
+      "[[1 3]\n",
+      " [2 4]]\n",
+      "\n",
+      "\n",
+      "10\n",
+      "\n",
+      "\n",
+      "[4 6]\n",
+      "\n",
+      "\n",
+      "[3 7]\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.array([1,2])\n",
+    "y = np.array([3,4])\n",
+    "\n",
+    "print(np.dot(x, y))          #Prints dot product of x and y\n",
+    "print(\"\\n\")\n",
+    "              \n",
+    "a = np.array([[1,2],[3,4]])\n",
+    "b = np.array([[5,6],[7,8]])\n",
+    "              \n",
+    "print(np.matmul(a, b))       #Prints the matrix product of a and b\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.transpose(a))               #Prints transpose of a\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.sum(a))  #Prints sum of all elements; prints \"10\"\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.sum(a, axis=0))  #Prints sum of each column; prints \"[4 6]\"\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "print(np.sum(a, axis=1))  #Prints sum of each row\n",
+    "print(\"\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9b1ae0b",
+   "metadata": {},
+   "source": [
+    "As we have seen Numpy is quite useful as it provides easier wways to do computations. Thus, it is quite popularly used in data science, machine learning, scientific computing, etc.To learn more about Numpy, feel free to go through its documentation at https://numpy.org/doc/."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "959266c1",
+   "metadata": {},
+   "source": [
+    "Sources:-\n",
+    "1.https://numpy.org/doc/.\n",
+    "2.https://cs231n.github.io/python-numpy-tutorial/#numpy/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 1/Introduction to Pandas.ipynb b/Sakt Launde/Week 1/Introduction to Pandas.ipynb
new file mode 100644
index 0000000..5564750
--- /dev/null
+++ b/Sakt Launde/Week 1/Introduction to Pandas.ipynb	
@@ -0,0 +1,324 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "89b70b4d",
+   "metadata": {},
+   "source": [
+    "# Introduction to Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c54e2be",
+   "metadata": {},
+   "source": [
+    "Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on top of the NumPy library. Pandas is fast and it has high performance & productivity for users.\n",
+    "\n",
+    "Before using Pandas we first need to import it as follows:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6329d536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45f8af2f",
+   "metadata": {},
+   "source": [
+    "Pandas is ow imported and we can call its functions by writin \"pd\".\n",
+    "\n",
+    "Pandas generally provide two data structures for manipulating data, They are: \n",
+    "\n",
+    "1.Series\n",
+    "2.DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c708604",
+   "metadata": {},
+   "source": [
+    "Series:\n",
+    "\n",
+    "Pandas Series is a one-dimensional labelled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called indexes. Pandas Series is nothing but a column in an excel sheet. Pandas Series can be created from the lists, dictionary, and from a scalar value etc. Here is how to create a Pandas Series:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b9223da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    1\n",
+      "1    2\n",
+      "2    3\n",
+      "3    4\n",
+      "4    5\n",
+      "dtype: int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np         #We need to import Numpy to use arrays\n",
+    "\n",
+    "data = np.array([1,2,3,4,5])\n",
+    "series=pd.Series(data)\n",
+    "print(series)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d3cc008",
+   "metadata": {},
+   "source": [
+    "DataFrame:\n",
+    "\n",
+    "A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns. Pandas DataFrame can be created from the lists, dictionary, and from a list of dictionaries, etc. Here is how to create a Pandas DataFrame:-"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bd879771",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   0\n",
+      "0  a\n",
+      "1  b\n",
+      "2  c\n",
+      "3  d\n",
+      "4  e\n"
+     ]
+    }
+   ],
+   "source": [
+    "list = ['a','b','c','d','e']\n",
+    "   \n",
+    "df = pd.DataFrame(list)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82b440b8",
+   "metadata": {},
+   "source": [
+    "Here are some ways to work with rows and columns in Pandas:-"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5773e49",
+   "metadata": {},
+   "source": [
+    "Selecting Columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "feaa28e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Alphabet       Type\n",
+      "0        a      Vowel\n",
+      "1        b  Consonant\n",
+      "2        c  Consonant\n",
+      "3        d  Consonant\n",
+      "4        e      Vowel\n",
+      "5        f  Consonant\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'Alphabet':['a', 'b', 'c', 'd','e','f'],\n",
+    "        'Number':['1', '2', '3', '4', '5', '6'],\n",
+    "        'Type':['Vowel', 'Consonant', 'Consonant', 'Consonant', 'Vowel', 'Consonant']\n",
+    "       }\n",
+    " \n",
+    "# Convert the dictionary into DataFrame \n",
+    "df = pd.DataFrame(data,columns=['Alphabet','Number','Type'])\n",
+    " \n",
+    "# select two columns\n",
+    "print(df[['Alphabet', 'Type']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d406dafe",
+   "metadata": {},
+   "source": [
+    "Selecting Rows:\n",
+    "DataFrame.loc[] method is used to retrieve rows from Pandas DataFrame. Rows can also be selected by passing integer location to an iloc[] function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7bce815a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alphabet        a\n",
+      "Type        Vowel\n",
+      "Name: 1, dtype: object \n",
+      " Alphabet            d\n",
+      "Type        Consonant\n",
+      "Name: 4, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "df=df.set_index('Number')\n",
+    "first= df.loc['1']      #Selecting the 1nd and 4th alphabet\n",
+    "fourth= df.loc['4']\n",
+    "\n",
+    "print(first, \"\\n\", fourth)\n",
+    "\n",
+    "row2 = df.iloc[3]   # retrieving rows by iloc method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e2471e2",
+   "metadata": {},
+   "source": [
+    "Iterating over rows :\n",
+    "In order to iterate over rows, we can use three function iteritems(), iterrows(), itertuples(). Here we use iterrows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "440055e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 Alphabet        a\n",
+      "Type        Vowel\n",
+      "Name: 1, dtype: object\n",
+      "2 Alphabet            b\n",
+      "Type        Consonant\n",
+      "Name: 2, dtype: object\n",
+      "3 Alphabet            c\n",
+      "Type        Consonant\n",
+      "Name: 3, dtype: object\n",
+      "4 Alphabet            d\n",
+      "Type        Consonant\n",
+      "Name: 4, dtype: object\n",
+      "5 Alphabet        e\n",
+      "Type        Vowel\n",
+      "Name: 5, dtype: object\n",
+      "6 Alphabet            f\n",
+      "Type        Consonant\n",
+      "Name: 6, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, j in df.iterrows():\n",
+    "    print(i, j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e74c7d59",
+   "metadata": {},
+   "source": [
+    "Iterating over Columns :\n",
+    "In order to iterate over columns, we need to create a list of dataframe columns and then iterating through that list to pull out the dataframe columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d03f6dfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Alphabet', 'Type']\n",
+      "b\n",
+      "Consonant\n"
+     ]
+    }
+   ],
+   "source": [
+    "# creating a list of dataframe columns\n",
+    "columns = df.columns.values.tolist()\n",
+    "\n",
+    "for i in columns:\n",
+    " \n",
+    "    # printing the second element of the column\n",
+    "    print (df[i][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9267a24f",
+   "metadata": {},
+   "source": [
+    "Now we have seen how useful Pandas is while working with DataFrames. To learn more about Pandas, feel free to go through its documentation at https://pandas.pydata.org/docs/."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "30bfe798",
+   "metadata": {},
+   "source": [
+    "Sources:-\n",
+    "1.https://pandas.pydata.org/docs/\n",
+    "2.https://www.geeksforgeeks.org/python-programming-language/?ref=shm"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/w1/mnist.ipynb b/Sakt Launde/Week 1/mnist.ipynb
similarity index 97%
rename from w1/mnist.ipynb
rename to Sakt Launde/Week 1/mnist.ipynb
index b11858c..2b6b91b 100644
--- a/w1/mnist.ipynb
+++ b/Sakt Launde/Week 1/mnist.ipynb	
@@ -1,850 +1,850 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PWcMvku301NL"
-      },
-      "source": [
-        "<h1>exploring the dataset<h1>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "V000gJ6X01NN",
-        "outputId": "421ad142-9ccc-4e84-a4b6-979744cec788"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 1
-        }
-      ],
-      "source": [
-        "from sklearn.datasets import fetch_openml\n",
-        "mnist = fetch_openml('mnist_784', version=1, as_frame=False)#as_frame because fetch_openML is giving back pandas dataframe.  \n",
-        "mnist.keys()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "rhC9C5T101NO"
-      },
-      "outputs": [],
-      "source": [
-        "x,y = mnist[\"data\"], mnist[\"target\"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "n7gD7ViD01NP",
-        "outputId": "51178674-43bf-4ae6-c46e-70accdc36b71"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(70000, 784) (70000,)\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(x.shape, y.shape)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "id": "LXTKbI3V01NP"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "y = y.astype(np.uint8)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "type(x)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HPMd7T484iva",
