PepsiCo_Stock_Price_Prediction_Model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('PEP.csv')\n",
        "df.head()"
      ],
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            "text/plain": [
              "         Date      Open      High       Low     Close  Volume\n",
              "0  1972-06-01  0.000000  0.399388  0.394770  0.399388  318600\n",
              "1  1972-06-02  0.399388  0.403428  0.398233  0.398233  140400\n",
              "2  1972-06-05  0.399391  0.399970  0.386078  0.393603  469800\n",
              "3  1972-06-06  0.390130  0.390130  0.388973  0.390130  140400\n",
              "4  1972-06-07  0.390130  0.390709  0.386657  0.387815  178200"
            ],
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              "      <td>1972-06-01</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.399388</td>\n",
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              "      <th>2</th>\n",
              "      <td>1972-06-05</td>\n",
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              "      <td>1972-06-06</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13167,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 13167,\n        \"samples\": [\n          \"2010-08-09\",\n          \"1992-09-04\",\n          \"1994-12-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.74157942501968,\n        \"min\": 0.0,\n        \"max\": 189.42517438903465,\n        \"num_unique_values\": 12343,\n        \"samples\": [\n          3.762318250849777,\n          84.56215831241593,\n          0.4208850946431653\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.05221077447793,\n        \"min\": 0.1468663393400827,\n        \"max\": 189.72390430412256,\n        \"num_unique_values\": 11998,\n        \"samples\": [\n          0.7101144430873488,\n          92.2838727931912,\n          31.50497226796008\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.39971109828093,\n        \"min\": 0.141448974609375,\n        \"max\": 187.86405966517287,\n        \"num_unique_values\": 12035,\n        \"samples\": [\n          75.12175466604374,\n          24.76794591169041,\n          44.4213575688388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.73730578729007,\n        \"min\": 0.141448974609375,\n        \"max\": 188.9915313720703,\n        \"num_unique_values\": 9347,\n        \"samples\": [\n          30.96912956237793,\n          51.26775741577149,\n          121.01261901855467\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2794869,\n        \"min\": 0,\n        \"max\": 46162800,\n        \"num_unique_values\": 10359,\n        \"samples\": [\n          4271500,\n          4186300,\n          2319400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 64
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    {
      "cell_type": "code",
      "source": [
        "# Check for null values in the entire DataFrame\n",
        "null_values = df.isnull().sum()\n",
        "# Display the result\n",
        "print(\"Null values in each column:\\n\", null_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FLb1stD1Aa_P",
        "outputId": "5e4fcdd6-9eb4-4e4d-888e-669f6dd81d03"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null values in each column:\n",
            " Date      0\n",
            "Open      0\n",
            "High      0\n",
            "Low       0\n",
            "Close     0\n",
            "Volume    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the most recent stock price\n",
        "df.sort_values(by='Date', ascending=False, inplace=True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "2x1j4h096EJ5",
        "outputId": "61342cce-248b-4ad3-ce0b-16f674e522bd"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date        Open        High         Low       Close   Volume\n",
              "13166  2024-08-20  173.529999  176.029999  172.500000  175.850006  5327200\n",
              "13165  2024-08-19  172.660004  173.979996  172.440002  173.820007  3521700\n",
              "13164  2024-08-16  172.520004  172.779999  171.110001  172.619995  4614600\n",
              "13163  2024-08-15  173.710007  174.300003  171.830002  172.520004  4472100\n",
              "13162  2024-08-14  172.229996  174.339996  171.929993  173.710007  3555800"
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              "    <tr>\n",
              "      <th>13163</th>\n",
              "      <td>2024-08-15</td>\n",
              "      <td>173.710007</td>\n",
              "      <td>174.300003</td>\n",
              "      <td>171.830002</td>\n",
              "      <td>172.520004</td>\n",
              "      <td>4472100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13162</th>\n",
              "      <td>2024-08-14</td>\n",
              "      <td>172.229996</td>\n",
              "      <td>174.339996</td>\n",
              "      <td>171.929993</td>\n",
              "      <td>173.710007</td>\n",
              "      <td>3555800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-312d0438-c33f-4d5a-b509-5c109e58215e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-312d0438-c33f-4d5a-b509-5c109e58215e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-312d0438-c33f-4d5a-b509-5c109e58215e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-69fc64f9-7db4-4871-b011-3a61708814ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69fc64f9-7db4-4871-b011-3a61708814ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69fc64f9-7db4-4871-b011-3a61708814ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13167,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 13167,\n        \"samples\": [\n          \"1986-05-27\",\n          \"2004-04-16\",\n          \"2001-12-21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.74157942501969,\n        \"min\": 0.0,\n        \"max\": 189.42517438903465,\n        \"num_unique_values\": 12343,\n        \"samples\": [\n          