Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbad24c5-0c72-44cd-9630-96dc129a07c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e853f50-96d4-4a2c-96e3-86c8c2edf197",
   "metadata": {},
   "outputs": [],
   "source": [
    "Transformed_Housing_Data = pd.read_csv('Transformed_Housing_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c70a340f-522e-44ef-afdb-b847127dc421",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21609 entries, 0 to 21608\n",
      "Data columns (total 21 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   ID                                         21609 non-null  int64  \n",
      " 1   Sale Price                                 21609 non-null  float64\n",
      " 2   No of Bedrooms                             21609 non-null  int64  \n",
      " 3   No of Bathrooms                            21609 non-null  float64\n",
      " 4   Flat Area (in Sqft)                        21609 non-null  float64\n",
      " 5   Lot Area (in Sqft)                         21609 non-null  float64\n",
      " 6   No of Floors                               21609 non-null  float64\n",
      " 7   Waterfront View                            21609 non-null  object \n",
      " 8   No of Times Visited                        21609 non-null  int64  \n",
      " 9   Condition of the House                     21609 non-null  object \n",
      " 10  Overall Grade                              21609 non-null  int64  \n",
      " 11  Area of the House from Basement (in Sqft)  21609 non-null  float64\n",
      " 12  Basement Area (in Sqft)                    21609 non-null  int64  \n",
      " 13  Age of House (in Years)                    21609 non-null  int64  \n",
      " 14  Zipcode                                    21609 non-null  float64\n",
      " 15  Latitude                                   21609 non-null  float64\n",
      " 16  Longitude                                  21609 non-null  float64\n",
      " 17  Living Area after Renovation (in Sqft)     21609 non-null  float64\n",
      " 18  Lot Area after Renovation (in Sqft)        21609 non-null  int64  \n",
      " 19  Ever Renovated                             21609 non-null  object \n",
      " 20  Years Since Renovation                     21609 non-null  int64  \n",
      "dtypes: float64(10), int64(8), object(3)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Transformed_Housing_Data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8d42909-4130-44bb-aa32-21c739f1a96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Transformed_Housing_Data.drop(columns = 'ID' , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a300481-b694-4952-9cfc-95595a31c348",
   "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>Sale Price</th>\n",
       "      <th>No of Bedrooms</th>\n",
       "      <th>No of Bathrooms</th>\n",
       "      <th>Flat Area (in Sqft)</th>\n",
       "      <th>Lot Area (in Sqft)</th>\n",
       "      <th>No of Floors</th>\n",
       "      <th>Waterfront View</th>\n",
       "      <th>No of Times Visited</th>\n",
       "      <th>Condition of the House</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Area of the House from Basement (in Sqft)</th>\n",
       "      <th>Basement Area (in Sqft)</th>\n",
       "      <th>Age of House (in Years)</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Living Area after Renovation (in Sqft)</th>\n",
       "      <th>Lot Area after Renovation (in Sqft)</th>\n",
       "      <th>Ever Renovated</th>\n",
       "      <th>Years Since Renovation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>98178.0</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>67</td>\n",
       "      <td>98125.0</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>98028.0</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>53</td>\n",
       "      <td>98136.0</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>98074.0</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21604</th>\n",
       "      <td>360000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>98103.0</td>\n",
       "      <td>47.6993</td>\n",
       "      <td>-122.346</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>1509</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21605</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2310.0</td>\n",
       "      <td>5813.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>2310.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>98146.0</td>\n",
       "      <td>47.5107</td>\n",
       "      <td>-122.362</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>7200</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21606</th>\n",
       "      <td>402101.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>98144.0</td>\n",
       "      <td>47.5944</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21607</th>\n",
       "      <td>400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>98027.0</td>\n",
       "      <td>47.5345</td>\n",
       "      <td>-122.069</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>1287</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21608</th>\n",
       "      <td>325000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>98144.0</td>\n",
       "      <td>47.5941</td>\n",
       "      <td>-122.299</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1357</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21609 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sale Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0        221900.0               3             1.00               1180.0   \n",
       "1        538000.0               3             2.25               2570.0   \n",
       "2        180000.0               2             1.00                770.0   \n",
       "3        604000.0               4             3.00               1960.0   \n",
       "4        510000.0               3             2.00               1680.0   \n",
       "...           ...             ...              ...                  ...   \n",
       "21604    360000.0               3             2.50               1530.0   \n",
       "21605    400000.0               4             2.50               2310.0   \n",
       "21606    402101.0               2             0.75               1020.0   \n",