-        "outputId": "57d2467f-9a8e-4a0a-8fde-125653a66459"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "numpy.ndarray"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "KrajhLv701NQ",
-        "outputId": "1622d9b2-8fbf-4a1e-c8ae-f20729a3bb65"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
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-              "         0.,   0.,   0.])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 6
-        }
-      ],
-      "source": [
-        "x[1]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 265
-        },
-        "id": "UndmtKDy01NQ",
-        "outputId": "d86970d8-d9b0-4ff7-cfb6-99ad3f7e3ccb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ],
-      "source": [
-        "%matplotlib inline\n",
-        "import matplotlib as mpl\n",
-        "import matplotlib.pyplot as plt\n",
-        "def num_img(a):\n",
-        "    plt.imshow(x[a].reshape(28,28),cmap=mpl.cm.binary)\n",
-        "    plt.axis(\"off\")\n",
-        "    print(y[a])\n",
-        "\n",
-        "num_img(1)\n",
-        "    "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YylM2HHo01NR"
-      },
-      "source": [
-        "<h1>using sgd to build a 0, non 0 classifier<h1>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "nwiGGjE-01NR"
-      },
-      "outputs": [],
-      "source": [
-        "x_train,x_test,y_train,y_test = x[:60000],x[60000:],y[:60000],y[60000:]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "25eZypTO01NS"
-      },
-      "outputs": [],
-      "source": [
-        "y_train_0 = (y_train==0)\n",
-        "Y_test_0= (y_test==0)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "LtgH5fWR01NS",
-        "outputId": "043a65a4-5a20-40ad-fcc1-767bdc031267"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "SGDClassifier(random_state=69)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 10
-        }
-      ],
-      "source": [
-        "from sklearn.linear_model import SGDClassifier\n",
-        "classf = SGDClassifier(random_state = 69)\n",
-        "classf.fit(x_train,y_train_0)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "AnoUz2tx01NT",
-        "outputId": "51ddc062-9f16-44a5-e8d6-ff353e9faa17"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([ True])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 11
-        }
-      ],
-      "source": [
-        "classf.predict([x[1]])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "kxm68pNS01NT",
-        "outputId": "bf3892e1-636c-4455-891a-93ef649d20e0"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0.9865 , 0.98725, 0.98745])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 12
-        }
-      ],
-      "source": [
-        "from sklearn.model_selection import cross_val_score\n",
-        "cross_val_score(classf,x_train,y_train_0,cv=3,scoring = \"accuracy\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "id": "BpeCwDmr01NT"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import cross_val_predict\n",
-        "y_train_0_predict = cross_val_predict(classf,x_train,y_train_0,cv = 3)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "mEWE2lIO01NT",
-        "outputId": "46076c5f-71ef-42dc-8271-415e12afe14c"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[53781,   296],\n",
-              "       [  480,  5443]])"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ],
-      "source": [
-        "from sklearn.metrics import confusion_matrix\n",
-        "confusion_matrix(y_train_0,y_train_0_predict)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Pl58IGgT01NU",
-        "outputId": "d5ffa4fc-f93d-4cdf-afed-d28e6dbe4678"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "0.9484230702212929\n",
-            "0.9189599864933311\n"
-          ]
-        }
-      ],
-      "source": [
-        "from sklearn.metrics import precision_score, recall_score \n",
-        "print(precision_score(y_train_0,y_train_0_predict))\n",
-        "print(recall_score(y_train_0,y_train_0_predict))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QapUSMb901NW"
-      },
-      "source": [
-        "<h1> using a simple neural network to train the entire dataset<h1>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "oOVmJC8a01NW"
-      },
-      "outputs": [],
-      "source": [
-        "import tensorflow as tf"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "mnist = tf.keras.datasets.mnist ##dunno what but using mnist fetched from openml was just not showing regularization result altho it was happening\n",
-        "(x_train, y_train),(x_test, y_test) = mnist.load_data() ##overall openml is just shit, first givin out pandas dataframe now this"
-      ],
-      "metadata": {
-        "id": "UIQ-jwQM4SDe"
-      },
-      "execution_count": 33,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "type(x_train)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "t9HResPn4Zim",
-        "outputId": "60f7d957-b52c-4a2d-f04f-d8282e36e739"
-      },
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "numpy.ndarray"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 35,
-      "metadata": {
-        "id": "6fO_Dyxz01NW"
-      },
-      "outputs": [],
-      "source": [
-        "x_train  = tf.keras.utils.normalize(x_train)\n",
-        "x_test  = tf.keras.utils.normalize(x_test)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "x_train[0].reshape(28,28)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Aqe8Zwvo5Ku7",
-        "outputId": "1ade7f92-add9-4872-85b6-a4b42d43f966"
-      },
-      "execution_count": 36,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
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-          "metadata": {},
-          "execution_count": 36
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "plt.imshow(x_train[1].reshape(28,28),cmap=mpl.cm.binary)\n",
-        "plt.axis(\"off\")\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 248
-        },
-        "id": "5q3ZAxQv2qT0",
-        "outputId": "bfeb9cc4-4ecf-48ec-95df-73d7bad55c98"
-      },
-      "execution_count": 37,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ],
-            "image/png": "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\n"
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
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-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model = tf.keras.models.Sequential()\n",
-        "model.add(tf.keras.layers.Flatten())\n",
-        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
-        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
-        "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))"
-      ],
-      "metadata": {
-        "id": "s9vk7nNH3AaP"
-      },
-      "execution_count": 40,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.compile(optimizer='adam',\n",
-        "              loss='sparse_categorical_crossentropy',\n",
-        "              metrics=['accuracy'])"
-      ],
-      "metadata": {
-        "id": "HpM8isWm7b1P"
-      },
-      "execution_count": 41,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.fit(x_train, y_train, epochs=3)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HzKiJcov7dDh",
-        "outputId": "e082389b-548a-4802-aedf-812400c9e70e"
-      },
-      "execution_count": 42,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/3\n",
-            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2614 - accuracy: 0.9218\n",
-            "Epoch 2/3\n",
-            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1103 - accuracy: 0.9661\n",
-            "Epoch 3/3\n",
-            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0766 - accuracy: 0.9764\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.callbacks.History at 0x7f4333762a50>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 42
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
-        "print(val_loss)\n",
-        "print(val_acc)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "de9E3mQd7f2p",
-        "outputId": "eee80bea-68a8-48a8-f542-87fe33227c9e"
-      },
-      "execution_count": 43,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "313/313 [==============================] - 1s 1ms/step - loss: 0.0868 - accuracy: 0.9734\n",
-            "0.08681446313858032\n",
-            "0.9733999967575073\n"
-          ]
-        }
-      ]
-    }
-  ],
-  "metadata": {
-    "interpreter": {
-      "hash": "6c685f40f4e372158c8452f24cd9ee0bbc1030a9f2f196d3595227ed632ab81f"
-    },
-    "kernelspec": {
-      "display_name": "Python 3.9.12 ('bcs_project')",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.12"
-    },
-    "orig_nbformat": 4,
-    "colab": {
-      "name": "mnist.ipynb",
-      "provenance": []
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PWcMvku301NL"
+      },
+      "source": [
+        "<h1>exploring the dataset<h1>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V000gJ6X01NN",
+        "outputId": "421ad142-9ccc-4e84-a4b6-979744cec788"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "from sklearn.datasets import fetch_openml\n",
+        "mnist = fetch_openml('mnist_784', version=1, as_frame=False)#as_frame because fetch_openML is giving back pandas dataframe.  \n",
+        "mnist.keys()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "rhC9C5T101NO"
+      },
+      "outputs": [],
+      "source": [
+        "x,y = mnist[\"data\"], mnist[\"target\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n7gD7ViD01NP",
+        "outputId": "51178674-43bf-4ae6-c46e-70accdc36b71"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(70000, 784) (70000,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x.shape, y.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "LXTKbI3V01NP"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "y = y.astype(np.uint8)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "type(x)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HPMd7T484iva",
+        "outputId": "57d2467f-9a8e-4a0a-8fde-125653a66459"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "numpy.ndarray"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KrajhLv701NQ",
