RealStatePricePrediction.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f7248f4f-b8fe-4eae-a2cf-f90b4f9b1919",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "af93af81-beca-4bdd-82fb-3abae591217b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('Real State Data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8b923b1b-bd28-4a90-9d87-0e5f21596879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42 entries, 0 to 41\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   PriceTotal           42 non-null     object\n",
      " 1   Meterage             42 non-null     object\n",
      " 2   Year                 42 non-null     object\n",
      " 3   Room No              42 non-null     object\n",
      " 4   Floor No             42 non-null     object\n",
      " 5   Building Floors      42 non-null     object\n",
      " 6   Units Per Floor      42 non-null     object\n",
      " 7   Parking Lot          42 non-null     object\n",
      " 8   Elevator             42 non-null     object\n",
      " 9   Warehouse            42 non-null     object\n",
      " 10  Balcony              42 non-null     object\n",
      " 11  Add Parking          42 non-null     object\n",
      " 12  Joint Parking        42 non-null     object\n",
      " 13  Living Room Window   42 non-null     object\n",
      " 14  Renovation Status    42 non-null     object\n",
      " 15  Floor Covering       42 non-null     object\n",
      " 16  Cabinet              42 non-null     object\n",
      " 17  Bathroom             42 non-null     object\n",
      " 18  Heating              42 non-null     object\n",
      " 19  Cooling              42 non-null     object\n",
      " 20  Water Heating        42 non-null     object\n",
      " 21  Hood                 42 non-null     object\n",
      " 22  Desktop Stove        42 non-null     object\n",
      " 23  Closet               42 non-null     object\n",
      " 24  Building Direction   42 non-null     object\n",
      " 25  Apartment Direction  42 non-null     object\n",
      " 26  Joint Status         42 non-null     object\n",
      " 27  Address              42 non-null     object\n",
      " 28  Address in Farsi     41 non-null     object\n",
      "dtypes: object(29)\n",
      "memory usage: 9.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5d4712ab-7d00-4d9c-840a-cf5957612112",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PriceTotal</th>\n",
       "      <th>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Room No</th>\n",
       "      <th>Floor No</th>\n",
       "      <th>Building Floors</th>\n",
       "      <th>Units Per Floor</th>\n",
       "      <th>Parking Lot</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>...</th>\n",
       "      <th>Cooling</th>\n",
       "      <th>Water Heating</th>\n",
       "      <th>Hood</th>\n",
       "      <th>Desktop Stove</th>\n",
       "      <th>Closet</th>\n",
       "      <th>Building Direction</th>\n",
       "      <th>Apartment Direction</th>\n",
       "      <th>Joint Status</th>\n",
       "      <th>Address</th>\n",
       "      <th>Address in Farsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In Milliard Tooman</td>\n",
       "      <td>in mm</td>\n",
       "      <td>Construction Year</td>\n",
       "      <td>Just bedrooms are counted</td>\n",
       "      <td>Apartment floor</td>\n",
       "      <td>Number of floors in the building</td>\n",
       "      <td>Number of apartments in the floor of this apar...</td>\n",
       "      <td>Number of certified Parkings</td>\n",
       "      <td>0: Not Available, 1: Available</td>\n",
       "      <td>0: Not Available, 1: Available</td>\n",
       "      <td>...</td>\n",
       "      <td>0: Water cooler, 1: Split/Duct, 2: Others</td>\n",
       "      <td>0: Water Heater, 1: Radiator Package, 2: Central</td>\n",
       "      <td>0: Not Available, 1: Hidden , 2: Old 3:  Modern</td>\n",
       "      <td>0: Not Available, 1: Available</td>\n",
       "      <td>Number of closets in rooms</td>\n",
       "      <td>If yard is in the front and is visible at the ...</td>\n",
       "      <td>View if apartment</td>\n",
       "      <td>Joint Property Status according to be cleaned,...</td>\n",
       "      <td>Region</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.55</td>\n",
       "      <td>90</td>\n",
       "      <td>1397</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>2</td>\n",
       "      <td>South</td>\n",
       "      <td>South</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Behnood</td>\n",
       "      <td>بهنود.کوچه شیربانی</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.25</td>\n",
       "      <td>37</td>\n",
       "      <td>1383</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>NAN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "      <td>قصرالدشت بهنود</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.95</td>\n",
       "      <td>59</td>\n",
       "      <td>1388</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "      <td>قصرالدشت پایینتر از آذربایجان</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.35</td>\n",
       "      <td>63</td>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>North</td>\n",
       "      <td>NAN</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mashouf_Behnood</td>\n",
       "      <td>بالای بهنود</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 29 columns</p>\n",
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      ],
      "text/plain": [
       "           PriceTotal Meterage               Year                    Room No  \\\n",
       "0  In Milliard Tooman    in mm  Construction Year  Just bedrooms are counted   \n",
       "1                7.55       90               1397                          2   \n",
       "2                2.25       37               1383                          1   \n",
       "3                3.95       59               1388                          2   \n",
       "4                3.35       63               1382                          2   \n",
       "\n",
       "          Floor No                   Building Floors  \\\n",
       "0  Apartment floor  Number of floors in the building   \n",
       "1                2                                 5   \n",
       "2                1                                 4   \n",
       "3                1                                 4   \n",
       "4                2                                 4   \n",
       "\n",
       "                                     Units Per Floor  \\\n",
       "0  Number of apartments in the floor of this apar...   \n",
       "1                                                  2   \n",
       "2                                                  1   \n",
       "3                                                  1   \n",
       "4                                                  2   \n",
       "\n",
       "                    Parking Lot                        Elevator  \\\n",
       "0  Number of certified Parkings  0: Not Available, 1: Available   \n",
       "1                             1                               1   \n",
       "2                             0                               0   \n",
       "3                             0                               0   \n",
       "4                             0                               0   \n",
       "\n",
       "                        Warehouse  ...  \\\n",
       "0  0: Not Available, 1: Available  ...   \n",
       "1                               1  ...   \n",
       "2                               1  ...   \n",
       "3                               1  ...   \n",
       "4                               1  ...   \n",
       "\n",
       "                                     Cooling  \\\n",
       "0  0: Water cooler, 1: Split/Duct, 2: Others   \n",
       "1                                        NAN   \n",
       "2                                          1   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "                                      Water Heating  \\\n",
       "0  0: Water Heater, 1: Radiator Package, 2: Central   \n",
       "1                                               NAN   \n",
       "2                                               NAN   \n",
       "3                                                 1   \n",
       "4                                                 0   \n",
       "\n",
       "                                              Hood  \\\n",
       "0  0: Not Available, 1: Hidden , 2: Old 3:  Modern   \n",
       "1                                              NAN   \n",
       "2                                                3   \n",
       "3                                                3   \n",
       "4                                              NAN   \n",
       "\n",
       "                    Desktop Stove                      Closet  \\\n",
       "0  0: Not Available, 1: Available  Number of closets in rooms   \n",
       "1                             NAN                           2   \n",
       "2                               1                         NAN   \n",
       "3                               1                           1   \n",
       "4                             NAN                         NAN   \n",
       "\n",
       "                                  Building Direction Apartment Direction  \\\n",
       "0  If yard is in the front and is visible at the ...   View if apartment   \n",
       "1                                              South               South   \n",
       "2                                                NAN                 NAN   \n",
       "3                                                NAN                 NAN   \n",
       "4                                              North                 NAN   \n",
       "\n",
       "                                        Joint Status              Address  \\\n",
       "0  Joint Property Status according to be cleaned,...               Region   \n",
       "1                                                NAN              Behnood   \n",
       "2                                                NAN  Ghasredasht_Behnood   \n",
       "3                                                NAN  Ghasredasht_Behnood   \n",
       "4                                                NAN      Mashouf_Behnood   \n",
       "\n",
       "                Address in Farsi  \n",
       "0                            NaN  \n",
       "1             بهنود.کوچه شیربانی  \n",
       "2                 قصرالدشت بهنود  \n",
       "3  قصرالدشت پایینتر از آذربایجان  \n",