       "21607    400000.0               3             2.50               1600.0   \n",
       "21608    325000.0               2             0.75               1020.0   \n",
       "\n",
       "       Lot Area (in Sqft)  No of Floors Waterfront View  No of Times Visited  \\\n",
       "0                  5650.0           1.0              No                    0   \n",
       "1                  7242.0           2.0              No                    0   \n",
       "2                 10000.0           1.0              No                    0   \n",
       "3                  5000.0           1.0              No                    0   \n",
       "4                  8080.0           1.0              No                    0   \n",
       "...                   ...           ...             ...                  ...   \n",
       "21604              1131.0           3.0              No                    0   \n",
       "21605              5813.0           2.0              No                    0   \n",
       "21606              1350.0           2.0              No                    0   \n",
       "21607              2388.0           2.0              No                    0   \n",
       "21608              1076.0           2.0              No                    0   \n",
       "\n",
       "      Condition of the House  Overall Grade  \\\n",
       "0                       Fair              7   \n",
       "1                       Fair              7   \n",
       "2                       Fair              6   \n",
       "3                  Excellent              7   \n",
       "4                       Fair              8   \n",
       "...                      ...            ...   \n",
       "21604                   Fair              8   \n",
       "21605                   Fair              8   \n",
       "21606                   Fair              7   \n",
       "21607                   Fair              8   \n",
       "21608                   Fair              7   \n",
       "\n",
       "       Area of the House from Basement (in Sqft)  Basement Area (in Sqft)  \\\n",
       "0                                         1180.0                        0   \n",
       "1                                         2170.0                      400   \n",
       "2                                          770.0                        0   \n",
       "3                                         1050.0                      910   \n",
       "4                                         1680.0                        0   \n",
       "...                                          ...                      ...   \n",
       "21604                                     1530.0                        0   \n",
       "21605                                     2310.0                        0   \n",
       "21606                                     1020.0                        0   \n",
       "21607                                     1600.0                        0   \n",
       "21608                                     1020.0                        0   \n",
       "\n",
       "       Age of House (in Years)  Zipcode  Latitude  Longitude  \\\n",
       "0                           63  98178.0   47.5112   -122.257   \n",
       "1                           67  98125.0   47.7210   -122.319   \n",
       "2                           85  98028.0   47.7379   -122.233   \n",
       "3                           53  98136.0   47.5208   -122.393   \n",
       "4                           31  98074.0   47.6168   -122.045   \n",
       "...                        ...      ...       ...        ...   \n",
       "21604                        9  98103.0   47.6993   -122.346   \n",
       "21605                        4  98146.0   47.5107   -122.362   \n",
       "21606                        9  98144.0   47.5944   -122.299   \n",
       "21607                       14  98027.0   47.5345   -122.069   \n",
       "21608                       10  98144.0   47.5941   -122.299   \n",
       "\n",
       "       Living Area after Renovation (in Sqft)  \\\n",
       "0                                      1340.0   \n",
       "1                                      1690.0   \n",
       "2                                      2720.0   \n",
       "3                                      1360.0   \n",
       "4                                      1800.0   \n",
       "...                                       ...   \n",
       "21604                                  1530.0   \n",
       "21605                                  1830.0   \n",
       "21606                                  1020.0   \n",
       "21607                                  1410.0   \n",
       "21608                                  1020.0   \n",
       "\n",
       "       Lot Area after Renovation (in Sqft) Ever Renovated  \\\n",
       "0                                     5650             No   \n",
       "1                                     7639            Yes   \n",
       "2                                     8062             No   \n",
       "3                                     5000             No   \n",
       "4                                     7503             No   \n",
       "...                                    ...            ...   \n",
       "21604                                 1509             No   \n",
       "21605                                 7200             No   \n",
       "21606                                 2007             No   \n",
       "21607                                 1287             No   \n",
       "21608                                 1357             No   \n",
       "\n",
       "       Years Since Renovation  \n",
       "0                           0  \n",
       "1                          26  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "...                       ...  \n",
       "21604                       0  \n",
       "21605                       0  \n",
       "21606                       0  \n",
       "21607                       0  \n",
       "21608                       0  \n",
       "\n",
       "[21609 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab17432b-cb85-4a99-a69d-869cc932b020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Fair\n",