43.99310997500513,\n          0.747001588344574,\n          93.998951281149\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.05221077447793,\n        \"min\": 0.1468663393400827,\n        \"max\": 189.72390430412256,\n        \"num_unique_values\": 11998,\n        \"samples\": [\n          82.83772008214943,\n          0.6301074552824641,\n          13.861455280187348\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.399711098280925,\n        \"min\": 0.141448974609375,\n        \"max\": 187.86405966517287,\n        \"num_unique_values\": 12035,\n        \"samples\": [\n          0.7572113527396481,\n          18.803381875694107,\n          8.45189107762705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45.73730578729007,\n        \"min\": 0.141448974609375,\n        \"max\": 188.9915313720703,\n        \"num_unique_values\": 9347,\n        \"samples\": [\n          34.71238708496094,\n          16.31263542175293,\n          0.6646879315376282\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2794869,\n        \"min\": 0,\n        \"max\": 46162800,\n        \"num_unique_values\": 10359,\n        \"samples\": [\n          3145000,\n          6819600,\n          3625900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lwLuaWCQ4C2B",
        "outputId": "da707a99-b099-49e4-9893-2ee3f0e00493"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Open          High           Low         Close        Volume\n",
              "count  13167.000000  13167.000000  13167.000000  13167.000000  1.316700e+04\n",
              "mean      35.700170     35.979474     35.422165     35.712414  4.161206e+06\n",
              "std       45.741579     46.052211     45.399711     45.737306  2.794869e+06\n",
              "min        0.000000      0.146866      0.141449      0.141449  0.000000e+00\n",
              "25%        1.247521      1.254301      1.239385      1.246165  2.472800e+06\n",
              "50%       18.823605     19.099758     18.564689     18.836313  3.747000e+06\n",
              "75%       45.157750     45.472708     44.838472     45.197689  5.259800e+06\n",
              "max      189.425174    189.723904    187.864060    188.991531  4.616280e+07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-437861d5-c518-4cea-a172-c382a0e4990f\" class=\"colab-df-container\">\n",
              "    <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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13167.000000</td>\n",
              "      <td>13167.000000</td>\n",
              "      <td>13167.000000</td>\n",
              "      <td>13167.000000</td>\n",
              "      <td>1.316700e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.700170</td>\n",
              "      <td>35.979474</td>\n",
              "      <td>35.422165</td>\n",
              "      <td>35.712414</td>\n",
              "      <td>4.161206e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45.741579</td>\n",
              "      <td>46.052211</td>\n",
              "      <td>45.399711</td>\n",
              "      <td>45.737306</td>\n",
              "      <td>2.794869e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.146866</td>\n",
              "      <td>0.141449</td>\n",
              "      <td>0.141449</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.247521</td>\n",
              "      <td>1.254301</td>\n",
              "      <td>1.239385</td>\n",
              "      <td>1.246165</td>\n",
              "      <td>2.472800e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>18.823605</td>\n",
              "      <td>19.099758</td>\n",
              "      <td>18.564689</td>\n",
              "      <td>18.836313</td>\n",
              "      <td>3.747000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>45.157750</td>\n",
              "      <td>45.472708</td>\n",
              "      <td>44.838472</td>\n",
              "      <td>45.197689</td>\n",
              "      <td>5.259800e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>189.425174</td>\n",
              "      <td>189.723904</td>\n",
              "      <td>187.864060</td>\n",
              "      <td>188.991531</td>\n",
              "      <td>4.616280e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-437861d5-c518-4cea-a172-c382a0e4990f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-437861d5-c518-4cea-a172-c382a0e4990f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-437861d5-c518-4cea-a172-c382a0e4990f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4b686bbe-68e6-44a5-84b2-f17bbf39fd42\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4b686bbe-68e6-44a5-84b2-f17bbf39fd42')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4b686bbe-68e6-44a5-84b2-f17bbf39fd42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4638.654873912243,\n        \"min\": 0.0,\n        \"max\": 13167.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.70017009811794,\n          18.82360492802699,\n          13167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4638.573052284448,\n        \"min\": 0.1468663393400827,\n        \"max\": 13167.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.97947408940884,\n          19.099757730328992,\n          13167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4638.780911516078,\n        \"min\": 0.141448974609375,\n        \"max\": 13167.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.42216456725609,\n          18.564689471270587,\n          13167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4638.664525860903,\n        \"min\": 0.141448974609375,\n        \"max\": 13167.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.71241403536721,\n          18.836313247680664,\n          13167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15501697.659007674,\n        \"min\": 0.0,\n        \"max\": 46162800.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4161205.9542796384,\n          3747000.0,\n          13167.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing\n",