       "4                    بالای بهنود  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "226680f6-4aff-453a-92d2-7b076b346b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('NAN',np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5ef23209-4c58-4251-a9d0-3b948d6af2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42 entries, 0 to 41\n",
      "Data columns (total 29 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   PriceTotal           42 non-null     object\n",
      " 1   Meterage             42 non-null     object\n",
      " 2   Year                 42 non-null     object\n",
      " 3   Room No              42 non-null     object\n",
      " 4   Floor No             42 non-null     object\n",
      " 5   Building Floors      36 non-null     object\n",
      " 6   Units Per Floor      40 non-null     object\n",
      " 7   Parking Lot          42 non-null     object\n",
      " 8   Elevator             42 non-null     object\n",
      " 9   Warehouse            42 non-null     object\n",
      " 10  Balcony              35 non-null     object\n",
      " 11  Add Parking          42 non-null     object\n",
      " 12  Joint Parking        42 non-null     object\n",
      " 13  Living Room Window   27 non-null     object\n",
      " 14  Renovation Status    31 non-null     object\n",
      " 15  Floor Covering       35 non-null     object\n",
      " 16  Cabinet              26 non-null     object\n",
      " 17  Bathroom             32 non-null     object\n",
      " 18  Heating              33 non-null     object\n",
      " 19  Cooling              33 non-null     object\n",
      " 20  Water Heating        29 non-null     object\n",
      " 21  Hood                 22 non-null     object\n",
      " 22  Desktop Stove        21 non-null     object\n",
      " 23  Closet               18 non-null     object\n",
      " 24  Building Direction   20 non-null     object\n",
      " 25  Apartment Direction  2 non-null      object\n",
      " 26  Joint Status         11 non-null     object\n",
      " 27  Address              42 non-null     object\n",
      " 28  Address in Farsi     41 non-null     object\n",
      "dtypes: object(29)\n",
      "memory usage: 9.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "62819497-045c-4790-8de3-f78e9fc3e2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The first row gives some information about the contents of each column, so droped.\n",
    "# Also Address in Farsi in not needed, so dropeed.\n",
    "df.drop(0,axis=0,inplace=True)\n",
    "df.drop('Address in Farsi',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "bca6af7f-4ae2-4d71-afbf-66657d7781e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Meterage</th>\n",
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       "      <th>Room No</th>\n",
       "      <th>Floor No</th>\n",
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       "      <td>7.55</td>\n",
       "      <td>90</td>\n",
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       "      <td>Ghasredasht_Behnood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.35</td>\n",
       "      <td>63</td>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>Mashouf_Behnood</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.85</td>\n",
       "      <td>70</td>\n",
       "      <td>1375</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Karoon_Behnood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PriceTotal Meterage  Year Room No Floor No Building Floors Units Per Floor  \\\n",
       "1       7.55       90  1397       2        2               5               2   \n",
       "2       2.25       37  1383       1        1               4               1   \n",
       "3       3.95       59  1388       2        1               4               1   \n",
       "4       3.35       63  1382       2        2               4               2   \n",
       "5       2.85       70  1375       1        2               3               2   \n",
       "\n",
       "  Parking Lot Elevator Warehouse  ... Heating Cooling Water Heating Hood  \\\n",
       "1           1        1         1  ...     NaN     NaN           NaN  NaN   \n",
       "2           0        0         1  ...       1       1           NaN    3   \n",
       "3           0        0         1  ...       1       0             1    3   \n",
       "4           0        0         1  ...       0       0             0  NaN   \n",
       "5           0        0         1  ...       0       0             0  NaN   \n",
       "\n",
       "  Desktop Stove Closet Building Direction Apartment Direction Joint Status  \\\n",
       "1           NaN      2              South               South          NaN   \n",
       "2             1    NaN                NaN                 NaN          NaN   \n",
       "3             1      1                NaN                 NaN          NaN   \n",
       "4           NaN    NaN              North                 NaN          NaN   \n",
       "5           NaN    NaN              South                 NaN          NaN   \n",
       "\n",
       "               Address  \n",
       "1              Behnood  \n",
       "2  Ghasredasht_Behnood  \n",
       "3  Ghasredasht_Behnood  \n",
       "4      Mashouf_Behnood  \n",
       "5       Karoon_Behnood  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b2916bf2-6e0a-47f2-b48f-e6cfbf3ba7af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 1 to 41\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   PriceTotal           41 non-null     object\n",
      " 1   Meterage             41 non-null     object\n",
      " 2   Year                 41 non-null     object\n",
      " 3   Room No              41 non-null     object\n",
      " 4   Floor No             41 non-null     object\n",
      " 5   Building Floors      35 non-null     object\n",
      " 6   Units Per Floor      39 non-null     object\n",
      " 7   Parking Lot          41 non-null     object\n",
      " 8   Elevator             41 non-null     object\n",
      " 9   Warehouse            41 non-null     object\n",
      " 10  Balcony              34 non-null     object\n",
      " 11  Add Parking          41 non-null     object\n",
      " 12  Joint Parking        41 non-null     object\n",
      " 13  Living Room Window   26 non-null     object\n",
      " 14  Renovation Status    30 non-null     object\n",
      " 15  Floor Covering       34 non-null     object\n",
      " 16  Cabinet              25 non-null     object\n",
      " 17  Bathroom             31 non-null     object\n",
      " 18  Heating              32 non-null     object\n",
      " 19  Cooling              32 non-null     object\n",
      " 20  Water Heating        28 non-null     object\n",
      " 21  Hood                 21 non-null     object\n",
      " 22  Desktop Stove        20 non-null     object\n",
      " 23  Closet               17 non-null     object\n",
      " 24  Building Direction   19 non-null     object\n",
      " 25  Apartment Direction  1 non-null      object\n",
      " 26  Joint Status         10 non-null     object\n",
      " 27  Address              41 non-null     object\n",
      "dtypes: object(28)\n",
      "memory usage: 9.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bedc8f18-d4ae-48e8-b403-ecc1666444e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to correct the RangeIndex\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "79c4d7b1-e644-44b2-85e5-6046d0db5bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   PriceTotal           41 non-null     object\n",
      " 1   Meterage             41 non-null     object\n",
      " 2   Year                 41 non-null     object\n",
      " 3   Room No              41 non-null     object\n",
      " 4   Floor No             41 non-null     object\n",
      " 5   Building Floors      35 non-null     object\n",
      " 6   Units Per Floor      39 non-null     object\n",
      " 7   Parking Lot          41 non-null     object\n",
      " 8   Elevator             41 non-null     object\n",
      " 9   Warehouse            41 non-null     object\n",
      " 10  Balcony              34 non-null     object\n",
      " 11  Add Parking          41 non-null     object\n",
      " 12  Joint Parking        41 non-null     object\n",
      " 13  Living Room Window   26 non-null     object\n",
      " 14  Renovation Status    30 non-null     object\n",
      " 15  Floor Covering       34 non-null     object\n",
      " 16  Cabinet              25 non-null     object\n",
      " 17  Bathroom             31 non-null     object\n",
      " 18  Heating              32 non-null     object\n",
      " 19  Cooling              32 non-null     object\n",
      " 20  Water Heating        28 non-null     object\n",
      " 21  Hood                 21 non-null     object\n",
      " 22  Desktop Stove        20 non-null     object\n",
      " 23  Closet               17 non-null     object\n",
      " 24  Building Direction   19 non-null     object\n",
      " 25  Apartment Direction  1 non-null      object\n",
      " 26  Joint Status         10 non-null     object\n",
      " 27  Address              41 non-null     object\n",
      "dtypes: object(28)\n",
      "memory usage: 9.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ceddb360-06e3-46dd-a6ae-28a85d239ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PriceTotal              0\n",
       "Meterage                0\n",
       "Year                    0\n",
       "Room No                 0\n",
       "Floor No                0\n",
       "Building Floors         6\n",
       "Units Per Floor         2\n",
       "Parking Lot             0\n",
       "Elevator                0\n",
       "Warehouse               0\n",
       "Balcony                 7\n",
       "Add Parking             0\n",
       "Joint Parking           0\n",
       "Living Room Window     15\n",
       "Renovation Status      11\n",
       "Floor Covering          7\n",
       "Cabinet                16\n",
       "Bathroom               10\n",
       "Heating                 9\n",
       "Cooling                 9\n",
       "Water Heating          13\n",
       "Hood                   20\n",
       "Desktop Stove          21\n",
       "Closet                 24\n",
       "Building Direction     22\n",
       "Apartment Direction    40\n",
       "Joint Status           31\n",
       "Address                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4e8a70d-9eed-49ae-b904-2220b70d84c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The sample numbers are too low. So many of them should be droped in order to reach an accurate solution. I decided to drop all the columns with null values. \n",
    "for i in df.columns:\n",
    "    if df[i].isnull().sum()>0:\n",
    "        df.drop(i,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cb52b43d-b977-4588-8ec2-1fc2aaedac2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 11 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   PriceTotal     41 non-null     object\n",