       "1         Fair\n",
       "2         Fair\n",
       "3    Excellent\n",
       "4         Fair\n",
       "5         Fair\n",
       "6         Fair\n",
       "7         Fair\n",
       "8         Fair\n",
       "9         Fair\n",
       "Name: Condition of the House, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eyeballing\n",
    "Transformed_Housing_Data['Condition of the House'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a84befd6-aa44-4559-a92e-14552b16bdec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Condition of the House\n",
       "Fair         14028\n",
       "Good          5678\n",
       "Excellent     1701\n",
       "Okay           172\n",
       "Bad             30\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data['Condition of the House'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "231ee91c-f1f3-49de-8ef3-b11fe74a922e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Condition of the House'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "Transformed_Housing_Data.groupby ('Condition of the House',\n",
    "                                 )['Sale Price'].mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46a2a557-d976-4e38-af9f-2a2c1014372f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Condition of the House'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "#in ascending order making it \n",
    "Transformed_Housing_Data.groupby ('Condition of the House',\n",
    "                                 )['Sale Price'].mean().sort_values().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96dc7e84-1be2-4371-a4c7-c9280654334f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Waterfront View'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "#Other categorical Variables can be done in same \n",
    "Transformed_Housing_Data.groupby ('Waterfront View',\n",
    "                                 )['Sale Price'].mean().sort_values().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d2e37d3-267c-4f2b-8641-5082757b62bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Ever Renovated'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "#Other categorical Variables can be done in same \n",
    "Transformed_Housing_Data.groupby ('Ever Renovated',\n",
    "                                 )['Sale Price'].mean().sort_values().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cbcda0f-59cd-4ab1-81f9-d8c5d2bd52b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Zipcode'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "#Other categorical Variables can be done in same \n",
    "Transformed_Housing_Data.groupby ('Zipcode',\n",
    "                                 )['Sale Price'].mean().sort_values().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7278089-bf78-4fef-8f7d-2fc282078c01",
   "metadata": {},
   "source": [
    "# Deleting Blankspaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aeb839f5-7479-45d2-9b2f-69fbd0e33d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import ols \n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cdb547d1-348f-41f3-9c7f-595ef0dc195c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the variable\n",
    "Transformed_Housing_Data = Transformed_Housing_Data.rename(columns = {'Sale Price' : 'Sale_Price'})\n",
    "Transformed_Housing_Data = Transformed_Housing_Data.rename(columns = {'Condition of the House' : 'Condition_of_the_House'})\n",
    "Transformed_Housing_Data = Transformed_Housing_Data.rename(columns = {'Ever Renovated' : 'Ever_Renovated'})\n",
    "Transformed_Housing_Data = Transformed_Housing_Data.rename(columns = {'Waterfront View' : 'Waterfront_View'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7587ef2-0d10-4b78-93d6-ec3784539857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21609 entries, 0 to 21608\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   Sale_Price                                 21609 non-null  float64\n",
      " 1   No of Bedrooms                             21609 non-null  int64  \n",
      " 2   No of Bathrooms                            21609 non-null  float64\n",
      " 3   Flat Area (in Sqft)                        21609 non-null  float64\n",
      " 4   Lot Area (in Sqft)                         21609 non-null  float64\n",
      " 5   No of Floors                               21609 non-null  float64\n",
      " 6   Waterfront_View                            21609 non-null  object \n",
      " 7   No of Times Visited                        21609 non-null  int64  \n",
      " 8   Condition_of_the_House                     21609 non-null  object \n",
      " 9   Overall Grade                              21609 non-null  int64  \n",
      " 10  Area of the House from Basement (in Sqft)  21609 non-null  float64\n",
      " 11  Basement Area (in Sqft)                    21609 non-null  int64  \n",
      " 12  Age of House (in Years)                    21609 non-null  int64  \n",
      " 13  Zipcode                                    21609 non-null  float64\n",
      " 14  Latitude                                   21609 non-null  float64\n",
      " 15  Longitude                                  21609 non-null  float64\n",
      " 16  Living Area after Renovation (in Sqft)     21609 non-null  float64\n",
      " 17  Lot Area after Renovation (in Sqft)        21609 non-null  int64  \n",
      " 18  Ever_Renovated                             21609 non-null  object \n",
      " 19  Years Since Renovation                     21609 non-null  int64  \n",
      "dtypes: float64(10), int64(7), object(3)\n",
      "memory usage: 3.3+ MB\n"
     ]
    },
    {
     "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>Sale_Price</th>\n",
       "      <th>No of Bedrooms</th>\n",
       "      <th>No of Bathrooms</th>\n",
       "      <th>Flat Area (in Sqft)</th>\n",
       "      <th>Lot Area (in Sqft)</th>\n",
       "      <th>No of Floors</th>\n",
       "      <th>Waterfront_View</th>\n",
       "      <th>No of Times Visited</th>\n",
       "      <th>Condition_of_the_House</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Area of the House from Basement (in Sqft)</th>\n",
       "      <th>Basement Area (in Sqft)</th>\n",
       "      <th>Age of House (in Years)</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Living Area after Renovation (in Sqft)</th>\n",