        "\n"
      ],
      "metadata": {
        "id": "MNr19X4b4ZhX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# convert Date column to datetime\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "# set date as index, preparing data for time series analysis\n",
        "df.set_index('Date', inplace=True)\n",
        "#df.describe()"
      ],
      "metadata": {
        "id": "B7bxKm-v4PAe"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the PepsiCo closing price over\n",
        "plt.figure(figsize=(16, 10))\n",
        "plt.plot(df['Close'], label='Close Price')\n",
        "plt.title('PepsiCo Closing Price Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Close Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "XMdin80M5moT",
        "outputId": "a4f45757-b926-484a-a65c-e392570235e0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": 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k5s2b69tvv9WhQ4e0YcMGGYahtm3bql69ehU/DAAAAAAAEIQNe3P1yNd/6raBbfTIV39Guhy/Rp3cTA9+scptrHOTlAhVA0S3oMLIUvXq1dPJJ58c6loAAAAAAACc/v7+Em3Ym6s56/Z5XTNc9rP5+e4Beu6Hdfpi2U6f77l9UNtwlShJirWalZYU61wvUpIa100I6zeB6ooFDAAAAAAAQFTaceiY8/ia/s0lSV2b1JUkmVxmQacmxMps8j8tevGW8K4ZKanc7wMoQxgJAAAAAACiksVl3cWUhBhJUq/mJUvFWS1l1+omxqi8LHDVzpzwFOjCdYnIEV0zwv49oLoijAQAAAAAAFGl2O7Q1KXblVtoc46V7pxtd5TMz/5rryxJUv9W9SWV35nYOTP86ze6Bqf9WzcI+/eA6oowEgAAAAAARJW3f83W2I+X+7w2Y9VuSVKb9Dpa9uDZev9vfSW5dyZ6Gj+yc8hr9LTrSIHzODHGEvbvAdVVpTawAQAAAAAACJeJ36/ze23v0ULncWpirPPYX2fkpb2z1CY9OXTFBSAxljAS8IfOSAAAAAAAEFWS4/33TrWon+hzPN5PN2JSXNX3YcUTRgJ+EUYCAAAAAICoklBOmNe0nu8w0l83YkJs1UcfTNMG/COMBAAAAAAAUWX7oWN+r1n8LA7pGUY2qBMnSRrYIT10hQUoMZZV8QB/+LcDAAAAAABUG/42qvlp7T638+9uP11bD+apV/O0KqjKXXmdnUBtRxgJAAAAAACqDZOfjWoWbznkdt4wOU4Nk+OqoiQvhJGAf0zTBgAAAAAAUaV+Uqzfa+d2a+xz/IO/9Q1XOUGLtxK3AP7wbwcAAAAAAIgqRXaHz/Epf+urC3s29Xnt1DYNlJESH86yytUmvY7zODk+JmJ1ANGOMBIAAAAAAFQJu8PQ96t2a9/RQr/3vDJ7g44W2HxeO6VNg3Lf36KB7522q0KcSzdkLJ2RgF/82wEAAAAAAKrElIVbNOa9JTr58ZkyDMPnPc/MWFvp95vkZ3ebKnDzWW0kSed09T2NHEAJNrABAAAAAABV4r0FW5zH2fvz1KphHbfrRTbf07MDlVbH/1qT4Taia2P9cs9ZykxNiFgNQHVAZyQAAAAAAKgSXZrUdR7nF9l1JL9YN09Zqllr9kiSjhXZT+j9D57bSX1apOmly3qc0HsqKystURZz5LozgeqAMBIAAAAAAFSJvi3TnMczVu1W90e+1zcrdun6SYslSceK3cPI+8/p6Dy+7tQWFb6/UUq8Pv57f53XPTM0BQMIOcJIAAAAAABQJVyXiXxp1gav69/8sct5/PPdA/S301s5z9mhGqgZCCMBAAAAAECVcPjes8bp0a//dB43r58kSTqrfUPFWc26om+zcJYGoIqwgQ0AAAAAAKgShipII334z9W9VWR3KDGWCAOoCfg3GQAAAAAAVInicnbLPpJf7HPcajHLamFiJ1BT8G8zAAAAAACoEnM3HvB7bdi/51RhJQAihTASAAAAAABUieQ4/xM0dx0pcB6f2qZ+VZQDIAIIIwEAAAAAQNgV2Rya+vuOgO61mokrgJqKNSMBAAAAAEBYHMkv1qj/LNA5XTM0qGOjgJ+LsZjCWBWASCKMBAAAAAAAYfHGnI1avStHq3fl6OxOGQE/V2wPftdtANUDfc8AAAAAACBgdoehT5dsV/b+vArvffWnjc7jYrv/nbQ9pSTEVKo2ANGPzkgAAAAAABCwT5ds072f/SFJ2vzkOQE/V2gLPIwcN7xD0HUBqB6ivjOyRYsWMplMXn9uvvlmSdKAAQO8rv3973+PcNUAAAAAANQMe3MK9PT0Ndp2MF+StHTL4YCeszvcp1p7dkY2qBOrTU+M8Hru7E6NlJmaULliAUS9qO+M/O2332S3253nK1eu1Nlnn62//vWvzrHRo0frkUcecZ4nJiZWaY0AAAAAANRUfZ74UZI0ZdFWLXtwiGKtZX1NUxZu1eV9m/l8zjN8LHLpjHzovE66rE8zmc0mDe+Soe9W7nZeO7V1/VCWDyDKRH1nZMOGDZWRkeH88/XXX6t169Y688wznfckJia63ZOSkhLBigEAAAAAqHkO5xfLMAy3tSL/7/M/nMdFNocO5hU5z13DyBiLye38ir7NFR9jkSR1z0p1jlvMJl3Zr3k4ygcQJaI+jHRVVFSk999/X9dff71MJpNz/IMPPlCDBg3UpUsXjRs3Tvn5+eW+p7CwUDk5OW5/AAAAAABA+V6ZvUG/btjv89o/P1mufhN+1OpdOXI4DN30/lLntViL2dkZeXKLem7dlVZz2X+/tzsMWS3VKqoAEKSon6btatq0aTp8+LCuvfZa59jll1+u5s2bKzMzUytWrNC9996rtWvXaurUqX7fM2HCBI0fP74KKgYAAAAAoGbISInXs9+v83v9q+U7JUkTv1+nc7s1dgstHYZUdLwz0jWIlKScApvzeFCH9FCWDCAKVasw8q233tLw4cOVmZnpHBszZozzuGvXrmrcuLEGDRqkjRs3qnXr1j7fM27cOI0dO9Z5npOTo6ysrPAVDgAAAABANfTnzrKZhHUTYmQ2STuPFHjd9+mS7c7jQptdXx4PJkvZHYYKi0vCyDirxe3aZpdp3w+c2ykkdQOIXtUmjNyyZYtmzpxZbsejJPXt21eStGHDBr9hZFxcnOLi4kJeIwAAAAAANcmIF39xHq/dc1TJ8d4xwuLNB3XXJ8ud57+s957GXexwqLC0M9JjGrbrrttWi0kAarZqsxDDO++8o/T0dJ1zzjnl3rds2TJJUuPGjaugKgAAAAAAao+jLlOqS138+vwKnzMMqbDYLsl7mrZrGGkxE0YCNV216Ix0OBx65513dM0118hqLSt548aNmjJlikaMGKH69etrxYoVuvPOO3XGGWeoW7duEawYAAAAAAC4yissCSPjPMJI1122zSbCSKCmqxZh5MyZM7V161Zdf/31buOxsbGaOXOmXnjhBeXl5SkrK0sXXXSR7r///ghVCgAAAABAzRFrMTs3njlR+UUlXZWenZHHjndMShJZJFDzVYswcsiQITIMw2s8KytLP//8cwQqAgAAAACgZjMMI2RBpCS9MWeTJGnuBvc1Je8d1kHnvzJXkpQYWy1iCgAngH/LAQAAAACAl00uu1yH0uYD+W7n3bNS9dJlPRRjMatOHDEFUNPxbzkAAAAAAPCSve/Ew8gmqQnadeSYXPaoUbemdb3uO6975gl/C0D1UG120wYAAAAAAFXn2e/XnvA7vrzlVFkt7tFDj6zUE34vgOqLMBIAAAAAAHjplJlywu+olxirIpv7upN5RXY/dwOoDQgjAQAAAACAl0Yp8ZKkK/o2q/Ded649WRf3auo1bjZ7b4/tY39aALUIYSQAAAAAAHCz7WC+XvtpoySpfp24Cu8/q0O6nv1rd5lcssdRJ2f5vPe6U1uEokQA1RRhJAAAAAAAcDPqzQUV3vPAuZ3Uo1mqZt81wDn25lW9ncd/P7O1z+e6NPHewAZA7cFu2gAAAAAAwMnhMLTj8DHn+Z4jBT7vu6JvM91wWku3sbM7NdKNZ7SSTFKLBklhrRNA9UQYCQAAAAAAnK6d9JvbeaO68V73LHvwbMXHWHw+P25Ex7DUBaBmYJo2AAAAAABwmrNun9u559qP/VvVV2pibKXefctZbSpdF4CagTASAAAAAABIkgqK7V5jaUllwWOb9Dr67zW9ve4J1G2D2lb6WQA1A9O0AQAAAACA8ots6vTgDK/xOKtZP/7zTE1fuVvXntJCSXGVixIGtG+oWCs9UUBtRxgJAAAAAAD04o8bfI6bTCa1blhHN5/gFOvM1IQTeh5AzcD/JAEAAAAAALR066FIlwCgFiCMBAAAAACglrPZHVqUfTCs3zCF9e0AqgvCSAAAAAAAarF9RwvV89Efwvb+9OQ4SdKwLhlh+waA6oM1IwEAAAAAqMU++m2rcgpsYXv/zH+eqa0H8tWlSd2wfQNA9UFnJAAAAAAAtdjXK3Z5jbVrVEeSlJESf8LvT4mPIYgE4ERnJAAAAAAAtdia3Ufdztc9NlyxVrNW7TyirLTECFUFoKYijAQAAAAAoJZyOAyvsVhrySTKzpl0MwIIPaZpAwAAAABQC209kK9W//dtpMsAUMsQRgIAAAAAUAu9NGt9pEsAUAsRRgIAAAAAUAuVTscGgKrEf/IAAAAAAFALxcdYIl0CgFqIMBIAAAAAgFroUF6R19hlfbIiUAmA2oQwEgAAAACAWmjq7zu8xhrWiYtAJQBqE8JIAAAAAABqmUKb3ed4u4zkKq4EQG1jjXQBAAAAAACgat33+Uq380//3l+/bz2sc7o2jlBFAGoLwkgAAAAAAGqZT5dsdzvv3SJNvVukRagaALUJ07QBAAAAAKhFHA5D9RJjnOe3DWwTwWoA1DZ0RgIAAAAAUIu0+r9vnce3DmyjsUPaR7AaALUNnZEAAAAAANQS8zcecDtPiY/xcycAhAdhJAAAAAAAtcST3612O4+PtUSoEgC1FWEkAAAAAAC1xPLtR9zOXdeOBICqQBgJAAAAAEA1dyC3UJ8s3qb8IltQz8Vb6YwEULUIIwEAAAAAqCaOFdnlcBhe41f8d6Hu/nSFej060++zvoLK7lmpoSwPACpEGAkAAAAAQDVwJL9Y3cd/rwtfm+d1bc3uo5KkY8V2v88fK3K/NvUfp6hhclxoiwSAChBGAgAAAABQDcxZv09FdoeWbTusgnJCx982H/Q5XmhzOI/XPTZcPZvVC3mNAFARwkgAAAAAAKqBzNR45/GmfXnO47xC9+nX10/6zefzpQFmcrxVsVbiAACRwX/6AAAAAABQLZicRwfzipzH2fvz3O46WuB7E5vBz/1c7nUAqAqEkQAAAAAAVAN2l41rvl6x0+99nTNTvMYcDkM+9r0BgCpHGAkAAAAAQDVQaCtbJ/LD37Y5z+0eKeOqnTlaueOIJs/fLMMoubb5gHv3JABEijXSBQAAAAAAgIpd9dYit/N9RwvVtF6iiu0Or3vPfelX5/GIro01cOLPYa8PAAJBGAkAAAAAQDVUuvbjpv3+ux4f/GKVjhW577z94LmdwloXAJSHMBIAAAAAgCi05UCe5m08oCPHivX3M1t7XT9WbNfRgmLd8+mKct9jMZvczq85pUUoywSAoBBGAgAAAAAQZVZsP6yRL891np/ZrqHO6dZY36zY5Rz7cNFWfbx4e4XvWrz5kNu5ZzgJAFWJDWwAAAAAAIgyX7uEjpL06k8btevwMbexQIJISZq+arfzuFNj7522AaAq0RkJAAAAAECUKbK5b0rz1fKdIXnv85eeFJL3AEBl0RkJAAAAAECUOZBXFPQzn910is7rnlnuPe0a1alsSQAQEoSRAAAAAABEmZl/7gn6mViLWbkFxX6v/2t4B5lMrBcJILIIIwEAAAAAiDIdGyf7vZYc53vFtUKbXd2zUv0+Z2XjGgBRgDASAAAAAIAo07dVfb/XstISfY7vO1qo0ae3UvP6ibqsT5bX9UKPdSgBIBIIIwEAAAAAiDIOhyFJOrdbY69r9uPXPJ3atoGS4qz66a4BmnBhN7VumOR2fdrvO0JfKAAEiTASAAAAAIAoUxo41vExJXvtnqM+n0mJj5Ek57qQHTJS3K63z/A/9RsAqgphJAAAAAAAUcZu+A8jA2VzuE/LvvGM1idUEwCEAmEkAAAAAABRZunWw5Kk+BiL7hnW3jmeWTdej/+lS0Dv8JzOnRBrCVl9AFBZhJEAAAAAAESRPTkFWr7tsCQpv8iufwxo47x2ca+mSk+OD+g9o09v5XbeqkGSnzsBoOpUvt8bAAAAAACE3N/fX+I83pNT4H7RZFJ8TFlf0ZX9mslqNmt4lwyv93juyG02m0JbKABUAmEkAAAAAABR5PfjU7QlKaegWJLULC1RWw/ma3iXDLVvVLYRzbjhHZVUzrqSibEW5RfZw1YrAASLMBIAAAAAgCjVon7J1Orv7zxD+44WKistUZK0+clzAnq+SWqC1u/NDVt9ABAs1owEAAAAACBKrNp5xO386v7NJZVsZFMaRAbjtkFtJUl/6dHkxIsDgBCgMxIAAAAAgCjhOqX66Yu7qa3LlOzKOK97pno2r6fGKYFtegMA4UYYCQAAAABAlKjjsv5jv5b1y7kzcE1SE0LyHgAIBaZpAwAAAAAQJcZ+vNx53Kx+8NOyASDaEUYCAAAAABAFjhYUa/WunEiXAQBhRRgJAAAAAEAU2H7oWKRLAICwI4wEAAAAACAK7M8tjHQJABB2hJEAAAAAAESBg3lFzuOhnRtFsBIACB/CSAAAAAAAokBpGDmoQ7peu6JXhKsBgPAgjAQAAAAAIAqM/+pPSVJcjFlmsynC1QBAeBBGAgAAAAAQYQ6H4Tz+9o/dEawEAMKLMBIAAAAAgAjLL7Y7j1+/smcEKwGA8CKMBAAAAAAgwvILbc7joZ0zIlgJAIQXYSQAAAAAABGWezyMTI6zymRivUgANRdhJAAAAAAAEZZfVDJNOynOGuFKACC8CCMBAAAAAIggwzB03aTfJEnxMfzXdAA1G/8pBwAAAABABC3YdFD7jhZKkjYfyI9wNQAQXoSRAAAAAACEyRs/b9QbP28s955XZm+oomoAIPJYjAIAAAAAgDA4VmTXhO/WSJLapNfRoI6NfN7364b9VVkWAEQUnZEAAAAAAITBZf9Z4Dy+4d3FPu+xO4yqKgcAogJhJAAAAAAAIXa0oFjLth12G/ty+U6v+3ILbVVUEQBEB8JIAAAAAABCbOtB741o/m/qH15jB3ILncetGiZp/MjOYa0LACItqsPIhx9+WCaTye1Phw4dnNcLCgp08803q379+qpTp44uuugi7dmzJ4IVAwAAAAAg5RXavcZyC236xwdLnFOzF2w6oFum/C5J6tIkRbP+OUDXnNKiKssEgCoX9RvYdO7cWTNnznSeW61lJd9555365ptv9Mknn6hu3bq65ZZbdOGFF2ru3LmRKBUAAAAAAEmSze7wOf7tH7t1UtYmDezQSKPeLFtTslFyfFWVBgARFfVhpNVqVUZGhtf4kSNH9NZbb2nKlCkaOHCgJOmdd95Rx44dtWDBAvXr16+qSwUAAAAAQAXFdi3eckiS1CEjWWt2H3W7/sS3azTzz71uY0lxUf9fzwEgJKJ6mrYkrV+/XpmZmWrVqpWuuOIKbd26VZK0ZMkSFRcXa/Dgwc57O3TooGbNmmn+/PnlvrOwsFA5OTlufwAAAAAACIXr3vlNz/2wTpJktZh83rNo80G386Q4S9jrAoBoENVhZN++fTVp0iRNnz5dr732mrKzs3X66afr6NGj2r17t2JjY5Wamur2TKNGjbR79+5y3zthwgTVrVvX+ScrKyuMPwUAAAAAoDaZv+mA83jljhw9c3G3Cp+pmxAbzpIAIGpEdR/48OHDncfdunVT37591bx5c3388cdKSEio9HvHjRunsWPHOs9zcnIIJAEAAADUGEfyi5WSYJXJ5LsrD+FjGIbX2F97Z6lfq/r69o9dmvDdGp/PndamQbhLA4CoENWdkZ5SU1PVrl07bdiwQRkZGSoqKtLhw4fd7tmzZ4/PNSZdxcXFKSUlxe0PAAAAANQEv289pO6PfK/bPlwW6VJqpSf9hI1ZaYm6rG8zt7H4mLL/St4wOS6sdQFAtKhWYWRubq42btyoxo0bq1evXoqJidGPP/7ovL527Vpt3bpV/fv3j2CVAAAAABAZRTaHXpi5XpL01fKdEa4men24aKue+Ha1zy7GE2EYht6Ys8nv9Riz+38FLygu23E7LYlp2v/f3n3HV1Xffxx/35WbnZCQEPZesmQvRVAEBLdVaxVcrQv9ValWcW/U1ta6a1tLtbUqClgHKAiCKCggyN57JBAgO7nz/P645JLLvVlwc7Nez8cjD8/5nu/5ns/No6dJPny+3y+AxqFOT9O+9957ddFFF6lt27Y6cOCAHnvsMVksFl1zzTVKSkrSzTffrClTpiglJUWJiYm66667NHToUHbSBgAAANAojfvLYu04XFjbYdR5D8xcK0k6/4xmGtguJWzjPv6/9RVej7IGJiPbpcZq77FixdutahpPMhJA41Cnk5H79u3TNddcoyNHjigtLU1nnXWWli1bprS0NEnSn//8Z5nNZl1xxRVyOBwaO3asXn/99VqOGgAAAAAizzAMEpFVkFfi8h9f+eZSSdKwjql67zenV9RyON+hfy3dHdDWLSNBt53T0X9uMZv014n9deu7KyVJT1/aS92aJyjaZmF9TwCNhskId116PZSXl6ekpCTl5uayfiQAAACAesnh9qjrw3MD2nY9N6GWoqm77v9ojT5YsTeo/adHzj+tqdK3vrtCX67P8p9fN6SNnr60V8i+bo9XWfkOtUw+9Y1ZAaCuqWp+rV6tGQkAAAAACC232FV5J8hTTj2Ow+05rXG/23Yk4PyJi3uW29dqMZOIBNBokYwEAAAAgAZg0DNfV94J+mjlvpDtWXmO0xq3XdPYgHOLmWnXABAKyUgAAAAAaKBYlSvQz3tzyr22/1jxaY09oG34NsIBgIaMZCQAAAAANFBecpEBdmaXv8HPW4u3n9KYK3cf08JNhzT9+12SJKvZpI9uG3pKYwFAY1Cnd9MGAAAAAJw6t9cri9lS22HUGRVtWP3zvtxK7zcMQyaTSR6vIYvZpCMFDl3xxvcBfV6+pq8GtKNKEgDKQzISAAAAAOq5Iqc7ZLuH0sgAaQl2//HPj45Rnye/qvK9z36xUW8t3uE/H9ujma4f1i6oX04RGwkBQEWYpg0AAAAA9dzLX2/zHz80vrv/2F3NZORPe46p75NfaVNmXthiq0tcHt/344zmiUqKtemtif3VOsW3q3VF+82UuDwBiUhJ+nJ9lrYfKgjqWzbhCQAIRjISAAAAAOq5sjtEn9c93X/s8VQvGXn569/rWJFL4176Nmyx1SWbjydZc4qckqQxPTI07bLekqQuzRLKvS+/JHTl6eas/KC287qlh+gJAChFMhIAAAAA6iG3x6tf/2uFXl2wVUM6nFijsF1qnH9txOpWRtY3RU63rnzze722cFvlnSU9+8UmSdKB3BJ/m9Xi+2a5PN5y7yt2ekK2b8kKrIzc9dwEmSsqsQQAsGYkAAAAANRHf1+yU/M3Zmn+xix/22/P6yyz2SSr2SSXx2jwa0a+98MeLd91TMt3HdPkUZ0CruWXuBQXZfUnBw0j9PfCdjwZWVHi9sbpP4Zs3xKiMhIAUDEqIwEAAACgHlqx62hQW0yUb+dsizm42u9YobNa46/bX/nu0rWtvM1ifv2v5er1+Ff67Qer/W25xSf6fnbXWf7jKIvve+Zwha6MXLr9iLYfLgxqT4i2+p9/56hOWv3o+dWOHwAaI5KRAAAAAFAP9W+bEtRW4vJNJ7aafX/qlVZGvrtst/o+NU/Pz91U5fGveOP7MERZc1wer14tMz27tPJxydZszd94SJL06c8H5D3+PViyLdvft2fLJP9xnN2XjCx0BK8LuWLXUV3zt2Uhn1+6jmSHpnG6d2xXJcdGnc7HAYBGg2QkAAAAANRDZSv9SqXG+RJipZWRN05friVbs/XI7HWSpDe+2e5PWJ7Me9I0ZYe7/DUU64LzXlwUcL5wsy8Bed0/fgho/3DFXknSqwtCrysZH+1bvazA6dY3mw/pkdnr/N+jZTuOVBpHq5TY6gUOAI0cyUgAAAAAqIeKncGVfFcOaC1Jslt9f+rtzC4MSs69/d3O0OOVk6SMtJwipzYe9O16/fPeHH2x9mDA9bX7cnXnez9pz9GigPY3vtmuKWWmZZd6YOZaSdKO41OtR3cP3O063u5LRhqGdMM/l+vdZbv1ztJdkqSTl5F86tKe+u6BcwPaShPAAICqYQMbAAAAAKiHZqzcF3A+845hirb5phzH2606lO8Ied8LczfrjpGBm73kFrk06e0fQvaPJMMwdOaT84Lan7ykhyYNbSdJuujVJSHvLd3I5mR9Wier3QOf+88LHYFJ15jj37OyDuT4dttevTfH3za8U6omDmkb1HfrITaxAYDqoDISAAAAAOqhImdgUq1zerz/2GQq/75xPTKC2n79znL9vC9ww5poW+T/XCyb/Cvr0U/WS5KOVnMTHslXXVlWQnRgTY4pxDfrvR/3qMTl0YJNh/xtqXH2kOOv259X7ZgAoDEjGQkAAAAA9UzpZi1llU43lhRy9+dSDnfwdOxQFYXXH69EjKSTp2SXOr4Eph6atbZK49gs5Wdjbx/ZsdL7nW6vfvFm4AY+ZaeFl03U/vHKPlWKCQDgQzISAAAAAOqZUJvLhKrwCyW7oGrVhdYKEno1JTU+dPXhTcPbS5LmrMsMaO/SLF7TbxwY1H/m7cN17eA2Icfq26ZJlWI5ueKxbEXlF/93th6e0F1f3j1CV/RrWaXxAAA+JCMBAAAAoJ4odnr06c8HdPu/V/rbrhvSRq9f26/KY6zdn6t2D3yuKR+urrCf++TdWyIgv8S3Q/jJm8ws2HRIv/vw56D+W7IKdE6XtKD2NqmxundM16D2DmlxIZ/7p6sqr24cW2Z6e4e0eP367A7qmpFQ5SQwAMCHDWwAAAAAoJ544tP1en/53oC2py/tdUpjzfxpv+4Z3UWp8aF3g/Z4Ip+M3H+sWJLULDE6oH1HdqF2ZIeeem4ymfTUpT31yOx1/rYYm0VR1uDam2hr8GY1kjSqa3rI9lLdMhL0q0GhKy0BANVDZSQAAAAA1EEuj1f/XrZb2w8X+NtOTkQmRoeuLxlzRrOgtuRYW1Db2S8s1N+/3ek/v7xfS/+U6NqojJy9+oAkqUls6ARpeSYOaatZdwyTJN09urM/Efl/5wbuGl7epjxmc/nVjQPbNdHcu0dU2AcAUHUkIwEAAACgDvpwxV49PHudzntxUbl9erVKCtk+cWhb//GQDim6f1w3/fOG4LUVJelP87b4j1+8so9/bUS3N3hdynDxeg09/r/1+