      " 1   Meterage       41 non-null     object\n",
      " 2   Year           41 non-null     object\n",
      " 3   Room No        41 non-null     object\n",
      " 4   Floor No       41 non-null     object\n",
      " 5   Parking Lot    41 non-null     object\n",
      " 6   Elevator       41 non-null     object\n",
      " 7   Warehouse      41 non-null     object\n",
      " 8   Add Parking    41 non-null     object\n",
      " 9   Joint Parking  41 non-null     object\n",
      " 10  Address        41 non-null     object\n",
      "dtypes: object(11)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f3d6ba05-5ffe-42b3-8db3-047c218a6857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Add Parking\n",
       "0    37\n",
       "1     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Add Parking'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a9b080dd-9b0b-4b58-a630-c15a1cb882e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Add Parking', ylabel='PriceTotal'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='PriceTotal',x='Add Parking',data=df,width=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b1e37ca9-7f93-41fc-8834-3d70be8b1297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Joint Parking\n",
       "0    37\n",
       "1     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Joint Parking'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8fc2bd9b-2370-42f6-8fd6-4b7ee3da7cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Joint Parking', ylabel='PriceTotal'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='PriceTotal',x='Joint Parking',data=df,width=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3c0bfbfe-73ce-4f45-bdcf-558284b2681c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Add Parking' doesn't seem to have a considerable effect on total price, so droped.\n",
    "df.drop('Add Parking',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a014e540-f223-4bca-85e6-7202eccd35eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   PriceTotal     41 non-null     object\n",
      " 1   Meterage       41 non-null     object\n",
      " 2   Year           41 non-null     object\n",
      " 3   Room No        41 non-null     object\n",
      " 4   Floor No       41 non-null     object\n",
      " 5   Parking Lot    41 non-null     object\n",
      " 6   Elevator       41 non-null     object\n",
      " 7   Warehouse      41 non-null     object\n",
      " 8   Joint Parking  41 non-null     object\n",
      " 9   Address        41 non-null     object\n",
      "dtypes: object(10)\n",
      "memory usage: 3.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ed52e3cd-ef80-469a-af9a-cf8891f02750",
   "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>PriceTotal</th>\n",
       "      <th>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Room No</th>\n",
       "      <th>Floor No</th>\n",
       "      <th>Parking Lot</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Joint Parking</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.55</td>\n",
       "      <td>90</td>\n",
       "      <td>1397</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Behnood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.25</td>\n",
       "      <td>37</td>\n",
       "      <td>1383</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.95</td>\n",
       "      <td>59</td>\n",
       "      <td>1388</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.35</td>\n",
       "      <td>63</td>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mashouf_Behnood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.85</td>\n",
       "      <td>70</td>\n",
       "      <td>1375</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Karoon_Behnood</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PriceTotal Meterage  Year Room No Floor No Parking Lot Elevator Warehouse  \\\n",
       "0       7.55       90  1397       2        2           1        1         1   \n",
       "1       2.25       37  1383       1        1           0        0         1   \n",
       "2       3.95       59  1388       2        1           0        0         1   \n",
       "3       3.35       63  1382       2        2           0        0         1   \n",
       "4       2.85       70  1375       1        2           0        0         1   \n",
       "\n",
       "  Joint Parking              Address  \n",
       "0             0              Behnood  \n",
       "1             0  Ghasredasht_Behnood  \n",
       "2             0  Ghasredasht_Behnood  \n",
       "3             1      Mashouf_Behnood  \n",
       "4             0       Karoon_Behnood  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2669642b-32c4-476b-a309-a3e6355732a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PriceTotal', ylabel='Address'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HjRpFu3bt2LdvH82bNycmJoZz584BYDabqVatGp988gkHDx5k+PDhvPbaayxbtgyAK1eu0KZNG5o0acK+ffvYunUr3bp1s2r/yJEjLF++nM8++8wybeCZZ57hzJkzfP311+zcuZP69evz6KOPWs67bNkyRo4cybhx4/jhhx/w8/Nj1qxZVtdw4cIFOnXqxJYtW/j+++8JDg6mefPmlnC4fPly3nrrLd59910OHz7MihUruPPOO63amDJlCg0aNGD37t3ExcXRo0cPkpOTAdi+fTsA69ev5+TJk3z22WeF/lpkZ2czf/58PD09CQ0NtTx/s+sHOHr0KCtWrGDVqlWsWrWKTZs28cYbb1i1/8EHH+Dq6sq2bduYOHEio0ePtgRTs9lM69atOXfuHJs2bWLdunX8/PPPtG/f3nL8559/Tp8+fejfvz8//vgjL730Ei+88AIbNmywtPHUU09Rrlw5tm3bxjvvvMOgQYMKdN3p6elWH8Xp2LFjHDp06KYf+b+QyfX7TP0jIiK3ymZTHY4cOUJeXp5V0AKoVKmSZdS0Z8+eTJgwAYDY2Fg6dOgAwLhx45g+fTrbt28nOjoaR0dHRo0aZWmjRo0abN26lWXLltGuXTvS09NJS0ujZcuW3HHHHQDUqVPH6ryXL19m4cKFVK5cGYAtW7awfft2zpw5g5OTEwCTJ09mxYoVfPrpp3Tr1o2pU6fSpUsXunTpAkB8fDzr16+3GvV95JFHrM4zZ84cPD092bRpEy1btiQ1NRVfX1+aNm2Ko6MjgYGBNGrUyOqY5s2bExcXB8CgQYN466232LBhA6GhoZZ6vb298fX1LXD/79+/nwoVKgBXR5Td3Nz4+OOPcXd3L/D1w9XQuWDBAtzc3ADo2LEjCQkJjB071nKu8PBwRowYAUBwcDAzZswgISGBxx57jISEBPbv309KSgoBAQEALFy4kLCwMHbs2EHDhg2ZPHkysbGxlj7o168f33//PZMnT+bhhx9m/fr1/PTTT6xduxZ/f3/g6vfIE088ccM+GD9+vNX3TXH7a59IwajPRESkKNl0ju/1bN++HbPZTExMDNnZ2Zbnw8PDLZ+7urri7u7OmTNnLM/NnDmT999/n9TUVDIzM7l8+bJlxQEvLy9iY2Np1qwZjz32GE2bNqVdu3b4+flZjg8KCrKESIC9e/eSkZGBt7e3VX2ZmZkcPXoUgKSkJLp37261PTIy0jISCXD69GmGDRvGxo0bOXPmDLm5uVy6dMmy+P4zzzzD1KlTqVmzJtHR0TRv3pxWrVrh4PD/X5q/XrvJZMLX19fq2m9FaGioZbrIhQsX+Pjjj3nmmWfYsGEDDRo0KND1w9VVJ/JDL4Cfn981tf21/v/dJykpiYCAAEvoBahbty6enp4kJSXRsGFDkpKSLEE7X1RUlGU6S34b+aEXrn4dbmbIkCH069fP8jg9Pd2qjqI2dOhQgoKCbrrfsWPHFPj+63p9pv4REZFbZbPgW6tWLUwmk+VP9vlq1qwJgIuLi9Xzjo6OVo9NJhNmsxmApUuX8uqrrzJlyhQiIyNxc3Nj0qRJbNu2zbL//Pnz6d27N2vWrOHjjz9m2LBhrFu3jvvuuw/gmukWGRkZ+Pn5sXHjxmtq9/T0LPB1durUiT/++INp06YRFBSEk5MTkZGRXL58GYCAgACSk5NZv34969atIy4ujkmTJrFp0ybLNd/o2m9VuXLlqFWrluVxREQEK1asYOrUqSxevLjA11+Q2oqj/qLg5ORkGc0uCUFBQYSEhJTY+coC9ZmIiBQlm83x9fb25rHHHmPGjBlcvHjxH7WVmJjI/fffT1xcHBEREdSqVctqVDJfREQEQ4YM4bvvvqNevXp8+OGHf9tm/fr1OXXqFA4ODtSqVcvqo1KlSsDV6RJ/DdcA33///TW19e7dm+bNmxMWFoaTkxNnz5612sfFxYVWrVoxffp0Nm7cyNatW9m/f3+Brj1/9Ync3NwC7X8j9vb2ZGZmAgW7/qJQp04djh8/zvHjxy3PHTx4kPPnz1O3bl3LPomJiVbHJSYmWm0/fvw4J0+etGz/36+DiIiIiE2nOsyaNYuoqCgaNGjAyJEjCQ8Px87Ojh07dvDTTz9xzz33FKid4OBgFi5cyNq1a6lRowaLFi1ix44dlhUPUlJSmDNnDk8++ST+/v4kJydz+PBhnn/++b9ts2nTpkRGRtKmTRsmTpxISEgIJ06c4KuvvqJt27Y0aNCAPn36EBsbS4MGDYiKimLJkiUcOHDAMmqdX9uiRYto0KAB6enpDBgwwGo0e8GCBeTm5nLvvfdSvnx5Fi9ejIuLS4H+JA5QpUoVXFxcWLNmDdWqVcPZ2blAy5FduXKFU6dOAf8/1eHgwYOWm8IKcv1FoWnTptx5553ExMQwdepUrly5QlxcHE2aNLGcY8CAAbRr146IiAiaNm3Kl19+yWeffWZZjq1p06aEhITQqVMnJk2aRHp6OkOHDi2S+kRERKTssOlyZnfccQe7d++madOmDBkyhLvuuosGDRrw9ttv8+qrrzJmzJgCtfPSSy/x1FNP0b59e+69917++OMPy41QAOXLl+enn37i6aefJiQkhG7dutGzZ09eeumlv23TZDKxevVqGjduzAsvvEBISAjPPvssx44dw8fHB4D27dvz+uuvM3DgQO655x6OHTtGjx49rNqZN28ef/75J/Xr16djx4707t2bKlWqWLZ7enoyd+5coqKiCA8PZ/369Xz55ZfXzK39Ow4ODkyfPp13330Xf39/WrduXaDjDhw4gJ+fH35+ftx9990sW7aM2bNnW34ZKMj1FwWTycQXX3xBxYoVady4MU2bNqVmzZp8/PHHln3atGnDtGnTmDx5MmFhYbz77rvMnz/fsnSbnZ0dn3/+OZmZmTRq1IiuXbtqDqiIiIhcw5SXl5dn6yJESoP09HQ8PDxIS0uzrG5RFA4dOkS3bt2YM2dOgear5u9/se6TmF2LblrJ9dhdPIvrwZUlcq7CyK/ren1W2P4UEZGyrTCv3zZ/y2IRERERkZKg4FsGVahQ4W8/vv32W1uXZzj577YXGBho61LKBPWniIjcqlK3jq/8c/nvPHc9VatWLblCBABnZ2f9Sb4IqT9FRORWKfiWQX9dn1dERERErtJUBxERERExBAVfERERETEEBV8RERERMQTN8RUppeyy0or/HJnnrf4tLUri2kVExHgUfEVKGQ8PDxzLOcHPm0rsnC4pm0vsXAXlWM6pQG+/LSIiUlAKviKljI+PD4sXLSQtzdijnh4eHkX69tgiIiIKviKlkI+Pj0KfiIhIEdPNbSIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggOti5ARKS0On36NGlpabYuQ6RM8vDwwMfHx9ZliMEo+IqIXMfp06d5ruPz5FzOtnUpImWSYzknFi9aqPArJUrBV0TkOtLS0si5nE1mzSaYnT1sXY4h2WWexyVlM5k1GmN28bR1OVKE7LLS4OdNpKWlKfhKiVLwFRG5AbOzB2bXSrYuw9DMLp76GohIkdDNbSIiIiJiCAq+IiIiImIICr4iIiIiYggKviJSaFlZWRw6dIisrCxblyIiIreB0vK6oeArIoWWmppKt27dSE1NtXUpIiJyGygtrxsKviIiIiJiCAq+IiIiImIICr4iIiIiYggKvsXIZDKxYsWKYj/PqVOneOyxx3B1dcXT07PYz5evevXqTJ06tcTOVxgLFiwo0b4QERGR0s9wwTc2NhaTyUT37t2v2dazZ09MJhOxsbElX9g/8NZbb3Hy5En27NnDoUOHbrp/9erVMZlMmEwm7O3t8ff3p0uXLvz5558lUK2IiIiIbRgu+AIEBASwdOlSMjMzLc9lZWXx4YcfEhgYaMPKbs3Ro0e55557CA4OpkqVKgU6ZvTo0Zw8eZLU1FSWLFnC5s2b6d27dzFXKiIiImI7DrYuwBbq16/P0aNH+eyzz4iJiQHgs88+IzAwkBo1alj2W7NmDfHx8fz444/Y29sTGRnJtGnTuOOOOwC4fPky/fr1Y/ny5fz555/4+PjQvXt3hgwZYmnj7NmztG3blrVr11K1alWmTJnCk08+adm+adMmBgwYwN69e/Hy8qJTp07Ex8fj4HD1S1O9enX69u1L3759LcfcfffdtGnThpEjR1K9enWOHTsGwMKFC+nUqRMLFiy4aR+4ubnh6+sLQNWqVenUqRMfffSR1T5btmxhyJAh/PDDD1SqVIm2bdsyfvx4XF1dLftcunSJzp0788knn1CxYkWGDRtGt27dAPjll1+oUaMGy5cv5+2332bbtm0EBwfzzjvvEBkZaWlj+fLlDB8+nCNHjuDn58fLL79M//79Ldv//PNP+vTpw5dffkl2djZNmjRh+vTpBAcHW/ZZsGABw4cP5+zZszRr1owHHnjgpn2QnZ1Ndna25XF6evpNjxFr+d97ZVFZvjaR0kL/z4yjtHytDRl8ATp37sz8+fMtwff999/nhRdeYOPGjZZ9Ll68SL9+/QgPDycjI4Phw4fTtm1b9uzZg52dHdOnT2flypUsW7aMwMBAjh8/zvHjx63OM2rUKCZOnMikSZN4++23iYmJ4dixY3h5efHbb7/RvHlzYmNjWbhwIT/99BMvvvgizs7OjBw5skDXsWPHDp5//nnc3d2ZNm0aLi4uhe6L3377jS+//JJ7773X8tzRo0eJjo4mPj6e999/n99//51evXrRq1cv5s+fb9lvypQpjBkzhtdee41PP/2UHj160KRJE0JDQy37DB06lMmTJxMcHMzQoUPp0KEDR44cwcHBgZ07d9KuXTtGjhxJ+/bt+e6774iLi8Pb29sy5SQ2NpbDhw+zcuVK3N3dGTRoEM2bN+fgwYM4Ojqybds2unTpwvjx42nTpg1r1qxhxIgRN73u8ePHM2rUqEL3l/y/sWPH2roEEbmN6WeIlDRTXl5enq2LKEmxsbGcP3+euXPnEhAQQHJyMgC1a9fm+PHjdO3aFU9Pz+uOmp49e5bKlSuzf/9+6tWrR+/evTlw4ADr16/HZDJds7/JZGLYsGGMGTMGuBqkK1SowNdff010dDRDhw5l+fLlJCUlWY6fNWsWgwYNIi0tDTs7u5uO+AK0adPmb2u+nurVq3Py5EkcHR3Jzc0lKyuLe++9lzVr1lhuCOvatSv29va8++67luO2bNlCkyZNuHjxIs7OzlSvXp0HH3yQRYsWAZCXl4evry+jRo2ie/fulhHf9957jy5dugBw8OBBwsLCSEpKonbt2sTExPD777/zzTffWM4zcOBAvvrqKw4cOMDhw4cJCQkhMTGR+++/H4A//viDgIAAPvjgA5555hn+/e9/k5aWxldffWVp49lnn2XNmjWcP3/+b/vheiO+AQEBpKWl4e7uXqC+NKpDhw7RrVs3hg4dSlBQkK3LKRbHjh1j7NixXKz7JGbXSrYux5DsLp7F9eBKfQ3KoPyvbVn+GSLW8n+mzpkzh5CQkCJtOz09HQ8PjwK9fht2xLdy5cq0aNGCBQsWkJeXR4sWLahUyfoH6+HDhxk+fDjbtm3j7NmzmM1m4Oq7j9SrV4/Y2Fgee+wxQkNDiY6OpmXLljz++ONWbYSHh1s+d3V1xd3dnTNnzgCQlJREZGSkVWiOiooiIyODX3/9tVjnGw8YMIDY2Fjy8vI4fvw4r732Gi1atGDz5s3Y29uzd+9e9u3bx5IlSyzH5OXlYTabSUlJoU6dOtdcn8lkwtfX13J9+f66j5+fHwBnzpyhdu3aJCUl0bp1a6v9o6KimDp1Krm5uSQlJeHg4GA1Gu3t7U1oaChJSUnA1X5s27atVRuRkZGsWbPmhn3g5OSEk5PTTftK/l5QUFCR/wATEePQzxApaYYNvnB1ukOvXr0AmDlz5jXbW7VqRVBQEHPnzsXf3x+z2Uy9evW4fPkycHWucEpKCl9//TXr16+nXbt2NG3alE8//dTShqOjo1WbJpPJEqALws7Ojv8dlM/JySnw8X+nUqVK1KpVC4Dg4GCmTp1KZGQkGzZsoGnTpmRkZPDSSy9d94a3vwbyglzfX/fJD/mF6QMRERGRomDo4BsdHc3ly5cxmUw0a9bMatsff/xBcnIyc+fO5cEHHwSu/qn/f7m7u9O+fXvat2/Pv/71L6Kjozl37hxeXl43PX+dOnVYvnw5eXl5lkCYmJiIm5sb1apVA66OTJ88edJyTHp6OikpKbd8zX/H3t4ewLLSRf369Tl48KAlHBeXOnXqkJiYaPVcYmIiISEh2NvbU6dOHa5cucK2bduspjokJydTt25dSxvbtm2zauP7778v1rpFRETk9mPo4Gtvb2/5c3l+8MtXsWJFvL29mTNnDn5+fqSmpjJ48GCrfd588038/PyIiIjAzs6OTz75BF9f3wK/cUJcXBxTp07l5ZdfplevXiQnJzNixAj69euHnd3VleYeeeQRFixYQKtWrfD09GT48OHX1HorLly4wKlTpyxTHQYOHEjlypUt4XLQoEHcd9999OrVi65du+Lq6srBgwdZt24dM2bM+Mfnz9e/f38aNmzImDFjaN++PVu3bmXGjBnMmjULuDoa3bp1a1588UXeffdd3NzcGDx4MFWrVrVMkejduzdRUVFMnjyZ1q1bs3bt2ptOcxARERHjKfQ6vsePH+fXX3+1PN6+fTt9+/Zlzpw5RVpYSXF3d7/uRGg7OzuWLl3Kzp07qVevHq+88gqTJk2y2sfNzY2JEyfSoEEDGjZsyC+//MLq1astofVmqlatyurVq9m+fTt33XUX3bt3p0uXLgwbNsyyz5AhQ2jSpAktW7akRYsWtGnTxrKc2j8xfPhw/Pz88Pf3p2XLlri6uvLNN9/g7e0NXJ2Xu2nTJg4dOsSDDz5IREQEw4cPx9/f/x+f+6/q16/PsmXLWLp0KfXq1WP48OGMHj3a6k1E5s+fzz333EPLli2JjIwkLy+P1atXW6ZQ3HfffcydO5dp06Zx11138c0331j1oYiIiAjcwqoODz74IN26daNjx46cOnWK0NBQwsLCOHz4MC+//DLDhw8vrlpFilVh7go1uvxVHYrj7tzSIv8ataKA7WhVh7Ir/2tbln+GiLXifN0ozOt3oUd8f/zxRxo1agTAsmXLqFevHt999x1Lliwp8HJaIiIiIiIlrdDBNycnx7IE1Pr16y3vQla7dm2rm7DENpYsWUKFChWu+xEWFmbr8qSMCAwMZM6cObflW3yLiEjJKy2vG4W+uS0sLIx33nmHFi1asG7dOsubM5w4ccIyP1Rs58knn7Ra8/av/nfpMZFb5ezsrD9PiohIgZWW141CB98JEybQtm1bJk2aRKdOnbjrrrsAWLlypWUKhNiOm5sbbm5uti5DREREpNQpdPB96KGHOHv2LOnp6VSsWNHyfLdu3ShfvnyRFiciIiIiUlQKPcc3MzOT7OxsS+g9duwYU6dOJTk5mSpVqhR5gSIiIiIiRaHQwbd169YsXLgQgPPnz3PvvfcyZcoU2rRpw+zZs4u8QBERERGRolDoqQ67du3irbfeAuDTTz/Fx8eH3bt3s3z5coYPH06PHj2KvEgREVuxy0qzdQmGZZd53upfKTv0/0pspdDB99KlS5abp7755hueeuop7OzsuO+++zh27FiRFygiYgseHh44lnOCnzfZuhTDc0nZbOsSpBg4lnPCw8PD1mWIwRQ6+NaqVYsVK1bQtm1b1q5dyyuvvALAmTNn9G5XIlJm+Pj4sHjRQtLSNDIlUhw8PDzw8fGxdRliMIUOvsOHD+ff//43r7zyCo888giRkZHA1dHfiIiIIi9QRMRWfHx89MIsIlKGmPLy8vIKe9CpU6c4efIkd911F3Z2V++P2759O+7u7tSuXbvIixQpCYV5r28REREpHQrz+l3oVR0AfH19cXNzY926dWRmZgLQsGFDhV4RERERKbUKHXz/+OMPHn30UUJCQmjevDknT54EoEuXLvTv37/ICxQRERERKQqFDr6vvPIKjo6OpKamWr1TW/v27VmzZk2RFiciIiIiUlQKfXPbN998w9q1a6lWrZrV88HBwVrOTERERERKrUKP+F68eNFqpDffuXPncHJyKpKiRERERESKWqGD74MPPmh5y2IAk8mE2Wxm4sSJPPzww0VanIiIiIhIUSn0VIeJEyfy6KOP8sMPP3D58mUGDhzIgQMHOHfuHImJicVRo4iIiIjIP1boEd969epx6NAhHnjgAVq3bs3Fixd56qmn2L17N3fccUdx1CgiIiIi8o8VasQ3JyeH6Oho3nnnHYYOHVpcNYmIiIiIFLlCjfg6Ojqyb9++4qpFRERERKTYFHqqw3PPPce8efOKoxYRERERkWJT6Jvbrly5wvvvv8/69eu55557cHV1tdr+5ptvFllxIiIiIiJFpdDB98cff6R+/foAHDp0yGqbyWQqmqpERERERIpYoYPvhg0biqMOEREREZFiVeg5viIiIiIit6MCjfg+9dRTBW7ws88+u+ViRERERESKS4FGfD08PCwf7u7uJCQk8MMPP1i279y5k4SEBDw8PIqtUBERERGRf6JAI77z58+3fD5o0CDatWvHO++8g729PQC5ubnExcXh7u5ePFWKiIiIiPxDpry8vLzCHFC5cmW2bNlCaGio1fPJycncf//9/PHHH0VaoEhJSU9Px8PDg7S0NP0SJyIicpsozOt3oW9uu3LlCj/99NM1z//000+YzebCNiciIiIiUiIKvZzZCy+8QJcuXTh69CiNGjUCYNu2bbzxxhu88MILRV6giIiIiEhRKHTwnTx5Mr6+vkyZMoWTJ08C4Ofnx4ABA+jfv3+RFygiIiIiUhQKPcf3r9LT0wE0H1LKBM3xFRERuf0U5vW70CO+f6VwICIiIiK3iwIF34iICEwmU4Ea3LVr1z8qSERERESkOBQo+LZp08byeVZWFrNmzaJu3bpERkYC8P3333PgwAHi4uKKpUgRKT1Onz5NWlqarcsocR4eHvj4+Ni6DBER+QcKFHxHjBhh+bxr16707t2bMWPGXLPP8ePHi7Y6ESlVTp8+zXMdnyfncratSylxjuWcWLxoocKviMhtrNBzfD/55BOrtyvO99xzz9GgQQPef//9IilMREqftLQ0ci5nk1mzCWZnD+wyz+OSspnMGo0xu3jaurxiY5eVBj9vIi0tTcFXROQ2Vujg6+LiQmJiIsHBwVbPJyYm4uzsXGSFiUjpZXb2wOxa6f8fu3haPRYRESmNCh18+/btS48ePdi1a5fVG1jMmzeP4cOHF3mBIiIiIiJFodDBd/DgwdSsWZNp06axePFiAOrWrcsHH3xAnTp1irxAEREREZGicEvr+LZr14527doBVxcN/uijj5g0aRI7d+4kNze3SAsUERERESkKdrd64ObNm+nUqRP+/v5MmTKFRx55hO+//74oaxMRERERKTKFGvE9deoUCxYsYN68eaSnp9OuXTuys7NZsWIFdevWLa4aRW57WVlZpKamEhgYqJtAb0fmKwBkZxtvGTcRkbKkwCO+rVq1IjQ0lH379jF16lROnDjB22+/XZy1iZQZqampdOvWjdTUVFuXIrfALjsDuPrLv4iI3L4KPOL79ddf07t3b3r06HHNUmYiIiIiIqVdgUd8t2zZwoULF7jnnnu49957mTFjBmfPni3O2kREREREikyBg+99993H3LlzOXnyJC+99BJLly7F398fs9nMunXruHDhQnHWKaWQyWRixYoVti7jukaOHMndd99t6zJERMqM3Nxcdu/eTUJCArt379YqTiVM/V80Cr2cmaurK507d6Zz584kJyczb9483njjDQYPHsxjjz3GypUri6POEhcbG8v58+etgt2nn37Kc889x9ixY+nfv7/tivuHTCaT5XN7e3v8/f3517/+xfjx43FycrJhZSIiUhpt3ryZWbNmWc1z9/X1JS4ujsaNG9uwMmNQ/xedW17ODCA0NJSJEyfy66+/8tFHHxVVTaXSe++9R0xMDLNnz76l0Jubm4vZbC6Gym7N/PnzOXnyJCkpKcyaNYtFixYRHx9v67JERKSU2bx5MyNGjKBmzZrMnDmT1atXM3PmTGrWrMmIESPYvHmzrUss09T/ResfBd989vb2tGnTpsyM9v6viRMn8vLLL7N06VJeeOEFAN58803uvPNOXF1dCQgIIC4ujoyMDMsxCxYswNPTk5UrV1K3bl2cnJxITU3lzz//5Pnnn6dixYqUL1+eJ554gsOHD1udb/ny5YSFheHk5ET16tWZMmWK1fbq1aszbtw4OnfujJubG4GBgcyZM6dQ1+Tp6Ymvry8BAQG0bNmS1q1bs2vXLqt9vvjiC+rXr4+zszM1a9Zk1KhRXLlyxWqfs2fP0rZtW8qXL09wcLDV98DGjRsxmUwkJCTQoEEDypcvz/33309ycrJVG7Nnz+aOO+6gXLlyhIaGsmjRIqvtqamptG7dmgoVKuDu7k67du04ffq01T5vvPEGPj4+uLm50aVLF7KysgrVHyIicq3c3FxmzZpFZGQk8fHxhIWFUb58ecLCwoiPjycyMpLZs2frz+7FRP1f9G7pnduMZNCgQcyaNYtVq1bx6KOPWp63s7Nj+vTp1KhRg59//pm4uDgGDhzIrFmzLPtcunSJCRMm8N577+Ht7U2VKlXo0KEDhw8fZuXKlbi7uzNo0CCaN2/OwYMHcXR0ZOfOnbRr146RI0fSvn17vvvuO+Li4vD29iY2NtbS9pQpUxgzZgyvvfYan376KT169KBJkyaEhoYW+hoPHTrEf/7zH6v2v/32W55//nmmT5/Ogw8+yNGjR+nWrRsAI0aMsOw3atQoJk6cyKRJk3j77beJiYnh2LFjeHl5WfYZOnQoU6ZMoXLlynTv3p3OnTuTmJgIwOeff06fPn2YOnUqTZs2ZdWqVbzwwgtUq1aNhx9+GLPZbAm9mzZt4sqVK/Ts2ZP27duzceNGAJYtW8bIkSOZOXMmDzzwAIsWLWL69OnUrFnzhtednZ1ttS5renp6ofuusI4dO1bs5yhOt3v9IlI4+/bt49SpU7z++uvY2VmPldnZ2RETE0PPnj3Zt28fERERNqqy7FL/Fz0F3xv4+uuv+eKLL0hISOCRRx6x2ta3b1/L59WrVyc+Pp7u3btbBd+cnBxmzZrFXXfdBWAJvImJidx///0ALFmyhICAAFasWMEzzzzDm2++yaOPPsrrr78OQEhICAcPHmTSpElWwbR58+bExcUBV8P5W2+9xYYNGwocfDt06IC9vT1XrlwhOzubli1bMmTIEMv2UaNGMXjwYDp16gRAzZo1GTNmDAMHDrQKvrGxsXTo0AGAcePGMX36dLZv3050dLRln7Fjx9KkSRMABg8eTIsWLcjKysLZ2ZnJkycTGxtruZZ+/frx/fffM3nyZB5++GESEhLYv38/KSkpBAQEALBw4ULCwsLYsWMHDRs2ZOrUqXTp0oUuXboAEB8fz/r162866jt+/HhGjRpVoP4qKmPHji3R84mI/BPnzp0DoEaNGtfdnv98/n5StNT/RU/B9wbCw8M5e/YsI0aMoFGjRlSoUMGybf369YwfP56ffvqJ9PR0rly5QlZWFpcuXaJ8+fIAlCtXjvDwcMsxSUlJODg4cO+991qe8/b2JjQ0lKSkJMs+rVu3tqojKiqKqVOnkpubi729vaW2fCaTCV9fX86cOVPga3vrrbdo2rQpubm5HDlyhH79+tGxY0eWLl0KwN69e0lMTLQKarm5uddc41/rcHV1xd3d/Zo6/rqPn58fAGfOnCEwMJCkpCTLSPJfr3fatGmW/ggICLCEXoC6devi6elJUlISDRs2JCkpie7du1u1ERkZyYYNG27YB0OGDKFfv36Wx+np6VbnKQ5Dhw4lKCioWM9RnI4dO6bwLmIg+X+9S0lJISws7JrtKSkpVvtJ0VL/Fz0F3xuoWrUqn376KQ8//DDR0dF8/fXXuLm58csvv9CyZUt69OjB2LFj8fLyYsuWLXTp0oXLly9bQqGLi4vVCgpFydHR0eqxyWQq1M1zvr6+1KpVC7h6k+KFCxfo0KED8fHx1KpVi4yMDEaNGsVTTz11zbF/fcvdgtTx133y+6M03Ojn5ORU4qtYBAUFERISUqLnFBG5VeHh4fj6+rJkyRLi4+Ot/txuNptZsmQJfn5+VgMcUnTU/0WvSG5uK8uCgoLYtGkTp06dIjo6mgsXLrBz507MZjNTpkzhvvvuIyQkhBMnTty0rTp16nDlyhW2bdtmee6PP/4gOTmZunXrWvbJn/+aLzExkZCQEMtob3HIbzszMxOA+vXrk5ycTK1ata75+N95Rv/E313vX/vj+PHjHD9+3LL94MGDnD9/3mqfv/YpwPfff19kNYqIGJW9vT1xcXFs3bqVYcOGceDAAS5dusSBAwcYNmwYW7dupUePHsX6+mRk6v+ipxHfAggICGDjxo08/PDDNGvWjNmzZ5OTk8Pbb79Nq1atSExM5J133rlpO8HBwbRu3ZoXX3yRd999Fzc3NwYPHkzVqlUt0xv69+9Pw4YNGTNmDO3bt2fr1q3MmDHDau5wUTh//jynTp3CbDZz+PBhRo8eTUhICHXq1AFg+PDhtGzZksDAQP71r39hZ2fH3r17+fHHH4t02bMBAwbQrl07IiIiaNq0KV9++SWfffYZ69evB6Bp06bceeedxMTEMHXqVK5cuUJcXBxNmjShQYMGAPTp04fY2FgaNGhAVFQUS5Ys4cCBAze9uU1ERG6ucePGjBo1ilmzZtGzZ0/L835+fowaNUrryBYz9X/RUvAtoGrVqlnCb/fu3Rk5ciQTJkxgyJAhNG7cmPHjx/P888/ftJ358+fTp08fWrZsyeXLl2ncuDGrV6+2TAeoX78+y5YtY/jw4YwZMwY/Pz9Gjx5tdWNbUchfli1/fnDjxo0ZN24cDg5XvyWaNWvGqlWrGD16NBMmTMDR0ZHatWvTtWvXIq2jTZs2TJs2jcmTJ9OnTx9q1KjB/Pnzeeihhyz1ffHFF7z88ss0btwYOzs7oqOjefvtty1ttG/fnqNHjzJw4ECysrJ4+umn6dGjB2vXri3SWkVEjKpx48ZERUWxb98+zp07h5eXF+Hh4RppLCHq/6JjysvLy7N1ESKlQXp6Oh4eHqSlpeHu7l6kbR86dIhu3boxZ86c23qOb/51XKz7JGbXSthdPIvrwZWWx2WVw9kjuKRsZujQoTz22GO2LkdERP6iMK/fmuMrIiIiIoag4FvGjBs3jgoVKlz344knnrB1eYaV/+56gYGBti5FboHZ6epShr6+vjauRERE/gnN8S1junfvTrt27a67zcXFpYSrkXzOzs639RQHw7O7+qOypJe/ExGRoqXgW8Z4eXlpIWsRERGR69BUBxERERExBAVfERERETEEBV8RERERMQTN8RWRQrPLSrv6b+Z5q3/LqvzrFRGR25uCr4gUmIeHB47lnODnTVbPu6RstlFFJcexnBMeHh62LkNERP4BBV8RKTAfHx8WL1pIWprxRkA9PDzw8fGxdRkiIvIPKPiKSKH4+PgoAIqIyG1JN7eJiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghONi6ABEpWqdPnyYtLc3WZUgp5+HhgY+Pj63LEBEpUQq+ImXI6dOnea7j8+RczrZ1KVLKOZZzYvGihQq/ImIoCr4iZUhaWho5l7PJrNkEs7NHkbdvl3kel5TNZNZojNnFs8jbl5Jhl5UGP28iLS1NwVdEDEXBV6QMMjt7YHatVHztu3gWa/siIiLFQTe3iYiIiIghKPiKiIiIiCEo+IqIiIiIISj4ioiIiIghKPiKlLCsrCwOHTpEVlaWrUsRKTP0/0pECkLBV6SEpaam0q1bN1JTU21dikiZof9XIlIQCr4iIiIiYggKviIiIiJiCAq+RcxkMrFixQpbl1EiFixYgKenp63L+FvVq1dn6tSpti5DRERESgnDB9/Y2FjatGlj6zJK1IIFCzCZTJaPChUqcM899/DZZ5/ZujQRERGRYmP44GtU7u7unDx5kpMnT7J7926aNWtGu3btSE5OtnVpIiIiIsXCwdYFlCZms5kJEyYwZ84cTp06RUhICK+//jr/+te/yMvLIzg4mO7du/Pqq69ajtmzZw8REREcPnyYWrVqAXD27Fnatm3L2rVrqVq1KlOmTOHJJ5+0HLNp0yYGDBjA3r178fLyolOnTsTHx+PgcPXLkZ2dzYABA1i6dCnp6ek0aNCAt956i4YNGwKwceNGHn74YdavX8+gQYM4ePAgd999N/Pnzyc0NLRA12oymfD19QXA19eX+Ph4Jk+ezL59+yxtZGdnM3ToUD766CPOnz9PvXr1mDBhAg899JBVW2vXrqVv374cP36cBx54gPnz5+Pn5wdcHVE/f/48DzzwAFOmTOHy5cs8++yzTJ06FUdHRwD+/PNP+vTpw5dffkl2djZNmjRh+vTpBAcHW86xfPlyhg8fzpEjR/Dz8+Pll1+mf//+lu1nzpyhS5curF+/3nI9N5OdnU12drblcXp6eoH6rqgcO3bstmhTyq6y9P1Slq5FRIqPgu9fjB8/nsWLF/POO+8QHBzM5s2bee6556hcuTJNmjShc+fOzJ8/3yr4zp8/n8aNG1tCL8CoUaOYOHEikyZN4u233yYmJoZjx47h5eXFb7/9RvPmzYmNjWXhwoX89NNPvPjiizg7OzNy5EgABg4cyPLly/nggw8ICgpi4sSJNGvWjCNHjuDl5WU5z9ChQ5kyZQqVK1eme/fudO7cmcTExEJfd25uLgsXLgSgfv36lud79erFwYMHWbp0Kf7+/nz++edER0ezf/9+Syi9dOkSkydPZtGiRdjZ2fHcc8/x6quvsmTJEks7GzZswM/Pjw0bNnDkyBHat2/P3XffzYsvvghcDceHDx9m5cqVuLu7M2jQIJo3b87BgwdxdHRk586dtGvXjpEjR9K+fXu+++474uLi8Pb2JjY21tLGiRMn2LBhA46OjvTu3ZszZ87c9Os9atSoQvdXURk7dqzNzi0C+h4UEeMx5eXl5dm6CFvKH5H8+OOP8fLyYv369URGRlq2d+3alUuXLvHhhx9y4sQJAgMD+e6772jUqBE5OTn4+/szefJkOnXqBFwdSR02bBhjxowB4OLFi1SoUIGvv/6a6Ohohg4dyvLly0lKSsJkMgEwa9YsBg0aRFpaGpmZmVSsWJEFCxbw73//G4CcnByqV69O3759GTBggNWI76OPPgrA6tWradGiBZmZmTg7O9/wmhcsWMALL7yAq6srAJmZmTg6OvLOO+9YgmRqaio1a9YkNTUVf39/y7FNmzalUaNGjBs3ztLOkSNHuOOOOyzXMnr0aE6dOmXp340bN3L06FHs7e0BaNeuHXZ2dixdupTDhw8TEhJCYmIi999/PwB//PEHAQEBfPDBBzzzzDPExMTw+++/880331jqGDhwIF999RUHDhzg0KFDhIaGsn37dsuo+E8//USdOnV466236Nu373X74XojvgEBAaSlpeHu7n7DPvwnDh06RLdu3Rg6dChBQUFF2vaxY8cYO3YsF+s+idm1UpG2DWB38SyuB1cWW/tSMvK/jsXxPWgr+d/7c+bMISQkxNbliEgJSk9Px8PDo0Cv3xrx/a8jR45w6dIlHnvsMavnL1++TEREBAD+/v60aNGC999/n0aNGln+NP/MM89YHRMeHm753NXVFXd3d8voY1JSEpGRkZbQCxAVFUVGRga//vor58+fJycnh6ioKMt2R0dHGjVqRFJS0t+eJ39qwZkzZwgMDLzp9bq5ubFr1y7g6qjt+vXr6d69O97e3rRq1Yr9+/eTm5t7zQtIdnY23t7elsfly5e3hN78Ov53pDUsLMwSevP32b9/v6U/HBwcuPfeey3bvb29CQ0NtVxvUlISrVu3tmozKiqKqVOnkpuba2njnnvusWyvXbv2TVeccHJywsnJ6Yb7FKegoCC9QItN6XtQRIxGwfe/MjIyAPjqq6+oWrWq1ba/hqOuXbvSsWNH3nrrLebPn0/79u0pX7681f75c1fzmUwmzGZzkdf81/PkB+mCnsfOzs5qekZ4eDjffPMNEyZMoFWrVmRkZGBvb8/OnTutQitAhQoVrltDfh3/+0eEkuoPERERkRtR8P2vunXr4uTkRGpqKk2aNPnb/Zo3b46rqyuzZ89mzZo1bN68uVDnqVOnDsuXLycvL88SVhMTE3Fzc6NatWp4e3tTrlw5EhMTLX+CzMnJYceOHX/7J/uiYm9vT2ZmJgARERHk5uZy5swZHnzwwWI7Z506dbhy5Qrbtm2zmuqQnJxM3bp1Lfv879zlxMREQkJCsLe3p3bt2ly5coWdO3dapjokJydz/vz5YqtbREREbj8Kvv/l5ubGq6++yiuvvILZbOaBBx4gLS2NxMRE3N3dLXN47e3tiY2NZciQIQQHB1vNBy6IuLg4pk6dyssvv0yvXr1ITk5mxIgR9OvXDzs7O1xdXenRowcDBgzAy8uLwMBAJk6cyKVLl+jSpUuRXW9eXp5lHm5mZibr1q1j7dq1DB8+HICQkBBiYmJ4/vnnmTJlChEREfz+++8kJCQQHh5OixYtiqSO4OBgWrduzYsvvsi7776Lm5sbgwcPpmrVqpbpDf3796dhw4aMGTOG9u3bs3XrVmbMmMGsWbMACA0NJTo6mpdeeonZs2fj4OBA3759cXFxKZIaRUREpGwwfPA1m82WZcTGjBlD5cqVGT9+PD///DOenp7Ur1+f1157zeqYLl26MG7cOF544YVCn69q1aqsXr2aAQMGcNddd+Hl5UWXLl0YNmyYZZ833ngDs9lMx44duXDhAg0aNGDt2rVUrFjxn13sX6Snp1vmBTs5OREUFMTo0aMZNGiQZZ/58+cTHx9P//79+e2336hUqRL33XcfLVu2LLI68s/Tp08fWrZsyeXLl2ncuDGrV6+2TJGoX78+y5YtY/jw4YwZMwY/Pz9Gjx5tuREvv42uXbvSpEkTfHx8iI+P5/XXXy/SOkVEROT2ZvhVHaKjo6lVqxYzZswo8DHffvstjz76KMePH8fHx6cYq5OSVJi7Qv+J/FUdiuPu8/y2taqD3Ej+17EsrYBQnP+vRKR0K8zrt2Hfue3PP/9k1apVbNy4kaZNmxbomOzsbH799VdGjhzJM888o9ArIiIichsxbPDt3Lkz3bt3p3///tcslfV3PvroI4KCgjh//jwTJ04s5gpvXVhYGBUqVLjux1/fWEJsIzAwkDlz5hRo2TkRKRj9vxKRgjDsHN/PP/+80MfExsZazSstrVavXk1OTs51t2mU2vacnZ31p1iRIqb/VyJSEIYNvmVZWXknJhEREZGiZNipDiIiIiJiLAq+IiIiImIICr4iIiIiYgia4ytSBtllpRVPu5nnrf6V21NxfX+IiJR2Cr4iZYiHhweO5Zzg503Feh6XlM3F2r4UP8dyTnh4eNi6DBGREqXgK1KG+Pj4sHjRQtLSNKInN+bh4aHlDUXEcBR8RcoYHx8fBRoREZHr0M1tIiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCA62LkBEbuz06dOkpaXZuoxC8fDwwMfHx9ZliIiIWFHwFSnFTp8+zXMdnyfncratSykUx3JOLF60UOFXRERKFQVfkVIsLS2NnMvZZNZsgtnZw9blWLHLPI9LymYyazTG7OL5/89npcHPm0hLS1PwFRGRUkXBV+Q2YHb2wOxaydZlXJfZxbPU1iYiIvJXurlNRERERAxBwVdEREREDEHBV0REREQMQcFXpJTJysri0KFDZGVl2bqUW2O+AkB29u21EoWIiJR9Cr4ipUxqairdunUjNTXV1qXcErvsDABOnTpl40pERESsKfiKiIiIiCEo+IqIiIiIISj4FkJsbCxt2rT5R2388ssvmEwm9uzZc93Ht5OHHnqIvn372rqM69q4cSMmk4nz58/buhQRm8vNzWX37t0kJCSwe/ducnNzbV2SiIhN3NZvYLF161YeeOABoqOj+eqrr2xdToEEBARw8uRJKlWqdN3HJeGhhx5i06ZNlsdVqlShcePGTJ48maCgoBKrQ0SK3+bNm5k1a5bVnGtfX1/i4uJo3LixDSsTESl5t/WI77x583j55ZfZvHkzJ06cKLbz5ObmYjabi6Qte3t7fH19cXBwuO7jkvLiiy9y8uRJTpw4wRdffMHx48d57rnnSrQGESlemzdvZsSIEdSsWZOZM2eyevVqZs6cSc2aNRkxYgSbN2+2dYkiIiXqtg2+GRkZfPzxx/To0YMWLVqwYMECy7bY2FhMJtM1Hxs3bgSuLrP06quvUrVqVVxdXbn33nst2wAWLFiAp6cnK1eupG7dujg5OVndYT9q1CgqV66Mu7s73bt35/Lly5Zta9as4YEHHsDT0xNvb29atmzJ0aNHLdtvNtUhNzeXLl26UKNGDVxcXAgNDWXatGlW154/5WLy5Mn4+fnh7e1Nz549ycnJKXD/lS9fHl9fX/z8/Ljvvvvo1asXu3btstrnxx9/5IknnqBChQr4+PjQsWNHzp49a7WP2Wxm4MCBeHl54evry8iRI622m0wm3nvvPdq2bUv58uUJDg5m5cqVVvts2rSJRo0a4eTkhJ+fH4MHD+bKlSuW7dnZ2fTu3ZsqVarg7OzMAw88wI4dO6zaWL16NSEhIbi4uPDwww/zyy+/FLgvRMqi3NxcZs2aRWRkJPHx8YSFhVG+fHnCwsKIj48nMjKS2bNna9qDiBjKbRt8ly1bRu3atQkNDeW5557j/fffJy8vD4Bp06Zx8uRJy0efPn2oUqUKtWvXBqBXr15s3bqVpUuXsm/fPp555hmio6M5fPiwpf1Lly4xYcIE3nvvPQ4cOECVKlUASEhIICkpiY0bN/LRRx/x2WefMWrUKMtxFy9epF+/fvzwww8kJCRgZ2dH27ZtCzxibDabqVatGp988gkHDx5k+PDhvPbaayxbtsxqvw0bNnD06FE2bNjABx98wIIFC6zCf2GcO3eOZcuWce+991qeO3/+PI888ggRERH88MMPrFmzhtOnT9OuXTurYz/44ANcXV3Ztm0bEydOZPTo0axbt85qn1GjRtGuXTv27dtH8+bNiYmJ4dy5cwD89ttvNG/enIYNG7J3715mz57NvHnziI+Ptxw/cOBAli9fzgcffMCuXbuoVasWzZo1s7Rx/PhxnnrqKVq1asWePXvo2rUrgwcPvul1Z2dnk56ebvVRmhw7doxjx47Zugy5Te3bt49Tp04RExODnZ31j3o7OztiYmI4efIk+/bts1GFIiIl77ad4ztv3jzLn+ajo6NJS0tj06ZNPPTQQ3h4eODh4QHAZ599xrvvvsv69evx9fUlNTWV+fPnk5qair+/PwCvvvoqa9asYf78+YwbNw6AnJwcZs2axV133WV13nLlyvH+++9bRk5Gjx7NgAEDGDNmDHZ2djz99NNW+7///vtUrlyZgwcPUq9evZtel6Ojo1WQrlGjBlu3bmXZsmVWobNixYrMmDEDe3t7ateuTYsWLUhISODFF18sUP/NmjWL9957j7y8PC5dukRISAhr1661bJ8xYwYRERGW/si/loCAAA4dOkRISAgA4eHhjBgxAoDg4GBmzJhBQkICjz32mOW42NhYOnToAMC4ceOYPn0627dvJzo6mlmzZhEQEMCMGTMwmUzUrl2bEydOMGjQIIYPH05mZiazZ89mwYIFPPHEEwDMnTuXdevWMW/ePAYMGMDs2bO54447mDJlCgChoaHs37+fCRMm3LAPxo8fb9XXpc3YsWNtXYLcxvJ/MaxRo8Z1t+c/n7+fiIgR3JYjvsnJyWzfvt0SphwcHGjfvj3z5s2z2m/37t107NiRGTNmEBUVBcD+/fvJzc0lJCSEChUqWD42bdpkNSWhXLlyhIeHX3Puu+66i/Lly1seR0ZGkpGRwfHjxwE4fPgwHTp0oGbNmri7u1O9enWAQr0ZwcyZM7nnnnuoXLkyFSpUYM6cOdccHxYWhr29veWxn58fZ86cKfA5YmJi2LNnD3v37mXLli3UqlWLxx9/nAsXLgCwd+9eNmzYYNVH+SPmf+2n/+2j69Xx131cXV1xd3e37JOUlERkZCQmk8myT1RUFBkZGfz6668cPXqUnJwcy9cPrv5y0KhRI5KSkixt/HW0Gq5+XW5myJAhpKWlWT7yv4alxdChQxk6dKity5DblJeXFwApKSnX3Z7/fP5+IiJGcFuO+M6bN48rV65YRmwB8vLycHJyYsaMGXh4eHDq1CmefPJJunbtSpcuXSz7ZWRkYG9vz86dO62CI0CFChUsn7u4uFiFsYJq1aoVQUFBzJ07F39/f8xmM/Xq1bOaB3wjS5cu5dVXX2XKlClERkbi5ubGpEmT2LZtm9V+jo6OVo9NJlOhbsDz8PCgVq1aANSqVYt58+bh5+fHxx9/TNeuXcnIyKBVq1bXHTX18/MrVB3/tNbi4uTkhJOTk63L+FtaYUP+ifDwcHx9fVmyZAnx8fFW0x3MZjNLlizBz8/vur/gi4iUVbdd8L1y5QoLFy5kypQpPP7441bb2rRpw0cffURsbCytW7emdu3avPnmm1b7REREkJuby5kzZ3jwwQcLff69e/eSmZmJi4sLAN9//z0VKlQgICCAP/74g+TkZObOnWtpe8uWLYVqPzExkfvvv5+4uDjLc38dYS0u+b8EZGZmAlC/fn2WL19O9erVi3XFiTp16rB8+XLy8vIsv2gkJibi5uZGtWrV8Pb2ply5ciQmJlqCYE5ODjt27LCsIVynTp1rbpj7/vvvi61mkduBvb09cXFxjBgxgmHDhhETE0ONGjVISUlhyZIlbN26lVGjRl0zACAiUpbddsF31apV/Pnnn3Tp0sUyjzff008/zbx589i6dSvHjx8nISGB33//3bLdy8uLkJAQYmJieP7555kyZQoRERH8/vvvJCQkEB4eTosWLW54/suXL9OlSxeGDRvGL7/8wogRI+jVqxd2dnZUrFgRb29v5syZg5+fH6mpqQW6yeqvgoODWbhwIWvXrqVGjRosWrSIHTt2/O08vVt16dIly7qep0+fZsyYMTg7O1t+mejZsydz586lQ4cOllUbjhw5wtKlS3nvvfeK7MUyLi6OqVOn8vLLL9OrVy+Sk5MZMWIE/fr1w87ODldXV3r06MGAAQPw8vIiMDCQiRMncunSJctIfvfu3ZkyZQoDBgyga9eu7Ny585Zv9BMpSxo3bsyoUaOYNWsWPXv2tDzv5+fHqFGjtI6viBjObRd8582bR9OmTa8JvXA1+E6cOJGjR4/y559/UrduXavtGzZs4KGHHmL+/PnEx8fTv39/fvvtNypVqsR9991Hy5Ytb3r+Rx99lODgYBo3bkx2djYdOnSwLOFlZ2fH0qVL6d27N/Xq1SM0NJTp06fz0EMPFfj6XnrpJXbv3k379u0xmUx06NCBuLg4vv766wK3URBz585l7ty5wNUb5cLDw1m9ejWhoaEA+Pv7k5iYyKBBg3j88cfJzs4mKCiI6Ojoa+4Q/yeqVq3K6tWrGTBgAHfddRdeXl6WXyzyvfHGG5jNZjp27MiFCxdo0KABa9eupWLFigAEBgayfPlyXnnlFd5++20aNWrEuHHj6Ny5c5HVKXK7aty4MVFRUezbt49z587h5eVFeHi4RnpFxJBMeflrgIlNJCcnU7t2bQ4fPmyZcyu2kZ6ejoeHB2lpabi7u9usjkOHDtGtWzfmzJkDQLdu3bhY90nMriX37n4FYXfxLK4HV15Tm8PZI7ikbGbo0KFWq3uIiIgUh8K8ft+WqzqUFefOnePTTz/F3d2dgIAAW5cjIiIiUqbddlMdypIuXbqwc+dOZs+eXWSrC3z77beW9W6vJyMjo0jOIyIiInK7UfC1oc8//7zI22zQoIHl7Y/l9hQYGMicOXMIDAws1PrPpYXZ6eqygL6+vjauRERExJqCbxnj4uKiucK3OWdnZ8s7492W7K7+WCnNaySLiIgxaY6viIiIiBiCgq+IiIiIGIKCr4iIiIgYgub4itwG7LLSbF3CNewyz1v9a3m+FNYqIiICCr4ipZqHhweO5Zzg5022LuVvuaRsvuY5x3JO1313RREREVtS8BUpxXx8fFi8aCFpabfXKKqHhwc+Pj62LkNERMSKgq9IKefj46MQKSIiUgR0c5uIiIiIGIKCr4iIiIgYgoKviIiIiBiCgq+IiIiIGIKCr4iIiIgYgoKviIiIiBiCgq+IiIiIGIKCr4iIiIgYgoKviIiIiBiCgq+IiIiIGIKCr4iIiIgYgoKviIiIiBiCg60LECkt8vLyAEhPT7dxJSIiIlJQ+a/b+a/jN6LgK/JfFy5cACAgIMDGlYiIiEhhXbhwAQ8PjxvuY8orSDwWMQCz2cyJEydwc3PDZDLdcN/09HQCAgI4fvw47u7uJVShgPreltT3tqX+tx31vW3drP/z8vK4cOEC/v7+2NndeBavRnxF/svOzo5q1aoV6hh3d3f9ELQR9b3tqO9tS/1vO+p727pR/99spDefbm4TEREREUNQ8BURERERQ1DwFbkFTk5OjBgxAicnJ1uXYjjqe9tR39uW+t921Pe2VZT9r5vbRERERMQQNOIrIiIiIoag4CsiIiIihqDgKyIiIiKGoOArIiIiIoag4CtSQOPHj6dhw4a4ublRpUoV2rRpQ3Jysq3LMqQ33ngDk8lE3759bV2KYfz2228899xzeHt74+Liwp133skPP/xg67LKvNzcXF5//XVq1KiBi4sLd9xxB2PGjEH3pRePzZs306pVK/z9/TGZTKxYscJqe15eHsOHD8fPzw8XFxeaNm3K4cOHbVNsGXOjvs/JyWHQoEHceeeduLq64u/vz/PPP8+JEycKfR4FX5EC2rRpEz179uT7779n3bp15OTk8Pjjj3Px4kVbl2YoO3bs4N133yU8PNzWpRjGn3/+SVRUFI6Ojnz99dccPHiQKVOmULFiRVuXVuZNmDCB2bNnM2PGDJKSkpgwYQITJ07k7bfftnVpZdLFixe56667mDlz5nW3T5w4kenTp/POO++wbds2XF1dadasGVlZWSVcadlzo76/dOkSu3bt4vXXX2fXrl189tlnJCcn8+STTxb6PFrOTOQW/f7771SpUoVNmzbRuHFjW5djCBkZGdSvX59Zs2YRHx/P3XffzdSpU21dVpk3ePBgEhMT+fbbb21diuG0bNkSHx8f5s2bZ3nu6aefxsXFhcWLF9uwsrLPZDLx+eef06ZNG+DqaK+/vz/9+/fn1VdfBSAtLQ0fHx8WLFjAs88+a8Nqy5b/7fvr2bFjB40aNeLYsWMEBgYWuG2N+IrcorS0NAC8vLxsXIlx9OzZkxYtWtC0aVNbl2IoK1eupEGDBjzzzDNUqVKFiIgI5s6da+uyDOH+++8nISGBQ4cOAbB37162bNnCE088YePKjCclJYVTp05Z/fzx8PDg3nvvZevWrTaszJjS0tIwmUx4enoW6jiH4ilHpGwzm8307duXqKgo6tWrZ+tyDGHp0qXs2rWLHTt22LoUw/n555+ZPXs2/fr147XXXmPHjh307t2bcuXK0alTJ1uXV6YNHjyY9PR0ateujb29Pbm5uYwdO5aYmBhbl2Y4p06dAsDHx8fqeR8fH8s2KRlZWVkMGjSIDh064O7uXqhjFXxFbkHPnj358ccf2bJli61LMYTjx4/Tp08f1q1bh7Ozs63LMRyz2UyDBg0YN24cABEREfz444+88847Cr7FbNmyZSxZsoQPP/yQsLAw9uzZQ9++ffH391ffiyHl5OTQrl078vLymD17dqGP11QHkULq1asXq1atYsOGDVSrVs3W5RjCzp07OXPmDPXr18fBwQEHBwc2bdrE9OnTcXBwIDc319Yllml+fn7UrVvX6rk6deqQmppqo4qMY8CAAQwePJhnn32WO++8k44dO/LKK68wfvx4W5dmOL6+vgCcPn3a6vnTp09btknxyg+9x44dY926dYUe7QUFX5ECy8vLo1evXnz++ef85z//oUaNGrYuyTAeffRR9u/fz549eywfDRo0ICYmhj179mBvb2/rEsu0qKioa5buO3ToEEFBQTaqyDguXbqEnZ31S7W9vT1ms9lGFRlXjRo18PX1JSEhwfJceno627ZtIzIy0oaVGUN+6D18+DDr16/H29v7ltrRVAeRAurZsycffvghX3zxBW5ubpY5XR4eHri4uNi4urLNzc3tmrnUrq6ueHt7a451CXjllVe4//77GTduHO3atWP79u3MmTOHOXPm2Lq0Mq9Vq1aMHTuWwMBAwsLC2L17N2+++SadO3e2dWllUkZGBkeOHLE8TklJYc+ePXh5eREYGEjfvn2Jj48nODiYGjVq8Prrr+Pv73/D1QekYG7U935+fvzrX/9i165drFq1itzcXMtrsJeXF+XKlSv4ifJEpECA637Mnz/f1qUZUpMmTfL69Olj6zIM48svv8yrV69enpOTU17t2rXz5syZY+uSDCE9PT2vT58+eYGBgXnOzs55NWvWzBs6dGhedna2rUsrkzZs2HDdn/OdOnXKy8vLyzObzXmvv/56no+PT56Tk1Peo48+mpecnGzbosuIG/V9SkrK374Gb9iwoVDn0Tq+IiIiImIImuMrIiIiIoag4CsiIiIihqDgKyIiIiKGoOArIiIiIoag4CsiIiIihqDgKyIiIiKGoOArIiIiIoag4CsiIiIihqDgKyIipVb16tWZOnWqrcu4Zb/88gsmk4k9e/bYuhQRQcFXRERKSGxsLCaTCZPJRLly5ahVqxajR4/mypUrf3vMjh076Nat2z8+d34AvdHHggUL/vb4jRs3YjKZOH/+/D+uRURsx8HWBYiIiHFER0czf/58srOzWb16NT179sTR0ZEhQ4ZY7Xf58mXKlStH5cqVi+S8AQEBnDx50vJ48uTJrFmzhvXr11ue8/DwKJJziUjppRFfEREpMU5OTvj6+hIUFESPHj1o2rQpK1euJDY2ljZt2jB27Fj8/f0JDQ0Frp3qcP78eV566SV8fHxwdnamXr16rFq1yrJ9y5YtPPjgg7i4uBAQEEDv3r25ePEi9vb2+Pr6Wj4qVKiAg4OD5XHFihUZNGgQVapUwdnZmQceeIAdO3YAV0eLH374YQAqVqyIyWQiNjYWgDVr1vDAAw/g6emJt7c3LVu25OjRoyXTmSJSaAq+IiJiMy4uLly+fBmAhIQEkpOTWbdunVWYzWc2m3niiSdITExk8eLFHDx4kDfeeAN7e3sAjh49SnR0NE8//TT79u3j448/ZsuWLfTq1eumdQwcOJDly5fzwQcfsGvXLmrVqkWzZs04d+4cAQEBLF++HIDk5GROnjzJtGnTALh48SL9+vXjhx9+ICEhATs7O9q2bYvZbC6qLhKRIqSpDiIiUuLy8vJISEhg7dq1vPzyy/z++++4urry3nvvUa5cueses379erZv305SUhIhISEA1KxZ07J9/PjxxMTE0LdvXwCCg4OZPn06TZo0Yfbs2Tg7O1+33YsXLzJ79mwWLFjAE088AcDcuXNZt24d8+bNY8CAAXh5eQFQpUoVPD09Lcc+/fTTVm29//77VK5cmYMHD1KvXr1b6hsRKT4a8RURkRKzatUqKlSogLOzM0888QTt27dn5MiRANx5551/G3oB9uzZQ7Vq1Syh93/t3buXBQsWUKFCBctHs2bNMJvNpKSk/G27R48eJScnh6ioKMtzjo6ONGrUiKSkpBtez+HDh+nQoQM1a9bE3d2d6tWrA5CamnrD40TENjTiKyIiJebhhx9m9uzZlCtXDn9/fxwc/v9lyNXV9YbHuri43HB7RkYGL730Er17975mW2Bg4K0VfBOtWrUiKCiIuXPn4u/vj9lspl69epbpGyJSuij4iohIiXF1daVWrVq3dGx4eDi//vorhw4duu6ob/369Tl48GCh27/jjjsoV64ciYmJBAUFAZCTk8OOHTss0ybyR6Jzc3Mtx/3xxx8kJyczd+5cHnzwQeDqzXUiUnppqoOIiNwWmjRpQuPGjXn66adZt24dKSkpfP3116xZswaAQYMG8d1339GrVy/27NnD4cOH+eKLL256c5urqys9evRgwIABrFmzhoMHD/Liiy9y6dIlunTpAkBQUBAmk4lVq1bx+++/k5GRQcWKFfH29mbOnDkcOXKE//znP/Tr16/Y+0FEbp2Cr4iI3DaWL19Ow4YN6dChA3Xr1mXgwIGWUdjw8HA2bdrEoUOHePDBB4mIiGD48OH4+/vftN033niDp59+mo4dO1K/fn2OHDnC2rVrqVixIgBVq1Zl1KhRDB48GB8fH3r16oWdnR1Lly5l586d1KtXj1deeYVJkyYV6/WLyD9jysvLy7N1ESIiIiIixU0jviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImIICr4iIiIiYggKviIiIiJiCAq+IiIiImII/wcwDnsIQ0nxcAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Address',x='PriceTotal',data=df,width=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1377792d-8f5e-4275-8717-271effdb0909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.55, 2.25, 3.95, 3.35, 2.85, 5.7, 3.38, 3.5, 10.7, 3.4, 8.7, 4.95,\n",
       "       2.32, 2.6, 2.5, 11.7, 8.2, 3.26, 6.8, 3.3, 4, 4.6, 3.45, 5.63, 3,\n",
       "       3.99, 2.4, 2.8, 2.45, 3.85, 2.86, 3.05, 2.53, 2.27], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PriceTotal'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "aba5f01a-14f0-4c87-9000-436269c0b699",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PriceTotal']=df['PriceTotal'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d1f350f6-2e47-47a5-a445-5dbfadb7a691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([90, 37, 59, 63, 70, 100, 49, 145, 125, 69, 44, 147.5, 98, 45, 85,\n",