       "      <th>Lot Area after Renovation (in Sqft)</th>\n",
       "      <th>Ever_Renovated</th>\n",
       "      <th>Years Since Renovation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>98178.0</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>67</td>\n",
       "      <td>98125.0</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>98028.0</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>53</td>\n",
       "      <td>98136.0</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>98074.0</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0    221900.0               3             1.00               1180.0   \n",
       "1    538000.0               3             2.25               2570.0   \n",
       "2    180000.0               2             1.00                770.0   \n",
       "3    604000.0               4             3.00               1960.0   \n",
       "4    510000.0               3             2.00               1680.0   \n",
       "\n",
       "   Lot Area (in Sqft)  No of Floors Waterfront_View  No of Times Visited  \\\n",
       "0              5650.0           1.0              No                    0   \n",
       "1              7242.0           2.0              No                    0   \n",
       "2             10000.0           1.0              No                    0   \n",
       "3              5000.0           1.0              No                    0   \n",
       "4              8080.0           1.0              No                    0   \n",
       "\n",
       "  Condition_of_the_House  Overall Grade  \\\n",
       "0                   Fair              7   \n",
       "1                   Fair              7   \n",
       "2                   Fair              6   \n",
       "3              Excellent              7   \n",
       "4                   Fair              8   \n",
       "\n",
       "   Area of the House from Basement (in Sqft)  Basement Area (in Sqft)  \\\n",
       "0                                     1180.0                        0   \n",
       "1                                     2170.0                      400   \n",
       "2                                      770.0                        0   \n",
       "3                                     1050.0                      910   \n",
       "4                                     1680.0                        0   \n",
       "\n",
       "   Age of House (in Years)  Zipcode  Latitude  Longitude  \\\n",
       "0                       63  98178.0   47.5112   -122.257   \n",
       "1                       67  98125.0   47.7210   -122.319   \n",
       "2                       85  98028.0   47.7379   -122.233   \n",
       "3                       53  98136.0   47.5208   -122.393   \n",
       "4                       31  98074.0   47.6168   -122.045   \n",
       "\n",
       "   Living Area after Renovation (in Sqft)  \\\n",
       "0                                  1340.0   \n",
       "1                                  1690.0   \n",
       "2                                  2720.0   \n",
       "3                                  1360.0   \n",
       "4                                  1800.0   \n",
       "\n",
       "   Lot Area after Renovation (in Sqft) Ever_Renovated  Years Since Renovation  \n",
       "0                                 5650             No                       0  \n",
       "1                                 7639            Yes                      26  \n",
       "2                                 8062             No                       0  \n",
       "3                                 5000             No                       0  \n",
       "4                                 7503             No                       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking is the above variables are changed\n",
    "Transformed_Housing_Data.info()\n",
    "Transformed_Housing_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c66eee50-30bc-488e-ba7e-1c4b001ae5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now using ANOVA\n",
    "mod = ols('Sale_Price ~ Condition_of_the_House', data = Transformed_Housing_Data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af983d3f-86ff-42e7-9efc-84922eb2a1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating One Way anova table\n",
    "Anova_Table = sm.stats.anova_lm(mod,typ = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a47a364c-7ffd-4b8d-8b83-d4943e345da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              sum_sq       df         F        PR(>F)\n",
      "Condition_of_the_House  1.441831e+13      4.0  58.25563  5.447605e-49\n",
      "Residual                1.336752e+15  21604.0       NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "print(Anova_Table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e26f8a5d-3e85-4260-a3d5-d35020e4025d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       sum_sq       df           F         PR(>F)\n",
      "Waterfront_View  3.283737e+13      1.0  538.192683  1.275558e-117\n",
      "Residual         1.318333e+15  21607.0         NaN            NaN\n"
     ]
    }
   ],
   "source": [
    "# Doing for other categoricals\n",
    "mod = ols('Sale_Price ~ Waterfront_View', data = Transformed_Housing_Data).fit()\n",
    "Anova_Table = sm.stats.anova_lm(mod,typ = 2)\n",
    "print(Anova_Table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa9b1ea6-6418-42cc-bc7c-540c6ad99bb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      sum_sq       df           F        PR(>F)\n",
      "Ever_Renovated  1.998463e+13      1.0  324.378469  5.419055e-72\n",
      "Residual        1.331185e+15  21607.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "mod = ols('Sale_Price ~ Ever_Renovated', data = Transformed_Housing_Data).fit()\n",
    "Anova_Table = sm.stats.anova_lm(mod,typ = 2)\n",
    "print(Anova_Table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "66293c3e-b2d4-4298-b4ad-2ac770e86fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                sum_sq       df          F        PR(>F)\n",
      "Zipcode   2.503765e+12      1.0  40.112844  2.444308e-10\n",