+        "outputId": "1622d9b2-8fbf-4a1e-c8ae-f20729a3bb65"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              "         0.,   0.,   0.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "x[1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 265
+        },
+        "id": "UndmtKDy01NQ",
+        "outputId": "d86970d8-d9b0-4ff7-cfb6-99ad3f7e3ccb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib as mpl\n",
+        "import matplotlib.pyplot as plt\n",
+        "def num_img(a):\n",
+        "    plt.imshow(x[a].reshape(28,28),cmap=mpl.cm.binary)\n",
+        "    plt.axis(\"off\")\n",
+        "    print(y[a])\n",
+        "\n",
+        "num_img(1)\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YylM2HHo01NR"
+      },
+      "source": [
+        "<h1>using sgd to build a 0, non 0 classifier<h1>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "nwiGGjE-01NR"
+      },
+      "outputs": [],
+      "source": [
+        "x_train,x_test,y_train,y_test = x[:60000],x[60000:],y[:60000],y[60000:]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "25eZypTO01NS"
+      },
+      "outputs": [],
+      "source": [
+        "y_train_0 = (y_train==0)\n",
+        "Y_test_0= (y_test==0)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LtgH5fWR01NS",
+        "outputId": "043a65a4-5a20-40ad-fcc1-767bdc031267"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SGDClassifier(random_state=69)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import SGDClassifier\n",
+        "classf = SGDClassifier(random_state = 69)\n",
+        "classf.fit(x_train,y_train_0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AnoUz2tx01NT",
+        "outputId": "51ddc062-9f16-44a5-e8d6-ff353e9faa17"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ True])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "classf.predict([x[1]])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kxm68pNS01NT",
+        "outputId": "bf3892e1-636c-4455-891a-93ef649d20e0"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0.9865 , 0.98725, 0.98745])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import cross_val_score\n",
+        "cross_val_score(classf,x_train,y_train_0,cv=3,scoring = \"accuracy\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "BpeCwDmr01NT"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import cross_val_predict\n",
+        "y_train_0_predict = cross_val_predict(classf,x_train,y_train_0,cv = 3)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mEWE2lIO01NT",
+        "outputId": "46076c5f-71ef-42dc-8271-415e12afe14c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[53781,   296],\n",
+              "       [  480,  5443]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "confusion_matrix(y_train_0,y_train_0_predict)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Pl58IGgT01NU",
+        "outputId": "d5ffa4fc-f93d-4cdf-afed-d28e6dbe4678"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9484230702212929\n",
+            "0.9189599864933311\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import precision_score, recall_score \n",
+        "print(precision_score(y_train_0,y_train_0_predict))\n",
+        "print(recall_score(y_train_0,y_train_0_predict))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QapUSMb901NW"
+      },
+      "source": [
+        "<h1> using a simple neural network to train the entire dataset<h1>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "oOVmJC8a01NW"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mnist = tf.keras.datasets.mnist ##dunno what but using mnist fetched from openml was just not showing regularization result altho it was happening\n",
+        "(x_train, y_train),(x_test, y_test) = mnist.load_data() ##overall openml is just shit, first givin out pandas dataframe now this"
+      ],
+      "metadata": {
+        "id": "UIQ-jwQM4SDe"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "type(x_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t9HResPn4Zim",
+        "outputId": "60f7d957-b52c-4a2d-f04f-d8282e36e739"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "numpy.ndarray"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "6fO_Dyxz01NW"
+      },
+      "outputs": [],
+      "source": [
+        "x_train  = tf.keras.utils.normalize(x_train)\n",
+        "x_test  = tf.keras.utils.normalize(x_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train[0].reshape(28,28)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Aqe8Zwvo5Ku7",
+        "outputId": "1ade7f92-add9-4872-85b6-a4b42d43f966"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(x_train[1].reshape(28,28),cmap=mpl.cm.binary)\n",
+        "plt.axis(\"off\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "id": "5q3ZAxQv2qT0",
+        "outputId": "bfeb9cc4-4ecf-48ec-95df-73d7bad55c98"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = tf.keras.models.Sequential()\n",
+        "model.add(tf.keras.layers.Flatten())\n",
+        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
+        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
+        "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))"
+      ],
+      "metadata": {
+        "id": "s9vk7nNH3AaP"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(optimizer='adam',\n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "HpM8isWm7b1P"
+      },
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(x_train, y_train, epochs=3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HzKiJcov7dDh",
+        "outputId": "e082389b-548a-4802-aedf-812400c9e70e"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/3\n",
+            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2614 - accuracy: 0.9218\n",
+            "Epoch 2/3\n",
+            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1103 - accuracy: 0.9661\n",
+            "Epoch 3/3\n",
+            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0766 - accuracy: 0.9764\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7f4333762a50>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
+        "print(val_loss)\n",
+        "print(val_acc)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "de9E3mQd7f2p",
+        "outputId": "eee80bea-68a8-48a8-f542-87fe33227c9e"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 1ms/step - loss: 0.0868 - accuracy: 0.9734\n",
+            "0.08681446313858032\n",
+            "0.9733999967575073\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "interpreter": {
+      "hash": "6c685f40f4e372158c8452f24cd9ee0bbc1030a9f2f196d3595227ed632ab81f"
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.12 ('bcs_project')",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.12"
+    },
+    "orig_nbformat": 4,
+    "colab": {
+      "name": "mnist.ipynb",
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/Sakt Launde/Week 2/Dataset Biases.ipynb b/Sakt Launde/Week 2/Dataset Biases.ipynb
new file mode 100644
index 0000000..40df8e9
--- /dev/null
+++ b/Sakt Launde/Week 2/Dataset Biases.ipynb	
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d6a8379e",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43b5acbc",
+   "metadata": {},
+   "source": [
+    "Data bias is a type of error in which certain elements of a dataset are more heavily weighted or represented than others .A biased dataset does not accurately represent a model's use case,resulting in skewed outcomes.low accuracy levels,and analytical errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d2143d9",
+   "metadata": {},
+   "source": [
+    "# Different types of Biases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cd973c0",
+   "metadata": {},
+   "source": [
+    "### Confirmation/Observer Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db80455a",
+   "metadata": {},
+   "source": [
+    "This bias is created when an oberver sees what he/she wants or expects to see .This can happen subconsciously through biases in how we search for,interpret,or recall information,or consciously, when we decide to cherry pick, by focusing on information that supports our arguments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80a74140",
+   "metadata": {},
+   "source": [
+    "#### How to avoid?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e750678",
+   "metadata": {},
+   "source": [
+    " - Seek contrary opinions, even if those opinions may seem uncomfortable to you at first. Try to understand the rationale behind the contrarian opinions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e5f2467",
+   "metadata": {},
+   "source": [
+    " - Look for multiple sources of information rather than focussing on one source."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9e4334c",
+   "metadata": {},
+   "source": [
+    " - It might be a good ideda to record your ideologies on the subject beforehand . So you could see if they have influenced the findingvs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a330d58",
+   "metadata": {},
+   "source": [
+    "### Sampling/Selection/Exclusion/Representation Bias "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2de1def",
+   "metadata": {},
+   "source": [
+    "Selection Bias is introduced when the dataset is not completely randomised , hence failing to ensure that the sample obtained is representative of the population intended to be analyzed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b2f8eb3",
+   "metadata": {},
+   "source": [
+    "#### How to avoid?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d666d04",
+   "metadata": {},
+   "source": [
+    " - Use randomization to ensure you have a representative sample rather than a convenient one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ad09f9f",
+   "metadata": {},
+   "source": [
+    " - To never erase data thinking of it as unsignificant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f52e043",
+   "metadata": {},
+   "source": [
+    "### Measurement/Detection Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1083d66f",
+   "metadata": {},
+   "source": [
+    "Measurement bias refers to any systematic or non-random error that occurs in the collection of data in a study."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fd65180",