       "       62, 60, 79, 56, 72, 50, 36, 40, 53, 39, 58], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Meterage'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f91a8395-e4f7-455b-b21b-4a8e715a4846",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Meterage']=df['Meterage'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "132270db-7486-46b3-9aee-e48217db4614",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year']=df['Year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b98c775c-67d1-4a8a-8532-1dbef078f095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   PriceTotal     41 non-null     float64\n",
      " 1   Meterage       41 non-null     float64\n",
      " 2   Year           41 non-null     int32  \n",
      " 3   Room No        41 non-null     object \n",
      " 4   Floor No       41 non-null     object \n",
      " 5   Parking Lot    41 non-null     object \n",
      " 6   Elevator       41 non-null     object \n",
      " 7   Warehouse      41 non-null     object \n",
      " 8   Joint Parking  41 non-null     object \n",
      " 9   Address        41 non-null     object \n",
      "dtypes: float64(2), int32(1), object(7)\n",
      "memory usage: 3.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "70a10559-85df-4409-b6f1-66ffd3471682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 4, 3], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Room No'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "70dc4385-1da0-4f2a-99b5-74e7f983af81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 5, 4], dtype=object)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Floor No'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1d1b2b67-c3ac-447c-a12a-b9d0a4e97082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Parking Lot'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "597429b9-19c7-41ab-840f-da71c262d4e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=object)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Elevator'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "813342d3-5c8c-4105-b364-ec6a3d787fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=object)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Warehouse'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "07e39822-e0b8-45c6-a2d4-f3828ccf0d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Floor No', ylabel='count'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(hue='Elevator',x='Floor No',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "dfa0fe86-fdcb-4057-bf30-8c7bbed9fc0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Elevator', ylabel='PriceTotal'>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Elevator',y='PriceTotal',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c7b6c02a-2b9a-4eb5-ba37-9573d48c31d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Floor No', ylabel='PriceTotal'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Floor No',y='PriceTotal',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e8fc4ef0-ab17-4376-a4f3-844bb8f2c67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Room No', ylabel='Meterage'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Room No',y='Meterage',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "631021ab-b9ca-4dba-a3a7-99f043be5334",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To minimise the number of dummy objects,'Floor Status' is defined to summarize the information about floors numbers.\n",
    "j=0\n",
    "for i in df['Elevator']:\n",
    "    if df.loc[j,'Floor No']>3:\n",
    "        df.loc[j,'Floor Status']=1\n",
    "    else:\n",
    "        df.loc[j,'Floor Status']=0\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "05d9acd5-0aee-4aaa-839d-d67c14987771",
   "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>PriceTotal</th>\n",
       "      <th>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Room No</th>\n",
       "      <th>Floor No</th>\n",
       "      <th>Parking Lot</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Joint Parking</th>\n",
       "      <th>Address</th>\n",
       "      <th>Floor Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3.05</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1382</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2.50</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1380</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.53</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1382</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Azerbaijan_Behnood</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1382</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Ghasredasht_Behnood</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.27</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1380</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Karoon_Behnood</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PriceTotal  Meterage  Year Room No Floor No Parking Lot Elevator  \\\n",
       "36        3.05      50.0  1382       1        3           1        0   \n",
       "37        2.50      40.0  1380       1        4           0        0   \n",
       "38        2.53      39.0  1382       1        3           0        0   \n",
       "39        3.00      58.0  1382       1        4           0        0   \n",
       "40        2.27      36.0  1380       1        3           0        0   \n",
       "\n",
       "   Warehouse Joint Parking              Address  Floor Status  \n",
       "36         1             0  Ghasredasht_Behnood           0.0  \n",
       "37         1             0  Ghasredasht_Behnood           1.0  \n",
       "38         1             0   Azerbaijan_Behnood           0.0  \n",
       "39         1             0  Ghasredasht_Behnood           1.0  \n",
       "40         1             0       Karoon_Behnood           0.0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "41b8203d-8830-43e0-81c3-54515f5134ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Floor Status' is defined to substitute 'Floor No'. \n",
    "# In addition, It seems that 'Room No' is highly correlated with 'Meterage'. \n",
    "# So 'Room No' and'Floor No' are droped.\n",
    "df.drop(['Room No','Floor No'],axis=1,inplace=True)\n",
    "df_object = df.drop(df[['PriceTotal','Meterage','Year']],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "cff7732d-2743-4675-856e-2e3c2ebf7078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Parking Lot    41 non-null     object \n",
      " 1   Elevator       41 non-null     object \n",
      " 2   Warehouse      41 non-null     object \n",
      " 3   Joint Parking  41 non-null     object \n",
      " 4   Address        41 non-null     object \n",
      " 5   Floor Status   41 non-null     float64\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_object.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fc3acf93-d867-44cb-9dd9-99441c81d735",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   PriceTotal     41 non-null     float64\n",
      " 1   Meterage       41 non-null     float64\n",
      " 2   Year           41 non-null     int32  \n",
      " 3   Parking Lot    41 non-null     object \n",
      " 4   Elevator       41 non-null     object \n",
      " 5   Warehouse      41 non-null     object \n",
      " 6   Joint Parking  41 non-null     object \n",
      " 7   Address        41 non-null     object \n",
      " 8   Floor Status   41 non-null     float64\n",
      "dtypes: float64(3), int32(1), object(5)\n",
      "memory usage: 2.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a289a142-e03b-4a8e-98b1-ce057312ac7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make dummies from object predictors.\n",
    "df_dummy=pd.get_dummies(df_object,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "eb4cad01-356c-491c-ae93-ea1b2fef0911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 10)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy = df_dummy.astype(int)\n",
    "df_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "731a4f63-a52e-4a4c-b3ac-7b7e795eca0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make output as y and and inputs as X\n",
    "df.drop(df_object,axis=1,inplace=True)\n",
    "y = df['PriceTotal']\n",
    "X = df.drop('PriceTotal',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "16c68daa-21c7-44c4-8a75-eb31df71cff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are now two columns 'Meterage' and 'Year' in X.\n",
    "# Normalize X (min=0, max=1)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X=pd.DataFrame(X_scaled,columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a1c6067c-99b5-406d-bd3b-f62e25da19c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 12)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=pd.concat([X,df_dummy],axis=1)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "3c32f3a7-a531-41c0-b510-466e2f3b3446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 12 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Meterage                     41 non-null     float64\n",
      " 1   Year                         41 non-null     float64\n",
      " 2   Floor Status                 41 non-null     int32  \n",
      " 3   Parking Lot_1                41 non-null     int32  \n",
      " 4   Elevator_1                   41 non-null     int32  \n",
      " 5   Warehouse_1                  41 non-null     int32  \n",
      " 6   Joint Parking_1              41 non-null     int32  \n",
      " 7   Address_Behnood              41 non-null     int32  \n",
      " 8   Address_Ghasredasht_Behnood  41 non-null     int32  \n",
      " 9   Address_Jeyhoon_Behnood      41 non-null     int32  \n",
      " 10  Address_Karoon_Behnood       41 non-null     int32  \n",
      " 11  Address_Mashouf_Behnood      41 non-null     int32  \n",
      "dtypes: float64(2), int32(10)\n",
      "memory usage: 2.4 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "20d96132-c811-413f-8b2d-a79bc4bb948f",
   "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>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Floor Status</th>\n",
       "      <th>Parking Lot_1</th>\n",
       "      <th>Elevator_1</th>\n",
       "      <th>Warehouse_1</th>\n",
       "      <th>Joint Parking_1</th>\n",
       "      <th>Address_Behnood</th>\n",
       "      <th>Address_Ghasredasht_Behnood</th>\n",
       "      <th>Address_Jeyhoon_Behnood</th>\n",
       "      <th>Address_Karoon_Behnood</th>\n",
       "      <th>Address_Mashouf_Behnood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.484305</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008969</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.206278</td>\n",
       "      <td>0.548387</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242152</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.304933</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Meterage      Year  Floor Status  Parking Lot_1  Elevator_1  Warehouse_1  \\\n",