      "Residual  1.348666e+15  21607.0        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "mod = ols('Sale_Price ~ Zipcode', data = Transformed_Housing_Data).fit()\n",
    "Anova_Table = sm.stats.anova_lm(mod,typ = 2)\n",
    "print(Anova_Table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e6d87e-864a-40ee-a258-8a772e8a4701",
   "metadata": {},
   "source": [
    "# We can see in all the three cases the p value is less than 0.05 and we reject the null hypothesis and accept alternate hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bdc62b66-ed4e-4633-b131-9ad5d841a855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Dummy Variables\n",
    "Transformed_Housing_Data = pd.get_dummies(Transformed_Housing_Data,\n",
    "                                         columns = ['Condition_of_the_House'],\n",
    "                                         drop_first = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "abcf188c-fd25-4a4d-b766-cc4e1d6c51b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>No of Bedrooms</th>\n",
       "      <th>No of Bathrooms</th>\n",
       "      <th>Flat Area (in Sqft)</th>\n",
       "      <th>Lot Area (in Sqft)</th>\n",
       "      <th>No of Floors</th>\n",
       "      <th>Waterfront_View</th>\n",
       "      <th>No of Times Visited</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Area of the House from Basement (in Sqft)</th>\n",
       "      <th>...</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Living Area after Renovation (in Sqft)</th>\n",
       "      <th>Lot Area after Renovation (in Sqft)</th>\n",
       "      <th>Ever_Renovated</th>\n",
       "      <th>Years Since Renovation</th>\n",
       "      <th>Condition_of_the_House_Excellent</th>\n",
       "      <th>Condition_of_the_House_Fair</th>\n",
       "      <th>Condition_of_the_House_Good</th>\n",
       "      <th>Condition_of_the_House_Okay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>...</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0    221900.0               3             1.00               1180.0   \n",
       "1    538000.0               3             2.25               2570.0   \n",
       "2    180000.0               2             1.00                770.0   \n",
       "3    604000.0               4             3.00               1960.0   \n",
       "4    510000.0               3             2.00               1680.0   \n",
       "\n",
       "   Lot Area (in Sqft)  No of Floors Waterfront_View  No of Times Visited  \\\n",
       "0              5650.0           1.0              No                    0   \n",
       "1              7242.0           2.0              No                    0   \n",
       "2             10000.0           1.0              No                    0   \n",
       "3              5000.0           1.0              No                    0   \n",
       "4              8080.0           1.0              No                    0   \n",
       "\n",
       "   Overall Grade  Area of the House from Basement (in Sqft)  ...  Latitude  \\\n",
       "0              7                                     1180.0  ...   47.5112   \n",
       "1              7                                     2170.0  ...   47.7210   \n",
       "2              6                                      770.0  ...   47.7379   \n",
       "3              7                                     1050.0  ...   47.5208   \n",
       "4              8                                     1680.0  ...   47.6168   \n",
       "\n",
       "   Longitude  Living Area after Renovation (in Sqft)  \\\n",
       "0   -122.257                                  1340.0   \n",
       "1   -122.319                                  1690.0   \n",
       "2   -122.233                                  2720.0   \n",
       "3   -122.393                                  1360.0   \n",
       "4   -122.045                                  1800.0   \n",
       "\n",
       "   Lot Area after Renovation (in Sqft)  Ever_Renovated  \\\n",
       "0                                 5650              No   \n",
       "1                                 7639             Yes   \n",
       "2                                 8062              No   \n",
       "3                                 5000              No   \n",
       "4                                 7503              No   \n",
       "\n",
       "   Years Since Renovation  Condition_of_the_House_Excellent  \\\n",
       "0                       0                             False   \n",
       "1                      26                             False   \n",
       "2                       0                             False   \n",
       "3                       0                              True   \n",
       "4                       0                             False   \n",
       "\n",
       "  Condition_of_the_House_Fair  Condition_of_the_House_Good  \\\n",
       "0                        True                        False   \n",
       "1                        True                        False   \n",
       "2                        True                        False   \n",
       "3                       False                        False   \n",
       "4                        True                        False   \n",
       "\n",
       "   Condition_of_the_House_Okay  \n",
       "0                        False  \n",
       "1                        False  \n",
       "2                        False  \n",
       "3                        False  \n",
       "4                        False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0da72ecf-9e0c-4122-a320-87c412793fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Transformed_Housing_Data = pd.get_dummies(Transformed_Housing_Data,\n",
    "                                         columns = ['Ever_Renovated','Waterfront_View'],\n",
    "                                         drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2e3f729-aefa-4b42-9140-21b6a4e8f618",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Basement Area (in Sqft)</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Living Area after Renovation (in Sqft)</th>\n",
       "      <th>Lot Area after Renovation (in Sqft)</th>\n",
       "      <th>Years Since Renovation</th>\n",
       "      <th>Condition_of_the_House_Excellent</th>\n",