+   "metadata": {},
+   "source": [
+    "#### How to avoid?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b301f82f",
+   "metadata": {},
+   "source": [
+    " - Use to proper tools/instruments in noting the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feaf61d6",
+   "metadata": {},
+   "source": [
+    "### Aggregation bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adf375bc",
+   "metadata": {},
+   "source": [
+    "Aggregation bias occurs when groups are inappropriately combined, resulting in a model that does not perform well for any group or only performs well for the majority group. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f891867",
+   "metadata": {},
+   "source": [
+    "#### For eg."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccf018d5",
+   "metadata": {},
+   "source": [
+    " - You might have data showing that inner city students tend to perform poorly on standardized tests. That doesn’t mean any one individual will perform poorly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb1b9965",
+   "metadata": {},
+   "source": [
+    "#### How to avoid?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d971d244",
+   "metadata": {},
+   "source": [
+    " - One need to design their studies at the appropriate unit of analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4b9243e",
+   "metadata": {},
+   "source": [
+    "### Deployment Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62be2e3",
+   "metadata": {},
+   "source": [
+    "This bias occurs when what the model is intended to solve is different from what it is used to solve. When the users don't use the model the way it is intended , there is no guarantee that the model will perform well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2657291",
+   "metadata": {},
+   "source": [
+    "#### For eg."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4ec9ea3",
+   "metadata": {},
+   "source": [
+    " - The criminal justice system uses tools to predict the likelihood that a convicted criminal will relapse into criminal behavior. The predictions are not designed for judges when deciding appropriate punishments at the time of sentencing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05fbafdb",
+   "metadata": {},
+   "source": [
+    "### Recall Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79ca5f4f",
+   "metadata": {},
+   "source": [
+    "This kind of measurement bias, and is common at the labeling stage of a project. Recall bias arises when you label similar types of data inconsistently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "475d83b4",
+   "metadata": {},
+   "source": [
+    "### Outlier Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d91737f7",
+   "metadata": {},
+   "source": [
+    "Some data is convenient to visualize as an average, but this simple operation hides the effect of outliers and anomalies, and skews our observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d9fe040",
+   "metadata": {},
+   "source": [
+    "#### For eg."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8c91ddb",
+   "metadata": {},
+   "source": [
+    "A start-up wants to be sure that their marketing site feels quick and responsive. They decide to track their average latency time to make sure that their site continues to load quickly. After a few months of roughly consistent average latency values, they start to see a decline in engagement on some of their most important pages. When they investigate further, they realize that the latency on those pages has skyrocketed. Their average latency time site-wide continued to paint a rosy image, because those pages were an outlier amongst many other otherwise well-performing pages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3d307b9",
+   "metadata": {},
+   "source": [
+    "Sources:\n",
+    " - https://www.metabase.com/blog/6-most-common-type-of-data-bias-in-data-analysis\n",
+    " - https://www.miraeassetmf.co.in/knowledge-center/confirmation-bias\n",
+    " - https://ori.hhs.gov/education/products/niu_authorship/mistakes/09mistake-a.htm\n",
+    " - https://www.kaggle.com/code/alexisbcook/identifying-bias-in-ai/tutorial\n",
+    " - https://www.telusinternational.com/articles/7-types-of-data-bias-in-machine-learning\n",
+    " - https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/4-measurement-error-and-bias\n",
+    " - https://www.google.com/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "896a7ffb",
+   "metadata": {},
+   "source": [
+    "##### - by team Sakt Launde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84871045",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Written by\n",
+    "Priyanshu Meena\n",
+    "Aditya Yadav"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 2/Perceptron.ipynb b/Sakt Launde/Week 2/Perceptron.ipynb
new file mode 100644
index 0000000..9fdbb59
--- /dev/null
+++ b/Sakt Launde/Week 2/Perceptron.ipynb	
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "01f5bcff",
+   "metadata": {},
+   "source": [
+    "# Perceptron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "889b6401",
+   "metadata": {},
+   "source": [
+    "# Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "866901e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87e44ab1",
+   "metadata": {},
+   "source": [
+    "# Sigmoid Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "183d32c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def SigmoidFunction(x):\n",
+    "    y = 1/(1+np.exp(x))\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23f41f4b",
+   "metadata": {},
+   "source": [
+    "# Setting Up Inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d03cf17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getInput(inputs):\n",
+    "        for i in range(0,3):\n",
+    "            element=float(input())\n",
+    "            inputs[0,i]=element          \n",
+    "        return inputs\n",
+    "def createWeights():\n",
+    "        weights=np.random.rand(3)\n",
+    "        return weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58d09403",
+   "metadata": {},
+   "source": [
+    "# Defining a Perceptron"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c6d16f57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Perceptron:\n",
+    "    def createWeights():\n",
+    "        weights=np.random.random((3,1))\n",
+    "        return weights\n",
+    "        \n",
+    "    def __init__(self):\n",
+    "        self.inputs=np.empty((1,3))\n",
+    "        self.inputs=getInput(self.inputs)\n",
+    "        self.weights=createWeights()\n",
+    "        self.bias=0.02\n",
+    "        self.activation=SigmoidFunction(np.dot(self.inputs,self.weights))\n",
+    "        self.output=self.activation - self.bias\n",
+    "        return None\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e677561",
+   "metadata": {},
+   "source": [
+    "# Testing Perceptron With Random Inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6686ea1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P=Perceptron()\n",
+    "print(P.inputs)\n",
+    "print(P.weights)\n",
+    "print(P.activation)\n",
+    "print(P.output)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "699f94a5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Sakt Launde/Week 2/Stochastic Gradient Descent.md b/Sakt Launde/Week 2/Stochastic Gradient Descent.md
new file mode 100644
index 0000000..7723008
--- /dev/null
+++ b/Sakt Launde/Week 2/Stochastic Gradient Descent.md	
@@ -0,0 +1,67 @@
+</heading1>Stochastic Gradient Descent
+</p>
+</p>Gradient Descent: It is an algorithm through which we increase efficiency of training of a neural 
+network on a dataset. In this algorithm, aim is to minimize the "cost function". The cost function 
+is the sum of squares of error in our result. To minimise the cost function, we move in the direction
+of negative gradient of the cost function in each iteration to get to the minimum. The weights and biases
+are adjusted accordingly at each iteration. How big the steps are gradient descent takes into the direction 
+of the local minimum are determined by the learning rate, which figures out how fast or slow we will move 
+towards the optimal weights. How big the steps are gradient descent takes into the direction of the local
+minimum are determined by the learning rate, which figures out how fast or slow we will move towards the 
+optimal weights. In Gradient Descent, "Batch” denotes the total number of samples from a dataset that is 
+used for calculating the gradient for each iteration. In typical Gradient Descent optimization the batch 
+is taken to be the whole dataset. Although, using the whole dataset is really useful for getting to the 
+minima in a less noisy and less random manner, but this method becomes computationally expensive for huge
+datasets. This problem is solved by Stochastic Gradient Descent.</p>
+More about the cost function-></p>
+A cost function is used to measure how well the model is finding a relation between the input and output. While accuracy functions tell you how well the model is performing, they do not provide you with an insight on how to better improve them. Hence, you need a correctional function that can help you compute when the model is the most accurate, as you need to hit that small spot between an undertrained model and an overtrained model.</p> 
+For a linear regression –</p>
+                                             Cost function= $\frac{sum_{i=0}^{n}{(h(x^{i})-y^{i})^{2}}}{n}$</p>
+Where $h(x^{i})$ =observed value and $y^{i}$ =predicted value</p>
+                                             Gradient descent $\theta = \theta-\alpha \frac{\partial x}{\partial y}$</p>
+Where $\alpha$ = learning rate</p>
+It decides how fast you move down the slope. If alpha is large, you take big steps, and if it is small; you take small steps. If alpha is too large, you can entirely miss the least error point and our results will not be accurate. If it is too small it will take too long to optimize the model and you will also waste computational power. Hence you need to choose an optimal value of alpha.