       "0  0.484305  0.838710             0              1           1            1   \n",
       "1  0.008969  0.387097             0              0           0            1   \n",
       "2  0.206278  0.548387             0              0           0            1   \n",
       "3  0.242152  0.354839             0              0           0            1   \n",
       "4  0.304933  0.129032             0              0           0            1   \n",
       "\n",
       "   Joint Parking_1  Address_Behnood  Address_Ghasredasht_Behnood  \\\n",
       "0                0                1                            0   \n",
       "1                0                0                            1   \n",
       "2                0                0                            1   \n",
       "3                1                0                            0   \n",
       "4                0                0                            0   \n",
       "\n",
       "   Address_Jeyhoon_Behnood  Address_Karoon_Behnood  Address_Mashouf_Behnood  \n",
       "0                        0                       0                        0  \n",
       "1                        0                       0                        0  \n",
       "2                        0                       0                        0  \n",
       "3                        0                       0                        1  \n",
       "4                        0                       1                        0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "53270b06-693e-4d11-a957-45a9c242d5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.55\n",
       "1    2.25\n",
       "2    3.95\n",
       "3    3.35\n",
       "4    2.85\n",
       "Name: PriceTotal, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8b3c2d58-1cd2-4b91-9484-31a2c98c60ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split tran and test datasets.\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8b911896-d377-4b4c-9280-54b7f228fce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Linear Regression to train data\n",
    "lm=LinearRegression()\n",
    "result=lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5716e766-eae1-4bce-9657-eeac39cf032c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict total price by the trained model and input test dataset.\n",
    "y_hat = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d9b962c2-4221-4364-89a4-c956a57dd3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9739673793700925"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy score of the model for train dataset.\n",
    "lm.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d9e3fd82-0d90-4c25-a2ba-f8c6e73b88a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9315573676523302"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy score of the model for test dataset.\n",
    "lm.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7eae0e9c-44ad-4e0b-869a-45d10dbec504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 6.97705775  3.3515428  -0.20072984  0.36490353  0.03882517 -0.56410156\n",
      "  0.09611207  0.12657573 -0.19486718 -0.67519391 -0.21465025 -0.01720056]\n",
      "intercept: \n",
      " 1.3693317918551267\n",
      "Mean squared error: 0.28\n",
      "Coefficient of determination: 0.93\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# The coefficients\n",
    "coefs = lm.coef_\n",
    "intercept = lm.intercept_ \n",
    "print(\"Coefficients: \\n\", coefs)\n",
    "print(\"intercept: \\n\", intercept)\n",
    "\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_test, y_hat))\n",
    "\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print(\"Coefficient of determination: %.2f\" % r2_score(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "80045ff7-d350-44ae-897d-c98bd1081ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5987fe87-987d-4a52-883d-8c7132fdb776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Meterage', 'Year', 'Floor Status', 'Parking Lot_1', 'Elevator_1',\n",
       "       'Warehouse_1', 'Joint Parking_1', 'Address_Behnood',\n",
       "       'Address_Ghasredasht_Behnood', 'Address_Jeyhoon_Behnood',\n",
       "       'Address_Karoon_Behnood', 'Address_Mashouf_Behnood'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fb92e322-c998-4455-bdeb-bb1907530e8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1fed2df7550>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot outputs\n",
    "sns.jointplot(y=y_hat, x=y_test, kind='reg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a5212ea2-7ccb-4fb4-a57d-d28c6c790eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7962abb2-6bea-41b6-bc21-2ec355c787e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year -----t= -4.948377820063441 -----p= 0.00014524960861465994\n",
      "Parking Lot_1 -----t= -5.1110778143983095 -----p= 0.00010467533063676632\n",
      "Elevator_1 -----t= -5.1110778143983095 -----p= 0.00010467533063676632\n",
      "Warehouse_1 -----t= -4.51443409537627 -----p= 0.00035285332201998406\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\Python311\\Lib\\site-packages\\scipy\\stats\\_axis_nan_policy.py:523: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "# Claculating t-stat and p-value for predictors.\n",
    "j=0\n",
    "k=0\n",
    "t_stat_all=[]\n",
    "p_value_all=[]\n",
    "\n",
    "# When p-value is more than 0.0001, maybe the relationship between price and the predictor is weaker.\n",
    "for i in X.columns:\n",
    "    t_stat, p_value = stats.ttest_ind(X_test[i], y_hat)\n",
    "    t_stat_all.append(t_stat)\n",
    "    p_value_all.append(p_value)\n",
    "    if p_value_all[j] >0.0001:\n",
    "        k=k+1\n",
    "        print(i,'-----t=',t_stat,'-----p=',p_value)\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3d01db4a-67c8-4862-92e3-fca7ee9d5bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Enter a sample to predict the price using the trained model. \n",
    "Example1 = pd.read_excel('Our House Prediction.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d38d2f6f-3235-491b-a610-b4ea0901b8a3",
   "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>PriceTotal</th>\n",
       "      <th>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Room No</th>\n",
       "      <th>Floor No</th>\n",
       "      <th>Building Floors</th>\n",
       "      <th>Units Per Floor</th>\n",
       "      <th>Parking Lot</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>...</th>\n",
       "      <th>Cooling</th>\n",
       "      <th>Water Heating</th>\n",
       "      <th>Hood</th>\n",
       "      <th>Desktop Stove</th>\n",
       "      <th>Closet</th>\n",
       "      <th>Building Direction</th>\n",
       "      <th>Apartment Direction</th>\n",
       "      <th>Joint Status</th>\n",
       "      <th>Address</th>\n",
       "      <th>Address in Farsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1381</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>North</td>\n",
       "      <td>North</td>\n",
       "      <td>1</td>\n",
       "      <td>Mashouf_Behnood</td>\n",
       "      <td>خیابان مشعوف آزادی</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PriceTotal  Meterage  Year  Room No  Floor No  Building Floors  \\\n",
       "0         NaN      43.1  1381        1         4                4   \n",
       "\n",
       "   Units Per Floor  Parking Lot  Elevator  Warehouse  ...  Cooling  \\\n",
       "0                2            0         0          1  ...        0   \n",
       "\n",
       "   Water Heating  Hood  Desktop Stove  Closet Building Direction  \\\n",
       "0              1     3              1       1              North   \n",
       "\n",
       "  Apartment Direction  Joint Status          Address    Address in Farsi  \n",
       "0               North             1  Mashouf_Behnood  خیابان مشعوف آزادی  \n",
       "\n",
       "[1 rows x 29 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5ea9440c-a13a-432f-af48-e61946e1d76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PriceTotal', 'Meterage', 'Year'], dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ccbd53c3-3d17-4585-a574-3578091e4be7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Example1['Floor Status']=1\n",
    "Example1_notneeded = Example1.drop(df.columns,axis=1)\n",
    "Example1_notneeded = Example1_notneeded.drop(df_object.columns,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8349cda3-5232-46e7-9c0a-bd74ef613e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "Example1.drop(Example1_notneeded,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6485220f-7729-4fdf-b1dc-01ff0b01e033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1 entries, 0 to 0\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   PriceTotal     0 non-null      float64\n",
      " 1   Meterage       1 non-null      float64\n",
      " 2   Year           1 non-null      int64  \n",
      " 3   Parking Lot    1 non-null      int64  \n",
      " 4   Elevator       1 non-null      int64  \n",
      " 5   Warehouse      1 non-null      int64  \n",
      " 6   Joint Parking  1 non-null      int64  \n",
      " 7   Address        1 non-null      object \n",
      " 8   Floor Status   1 non-null      int64  \n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 204.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "Example1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "bd714f3e-712f-4a64-b199-69db48533e20",
   "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>PriceTotal</th>\n",
       "      <th>Meterage</th>\n",
       "      <th>Year</th>\n",
       "      <th>Parking Lot</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Joint Parking</th>\n",
       "      <th>Address</th>\n",
       "      <th>Floor Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1381</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Mashouf_Behnood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PriceTotal  Meterage  Year  Parking Lot  Elevator  Warehouse  \\\n",
       "0         NaN      43.1  1381            0         0          1   \n",
       "\n",
       "   Joint Parking          Address  Floor Status  \n",
       "0              0  Mashouf_Behnood             1  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "b9809c20-02a9-4dae-a514-e8a635934b0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "MinMeterage = df['Meterage'].min()\n",
    "MaxMeterage = df['Meterage'].max()\n",
    "\n",
    "MinYear = df['Year'].min()\n",
    "MaxYear = df['Year'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "ed8e01ce-4172-4be1-ae7e-e2a3eff02d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1381\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example1['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "1017bb64-e2c5-42d3-8939-1314460ca0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Example1['Meterage']=(Example1['Meterage']-MinMeterage)/(MaxMeterage-MinMeterage)\n",