       "      <th>Condition_of_the_House_Fair</th>\n",
       "      <th>Condition_of_the_House_Good</th>\n",
       "      <th>Condition_of_the_House_Okay</th>\n",
       "      <th>Ever_Renovated_Yes</th>\n",
       "      <th>Waterfront_View_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0    221900.0               3             1.00               1180.0   \n",
       "1    538000.0               3             2.25               2570.0   \n",
       "2    180000.0               2             1.00                770.0   \n",
       "3    604000.0               4             3.00               1960.0   \n",
       "4    510000.0               3             2.00               1680.0   \n",
       "\n",
       "   Lot Area (in Sqft)  No of Floors  No of Times Visited  Overall Grade  \\\n",
       "0              5650.0           1.0                    0              7   \n",
       "1              7242.0           2.0                    0              7   \n",
       "2             10000.0           1.0                    0              6   \n",
       "3              5000.0           1.0                    0              7   \n",
       "4              8080.0           1.0                    0              8   \n",
       "\n",
       "   Area of the House from Basement (in Sqft)  Basement Area (in Sqft)  ...  \\\n",
       "0                                     1180.0                        0  ...   \n",
       "1                                     2170.0                      400  ...   \n",
       "2                                      770.0                        0  ...   \n",
       "3                                     1050.0                      910  ...   \n",
       "4                                     1680.0                        0  ...   \n",
       "\n",
       "   Longitude  Living Area after Renovation (in Sqft)  \\\n",
       "0   -122.257                                  1340.0   \n",
       "1   -122.319                                  1690.0   \n",
       "2   -122.233                                  2720.0   \n",
       "3   -122.393                                  1360.0   \n",
       "4   -122.045                                  1800.0   \n",
       "\n",
       "   Lot Area after Renovation (in Sqft)  Years Since Renovation  \\\n",
       "0                                 5650                       0   \n",
       "1                                 7639                      26   \n",
       "2                                 8062                       0   \n",
       "3                                 5000                       0   \n",
       "4                                 7503                       0   \n",
       "\n",
       "   Condition_of_the_House_Excellent  Condition_of_the_House_Fair  \\\n",
       "0                             False                         True   \n",
       "1                             False                         True   \n",
       "2                             False                         True   \n",
       "3                              True                        False   \n",
       "4                             False                         True   \n",
       "\n",
       "   Condition_of_the_House_Good  Condition_of_the_House_Okay  \\\n",
       "0                        False                        False   \n",
       "1                        False                        False   \n",
       "2                        False                        False   \n",
       "3                        False                        False   \n",
       "4                        False                        False   \n",
       "\n",
       "   Ever_Renovated_Yes  Waterfront_View_Yes  \n",
       "0               False                False  \n",
       "1                True                False  \n",
       "2               False                False  \n",
       "3               False                False  \n",
       "4               False                False  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b68459-3b57-4e98-9d8c-4312637dbd09",
   "metadata": {},
   "source": [
    "# Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d54cc638-5091-4d3e-a5d2-64657ece5b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Zip_Table = Transformed_Housing_Data.groupby('Zipcode').agg({'Sale_Price' : 'mean'}).sort_values('Sale_Price',ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c75e88aa-d737-45ac-9065-d8ee39fcd2d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sale_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zipcode</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98002.0</th>\n",
       "      <td>234284.035176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98168.0</th>\n",
       "      <td>240328.371747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98032.0</th>\n",
       "      <td>251296.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98001.0</th>\n",
       "      <td>280804.690608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98148.0</th>\n",
       "      <td>284908.596491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sale_Price\n",
       "Zipcode               \n",
       "98002.0  234284.035176\n",
       "98168.0  240328.371747\n",
       "98032.0  251296.240000\n",
       "98001.0  280804.690608\n",
       "98148.0  284908.596491"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Zip_Table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "145eb937-88f3-4fef-8cb7-d8fabdf12b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating 10 bins\n",
    "Zip_Table['Zipcode_Group'] = pd.cut(Zip_Table['Sale_Price'],bins =10 ,\n",
    "                                    labels = ['Zipcode_Group_0',\n",
    "                                             'Zipcode_Group_1',\n",
    "                                             'Zipcode_Group_2',\n",
    "                                             'Zipcode_Group_3',\n",
    "                                             'Zipcode_Group_4',\n",
    "                                             'Zipcode_Group_5',\n",
    "                                             'Zipcode_Group_6',\n",
    "                                             'Zipcode_Group_7',\n",