+</p></p>
+
+Stochastic Gradient Descent: In SGD, it uses only a single sample, i.e., a batch size of one,instead of 
+the whole dataset to perform each iteration. The word ‘stochastic‘ means a system or a process that is 
+linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly
+instead of the whole data set for each iteration. As the sample is randomly chosen, the path taken to reach 
+minimum in SGD is generally nosier than typical Gradient Descent algorithm. But it doesn't matter much as we 
+still reach minimum and in shorter time. Also, it usually takes a higher number of iterations to reach the minima, 
+because of its randomness in its descent. Despite this, SGD is still computationally much less expensive than 
+typical Gradient Descent. Hence, in most scenarios, SGD is preferred over Batch Gradient Descent for optimizing 
+a learning algorithm.</p>
+There is a downside of the Stochastic nature of SGD i.e once it reaches close to the minimum value then it doesn’t settle down, instead bounces around which gives us a good value for model parameters but not optimal which can ve solved by reducing the learning rate at each step which can reduce the bouncing and SGD might settle down at global minimum after some time.
+</p> 
+Algorithm: For iteration 'i', a parameter 'a(i)' and cost function C(a(i)), we have the algorithm as follows:-
+</p>
+Step 1: Randomly shuffle the data set of size m
+
+Step 2: Select a learning rate l
+
+Step 3: Select initial parameter values 'a(0)' as the starting point
+
+Step 4: Update all parameters from the gradient of a single training example, i.e, compute 
+	 a(i+1)=a(i)-l*Gradient(C(a(i))).
+
+Step 5: Repeat Step 4 until a local minimum is reached
+</p>For Stochastic Gradient Descent-></p>
+for i in range m</p>
+$\theta_{j} = \theta_{j} - \alpha (y^{^i}_{j}-y^{i}_{j})x_{j}$
+
+
+
+
+Written by
+</p>
+Kumar Kanishk Singh</p>
+Vibhansh Bhatia
+
+
+Sources:
+1. https://optimization.cbe.cornell.edu/index.php?title=Stochastic_gradient_descent#:~:text=Stochastic%20gradient%20descent%20%28abbreviated%20as%20SGD%29%20is%20an,search%20once%20a%20random%20weight%20vector%20is%20picked.
+2. https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31
+3. https://www.geeksforgeeks.org/ml-stochastic-gradient-descent-sgd/
+4. https://www.geeksforgeeks.org/difference-between-batch-gradient-descent-and-stochastic-gradient-descent/
+
diff --git a/Sakt Launde/Week 3/Simple Neural Network.ipynb b/Sakt Launde/Week 3/Simple Neural Network.ipynb
new file mode 100644
index 0000000..fc23f0b
--- /dev/null
+++ b/Sakt Launde/Week 3/Simple Neural Network.ipynb	
@@ -0,0 +1,276 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Simple Neural Network.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Implementing Neural Network From Scratch"
+      ],
+      "metadata": {
+        "id": "uEC77ZPUrGVM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "First we import the required libraries."
+      ],
+      "metadata": {
+        "id": "WzlJlgvnrO_Z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "id": "WRze9m_V2wBU"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We now define our Activation Function. Activation Function is used to add non-linearity in our model and normalise the output of our model. In this case, we choose the Sigmoid Function. This function returns values between 0 and 1."
+      ],
+      "metadata": {
+        "id": "NAhIQqZ_rUas"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def sigmoid(x):\n",
+        "  return 1/(1+np.exp(-x))"
+      ],
+      "metadata": {
+        "id": "_6M61K7l234a"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We now define the derivative of the derivative of the Sigmoid Function. This will be used later on in Back Propogation."
+      ],
+      "metadata": {
+        "id": "rSwUovw0sGw1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def sigmoid_deriv(x):\n",
+        "  return (np.exp(-x)/(1 + np.exp(-x))**2)"
+      ],
+      "metadata": {
+        "id": "swYd26gQ9jcg"
+      },
+      "execution_count": 57,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, to define our neural network we create a class NeuralNetwork. Our neural network takes 3 inputs, passes it through 2 hidden layers of 5 and 4 neurons and then these neurons fire into an output layer of 3 nodes. "
+      ],
+      "metadata": {
+        "id": "cDL2GJ2gsee6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Each neuron corresponds to a particular feature in our dataset. We then assign different weights to the features marking their importance. We then meausre our error by comparing the output with desired output. Now we keep tweaking the weights for a number of iterations until we minimise our error and start getting the desired result."
+      ],
+      "metadata": {
+        "id": "qSGooJWB_sTJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In Forward Propogation, input to one layer is multiplied by corresponding weights and is passes on to next layer. This proccess is carried out starting from our input layer to the last hidden layer. Then the result is passed onto the output layer. "
+      ],
+      "metadata": {
+        "id": "4siJtn2PTTet"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class NeuralNetwork(object):\n",
+        "  def __init__(self,Inputs=3,HiddenLayers=[5,4],Outputs=2):\n",
+        "    self.Inputs=Inputs\n",
+        "    self.HiddenLayers=HiddenLayers\n",
+        "    self.Outputs=Outputs\n",
+        "\n",
+        "    layers=[self.Inputs]+self.HiddenLayers+[self.Outputs]\n",
+        "# We implement random weights to train the model. Weights are multiplied by inputs at each layer. Weights are a measure of importance of a neuron in getting the desired output from our model. \n",
+        "    weights = []\n",
+        "    for i in range(len(layers)-1):\n",
+        "      w=np.random.rand(layers[i], layers[i+1])\n",
+        "      weights.append(w)\n",
+        "    self.weights=weights\n",
+        "\n",
+        "# We create an empty array for activations. Activation are the activation function applied on result of multiplication of inputs and weights\n",
+        "    activations =[]\n",
+        "    for i in range(len(layers)):\n",
+        "      a=np.zeros(layers[i])\n",
+        "      activations.append(a)\n",
+        "    self.activations=activations\n",
+        "\n",
+        "# We create an empty array for derivatives to calculate the gradient\n",
+        "    derivatives=[]\n",
+        "    for i in range(len(layers)-1):\n",
+        "      a=np.zeros((layers[i],layers[i+1]))\n",
+        "      derivatives.append(a)\n",
+        "    self.derivatives=derivatives\n",
+        "\n",
+        "# We create a function forward to iterate over layers, apply weight and sigmoid function,get activations and get output after passing through all the hidden layers\n",
+        "  def forward(self,Inputs):\n",
+        "    activation=Inputs\n",
+        "    self.activations[0]=Inputs\n",
+        "    for i, w in enumerate(self.weights):\n",
+        "      unactivatedInputs=np.dot(activation,w)\n",
+        "      activation=sigmoid(unactivatedInputs)\n",
+        "      self.activations[i+1]=activation\n",
+        "    # print(self.activations)\n",
+        "    return activation\n",
+        "# We created a loss function which is a measure of how much the output given by the model deviates from the desired output. \n",
+        "  def loss(self,Output,RightOutput):\n",
+        "    return (RightOutput-Output)**2\n",
+        "# We created a function to calculate the derivative of loss function.\n",
+        "  def loss_deriv(self,Output,RightOutput):\n",
+        "    return (2*(RightOutput-Output))\n",
+        "# Gradient Descent is an algorithm through which we increase efficiency of training of a neural network on a dataset. In this algorithm, aim is to minimize the \"loss function\".To do this, we calculate the gradient and move in the direction\n",
+        "# of negative gradient of the loss function in each iteration to get to the minimum. The weights are adjusted accordingly at each iteration.\n",
+        "# So for each iteration we have,\n",
+        "# New weights= weights+(learning rate x Gradient)\n",
+        "  def GradientDescent(self,LearningRate):\n",
+        "    deriv_lr=[]\n",
+        "    for d in self.derivatives:\n",
+        "      for i in range(len(d)):\n",
+        "        d[i]=LearningRate*d[i]\n",
+        "      deriv_lr.append(d)\n",
+        "    New_weights=self.weights+deriv_lr\n",
+        "    self.weights=New_weights\n",
+        "    return New_weights\n",
+        "# We create Back function for back propogation. Through back propogation we calculate gradient of the loss function i.e dL/dW.\n",
+        "# Now we use chain rule of derivative,\n",
+        "# dL/d(W for current layer)=dL/d(activationsfor current layer) x d(activations for current layer)/d(unactivated inputs for current layer) x d(unactivated inputs for current layer)/d(W for previous layer)\n",