seSnQFtpTqlx2tQu5Ry7+94fLp1y+QYf1vfNk2067kJunt0F3/blDFddds5JzasuevcziHHqyjPOOO2YeVfBABUG9O0AQAAAKAOWrc/13/83bZs9Q6RePxu25GQ93rKJPaaxtt1+8iO/vUYK2IymWQ9npnz1GBl5MLNhzT9+12SpJvP8lViFjjd/uvjemZoQu/memXBNr389dag+5+6tKfW7svVuJ4ZQddO9sAF3XTf2K46mFusVk1iQ/Zh3UcAiBwqIwEAAACgDspIPFH1d+3ff9CrC7cF9fn18UTeycqul+g1fEnFhGib3prYv9LnWi2+e901uGbkw2XWdyyVW+RLltqtZkXbLLJZzBrWMTXk/fF2q249p6PapobekOZkFrOp3ERkRW4d0aHa9wAAKkYyEgAAAADqoJPXNzyU5wjq8/CFZ4S8d0j7E0m8sknFMT0y9OerQ+8cPbi9b1p0JCojD+aW+I+zCxzamV2oY0VOSYFrW8bYQm84U+Bwh2wPt9tHdqy8EwCgWpimDQAAAAB1jMvj1bQ5mwLaZq3aH3A+oG2Tcu83m01qlxqrXUeKNKF384Brl/VtpXs++Dnonqnju0vyVRFKkdvAZsDT8yVJ7VJ9lYtVSYKWXSsyHGJtFjWNt6vE5dFFfVpoeKdUXdi7RVifAQDwIRkJAAAAAHXM059tqPB6alyUZtw2tMI+/7vrLG3JzFf/EEnLge2aaPmuYwFtluPrJlotpcnI8G5gk1vk0sS3f1BidPCu3pK060iRJCm7wOlvM4dYy/GGYe2qPD27qsxmk5ZOPVdew5DdGroaEwAQHkzTBgAAAIA65l9Ld1d4/W/XD6h005XEaJsGtEsJ2e+vEwcEtRW7PJLKVEaGec3Id5ft0pp9uVqyLbvK94T6iDeXs07m6bJZzCQiASACSEYCAAAAQB3icHsq7RNlOb0/5VLiooLaSqdH28zmgPNwSSinIvJkZ3du6j9uGm8Put4izFO0AQCRxTRtAAAAAKhhHq8hs0mVVjNKUqGj8mRkqCTd6ZjQu7l/A5uaWjMy3l61Pz+fvayX/zgjKVqv/aqfYqLM2n+sWIPap/rjAwDUT1RGAgAAAEAN8XgNHc53aPSfFunav/8Qss+2QwV68avNyjm+m3RhmZ2itz87Xi9dfWbQPRlJ0acd26/LTHd+7Vf9ZDYHrhkZ7srI380I3jQnlFZNAisfJ/RurnO7NdPEoe3UNSMhrDEBACKPykgAAAAAqCE3Tl+uxVsOS5J2ZhfK6fYqynqiJmTGir2676M1kqRXFmzTB7cMUVKsbzpz0/goWcwmtW8auFnLuB4ZYYnt/gu66XCBQ90yEgPaSysPXZ7wbWATauq53WqWwx38jKpUjwIA6i+SkQAAAABQQ0oTkaU2HMzTma2T/eelichSV7+1TCO6pEk6scZin9bJevKSHtp/rFgxURZdP7RdWGKzWcz6yy/7BrVbzeGvjHSGSDrG261yuJ0hegMAGjKSkQAAAAAQIb99f5UW3TdKhQ53yJ2ipRMJzF4tk/xtk8KUgKwK6/ENbFbsPiav1/BP3z4dt767MqgtLcGuI4UkIwGgsSEZCQAAAAA1wBuisnD3kSIdKXBo/MvfKrvAWe5UZUnKLnDUdIghWSwnko+r9h5T/7Yppz3m99uP+I/fmthfzZNiZLOa9Ojs9brn/C665m/LTvsZAID6gWQkAAAAAIRZTpFTl73+fchr/Z+e7z+uaCp0Vl5J2OOqCmsYd6v2eA2dPNr5ZzTzrwv54W1DJUn//c0QXfv3ZfrdmK5hezYAoG4iGQkAAAAAYWQYhs58cl5A2x+v7KN7q7ibdKkHx3cPZ1hVZi4zf9xiNlfQs2Jfb8zSXf9dpcv6tgxoD7VBzdCOqVr/xDjFRFlO+XkAgPrh1H+yAAAAAACCfLk+M6itY1pciJ4n/PjgeQHnsycP13ndm4U1rlNxOlWSN/9rhYqcHv3nhz1V6k8iEgAaB5KRAAAAABBGH67YF9RWUaJt1h3DlJ4YrTev6ydJurxfy4AdtyPNKDNz3BLGKduSNH/KOWEdDwBQ/zBNGwAAAADCKL/EFXD+398MUddmCSH77npugv94XM/m2vbMBbJaardmxNCJbOTpJCOjLGY5PSc25xnZNU2d0uNPKzYAQP1HZSQAAAAAhNHyXcf8x7uem6ChHVNlMpkCEo/lqe1EpBRYGWkOsb5jVTw0a21AIlJS0NqRAIDGqfZ/0gEAAABAA2AYhgzDUFqCXZL0q8Ftgvq8eGUf//EHtwyJWGynavxfvtW2QwXVuuebzYdCrhMZY2NNSAAA07QBAAAA4LS5PV5d/sb3So6NUovkGB3Od2hE57Sgflf0b6WEaKs6pMXX2SnLZQoj5fR4de+MnzV78vAq3//EpxtCthc5PacZGQCgISAZCQAAAADVtHjLYS3dcUT3jukqi9mkj3/apzX7cgP6dEoPvYP2mB4ZkQjxlHVoGhh3kdNdrft3ZheGbLdawrsZDgCgfiIZCQAAAADV4PUamvT2j5KkpvF23XxWe81atT+oX/umdbPysTKtU2IDzi1mswzDkKmK60f2aJGo9QfyAtrO7ZauC3o2D1uMAID6i2QkAAAAAJTjWKFTc9dnanNmvvq1baKL+7TQ7qNF/utPfbZBNw5rp2U7jgbdezo7UdclGw/m6Xcf/qxf9G+lZknR6phWfpI1r8Qlu/XE1gRjezTTXycOiESYAIB6gmQkAAAAAIRwKL9Eg5752n8+/ftdyit26eHZ6wL6zV4dXBXZ0MxctV8zj1d/7pw2PmSV5I7DBTr3xUUBbQ9POCMi8QEA6g920wYAAACAk+SVuHTZa98Htf953pagtikf/hzUdts5HWskrrpgc1Z+yPY/z98a1JaeaK/pcAAA9QyVkQAAAABwkt6PfxWyPb+k4s1cJo/qqBGd0zSwXUpNhFUnWMpZO7JbRoI+LZOX/f24rrJbLRGKCgBQX1AZCQAAAABlHMwtDmr768T+kiSnx1vhvfeO6arBHVJlbiDrRYbi9hoh23u2TAo4P797s0iEAwCoZ0hGAgAAAMBxTrdXQ6ctCGgzmaSxPTIqvXf1o+dXecfp+sxTTjLSe1K71cKfmwCAYPx0AAAAAIDjvtueHXDeq2WSNj45LqhfjM2iHx86L6AtOTaqRmOrK15buE0frtirvBKXJrz8rZ6bs0lScJIyOcZWG+EBAOo41owEAAAAgOOsJ02vnnnHMNmOV/jZrWY53L5p2lPHd1N6QrS+umeEHpy5Vo9f3CPisdaWOesyNWddpg7kFGv9gTytP5CnEZ2bynXSFPYmcY0jOQsAqB4qIwEAAAA0GrNX7Ve7Bz5Xuwc+V4nLE3T945X7/MfrnhjrT0RK0uanL1DS8Wq/cT1907a7NEvQR7cPC1ovsTHILnD4j3/19x+0/XCB//yh8d1rIyQAQD1AMhIAAABAo3Cs0Km7P1jtP3/8f+sDrhc7PZq9+oD/PN4ePJFs+UOj9dMj5ys9IbrG4qwv/r1sT8D5H7/aIkka3ilVvxnRoTZCAgDUAyQjAQAAADQKLy/YGnD+/vK9euXrrXIfn1485cPV/muTR3UMOUaU1awUph9X6GBOSW2HAACow0hGAgAAAGgU8ordQW0vztuiTg/N0aG8Es1Zl+lvT42zRzK0BmXnkcLaDgEAUIeRjAQAAADQ4GzOzFe7Bz7X699skyQdyivRxz/tK7f/oGe/DjiPtllqNL76ZmTXtCr3tZn5MxMAUD5+SgAAAACo0ya9/aMm/uMHeb1Gle8Z+9JiSdILczdLkh4rsz7kDcPa6Z7RXSq8P7/EdQqRNlzV+NbLZKq8DwCg8SIZCQAAAKBOWbn7mN5dtlter6FXvt6qxVsO69ut2dqclX9K42UXODR/Y5b//FeD26hFcsUb0Axo1+SUntVQ/HVif10zqLX/3DAMXdm/VZXuHdgupabCAgA0AMHbwwEAAABALflk9X799v3VkqRHZq8LuOb2VK08b8tJScsBT89XbJRFLo9HktQ5PV6tm8Tqvo/WBN17/hnN1C0jQf3bNu6E2tgeGRrbI0P//XGvJMluNeuZy3ppxsryp7qX+vPVZ9ZwdACA+oxkJAAAAIA64/6PgxOEpX7el6NerZIqvN/t8WrMnxcHtRc5fYnIAW2byGQyKSYqcE3Ie0Z30Q3D2ykpxnYKUTdcz1/RS3/7dqceu6iHoqxm7Zw2Xu2nflFu/6QYm9IS2PwHAFA+pmkDAAAAqDNKXN5yrz08e12l60YeyndUeD0jKfT07N+O7kwiMoSrB7bR/CnnqHVKrCTJZDLp6Ut7ltv/uwfOjVRoAIB6imQkAAAAgHpj5qr95V5buPmQhj23oML77zq3s/94SAffVOzL+rYMT3CNxHVD2oZsT461Kd7O5DsAQMVIRgIAAACoE0pcHv9xy+SYkH2+XJ8Z1Ob2eLXxYJ5u/OfygPblD43WdUPaBLR1zUjwH795XX/98co+FVb6oXJ2q+/PyvO7N6vlSAAA9QHJSAAAAAC1LiuvRN0emes//8cNA/zH3/5+lP9479GioKna7y/fqwv+8m3QmGkJdj04vrv//P/O6xxwPTk2Sr/o30pxVPOdlk/vOksTh7TVvWO71nYoAIB6gJ+6AAAAQCPg8vjWYrRZ6l49gmEYGvzs1wFt6QnRWjr1XDndXv96hZK0KTNfHR78Qruem+Bve/x/68sdOzbKqh8fPE+r9+bo/DOo3KsJXZol6CmqSwEAVUQyEgAAAGgEOj80R5Jks5i09vGxirZZKrkjcnZmFwa1JUZbZbVE+c8v7N1cn6056D9fuv2I+rdtIpvFJPdJlZI2i0kf3z7Mf56eGK0xPTJqIHIAAFBdde+fRQEAAACEVdm1GF0eQ7/78Gc53eXvWh1pM1buC2qznlTBeW639IDza/62TF0enhNy9+0VD5+v3q2SwxojAAAID5KRAAAAQAOWW+QKWItRkj5fe1BdHp6jfyzZWUtRBXrjm+2V9jGM0O1/nr8lqC2mDlV9NlQd0+JqOwQAQD1FMhIAAABowB6Yuabca099tiGCkVTdVQNaBbWN79U8ZN+3Fu8Iaouy8mdOTfvqnnN0zaDW+ui2obUdCgCgnmHNSAAAAKABMgxDry3cpjnrMms7lAodLXT6j9+a2L/ctR1joizq1TJJa/fnljvWhF7NdcuIDmGPEcEsZpOmXd67tsMAANRDJCMBAACABiYrr0SXvPqdMvNKajuUSpVdz3Jk1/QKekof3jpUT3++Qf/5YU/I669d2y+ssQEAgPBj/gIAAABQjxmGob9/u0OLtxz2t7341eaQicinLu0ZcB5vr53ahBKXR8VOXxLS5fFtQBMbZal0enVMlEWTR3UKea1zenx4gwQAADWCykgAAACgnjEMQ3uPFuu5uRsVb7fqwxW+3ag3PTVO+SVu//nJ+rVJ1qShbfX1xkPan1OsAodbLo9XNkvN1yh8tuaA7nxvle4f103Pz93kj9fl8e1MU9UYWiTHaOXDozV79YGANS9vGN4u7DEDAIDwozISAAAAiLAFm7I0Y8XegCnK1fHvZbs14g8L9cXazIDEY7dH5mrgM/ND3vPar/qpR4skPXlJTy3+/Sh/+7tLd59SDNXh9Rq6871VkuRPRErSawu3Ket4BWdusavK46XG23Xt4Dbq1TJJkmQ1m3TVgNZhjBgAANQUk2EYRm0HUdvy8vKUlJSk3NxcJSYm1nY4AAAAaMAMw1D7qV/4zzc9NU7RNku1xmj3wOfV6v+3SQN0/hnNyh1j13MTqjVeRTxeQw/NWqt+bZv4E4Tr9ufqwleWVHrvqcRxON+hpBgbO2gDAFDLqppfY5o2AAAAEAGzV+3X3R+s1qVntgho35yZrz6tk2UYhkwmU9if+/NjY5QUYwv7uOW5d8bPmrVqv95fvleXntlSUVZzuRvOhENagr3GxgYAAOHHPx8CAAAAEXD3B6slSbNXHwhod3u