    "Example1['Year']=(Example1['Year']-MinYear)/(MaxYear-MinYear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7c445504-a949-456a-8961-80678279f461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.063677\n",
       "Name: Meterage, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example1['Meterage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "4cff452e-840d-4fa5-970a-23298b9ab74d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.322581\n",
       "Name: Year, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example1['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "cb2e2a0b-db83-4ffd-8886-7f39a6f37446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1 entries, 0 to 0\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   PriceTotal     0 non-null      float64\n",
      " 1   Meterage       1 non-null      float64\n",
      " 2   Year           1 non-null      float64\n",
      " 3   Parking Lot    1 non-null      int64  \n",
      " 4   Elevator       1 non-null      int64  \n",
      " 5   Warehouse      1 non-null      int64  \n",
      " 6   Joint Parking  1 non-null      int64  \n",
      " 7   Address        1 non-null      object \n",
      " 8   Floor Status   1 non-null      int64  \n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 204.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "Example1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "12d28f03-67f7-49ed-9557-3e0e6ee7ed15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41 entries, 0 to 40\n",
      "Data columns (total 12 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Meterage                     41 non-null     float64\n",
      " 1   Year                         41 non-null     float64\n",
      " 2   Floor Status                 41 non-null     int32  \n",
      " 3   Parking Lot_1                41 non-null     int32  \n",
      " 4   Elevator_1                   41 non-null     int32  \n",
      " 5   Warehouse_1                  41 non-null     int32  \n",
      " 6   Joint Parking_1              41 non-null     int32  \n",
      " 7   Address_Behnood              41 non-null     int32  \n",
      " 8   Address_Ghasredasht_Behnood  41 non-null     int32  \n",
      " 9   Address_Jeyhoon_Behnood      41 non-null     int32  \n",
      " 10  Address_Karoon_Behnood       41 non-null     int32  \n",
      " 11  Address_Mashouf_Behnood      41 non-null     int32  \n",
      "dtypes: float64(2), int32(10)\n",
      "memory usage: 2.4 KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9e891303-e6df-4e1e-809d-eaf458e91313",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_8132\\810785656.py:2: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  X_me['Meterage']=Example1['Meterage']\n",
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_8132\\810785656.py:3: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
      "  X_me['Year']=Example1['Year']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Meterage                       0.063677\n",
       "Year                           0.322581\n",
       "Floor Status                   0.000000\n",
       "Parking Lot_1                  1.000000\n",
       "Elevator_1                     1.000000\n",
       "Warehouse_1                    1.000000\n",
       "Joint Parking_1                0.000000\n",
       "Address_Behnood                1.000000\n",
       "Address_Ghasredasht_Behnood    0.000000\n",
       "Address_Jeyhoon_Behnood        0.000000\n",
       "Address_Karoon_Behnood         0.000000\n",
       "Address_Mashouf_Behnood        0.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_me = X.loc[0,X.columns]\n",
    "X_me['Meterage']=Example1['Meterage']\n",
    "X_me['Year']=Example1['Year']\n",
    "X_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "0039fcdb-f769-4683-bb80-954ad8c69d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_me['Floor Status']=1\n",
    "X_me['Parking Lot_1']=0\n",
    "X_me['Elevator_1']=0\n",
    "X_me['Address_Behnood']=0\n",
    "X_me['Address_Mashouf_Behnood']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "1d3cbf14-a246-423b-abdd-f3fbd4107561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meterage                       0.063677\n",
       "Year                           0.322581\n",
       "Floor Status                   1.000000\n",
       "Parking Lot_1                  0.000000\n",
       "Elevator_1                     0.000000\n",
       "Warehouse_1                    1.000000\n",
       "Joint Parking_1                0.000000\n",
       "Address_Behnood                0.000000\n",
       "Address_Ghasredasht_Behnood    0.000000\n",
       "Address_Jeyhoon_Behnood        0.000000\n",
       "Address_Karoon_Behnood         0.000000\n",
       "Address_Mashouf_Behnood        1.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "ad046bb9-0408-47df-bb71-8815afffae99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_me.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "27d5d52e-a62e-477a-a02f-99724f7886b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "18bfca43-327d-4441-a9a1-fd7b90ffab3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.97705775,  3.3515428 , -0.20072984,  0.36490353,  0.03882517,\n",
       "       -0.56410156,  0.09611207,  0.12657573, -0.19486718, -0.67519391,\n",
       "       -0.21465025, -0.01720056])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f759e987-b13b-4b25-a80a-f926d3e676b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.112721688131823\n"
     ]
    }
   ],
   "source": [
    "# Price prediction for new sample.\n",
    "j=0\n",
    "price=0\n",
    "for i in X_me:\n",
    "    price=price+coefs[j]*i\n",
    "    j=j+1\n",
    "print(price+intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "044d6c4d-0a5f-4758-b498-21883e80cc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(X.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b4ab2e00-7321-423b-9f1f-c464927f8b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate predicted price values using coefficients of the model.\n",
    "\n",
    "j=0 #column\n",
    "k=0 #row\n",
    "\n",
    "y_me=[]\n",
    "price=0\n",
    "for k in np.arange(X.shape[0]):\n",
    "    for i in X.iloc[k]:\n",
    "        price=price+coefs[j]*i\n",
    "        j=j+1\n",
    "    y_me.append(price+intercept)\n",
    "    price=0\n",
    "    j=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "92195b34-9428-4393-af0f-23e6d421af2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7.525529529190743,\n",
       " 1.9703089622497862,\n",
       " 3.8875195795587083,\n",
       " 3.7629106114259825,\n",
       " 3.150570423088239,\n",
       " 5.905194109294575,\n",
       " 3.832674052296676,\n",
       " 3.4972023458544537,\n",
       " 10.196940925132742,\n",
       " 3.6180238047566022,\n",
       " 8.51299360970219,\n",
       " 4.893930157513154,\n",
       " 2.1442323644149264,\n",
       " 2.8194262757300423,\n",
       " 2.057061861101821,\n",
       " 11.343159231060861,\n",
       " 8.422920730825144,\n",
       " 2.5790193180866803,\n",
       " 6.843344474621665,\n",
       " 3.270573526290499,\n",
       " 4.761217979615914,\n",
       " 3.6214245859290046,\n",
       " 3.6180238047566022,\n",
       " 4.277154853739088,\n",
       " 4.90299812684022,\n",
       " 3.6968961175044215,\n",
       " 5.963768511543972,\n",
       " 3.362009737028263,\n",
       " 3.436210360816839,\n",
       " 2.48596661112504,\n",
       " 4.210630408122297,\n",
       " 2.049918205336853,\n",
       " 3.6128778899726646,\n",
       " 2.9460020095873736,\n",
       " 4.977446547400305,\n",
       " 3.059339268916863,\n",
       " 3.040566820426702,\n",
       " 1.6329597995307885,\n",
       " 2.1822108790879957,\n",
       " 2.9755295291907524,\n",
       " 1.563608536784269]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "cfcb394c-49a0-4c38-a008-de2d2e38be3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7.55\n",
       "1      2.25\n",
       "2      3.95\n",
       "3      3.35\n",
       "4      2.85\n",
       "5      5.70\n",
       "6      3.38\n",
       "7      3.50\n",
       "8     10.70\n",
       "9      3.40\n",
       "10     8.70\n",
       "11     4.95\n",
       "12     2.32\n",
       "13     2.60\n",
       "14     2.50\n",
       "15    11.70\n",
       "16     8.20\n",
       "17     3.26\n",
       "18     6.80\n",
       "19     3.30\n",
       "20     4.00\n",
       "21     3.50\n",
       "22     3.40\n",
       "23     4.00\n",
       "24     4.60\n",
       "25     3.45\n",
       "26     5.63\n",
       "27     3.30\n",
       "28     3.30\n",
       "29     3.00\n",
       "30     3.99\n",
       "31     2.40\n",
       "32     2.80\n",
       "33     2.45\n",
       "34     3.85\n",
       "35     2.86\n",
       "36     3.05\n",
       "37     2.50\n",
       "38     2.53\n",
       "39     3.00\n",
       "40     2.27\n",
       "Name: PriceTotal, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PriceTotal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "827b4288-938f-441d-990d-366cf6db3758",
   "metadata": {},
   "outputs": [],
   "source": [
    "error = df['PriceTotal']-y_me"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "cdfbc629-49d2-472f-9864-b45acee2d80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the predicted price values with real ones.\n",
    "\n",
    "h=sns.jointplot(y=y_me,x=df['PriceTotal'],kind='reg')\n",
    "\n",
    "h.set_axis_labels('Real Prices', 'Predicted Prices', fontsize=16)\n",
    "\n",
    "h.ax_marg_y.grid('on')\n",
    "h.ax_marg_x.grid('on')\n",
    "\n",
    "# labels appear outside of plot area, so auto-adjust\n",
    "h.figure.tight_layout() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "6941ed6b-7853-4592-b873-41dc3a331db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2=sns.jointplot(y=error,x=df['PriceTotal'],kind='resid')\n",
    "h2.set_axis_labels('Real Prices', 'Error', fontsize=16)\n",
    "\n",
    "h2.ax_marg_y.grid('on')\n",
    "h2.ax_marg_x.grid('on')\n",
    "\n",
    "# labels appear outside of plot area, so auto-adjust\n",
    "h2.figure.tight_layout() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "72d68e8f-7244-467a-a086-b471fd40f94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h3=sns.jointplot(y=error,x=df['PriceTotal'],kind='kde')\n",
    "h3.set_axis_labels('Real Prices', 'Error', fontsize=16)\n",
    "# labels appear outside of plot area, so auto-adjust\n",
    "h3.figure.tight_layout() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b1fa7d-6e43-419e-8f37-66f2d548a364",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}