+        "  def Back(self,loss_deriv):\n",
+        "    for i in reversed(range(len(self.derivatives))):\n",
+        "        Sigmoid_Deriv=sigmoid_deriv(self.activations[i+1])\n",
+        "        delta=loss_deriv*Sigmoid_Deriv\n",
+        "        delta_reshaped=delta.reshape(delta.shape[0],-1).T\n",
+        "        activation=self.activations[i]\n",
+        "        activation=activation.reshape(activation.shape[0],-1)\n",
+        "\n",
+        "        self.derivatives[i]=np.dot(activation,delta_reshaped)\n",
+        "\n",
+        "        loss_deriv=np.dot(delta,self.weights[i].T)\n",
+        "        \n",
+        "    return loss_deriv\n",
+        "# We create the Train function giving instructions on how to train the model\n",
+        "  def Train(self,Inputs,RightOutput,epochs,LearningRate):\n",
+        "    losses=[]\n",
+        "    for i in range(epochs):\n",
+        "      Output=self.forward(Inputs[i])\n",
+        "\n",
+        "      Loss=self.loss(Output,RightOutput[i])\n",
+        "      losses.append(Loss)\n",
+        "      Loss_Deriv=self.loss_deriv(Output,RightOutput[i])\n",
+        "\n",
+        "      self.Back(Loss_Deriv)\n",
+        "\n",
+        "      self.GradientDescent(LearningRate)\n",
+        "\n",
+        "      print(\"epoch:{}    loss:{}\".format(i+1,Loss))\n",
+        "    return losses"
+      ],
+      "metadata": {
+        "id": "6WvBgthul2Fb"
+      },
+      "execution_count": 164,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We now create a random dataset and train our model on it"
+      ],
+      "metadata": {
+        "id": "0MaX8g47AoIl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if __name__ ==\"__main__\":\n",
+        "  NN=NeuralNetwork()\n",
+        "  # inputs=[]\n",
+        "  # Inputs=np.random.rand(NN.Inputs)\n",
+        "  # Inputs=Inputs.reshape()\n",
+        "  \n",
+        "  # Creating a random dataset and its correct outputs to train our model \n",
+        "  dataset=np.random.rand(NN.Inputs,1000) #for i in range(0,NN.Inputs) for j in range(0,1000)])\n",
+        "  dataset=dataset.reshape([1000,3])\n",
+        "  rightOutput=np.random.choice([0,1],size=1000)\n",
+        "# Calling the Train() function to train our dataset\n",
+        "  NN.Train(dataset,rightOutput,100,0.001)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 398
+        },
+        "id": "XAIazsRFxz_1",
+        "outputId": "740a7cee-465f-42a9-e516-fddfdd9ab0c5"
+      },
+      "execution_count": 165,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "epoch:1    loss:[0.75595361 0.62072168]\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-165-6dc6f1e224b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m   \u001b[0mrightOutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Calling the Train() function to train our dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m   \u001b[0mNN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrightOutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<ipython-input-164-520986cb53dd>\u001b[0m in \u001b[0;36mTrain\u001b[0;34m(self, Inputs, RightOutput, epochs, LearningRate)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0mlosses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m       \u001b[0mOutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mInputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m       \u001b[0mLoss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mOutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRightOutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-164-520986cb53dd>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, Inputs)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mInputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m       \u001b[0munactivatedInputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m       \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munactivatedInputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivations\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: shapes (2,) and (3,5) not aligned: 2 (dim 0) != 3 (dim 0)"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Sakt Launde/Week 4/Part1_TensorFlow.ipynb b/Sakt Launde/Week 4/Part1_TensorFlow.ipynb
new file mode 100644
index 0000000..53745a7
--- /dev/null
+++ b/Sakt Launde/Week 4/Part1_TensorFlow.ipynb	
@@ -0,0 +1,675 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "57knM8jrYZ2t"
+   },
+   "source": [
+    "# Part 1: Intro to TensorFlow\n",
+    "\n",
+    "## 0.1 Install TensorFlow\n",
+    "\n",
+    "TensorFlow is a software library extensively used in machine learning. Here we'll learn how computations are represented and how to define a simple neural network in TensorFlow. For all the labs in 6.S191 2022, we'll be using the latest version of TensorFlow, TensorFlow 2, which affords great flexibility and the ability to imperatively execute operations, just like in Python. You'll notice that TensorFlow 2 is quite similar to Python in its syntax and imperative execution. Let's install TensorFlow and a couple of dependencies.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "LkaimNJfYZ2w"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# %tensorflow_version 2.x\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Download and import the MIT package\u001b[39;00m\n\u001b[0;32m      5\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpip\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minstall mitdeeplearning\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
+   "source": [
+    "# %tensorflow_version 2.x\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Download and import the MIT package\n",
+    "%pip install mitdeeplearning\n",
+    "import mitdeeplearning as mdl\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2QNMcdP4m3Vs"
+   },
+   "source": [
+    "## 1.1 Why is TensorFlow called TensorFlow?\n",
+    "\n",
+    "TensorFlow is called 'TensorFlow' because it handles the flow (node/mathematical operation) of Tensors, which are data structures that you can think of as multi-dimensional arrays. Tensors are represented as n-dimensional arrays of base dataypes such as a string or integer -- they provide a way to generalize vectors and matrices to higher dimensions.\n",
+    "\n",
+    "The ```shape``` of a Tensor defines its number of dimensions and the size of each dimension. The ```rank``` of a Tensor provides the number of dimensions (n-dimensions) -- you can also think of this as the Tensor's order or degree.\n",
+    "\n",
+    "Let's first look at 0-d Tensors, of which a scalar is an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tFxztZQInlAB"
+   },
+   "outputs": [],
+   "source": [
+    "sport = tf.constant(\"Tennis\", tf.string)\n",
+    "number = tf.constant(1.41421356237, tf.float64)\n",
+    "\n",
+    "print(\"`sport` is a {}-d Tensor\".format(tf.rank(sport).numpy()))\n",
+    "print(\"`number` is a {}-d Tensor\".format(tf.rank(number).numpy()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-dljcPUcoJZ6"
+   },
+   "source": [
+    "Vectors and lists can be used to create 1-d Tensors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oaHXABe8oPcO"
+   },
+   "outputs": [],
+   "source": [
+    "sports = tf.constant([\"Tennis\", \"Basketball\"], tf.string)\n",
+    "numbers = tf.constant([3.141592, 1.414213, 2.71821], tf.float64)\n",
+    "\n",
+    "print(\"`sports` is a {}-d Tensor with shape: {}\".format(tf.rank(sports).numpy(), tf.shape(sports)))\n",
+    "print(\"`numbers` is a {}-d Tensor with shape: {}\".format(tf.rank(numbers).numpy(), tf.shape(numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gvffwkvtodLP"
+   },
+   "source": [
+    "Next we consider creating 2-d (i.e., matrices) and higher-rank Tensors. For examples, in future labs involving image processing and computer vision, we will use 4-d Tensors. Here the dimensions correspond to the number of example images in our batch, image height, image width, and the number of color channels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tFeBBe1IouS3"
+   },
+   "outputs": [],
+   "source": [
+    "### Defining higher-order Tensors ###\n",
+    "\n",
+    "'''TODO: Define a 2-d Tensor'''\n",
+    "matrix = # TODO\n",
+    "\n",
+    "assert isinstance(matrix, tf.Tensor), \"matrix must be a tf Tensor object\"\n",
+    "assert tf.rank(matrix).numpy() == 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Zv1fTn_Ya_cz"
+   },
+   "outputs": [],
+   "source": [
+    "'''TODO: Define a 4-d Tensor.'''\n",
+    "# Use tf.zeros to initialize a 4-d Tensor of zeros with size 10 x 256 x 256 x 3. \n",
+    "#   You can think of this as 10 images where each image is RGB 256 x 256.\n",
+    "images = # TODO\n",
+    "\n",
+    "assert isinstance(images, tf.Tensor), \"matrix must be a tf Tensor object\"\n",
+    "assert tf.rank(images).numpy() == 4, \"matrix must be of rank 4\"\n",
+    "assert tf.shape(images).numpy().tolist() == [10, 256, 256, 3], \"matrix is incorrect shape\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wkaCDOGapMyl"