9uv3fK3XJa9/JfXwzl4p4vNWb2BTJRKQkzVq133989we+qdnmKuRY+7ROrqGIAABAXUIyEgAAAKhhB3OLy71W7PRqzrpMrdmXq62HCiod6z8/VH2Nx4v6tCj3Ws+WNbM80fBOqf7jL9ZmSpK/MvL/zuusndPG66dHzg+6L7+k6mtGAgCA+otkJAAAAFCDDMPQ0GkLyr2eeXwDF6n8HaUNw9CMFXu1as8xPfrJ+qDrt57TIeC8c3q8RnZN0yMXdi/3uU9e0lOSlGAP78pNHdPiA87//u0O//HHK/fJZDIpJS5Kf/nlmYH9Jg0IaxwAAKBuIhkJAAAA1IDNmfk6WujU52sPVtjv3hk/+48vfe07eY9Pwy5wuFW61+S3W7N130drdNnr3wfcmxRj05Tzu2jK+V38bT1aJGrelHM0/cZBSk+ILve5pUlIWxg3frn93yv1zkm7cz/9+Ub/8VOX9vAfX3JmS3XLSPCfdzgpiQkAABqmOp2MnDZtmgYOHKiEhASlp6fr0ksv1ebNmwP6jBw5UiaTKeDrtttuq6WIAQAAAGlLVr7GvrRYQ6d9rbnrMqt8X4HDrQO5xdp2qEC9Hv9SV765VJIvsXmyK/q10vKHRuv/zussu9WiWXcM0/lnNNMr1/St0rMsxxdydFVhncqqOFLg0JxKPus5XdIDzj+96yzdPbqzPpk8PCwxAACAuq9O76a9aNEiTZ48WQMHDpTb7daDDz6oMWPGaMOGDYqLi/P3+81vfqMnn3zSfx4bG1sb4QIAAACSpMVbDkuSHG6vCh3uat2bV+zWv3/YLcOQVuw+pn99v0vPfLExqN+LV/UJOO/bpon+Vo2pzqVTwj1eQ1+uz5TNYtK53ZpJkt5eslMbD+Zpxsp9+sMveuvKAa0rHe/iV7+r8PofftHbnwAtG8Pdo7uUcwcAAGiI6nQycu7cuQHn06dPV3p6ulauXKkRI0b422NjY5WRkRHp8AAAAIAghmEETE1euz+3WvcfKXSoa7MT05cf+1/wGpHhUJoYLHJ6dOu7KyVJm54aJ4fbqyc/2+Dvd99HaypNRro8Xu3PKX+THkm6sHf5m+kAAIDGo05P0z5Zbq7vF7mUlJSA9v/85z9q2rSpevbsqalTp6qoqKjCcRwOh/Ly8gK+AAAAgHDILQ7cFTq7wFnt+ytLQF5cwS7ZVWU9qUpRko4VOXWozIY6pc794zdaufuYZq/aH3Ttj19uVueH5gS1f3PvSP/xTcPbKybKcnoBAwCABqFOV0aW5fV6dffdd2v48OHq2bOnv/1Xv/qV2rZtqxYtWmjNmjW6//77tXnzZs2cObPcsaZNm6YnnngiEmEDAACgETEMQ/uOVVwhWJlHZq+rtM/DE8rfJbuqrCF27p6/IUvtmwZvJLMju1BXvOHbPMdiNumi48lQh9ujVxduC+rfo0Wi2qTEqlWTGB3IKdYdozqedrwAAKBhqDfJyMmTJ2vdunVasmRJQPstt9ziP+7Vq5eaN2+u8847T9u3b1fHjqF/6Zk6daqmTJniP8/Ly1Pr1pWvgwMAAABU5M73VpW7e3aHpnHakV1Y6RjHilyV9mkab692bCc7ef1GSZq1ar+uHdy2wvs+WL7Xn4xctuNo0PWlU89VRmK0TCaT/nfnWXJ7vGGJFwAANAz1Ypr2nXfeqc8++0wLFy5Uq1atKuw7ePBgSdK2bcH/QlvKbrcrMTEx4AsAAAA4HSt2HS03ESlJf7yqj1LjovznZY8lqX/bJhWOf8Owdv5jc4hEYnWFmqbdNSNR2w4XVHhfh7QTG0k+P2eT/7hbRoJWPDxazZNiZDL5xk6Ji1J6YvRpxwoAABqOOp2MNAxDd955p2bNmqUFCxaoffv2ld6zevVqSVLz5s1rODoAAACEk9vj1R++3KTvt2XXdiin5MFZayu8nhZv139vGeI/75qREHDdMIyge5Y/NFpRVrMyEqP14Pju+u9vhmjt42PCEq/VEiqhaWjP0YrXXy+tqDxS4NCGgyfWXp979wgqIAEAQKXq9DTtyZMn67333tMnn3yihIQEZWZmSpKSkpIUExOj7du367333tP48eOVmpqqNWvW6J577tGIESPUu3fvWo4eAAAA1fHBir16beF2vbZwu164oreuGli/ltHZklVxRaHFbFKXMrtkx5bZ0KVlcoyaxEYF3ZOWYNfi+0YpIdqqKKtZQzumhi1eqzm4LqHI6VFBiVuSNKFX85CVnluzCpRX4lL/p+eHLRYAANB41OnKyDfeeEO5ubkaOXKkmjdv7v/64IMPJElRUVGaP3++xowZo27duul3v/udrrjiCn366ae1HDkAAACqwzAM/XXRDv/57z9eI4/XVyk4deYaXfXXpXK6vbUVXqWKnZ5q35OWEK2Pbx+moR1S9bdJA5QUawvZLyMpWnH28NcQhFozcsnWbK3Y5VsHcnyv0DONlmzL1ofL9wa0De0QviQpAABo2Op0ZWSoqSpltW7dWosWLYpQNAAAAKgp//v5QND04Nxil1LiovTfH32Jr683ZumCchJktW3BpkNBbR3S4rTj8IkNa7zHf7d94Yre+nDFXt07potSy0zdjq+BhGN1HSl0+o/jo61656ZBmvT2j0H9Tt5Be+mOIzUeGwAAaBjqdGUkAAAAGoe7P1gd1HZycnLnkcp3oq4thU53UNszl/YKOE+K8VU+XjWwtT66fZhST1pf8f/O6xywOUzZady1wWIyaUSXNF3er6Uk3wY1pXKqsOM3AABAKLX/z68AAABo9EJNiCkocesXb3zvP39h7mbdMbJTheP887udWrDpkN6aOEAxEUzmTf9uV1Bbh7Q47Xh2vDZm5sntMZQQHXoadqmm8XYt+N1ILdpyWG8v2aknL+lRQ9FWzfENsfXsZb10Wd+WSoy26ZLXvgvZ9zdnV77RJAAAgEQyEgAAAHVAk1ibjp1UbXfdP36o9jhPfLpBkvT83E16/OLIJfPK7ipdKiHaKrPZpB4tkqo11jld0nROl7RwhVah2CiLispZ77J0Rclom0Vnd07Tlqz8cse5f1y3GogOAAA0REzTBgAAQK0b1qnpad3/wtxN6vPEV/7z6d/v8h9PnblWt7yzImg9csMw9MXag9p+uOJdsCvzwfI9IdtjbLU7zboq3N7y12g3mQI3uAm14U0pq4U/KwAAQNVQGQkAAIA6IznWVu56hM0S7SHbJen1b7YHtR0rdGrV3mP674++ZOHeo8Vqkxrrv75oy2Hd8Z+fJEk7nh0vcwXJtorc//HakO0nJ/Pqooo2jDw5/HapcaE7AgAAVAP/hAkAAIBa53J7JUm/H9tNqXFRIftk5Tm0Zl9OUHt5CbUL/vKtbpq+wn++7KQdn1fsOuY//m57dnVDDmlYx1S9cW0/zbpjWFjGq2kV5CJlDlEZufrR84P6/fnqPuEOCwAANGAkIwEAAFDjHvtknW7854/yljMt2OXxJSOtFpOOFDrLHefiVwM3UDEMo9y1JTPzSgLOf//xGjmPJz0l6dWF2/zHry8Mrqw8FW/fMFAX9Gquvm2ahGW8mlZBLjKoMlKSkmOjtPi+Uf7z928Zosv6tgp/YAAAoMEiGQkAAIAatXDzIf1r6W4t3HxYq/YeC9nH5fGlxaJCrD346q/6Bpx7yiQ080rc+m7bkZNvKdeLX21WbrFL7yzdFdC+dMcR7TlSVOVxSq3bn+s//vTOsxRdD9aJLKuiadrlJY5bNYlRvzbJ6t0qSYPbp9RUaAAAoIEiGQkAAIAadeM/l/uPHWUqE8sqcfl2dLZbzbJZTpTkDWjbJGi6cGkVpSS5PaHHK89fF+9Qnye+0qOfrA+69vzcTSHv8XoNvfjVZs3bkBXUfuErS/znDnfoXanrsrLpxn/fPDjgWn6JO+Q9ZrNJH98+TJ9MHl4v1sUEAAB1C8lIAAAARE45hXg5xb5Na5Jibbp9ZCd/e5zdGrSLc9kdoMtLbp6KM1okBrW9tnCbfvm3ZXplwTb95p0VAdemzgzcuKZP6+SwxRIpZQsjz+rcVMumnuc/zy0OvZGQ5Nuch0QkAAA4FSQjAQAAUGNOngb8q7//oHYPfK6tWfk6lF+iv3+7Q7lFLuUU+daJTI6JUoLd6u/v9nrVKT0+YIzdRwr9x+FMRiZGWwPO1+3P1R++3Kwfdx4t87wT1Y8frNgb0N8WYop5XZdw0mfOSIr2H7dryu7ZAAAg/Orfb0wAAACoN75cnxWy/Z4PV+um6cv19Ocb9eCstcou8CUjY6Msum5IW3+/C3u3UMe0eMWUWYtxa1aBJN806RdCTK2+ZUSHKsd3Zf8Tm6+UrltZ6oGZa4L6PzhznaQT08pLnVkPqyIl6a5zfVWoo7un+9u+umeE3ryun/q3rR+b8AAAgPqFZCQAAABqzA87Q28us25/ntbtz5Mkfb72oL/dYxiKibLouwfO1fQbB+qaQW0kSeufGOvvExPlS0x+tHKf5qzL9Lf3aZWkv00aoAfHd9d3D5yrQe1SZDWb9N6vA9dCLKt362SN75UhSdqfUxwU48k+/mmfJOn1bwJ33549eXi5z6jLbj6rg96/ZYhevubEJkFdmiVoXM/mtRgVAABoyEhGAgAAIGwMw9DmzHxd+Mq3WrApS85qTqO2W32/nrZMjtHIrieq9cxmk87pkiZJyju+luG327ID7v3kzrN0/hnN/Pd/eNtQbX3mAg3r1FRXDWilUHZlF/p34/7Hkp3+9vJ2ku5wfOry9yc9u76ymE0a0iFVsVHWyjsDAACEAb91AAAA4LRtOJCnh2av1Zp9ufIcT+TdNH1FwPqPVdGqSWy51xJjbJJO7PKcFFP52KWbrLzwiz76388HVOIKTI72bpUUkIR0ebya9dN+7cguVCg7sgvV+/EvA/bheXhC90rjAAAAgA/JSAAAAJy28S9/G7I93+Gu8hhRlWwAU7rZypOfbdAV/VopLf7EZiu/H9e10vFjbJagZOTFfVpo2Y6j+u+PeyRJo/74jfYdKw51u19eSeBn+vXZVV+jEgAAoLFjmjYAAABOS7HTU3mnKvjloNYVXi+78/NLX28JuFZ2g5vy3HN+F0nSL/q30q7nJmjXcxNkMpl06Zkt/H3KS0QeL7AEAADAaSIZCQAAgFPmdHvV/dG5lfZ76tKeiouy6C+/PDMgqZiWYNfr1/bTP64foIcqme68dl+u/zi7wKk1+3L85z/tyQm+4SQTh7TV/Ckj9PwVvQPaB3dIrfC+K/q10hf/d3al4wMAAKByTNMGAADAKbvu7z9Uqd/EIW117aA2MptNGtQ+RUOnLdAZzRP1yZ3DZatkenapni2T9P1232YzW7PytSkz33+td8ukSu83mUzqlJ4Q8lpKXJSOFjpDXnvxqj5Vig8AAACVozISAAAAp+zHXUcr7fPZXWdJ8u2ILUnNk2L082Nj9OldZ1U5ESlJNw1v7z8um4iUpBuGt6vyOKF8d/+5p3Rf0/io03ouAABAY0MyEgAAAKfE5fFW3klS+6ZxQW1JMTZZzNVbiLFJnC1k+0V9WlQrqRlKtC30/U9d2tN/PO+eEWoSGxjD9BsHndZzAQAAGhuSkQAAAI1MgcOtdg98rmlfbKzyPVM+WK1fvrVUXq/hbzt5WnOHEEnHUV3TFGcPz8pA5e223atl4mmPbTKZlJ5g959bzSbtem6CJg5p62/r3CxBPz1yfsB9PaswPRwAAAAnkIwEAABoZJ6fs0mS9NfFO6rU3zAMzVy1X8t2HNWf52/xV0Qeznf4+5hM0rwp5+inR87X2Z2bavbk4Vo29Tz9M4yVg6ZytrT+dmt2WMZ/4RcnNrb5z68HVysGAAAAVA0b2AAAADQS6/bnau66TH23/UTyzuH2yG61VHifp0w15CsLtmnNvlz966ZBOlKmMnLHs+NlMpmUEheld28OncirKZ3L2ZSmujo0jfcfU/EIAABQM0hGAgAANBKT3v4xaGr10UKnmifFVHify2MEnC/acljZBQ59sHyPJKl5UnStVgye1Tk1LOO0ahKjQe1SZLeZFRtVfoJ2yf2j9L+fD+j6oe3C8lwAAIDGhGQkAABAA7U5M1/x0Va1TPYlG09ORErS0GkLdM/oLvrt6M4B7R6vIbPJNy3ZGWKjmgFPz/cfH8wtCXPk5UtPsOtQmenhkjSqa3pYxjabTfrg1iGSKp6O3apJrO4Y2SkszwQAAGhsWDMSAACgAVqwKUtjX1qs4c8tkCSVuDzl9v3z/C0yjBPVjw63R+e9+I0m/uNHSVXfNTsSTt41u0WYqzJNJhPrQgIAANQgKiMBAAAamAM5xbpp+gr/+TtLd6lZYnSF9zjcXkXbfFOTN2fma9eRIu06UqRCh7tOJSMt5sBE4WvX9qulSAAAAHAqqIwEAABoYP745eaA80c/Wa9b310pSUpLsGvXcxN081ntA/pMnbnWXx2ZlXdiGvTeY0VyuQPXjKxNVsuJZOTtIzuqb5smtRgNAAAAqotkJAAAQAMzc9X+cq8dPr7eYlqCPaB91qr9+nHnUUnSgk2H/O1HC5wqLjPFu0NaXNCYzZMqrroMJ5v5xK+vt5zdIWLPBQAAQHiQjAQAAGhEzuvm2+zlin6tgq7N25Clpz7boP/+uMff9t32bO0+UihJ6tEiUQt+N1Iju6Yp2mbWkvtH6YVf9NaM24ZGJnhJE4e2lSQN7ZCqJnFREXsuAAAAwsNklF2tvJHKy8tTUlKScnNzlZiYWNvhAAAAnJZ2D3zuP75xeDv987td/vMv7x6hrhkJknyb2nR7ZG6Vx724Twu9fE1febyGCp1uJUbbwhZzVRmGoTX7ctU1I8G/xiUAAABqX1Xza1RGAgAANCAfrtjrP26RFK0Hx3cPuN6uaaz/ONpm0Z+u6lPlsXOLXZJ8m8jURiJS8u123ad1MolIAACAeopkJAAAQD1X4HBrwNPzNO2LjZq/IcvfvuT+c2WzmLXi4dFKirHpgp4ZslsDk3iX92ul2KiqJfYm9Goe1rgBAADQ+FhrOwAAAACcnhkr9iq7wKm/Lt7hb7t9ZEeZzb6dp5vG2/XTI+fLYjaFvL/I6QlqW3TfSG3KzPfvwv3IhWfoygHB60wCAAAA1UEyEgAAoJ7bdqggqO2XA1sHnJeXiJSkxy46Q098usF/3qd1stqmxqltapw+u+ssNYmLUsvkmPAFDAAAgEaLZCQAAEA9tv5Arv7zw56g9rapcVUe48bh7TWhV3MdyneowOHWkA6p/ms9WyaFJU4AAABAIhkJAABQLy3aclgHc4r1wMy1QdfuH9et2uOlJ0YrPTE6HKEBAAAA5SIZCQAAUM9sPJin69/+Maj9wfHddPWANkqKrZ2drgEAAIDKkIwEAACoZy74y7dBbf+6aZDO6ZJWC9EAAAAAVWeu7QAAAABQdW6PN6jtrYn9SUQCAACgXiAZCQAAUAd5vIa2Hy6QYRgB7XuPFQecz7xjmMb0yIhkaAAAAMApIxkJAABQBz3x6Xqd9+Iivbtsd0D7jsMF/uNdz01QvzZNIh0aAAAAcMpYMxIAAKAOemepLwn56CfrdaTAqaOFTr27bLd6tUySJE3o3bw2wwMAAABOCclIAACAOmZLVn7A+V++3uo/Xrs/V5KUX+KOaEwAAABAODBNGwAAoA5xebwa8+fFlfa7sBeVkQAAAKh/SEYCAADUIZ/+fKBK/a4a2LqGIwEAAADCj2naAAAAdYjNcuLfivu2SdblfVvqkU/WS5LevK6/SlweXdCL3bMBAABQP5GMBAAA9V52gUMvf71Vo7qma0SXNJlNUonLq02ZeVq644hGd2+mLs0Sqj2u12soK79EzRKiZTabaiDyE1wer44WOnXXf1dJklo1idGsO4ZLki7v10oxNkuNxwAAAADUNJKRAACg3ipxeXThK0u07VCBpBM7UEvSgLZNtGL3MUnSJ6sO6Mt7RpQ7zr5jRZr83ir1aJGo7hkJunpgG9ksJp39wkLtzylW71ZJ+vj2YQFVi+FkGIYufHmJNpfZuKbQcWKDmjg7v7IBAACgYTAZhmHUdhC1LS8vT0lJScrNzVViYmJthwMAAKrA4zX0yoKtemn+1so7S9r13ISQ7St2HdW1f/9BDre30jHG9cjQQxO6q3VKbLViLY/L49WX6zN153urgq59/8C5apEcE5bnAAAAADWtqvk1/pkdAADUOwdzizV02oJq3bP7SKHapsYFtJW4PPrFm0urPMbc9Zmauz5TM+8Ypn5tmlTr+aG8s3S3nvpsQ1D7/CkjSEQCAACgQaIyUlRGAgBQH3y4fK/mbczSvA1ZQdfsVrOuH9ZOby3eUe79UVaz3prYX4PbpyomyqIdhwt074yf9dOenAqf+8uBrfX+8r1B7YPbp+iMFolavOWwXr+2v7pmVLwmZX6JS6P++I06pcerT+tkJcXY9MY325VfcmI69qiuaXr0oh5q3zSugpEAAACAuqeq+TWSkSIZCQBAXVbocKv/0/NU4go9jTo9wa4l958rl8erye/9pD6tkrX3WJFm/rRfb17XX498sk6H8x2VPmdCr+b6cddRFTncKnR6lBBt1drHx0qSHG6P+jzxVbkxlNo5bbxMptCbzJzzh4XafaSo3Htn3TFMZ7ZOLvd+AAAAoC5jmjYAAKh3PF5DHR/8wn/eMS1O2w8Xltu/R4tEfXbXWTKZTIqymjX9xkH+a3+66kxJ0qIth/XfH/dU+uzXru0nyZd4XLI1W4Pap/iv2a0WbXrqApW4POr2yNxyxziU71CzxGhJUmZuieKjrYq2mnX+nxdXmIi8b2xX9Q3DtG8AAACgrquZLSEBAECDdSivRA/OWqsNB/JOeYxPVu/XwGfmK7fI5W+bsWJvQCJSUlAiMsZmUZ/WyRrWMVW3ntNBs+4YXmkl4QMXdKvweuf0eO2cNt5/brdadF73ZkqItgX1jbZZ9PI1fXVh7+bqlB4fdP2zNQclSXuPFmnItK/V87Ev1emhOdqZXX5CVZImj+pU4XUAAACgoaAyEgAAVNnB3GKN/MM3cri9eu+HPdr6zAWyWar3b5s7Dhfot++vliT1efIrvXPTIPVplaz7PlpT4X3XDm6jZy7rVe2Yk2Js2vXcBB3IKdaCTYf08Ox1irFZVOzyyG4166t7RlRravTFfVro4j4t/OebMvM07qVvJUlxURZJ0j+W7Ax578MTuuuaQW0UG2XRWc8v1P6cYr36q77V/kwAAABAfcWakWLNSAAAquKR2ev07rLdAW2J0VYtum+UmsRF+dv2Hi3S2S8slCQtum9k0A7W7R74vNJnxUZZNHFoW/11kW9DmtWPnq/k2KhK7qoawzBkMpn8/w2Hwc/OV1aeb13K287pqDcXbQ/Zb9dzEwLikMQakQAAAGgQWDMSAACEjddrBCUiJSmvxK2+T82T3WrW5qcvUG6Ry5+IlKRpX2zSmxP7+893HC6o9Fn/vHGg+rRKVkpclKZe0D08H6CM0uRfOJOAZatDyyYirxrQSilxdq3cfVQvXxNYAUkSEgAAAI0RyUgAAFCpZTuOVHjd4faq3QOf68zWyQHtu4+e2LTlSIFD5764yH/+8e1DdcUbSwP6v3ldP43qmn76AUfYWxMHaPzL3wa1P3pRD8Xb+XULAAAAKMU0bTFNGwCAypw8tXrhvSM16o/fnPJ4v+jfSn+8so9W7j6qG/+5XJf2bak7RnZSRlL0aUZau/701Wa9vGCbJOmnR85XSlx4ppYDAAAAdV1V82skI0UyEgCAipS4POr2yFz/eem6hz/tOaYNB/J0RotEXf769wH39GuTrJ/25JQ75vZnx8tiZpoyAAAA0FBUNb9Wve0vAQBAg/DoJ+s0/LkF2n2kMOR1t8ertxZv15y1B9XzsS/97a9f289/3K9NE103pK36tWmiz+46y9/evmmcPr59mC4qs+N0WW9N7E8iEgAAAGikqIwUlZEAgMbD4fboN++s1OIth/1tF/dpoeuHtVO3jATF2a3afrhA55VZ27Gs/905XL1bJVfrmfM3ZOnX76zQxX1aBG3iAgAAAKBhYJp2NZCMBAA0Bpsy8zTupeBNVqpj57Txp7QLdInLo2ib5bSeDQAAAKDuYpo2AADwyy1ynXYicsXDo08pESmJRCQAAAAASZK1tgMAAACnz+XxymYJ/jfGlbuP6Yo3vg9q//PVfZQSZ9f1b/9Y7pjrnhirowVOjfjDQrVNjVXTeHtYYwYAAADQ+JCMBACgHihyuhVjswRUJpa4PPrjl5v19yU7/W1jezTTXycOkCTtyi4MmYgsO9W6WaJdWXmOoD7f/n6U4u1Wxdut/t2zAQAAAOB0sWakWDMSAFC3vbtstx6ZvU6S1CEtTpee2VJ/mrel2uPYLCZtfWZ8QNvO7EL96/td6pAWpzlrM/XiVX3UIjkmLHEDAAAAaDzYwKYaSEYCAOqaAodbGw/m6YW5m7R817HTGuu6IW302EU9Qk7jBgAAAIBwqGp+jWnaAADUIYZhqP3UL6rcPzUuSot/P0pxdqv+88NuPTRrXcD14Z1S9fSlvcIdJgAAAACcEpKRAADUIZPf+ylk+9QLumlMjwzN25CpZ7/YJEka1yNDb07s7+9z7eC2GtguRUcKnEqJi5LJJHVplhCRuAEAAACgKpimLaZpAwBql8vj1RdrD+q3768OumY2STNuG6b+bZsEtBuGEbCZDQAAAADUJqZpAwBQCa/X0NZDBWoSa1PTeLtMJoU9wZdb5FKfJ7+SJD1xcQ91To9Xl4wEWc0mzV2Xqac+26BCpyfovi/+72yd0aKCRZ9JRAIAAACoh0hGAgAalYO5xXr566367497Q16f89uz1b15xVXyLo9XmbklatUkRm6voXs+WK2DuSW6bkgbndMlXR6voc/WHNATn24IuO+x/62vNL6RXdP090kDZGWzGQAAAAANEMlIAECd8+3Ww5q96oASoq0a2jFVo7qmK8pa9eTcvmNFOuv5hZKkHi0S9d5vhqjA4dYFLy1WXom7wnsv+Mu3eu7yXhrUPkWLthwOSihWZOXuU9/12myS1j0xVrFR/GgGAAAA0HCxZqRYMxIA6gK3x6sPV+yT0+3R4xUkAP8+aYBGn9EsqN0wDHV6aI483ur9WBvUPkU/7jxa7Xir476xXXXj8HbalJmvYqdHbVJitedokQ7mlujSM1vI6fGShAQAAABQr1U1v0YyUiQjAaA2bTyYp8te/04lLm9EnvfkJT0UbbPo/O7N1CQuyt9e7PSo+6NzK7y3Y1qcth8ulCR1aBqnHdm+43vHdNGvz+6gw/kO3fbvleqQFq+svBK9c9MgRdssNfdhAAAAAKCOIBlZDSQjAaBmOd1eLdtxRJPe/lFSYCKvPM9c1lOd0xN01V+XntIzVzw8WimxUXpj0XYt2nxYq/fl6Pdju+rXZ3co955PVu/Xb99frVFd07Rw82ENbNdEk0d1UpTFrGGdmp5SHAAAAADQGJCMrAaSkQBQdR6vIZMkh9urvceKtGjzYW0/XKDthwu0/1ixBndIVesmMSp0evTdtmztOlJY5arH16/tpxFd0lTkcCs9MTro+syf9mnKhz+Xe/9Tl/TQdUPastM0AAAAAEQYychqIBkJINwMw9C+Y8X6ac8xDe/UVE3j7bUdkiQpt8ilErdHSTE25ZW4lFfsUm6xWw63R/F2q/JL3DpS6JRJUu9WSYqymlXo8OidpbuUlVeixVuyVezynHYcLZNjdKTQoaQYm341qK3uOreTzGYSiAAAAABQX1U1v9ZgVst/7bXX9Ic//EGZmZnq06ePXnnlFQ0aNKi2wwJQC7xeQ/klbhW53MrMLVHzpBhJ0tFCp3KKnLJZzWqWEK1Cp1vpCXZFWc1yewxfZV9OsSxmkzxeQzaLWXF2q3KKnMoucOqsTk11ILdY2w8VKKfIJZNJio2yyuP1qsjp0c7sQnm8hryGoTX7cnUo3+GPKSHaqjYpsUqOtWlw+1RlJEXrYE6JXB5fxWByrE3fbctWQrRNRU63BrZLUWyURXarRXabWXarRWaT5DUMHSty6WihUx6voYO5JSp0uLV2f676tk5Wk7goRdvMyit2q8jp0eECh3YfKdSxQqcMQ8p3VLyT9Kk4p0uaOqXHy+3xKrvAqaOFTjVPjlZyTJTyS1zKSIrWrwa3UXpCtCwkHAEAAACgUWsQlZEffPCBJk2apDfffFODBw/WSy+9pBkzZmjz5s1KT0+v9P6GXBnp9RpatPWwLCaTzCaTPIYht8crryGZJBnyVXCV/leSTCaT4qKsMpskmSSzySSn2yu31yubxSyL2XTiv2azzGap0OGR2+tVYrRNTo9XuUUueQ1fMic51qa2KXGyWU2yms2yWUwRm0JpGIYMQ/6Kq7Kf8eTvk9Pj9X25vXJ7fP28hi+xZBilxzp+fuLY65WcHq+OFTpV7PKoxOXxJ5gsxz9vlMWsKKtZZrNJVrNJqXF2WcwmeQ1DJpP8z7NZfP1tFrOsFpMKStwqdnmUXeCQyeS71+0xFGe3KrfYpQKHS26PoQKHW2aTSVaLr4/V7Lv/aKFTu48UKbvA4U9sGTKUV+zWsSKnrGaTEmNsSoy2KTHGqiaxUYq2WXwxW80qcHhkNZsUbTMfj80sr9dQlNWsGJtFhnyf3eX2yuUxdLTIqYM5xYqympUQbVNCtFXxdqsMQypxeXSsyKnEGJuibRbF2iyKj/Y902SSCh1uZRc4VeLyyOM15PJ45fYaKnS4tSO7UBaTSXarWRsz8zSsY1PZLCaV/r9XocOtzVn5KnJ6dCCnWHuPFsvpicxmKPWRyST/9y4pxiavYajE5ZHZZJLJpIAp1Vaz7/seE2WRzWJWh7Q4ndUpTZf3aymP11DTeF8yFwAAAADQuDWqadqDBw/WwIED9eqrr0qSvF6vWrdurbvuuksPPPBApfc35GSk0+1Vl4fn1HYYAexWs5rG232VZ1Zf4s0w5E+AHT5eTRZvt6plk1ilxkWpSWyU4u0WpcRF6XCBQ4UOj+w2X0Ks0OFWbrFL2QW+qre8Erc8x5NYOcUuOd1emUy+pIokeQ1fAiY2yiKr2aSC40kwNEzJsTZ/FWNKbJTMZpO8XkOFTrccbq/K/j9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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the volume over time\n",
        "plt.figure(figsize=(16, 10))\n",
        "plt.plot(df['Volume'], label='Volume', color='red')\n",
        "plt.title('PepsiCo Trading Volume Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Volume')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "VEPmQXJW6iD7",
        "outputId": "474d1c0c-f055-4229-cd45-eaea7426dd8e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predictive Modeling with Linear Regression\n",
        "\n",
        "\n",
        "\n",
        "1. Setting up features and target\n",
        "2. Build a pipeline of standard scalar and model for five different regressors.\n",
        "3. Fit all the models on training data\n",
        "4. Get mean of cross-validation on the training set for all the models for\n",
        "5. negative root mean square error\n",
        "6. Pick the model with the best cross-validation score\n",
        "7. Fit the best model on the training set and get\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_m8CKUCY_0TQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the features (X) and the target (y)\n",
        "X = df.drop(columns=['Close'])\n",
        "y = df['Close']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "SdXCfgmc_2y2"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Building pipelins of standard scaler and model for varios regressors.\n",
        "\n",
        "pipeline_lr=Pipeline([(\"scalar1\",StandardScaler()),\n",
        "                     (\"lr_classifier\",LinearRegression())])\n",
        "\n",
        "pipeline_dt=Pipeline([(\"scalar2\",StandardScaler()),\n",
        "                     (\"dt_classifier\",DecisionTreeRegressor())])\n",
        "\n",
        "pipeline_rf=Pipeline([(\"scalar3\",StandardScaler()),\n",
        "                     (\"rf_classifier\",RandomForestRegressor())])\n",
        "\n",
        "\n",
        "pipeline_kn=Pipeline([(\"scalar4\",StandardScaler()),\n",
        "                     (\"rf_classifier\",KNeighborsRegressor())])\n",
        "\n",
        "\n",
        "pipeline_xgb=Pipeline([(\"scalar5\",StandardScaler()),\n",
        "                     (\"rf_classifier\",XGBRegressor())])\n",
        "\n",
        "# List of all the pipelines\n",
        "pipelines = [pipeline_lr, pipeline_dt, pipeline_rf, pipeline_kn, pipeline_xgb]\n",
        "\n",
        "# Dictionary of pipelines and model types for ease of reference\n",
        "pipe_dict = {0: \"LinearRegression\", 1: \"DecisionTree\", 2: \"RandomForest\",3: \"KNeighbors\", 4: \"XGBRegressor\"}\n",
        "\n",
        "# Fit the pipelines\n",
        "for pipe in pipelines:\n",
        "    pipe.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "5ptnlkahC2UX"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results_rms = []\n",
        "for i, model in enumerate(pipelines):\n",
        "    cv_score = cross_val_score(model, X_train,y_train,scoring=\"neg_root_mean_squared_error\", cv=10)\n",
        "    cv_results_rms.append(cv_score)\n",
        "    print(\"%s: %f \" % (pipe_dict[i], cv_score.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8Ei3KiTDBIa",
        "outputId": "2708d917-faf1-49f6-d0c2-483769e5d464"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LinearRegression: -0.286680 \n",
            "DecisionTree: -0.478223 \n",
            "RandomForest: -0.359614 \n",
            "KNeighbors: -0.950247 \n",
            "XGBRegressor: -0.559896 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model prediction on test data\n",
        "It tells us that in this case, the fancy models don't necessarily perform better than a simple regression model because of the nature of the dateset. I'm going to test linear regression model on a test set and evaluate it with different parameters."
      ],
      "metadata": {
        "id": "phaLhumxEEVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = pipeline_lr.predict(X_test)"
      ],
      "metadata": {
        "id": "bz5cRMXZERKG"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "print(\"R^2:\",metrics.r2_score(y_test, pred))\n",
        "print(\"Adjusted R^2:\",1 - (1-metrics.r2_score(y_test, pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
        "print(\"MAE:\",metrics.mean_absolute_error(y_test, pred))\n",
        "print(\"MSE:\",metrics.mean_squared_error(y_test, pred))\n",
        "print(\"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pk9Shjk-EW1B",
        "outputId": "fa674412-83a2-4ae0-f3b1-09c86dc460cd"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2: 0.9999692060616282\n",
            "Adjusted R^2: 0.9999691748462827\n",
            "MAE: 0.12398381429578921\n",
            "MSE: 0.06449593093927482\n",
            "RMSE: 0.2539604909021772\n"
          ]
        }
      ]
    }
  ]
}