    "                                             'Zipcode_Group_8',\n",
    "                                             'Zipcode_Group_9'],\n",
    "                                    include_lowest = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "36226ca6-45a7-4295-bf8d-793253fdba64",
   "metadata": {},
   "outputs": [],
   "source": [
    "Zip_Table = Zip_Table.drop(columns = \"Sale_Price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e648c39-298d-405c-8917-2536b4a66c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining Table with Original Table using merge function in pandas library\n",
    "Transformed_Housing_Data = pd.merge(Transformed_Housing_Data,Zip_Table,\n",
    "                                    left_on = 'Zipcode',\n",
    "                                    how = 'left',\n",
    "                                    right_index = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d6a6d9c3-bd82-492b-b075-c5ba966a6e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "Transformed_Housing_Data = Transformed_Housing_Data.drop(columns = 'Zipcode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "46df6fba-3f1c-44ec-a624-7ae9c5899470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>No of Bedrooms</th>\n",
       "      <th>No of Bathrooms</th>\n",
       "      <th>Flat Area (in Sqft)</th>\n",
       "      <th>Lot Area (in Sqft)</th>\n",
       "      <th>No of Floors</th>\n",
       "      <th>No of Times Visited</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Area of the House from Basement (in Sqft)</th>\n",
       "      <th>Basement Area (in Sqft)</th>\n",
       "      <th>...</th>\n",
       "      <th>Living Area after Renovation (in Sqft)</th>\n",
       "      <th>Lot Area after Renovation (in Sqft)</th>\n",
       "      <th>Years Since Renovation</th>\n",
       "      <th>Condition_of_the_House_Excellent</th>\n",
       "      <th>Condition_of_the_House_Fair</th>\n",
       "      <th>Condition_of_the_House_Good</th>\n",
       "      <th>Condition_of_the_House_Okay</th>\n",
       "      <th>Ever_Renovated_Yes</th>\n",
       "      <th>Waterfront_View_Yes</th>\n",
       "      <th>Zipcode_Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>5650</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zipcode_Group_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>7639</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Zipcode_Group_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>8062</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zipcode_Group_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>...</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zipcode_Group_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>7503</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zipcode_Group_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0    221900.0               3             1.00               1180.0   \n",
       "1    538000.0               3             2.25               2570.0   \n",
       "2    180000.0               2             1.00                770.0   \n",
       "3    604000.0               4             3.00               1960.0   \n",
       "4    510000.0               3             2.00               1680.0   \n",
       "\n",
       "   Lot Area (in Sqft)  No of Floors  No of Times Visited  Overall Grade  \\\n",
       "0              5650.0           1.0                    0              7   \n",
       "1              7242.0           2.0                    0              7   \n",
       "2             10000.0           1.0                    0              6   \n",
       "3              5000.0           1.0                    0              7   \n",
       "4              8080.0           1.0                    0              8   \n",
       "\n",
       "   Area of the House from Basement (in Sqft)  Basement Area (in Sqft)  ...  \\\n",
       "0                                     1180.0                        0  ...   \n",
       "1                                     2170.0                      400  ...   \n",
       "2                                      770.0                        0  ...   \n",
       "3                                     1050.0                      910  ...   \n",
       "4                                     1680.0                        0  ...   \n",
       "\n",
       "   Living Area after Renovation (in Sqft)  \\\n",
       "0                                  1340.0   \n",
       "1                                  1690.0   \n",
       "2                                  2720.0   \n",
       "3                                  1360.0   \n",
       "4                                  1800.0   \n",
       "\n",
       "   Lot Area after Renovation (in Sqft)  Years Since Renovation  \\\n",
       "0                                 5650                       0   \n",
       "1                                 7639                      26   \n",
       "2                                 8062                       0   \n",
       "3                                 5000                       0   \n",
       "4                                 7503                       0   \n",
       "\n",
       "   Condition_of_the_House_Excellent  Condition_of_the_House_Fair  \\\n",
       "0                             False                         True   \n",
       "1                             False                         True   \n",
       "2                             False                         True   \n",
       "3                              True                        False   \n",
       "4                             False                         True   \n",
       "\n",
       "   Condition_of_the_House_Good  Condition_of_the_House_Okay  \\\n",
       "0                        False                        False   \n",
       "1                        False                        False   \n",
       "2                        False                        False   \n",
       "3                        False                        False   \n",
       "4                        False                        False   \n",
       "\n",
       "   Ever_Renovated_Yes  Waterfront_View_Yes    Zipcode_Group  \n",
       "0               False                False  Zipcode_Group_0  \n",