+   },
+   "source": [
+    "As you have seen, the ```shape``` of a Tensor provides the number of elements in each Tensor dimension. The ```shape``` is quite useful, and we'll use it often. You can also use slicing to access subtensors within a higher-rank Tensor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FhaufyObuLEG"
+   },
+   "outputs": [],
+   "source": [
+    "row_vector = matrix[1]\n",
+    "column_vector = matrix[:,2]\n",
+    "scalar = matrix[1, 2]\n",
+    "\n",
+    "print(\"`row_vector`: {}\".format(row_vector.numpy()))\n",
+    "print(\"`column_vector`: {}\".format(column_vector.numpy()))\n",
+    "print(\"`scalar`: {}\".format(scalar.numpy()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iD3VO-LZYZ2z"
+   },
+   "source": [
+    "## 1.2 Computations on Tensors\n",
+    "\n",
+    "A convenient way to think about and visualize computations in TensorFlow is in terms of graphs. We can define this graph in terms of Tensors, which hold data, and the mathematical operations that act on these Tensors in some order. Let's look at a simple example, and define this computation using TensorFlow:\n",
+    "\n",
+    "![alt text](https://raw.githubusercontent.com/aamini/introtodeeplearning/master/lab1/img/add-graph.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "X_YJrZsxYZ2z"
+   },
+   "outputs": [],
+   "source": [
+    "# Create the nodes in the graph, and initialize values\n",
+    "a = tf.constant(15)\n",
+    "b = tf.constant(61)\n",
+    "\n",
+    "# Add them!\n",
+    "c1 = tf.add(a,b)\n",
+    "c2 = a + b # TensorFlow overrides the \"+\" operation so that it is able to act on Tensors\n",
+    "print(c1)\n",
+    "print(c2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Mbfv_QOiYZ23"
+   },
+   "source": [
+    "Notice how we've created a computation graph consisting of TensorFlow operations, and how  the output is a Tensor with value 76 -- we've just created a computation graph consisting of operations, and it's executed them and given us back the result.\n",
+    "\n",
+    "Now let's consider a slightly more complicated example:\n",
+    "\n",
+    "![alt text](https://raw.githubusercontent.com/aamini/introtodeeplearning/master/lab1/img/computation-graph.png)\n",
+    "\n",
+    "Here, we take two inputs, `a, b`, and compute an output `e`. Each node in the graph represents an operation that takes some input, does some computation, and passes its output to another node.\n",
+    "\n",
+    "Let's define a simple function in TensorFlow to construct this computation function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "PJnfzpWyYZ23",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "### Defining Tensor computations ###\n",
+    "\n",
+    "# Construct a simple computation function\n",
+    "def func(a,b):\n",
+    "  '''TODO: Define the operation for c, d, e (use tf.add, tf.subtract, tf.multiply).'''\n",
+    "  c = # TODO\n",
+    "  d = # TODO\n",
+    "  e = # TODO\n",
+    "  return e"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AwrRfDMS2-oy"
+   },
+   "source": [
+    "Now, we can call this function to execute the computation graph given some inputs `a,b`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pnwsf8w2uF7p"
+   },
+   "outputs": [],
+   "source": [
+    "# Consider example values for a,b\n",
+    "a, b = 1.5, 2.5\n",
+    "# Execute the computation\n",
+    "e_out = func(a,b)\n",
+    "print(e_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6HqgUIUhYZ29"
+   },
+   "source": [
+    "Notice how our output is a Tensor with value defined by the output of the computation, and that the output has no shape as it is a single scalar value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1h4o9Bb0YZ29"
+   },
+   "source": [
+    "## 1.3 Neural networks in TensorFlow\n",
+    "We can also define neural networks in TensorFlow. TensorFlow uses a high-level API called [Keras](https://www.tensorflow.org/guide/keras) that provides a powerful, intuitive framework for building and training deep learning models.\n",
+    "\n",
+    "Let's first consider the example of a simple perceptron defined by just one dense layer: $ y = \\sigma(Wx + b)$, where $W$ represents a matrix of weights, $b$ is a bias, $x$ is the input, $\\sigma$ is the sigmoid activation function, and $y$ is the output. We can also visualize this operation using a graph: \n",
+    "\n",
+    "![alt text](https://raw.githubusercontent.com/aamini/introtodeeplearning/master/lab1/img/computation-graph-2.png)\n",
+    "\n",
+    "Tensors can flow through abstract types called [```Layers```](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) -- the building blocks of neural networks. ```Layers``` implement common neural networks operations, and are used to update weights, compute losses, and define inter-layer connectivity. We will first define a ```Layer``` to implement the simple perceptron defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HutbJk-1kHPh"
+   },
+   "outputs": [],
+   "source": [
+    "### Defining a network Layer ###\n",
+    "\n",
+    "# n_output_nodes: number of output nodes\n",
+    "# input_shape: shape of the input\n",
+    "# x: input to the layer\n",
+    "\n",
+    "class OurDenseLayer(tf.keras.layers.Layer):\n",
+    "  def __init__(self, n_output_nodes):\n",
+    "    super(OurDenseLayer, self).__init__()\n",
+    "    self.n_output_nodes = n_output_nodes\n",
+    "\n",
+    "  def build(self, input_shape):\n",
+    "    d = int(input_shape[-1])\n",
+    "    # Define and initialize parameters: a weight matrix W and bias b\n",
+    "    # Note that parameter initialization is random!\n",
+    "    self.W = self.add_weight(\"weight\", shape=[d, self.n_output_nodes]) # note the dimensionality\n",
+    "    self.b = self.add_weight(\"bias\", shape=[1, self.n_output_nodes]) # note the dimensionality\n",
+    "\n",
+    "  def call(self, x):\n",
+    "    '''TODO: define the operation for z (hint: use tf.matmul)'''\n",
+    "    z = # TODO\n",
+    "\n",
+    "    '''TODO: define the operation for out (hint: use tf.sigmoid)'''\n",
+    "    y = # TODO\n",
+    "    return y\n",
+    "\n",
+    "# Since layer parameters are initialized randomly, we will set a random seed for reproducibility\n",
+    "tf.random.set_seed(1)\n",
+    "layer = OurDenseLayer(3)\n",
+    "layer.build((1,2))\n",
+    "x_input = tf.constant([[1,2.]], shape=(1,2))\n",
+    "y = layer.call(x_input)\n",
+    "\n",
+    "# test the output!\n",
+    "print(y.numpy())\n",
+    "mdl.lab1.test_custom_dense_layer_output(y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Jt1FgM7qYZ3D"
+   },
+   "source": [
+    "Conveniently, TensorFlow has defined a number of ```Layers``` that are commonly used in neural networks, for example a [```Dense```](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable). Now, instead of using a single ```Layer``` to define our simple neural network, we'll use the  [`Sequential`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Sequential) model from Keras and a single [`Dense` ](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dense) layer to define our network. With the `Sequential` API, you can readily create neural networks by stacking together layers like building blocks. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7WXTpmoL6TDz"
+   },
+   "outputs": [],
+   "source": [
+    "### Defining a neural network using the Sequential API ###\n",
+    "\n",
+    "# Import relevant packages\n",
+    "from tensorflow.keras import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "\n",
+    "# Define the number of outputs\n",
+    "n_output_nodes = 3\n",
+    "\n",
+    "# First define the model \n",
+    "model = Sequential()\n",
+    "\n",
+    "'''TODO: Define a dense (fully connected) layer to compute z'''\n",
+    "# Remember: dense layers are defined by the parameters W and b!\n",
+    "# You can read more about the initialization of W and b in the TF documentation :) \n",
+    "# https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense?version=stable\n",
+    "dense_layer = # TODO\n",
+    "\n",
+    "# Add the dense layer to the model\n",
+    "model.add(dense_layer)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HDGcwYfUyR-U"
+   },
+   "source": [
+    "That's it! We've defined our model using the Sequential API. Now, we can test it out using an example input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "sg23OczByRDb"
+   },
+   "outputs": [],
+   "source": [
+    "# Test model with example input\n",
+    "x_input = tf.constant([[1,2.]], shape=(1,2))\n",
+    "\n",
+    "'''TODO: feed input into the model and predict the output!'''\n",
+    "model_output = # TODO\n",
+    "print(model_output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "596NvsOOtr9F"
+   },
+   "source": [
