preproc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the csv file\n",
    "data = pd.read_csv('housedata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_frame = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace missing values by mean\n",
    "for i in data.columns:\n",
    "    j=data[i].mode()\n",
    "    completed_frame = completed_frame.replace({i:np.NaN},j[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Area</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Furnishing</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Price</th>\n",
       "      <th>Status</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Type</th>\n",
       "      <th>Per_Sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Rohini Sector 25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>J R Designers Floors, Rohini Sector 24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>6667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Furnished</td>\n",
       "      <td>Citizen Apartment, Rohini Sector 13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>Resale</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>6667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Rohini Sector 24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>Resale</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>6667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Rohini Sector 24 carpet area 650 sqft status R...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6200000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>6667.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>4118.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Chittaranjan Park</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55000000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Chittaranjan Park</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>Resale</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>875.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Semi-Furnished</td>\n",
       "      <td>Chittaranjan Park</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>990.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Chittaranjan Park Block A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>Resale</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>11050.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>Chittaranjan Park</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18500000</td>\n",
       "      <td>Ready_to_move</td>\n",
       "      <td>New_Property</td>\n",
       "      <td>Builder_Floor</td>\n",
       "      <td>12916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Area  BHK  Bathroom      Furnishing  \\\n",
       "0       800.0    3       2.0  Semi-Furnished   \n",
       "1       750.0    2       2.0  Semi-Furnished   \n",
       "2       950.0    2       2.0       Furnished   \n",
       "3       600.0    2       2.0  Semi-Furnished   \n",
       "4       650.0    2       2.0  Semi-Furnished   \n",
       "...       ...  ...       ...             ...   \n",
       "1254   4118.0    4       5.0     Unfurnished   \n",
       "1255   1050.0    3       2.0  Semi-Furnished   \n",
       "1256    875.0    3       3.0  Semi-Furnished   \n",
       "1257    990.0    2       2.0     Unfurnished   \n",
       "1258  11050.0    3       3.0     Unfurnished   \n",
       "\n",
       "                                               Locality  Parking     Price  \\\n",
       "0                                      Rohini Sector 25      1.0   6500000   \n",
       "1                J R Designers Floors, Rohini Sector 24      1.0   5000000   \n",
       "2                   Citizen Apartment, Rohini Sector 13      1.0  15500000   \n",
       "3                                      Rohini Sector 24      1.0   4200000   \n",
       "4     Rohini Sector 24 carpet area 650 sqft status R...      1.0   6200000   \n",
       "...                                                 ...      ...       ...   \n",
       "1254                                  Chittaranjan Park      3.0  55000000   \n",
       "1255                                  Chittaranjan Park      3.0  12500000   \n",
       "1256                                  Chittaranjan Park      3.0  17500000   \n",
       "1257                          Chittaranjan Park Block A      1.0  11500000   \n",
       "1258                                  Chittaranjan Park      1.0  18500000   \n",
       "\n",
       "             Status   Transaction           Type  Per_Sqft  \n",
       "0     Ready_to_move  New_Property  Builder_Floor   12500.0  \n",
       "1     Ready_to_move  New_Property      Apartment    6667.0  \n",
       "2     Ready_to_move        Resale      Apartment    6667.0  \n",
       "3     Ready_to_move        Resale  Builder_Floor    6667.0  \n",
       "4     Ready_to_move  New_Property  Builder_Floor    6667.0  \n",
       "...             ...           ...            ...       ...  \n",
       "1254  Ready_to_move  New_Property  Builder_Floor   12916.0  \n",
       "1255  Ready_to_move        Resale  Builder_Floor   12916.0  \n",
       "1256  Ready_to_move  New_Property  Builder_Floor   12916.0  \n",
       "1257  Ready_to_move        Resale  Builder_Floor   12916.0  \n",
       "1258  Ready_to_move  New_Property  Builder_Floor   12916.0  \n",
       "\n",
       "[1259 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completed_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "local = list(completed_frame.iloc[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_l = ['Rohini' , 'Lajpat Nagar', 'Dwarka', 'Budh Vihar', 'Patel Nagar', 'Uttam Nagar', 'Greater Kailash', 'Okhla'\n",
    "           ,'New Friends Colony','Shahdara','Laxmi Nagar','Vasant Kunj','Geeta Colony','Saket','Safdarjung','Paschim Vihar'\n",
    "           ,'Commonwealth Games Village','Alaknanda','Sultanpur','Sarita Vihar','Chhattarpur','Mehrauli','Mahavir'\n",
    "           ,'Narela','Malviya Nagar','Dilshad Garden','Vasundhara','Karol Bagh','Kirti Nagar','Sheikh Sarai','Punjabi Bagh'\n",
    "           ,'Kalkaji','Hauz Khas' , 'Chittaranjan Park']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in final_l:  \n",
    "    for i in range(len(local)):\n",