       "1                True                False  Zipcode_Group_2  \n",
       "2               False                False  Zipcode_Group_2  \n",
       "3               False                False  Zipcode_Group_3  \n",
       "4               False                False  Zipcode_Group_4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4e8c5d35-566f-4336-8688-ffb07d11362e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dummy variable for Zipcode Group\n",
    "Transformed_Housing_Data = pd.get_dummies(Transformed_Housing_Data,\n",
    "                                         columns = ['Zipcode_Group'],\n",
    "                                         drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "93ef314d-95a4-401f-85ef-82e266857eac",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sale_Price</th>\n",
       "      <th>No of Bedrooms</th>\n",
       "      <th>No of Bathrooms</th>\n",
       "      <th>Flat Area (in Sqft)</th>\n",
       "      <th>Lot Area (in Sqft)</th>\n",
       "      <th>No of Floors</th>\n",
       "      <th>No of Times Visited</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Area of the House from Basement (in Sqft)</th>\n",
       "      <th>Basement Area (in Sqft)</th>\n",
       "      <th>...</th>\n",
       "      <th>Waterfront_View_Yes</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_1</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_2</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_3</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_4</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_5</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_6</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_7</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_8</th>\n",
       "      <th>Zipcode_Group_Zipcode_Group_9</th>\n",
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       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570.0</td>\n",
       "      <td>7242.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>8080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sale_Price  No of Bedrooms  No of Bathrooms  Flat Area (in Sqft)  \\\n",
       "0    221900.0               3             1.00               1180.0   \n",
       "1    538000.0               3             2.25               2570.0   \n",
       "2    180000.0               2             1.00                770.0   \n",
       "3    604000.0               4             3.00               1960.0   \n",
       "4    510000.0               3             2.00               1680.0   \n",
       "\n",
       "   Lot Area (in Sqft)  No of Floors  No of Times Visited  Overall Grade  \\\n",
       "0              5650.0           1.0                    0              7   \n",
       "1              7242.0           2.0                    0              7   \n",
       "2             10000.0           1.0                    0              6   \n",
       "3              5000.0           1.0                    0              7   \n",
       "4              8080.0           1.0                    0              8   \n",
       "\n",
       "   Area of the House from Basement (in Sqft)  Basement Area (in Sqft)  ...  \\\n",
       "0                                     1180.0                        0  ...   \n",
       "1                                     2170.0                      400  ...   \n",
       "2                                      770.0                        0  ...   \n",
       "3                                     1050.0                      910  ...   \n",
       "4                                     1680.0                        0  ...   \n",
       "\n",
       "   Waterfront_View_Yes  Zipcode_Group_Zipcode_Group_1  \\\n",
       "0                False                          False   \n",
       "1                False                          False   \n",
       "2                False                          False   \n",
       "3                False                          False   \n",
       "4                False                          False   \n",
       "\n",
       "   Zipcode_Group_Zipcode_Group_2  Zipcode_Group_Zipcode_Group_3  \\\n",
       "0                          False                          False   \n",
       "1                           True                          False   \n",
       "2                           True                          False   \n",
       "3                          False                           True   \n",
       "4                          False                          False   \n",
       "\n",
       "   Zipcode_Group_Zipcode_Group_4  Zipcode_Group_Zipcode_Group_5  \\\n",
       "0                          False                          False   \n",
       "1                          False                          False   \n",
       "2                          False                          False   \n",
       "3                          False                          False   \n",
       "4                           True                          False   \n",
       "\n",
       "   Zipcode_Group_Zipcode_Group_6  Zipcode_Group_Zipcode_Group_7  \\\n",
       "0                          False                          False   \n",
       "1                          False                          False   \n",
       "2                          False                          False   \n",
       "3                          False                          False   \n",
       "4                          False                          False   \n",
       "\n",
       "   Zipcode_Group_Zipcode_Group_8  Zipcode_Group_Zipcode_Group_9  \n",
       "0                          False                          False  \n",
       "1                          False                          False  \n",
       "2                          False                          False  \n",
       "3                          False                          False  \n",
       "4                          False                          False  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transformed_Housing_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e637e7d3-38da-4a30-b294-c6975fc0974a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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