+    "In addition to defining models using the `Sequential` API, we can also define neural networks by directly subclassing the [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model?version=stable) class, which groups layers together to enable model training and inference. The `Model` class captures what we refer to as a \"model\" or as a \"network\". Using Subclassing, we can create a class for our model, and then define the forward pass through the network using the `call` function. Subclassing affords the flexibility to define custom layers, custom training loops, custom activation functions, and custom models. Let's define the same neural network as above now using Subclassing rather than the `Sequential` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K4aCflPVyViD"
+   },
+   "outputs": [],
+   "source": [
+    "### Defining a model using subclassing ###\n",
+    "\n",
+    "from tensorflow.keras import Model\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "\n",
+    "class SubclassModel(tf.keras.Model):\n",
+    "\n",
+    "  # In __init__, we define the Model's layers\n",
+    "  def __init__(self, n_output_nodes):\n",
+    "    super(SubclassModel, self).__init__()\n",
+    "    '''TODO: Our model consists of a single Dense layer. Define this layer.''' \n",
+    "    self.dense_layer = '''TODO: Dense Layer'''\n",
+    "\n",
+    "  # In the call function, we define the Model's forward pass.\n",
+    "  def call(self, inputs):\n",
+    "    return self.dense_layer(inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U0-lwHDk4irB"
+   },
+   "source": [
+    "Just like the model we built using the `Sequential` API, let's test out our `SubclassModel` using an example input.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "LhB34RA-4gXb"
+   },
+   "outputs": [],
+   "source": [
+    "n_output_nodes = 3\n",
+    "model = SubclassModel(n_output_nodes)\n",
+    "\n",
+    "x_input = tf.constant([[1,2.]], shape=(1,2))\n",
+    "\n",
+    "print(model.call(x_input))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HTIFMJLAzsyE"
+   },
+   "source": [
+    "Importantly, Subclassing affords us a lot of flexibility to define custom models. For example, we can use boolean arguments in the `call` function to specify different network behaviors, for example different behaviors during training and inference. Let's suppose under some instances we want our network to simply output the input, without any perturbation. We define a boolean argument `isidentity` to control this behavior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P7jzGX5D1xT5"
+   },
+   "outputs": [],
+   "source": [
+    "### Defining a model using subclassing and specifying custom behavior ###\n",
+    "\n",
+    "from tensorflow.keras import Model\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "\n",
+    "class IdentityModel(tf.keras.Model):\n",
+    "\n",
+    "  # As before, in __init__ we define the Model's layers\n",
+    "  # Since our desired behavior involves the forward pass, this part is unchanged\n",
+    "  def __init__(self, n_output_nodes):\n",
+    "    super(IdentityModel, self).__init__()\n",
+    "    self.dense_layer = tf.keras.layers.Dense(n_output_nodes, activation='sigmoid')\n",
+    "\n",
+    "  '''TODO: Implement the behavior where the network outputs the input, unchanged, \n",
+    "      under control of the isidentity argument.'''\n",
+    "  def call(self, inputs, isidentity=False):\n",
+    "    x = self.dense_layer(inputs)\n",
+    "    '''TODO: Implement identity behavior'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ku4rcCGx5T3y"
+   },
+   "source": [
+    "Let's test this behavior:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NzC0mgbk5dp2"
+   },
+   "outputs": [],
+   "source": [
+    "n_output_nodes = 3\n",
+    "model = IdentityModel(n_output_nodes)\n",
+    "\n",
+    "x_input = tf.constant([[1,2.]], shape=(1,2))\n",
+    "'''TODO: pass the input into the model and call with and without the input identity option.'''\n",
+    "out_activate = # TODO\n",
+    "out_identity = # TODO\n",
+    "\n",
+    "print(\"Network output with activation: {}; network identity output: {}\".format(out_activate.numpy(), out_identity.numpy()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7V1dEqdk6VI5"
+   },
+   "source": [
+    "Now that we have learned how to define `Layers` as well as neural networks in TensorFlow using both the `Sequential` and Subclassing APIs, we're ready to turn our attention to how to actually implement network training with backpropagation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dQwDhKn8kbO2"
+   },
+   "source": [
+    "## 1.4 Automatic differentiation in TensorFlow\n",
+    "\n",
+    "[Automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation)\n",
+    "is one of the most important parts of TensorFlow and is the backbone of training with \n",
+    "[backpropagation](https://en.wikipedia.org/wiki/Backpropagation). We will use the TensorFlow GradientTape [`tf.GradientTape`](https://www.tensorflow.org/api_docs/python/tf/GradientTape?version=stable) to trace operations for computing gradients later. \n",
+    "\n",
+    "When a forward pass is made through the network, all forward-pass operations get recorded to a \"tape\"; then, to compute the gradient, the tape is played backwards. By default, the tape is discarded after it is played backwards; this means that a particular `tf.GradientTape` can only\n",
+    "compute one gradient, and subsequent calls throw a runtime error. However, we can compute multiple gradients over the same computation by creating a ```persistent``` gradient tape. \n",
+    "\n",
+    "First, we will look at how we can compute gradients using GradientTape and access them for computation. We define the simple function $ y = x^2$ and compute the gradient:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tdkqk8pw5yJM"
+   },
+   "outputs": [],
+   "source": [
+    "### Gradient computation with GradientTape ###\n",
+    "\n",
+    "# y = x^2\n",
+    "# Example: x = 3.0\n",
+    "x = tf.Variable(3.0)\n",
+    "\n",
+    "# Initiate the gradient tape\n",
+    "with tf.GradientTape() as tape:\n",
+    "  # Define the function\n",
+    "  y = x * x\n",
+    "# Access the gradient -- derivative of y with respect to x\n",
+    "dy_dx = tape.gradient(y, x)\n",
+    "\n",
+    "assert dy_dx.numpy() == 6.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JhU5metS5xF3"
+   },
+   "source": [
+    "In training neural networks, we use differentiation and stochastic gradient descent (SGD) to optimize a loss function. Now that we have a sense of how `GradientTape` can be used to compute and access derivatives, we will look at an example where we use automatic differentiation and SGD to find the minimum of $L=(x-x_f)^2$. Here $x_f$ is a variable for a desired value we are trying to optimize for; $L$ represents a loss that we are trying to  minimize. While we can clearly solve this problem analytically ($x_{min}=x_f$), considering how we can compute this using `GradientTape` sets us up nicely for future labs where we use gradient descent to optimize entire neural network losses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "attributes": {
+     "classes": [
+      "py"
+     ],
+     "id": ""
+    },
+    "id": "7g1yWiSXqEf-"
+   },
+   "outputs": [],
+   "source": [
+    "### Function minimization with automatic differentiation and SGD ###\n",
+    "\n",
+    "# Initialize a random value for our initial x\n",
+    "x = tf.Variable([tf.random.normal([1])])\n",
+    "print(\"Initializing x={}\".format(x.numpy()))\n",
+    "\n",
+    "learning_rate = 1e-2 # learning rate for SGD\n",
+    "history = []\n",
+    "# Define the target value\n",
+    "x_f = 4\n",
+    "\n",
+    "# We will run SGD for a number of iterations. At each iteration, we compute the loss, \n",
+    "#   compute the derivative of the loss with respect to x, and perform the SGD update.\n",
+    "for i in range(500):\n",
+    "  with tf.GradientTape() as tape:\n",
+    "    '''TODO: define the loss as described above'''\n",
+    "    loss = # TODO\n",
+    "\n",
+    "  # loss minimization using gradient tape\n",
+    "  grad = tape.gradient(loss, x) # compute the derivative of the loss with respect to x\n",
+    "  new_x = x - learning_rate*grad # sgd update\n",
+    "  x.assign(new_x) # update the value of x\n",
+    "  history.append(x.numpy()[0])\n",
+    "\n",
+    "# Plot the evolution of x as we optimize towards x_f!\n",
+    "plt.plot(history)\n",
+    "plt.plot([0, 500],[x_f,x_f])\n",
+    "plt.legend(('Predicted', 'True'))\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.ylabel('x value')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pC7czCwk3ceH"
+   },
+   "source": [
+    "`GradientTape` provides an extremely flexible framework for automatic differentiation. In order to back propagate errors through a neural network, we track forward passes on the Tape, use this information to determine the gradients, and then use these gradients for optimization using SGD."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [
+    "WBk0ZDWY-ff8"
+   ],
+   "name": "Part1_TensorFlow.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
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+}