    "        k=re.findall(r''+re.escape(j)+'' , local[i])\n",
    "        if len(k)!=0:\n",
    "            local[i] = k[0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ni = list(pd.unique(local))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_frame['Locality'] = local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_frame_demo = completed_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "garbage_local = list(set(ni) - set(final_l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_frame_demo = completed_frame_demo.replace({'Locality':garbage_local},'garbage')\n",
    "completed_frame_demo = completed_frame_demo.replace({'Parking':[39,114]},1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b13f2e2e80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5040x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(70,30))\n",
    "sns.countplot(completed_frame_demo['Locality'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b141f1b430>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(range(2003,2012,1),range(200300,201200,100))\n",
    "plt.ticklabel_format(useOffset=False)\n",
    "sns.scatterplot(completed_frame_demo['Area'],completed_frame_demo['Price'], marker = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_frame_demo.to_csv('final.csv' , index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo = pd.get_dummies(completed_frame_demo,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo = completed_frame_demo[completed_frame_demo['Locality']!= 'garbage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo = pd.get_dummies(demo,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "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>Area</th>\n",
       "      <th>BHK</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Price</th>\n",
       "      <th>Per_Sqft</th>\n",
       "      <th>Furnishing_Semi-Furnished</th>\n",
       "      <th>Furnishing_Unfurnished</th>\n",
       "      <th>Locality_Budh Vihar</th>\n",
       "      <th>Locality_Chhattarpur</th>\n",
       "      <th>...</th>\n",
       "      <th>Locality_Sarita Vihar</th>\n",
       "      <th>Locality_Shahdara</th>\n",
       "      <th>Locality_Sheikh Sarai</th>\n",
       "      <th>Locality_Sultanpur</th>\n",
       "      <th>Locality_Uttam Nagar</th>\n",
       "      <th>Locality_Vasant Kunj</th>\n",
       "      <th>Locality_Vasundhara</th>\n",
       "      <th>Status_Ready_to_move</th>\n",
       "      <th>Transaction_Resale</th>\n",
       "      <th>Type_Builder_Floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6500000</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5000000</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>950.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15500000</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200000</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6200000</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>4118.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55000000</td>\n",
       "      <td>12916.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1050.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12500000</td>\n",
       "      <td>12916.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>875.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17500000</td>\n",
       "      <td>12916.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>990.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11500000</td>\n",
       "      <td>12916.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>11050.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18500000</td>\n",
       "      <td>12916.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1240 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Area  BHK  Bathroom  Parking     Price  Per_Sqft  \\\n",
       "0       800.0    3       2.0      1.0   6500000   12500.0   \n",
       "1       750.0    2       2.0      1.0   5000000    6667.0   \n",
       "2       950.0    2       2.0      1.0  15500000    6667.0   \n",
       "3       600.0    2       2.0      1.0   4200000    6667.0   \n",
       "4       650.0    2       2.0      1.0   6200000    6667.0   \n",
       "...       ...  ...       ...      ...       ...       ...   \n",
       "1254   4118.0    4       5.0      3.0  55000000   12916.0   \n",
       "1255   1050.0    3       2.0      3.0  12500000   12916.0   \n",
       "1256    875.0    3       3.0      3.0  17500000   12916.0   \n",
       "1257    990.0    2       2.0      1.0  11500000   12916.0   \n",
       "1258  11050.0    3       3.0      1.0  18500000   12916.0   \n",
       "\n",
       "      Furnishing_Semi-Furnished  Furnishing_Unfurnished  Locality_Budh Vihar  \\\n",
       "0                             1                       0                    0   \n",
       "1                             1                       0                    0   \n",
       "2                             0                       0                    0   \n",
       "3                             1                       0                    0   \n",
       "4                             1                       0                    0   \n",
       "...                         ...                     ...                  ...   \n",
       "1254                          0                       1                    0   \n",
       "1255                          1                       0                    0   \n",
       "1256                          1                       0                    0   \n",
       "1257                          0                       1                    0   \n",
       "1258                          0                       1                    0   \n",
       "\n",
       "      Locality_Chhattarpur  ...  Locality_Sarita Vihar  Locality_Shahdara  \\\n",
       "0                        0  ...                      0                  0   \n",
       "1                        0  ...                      0                  0   \n",
       "2                        0  ...                      0                  0   \n",
       "3                        0  ...                      0                  0   \n",
       "4                        0  ...                      0                  0   \n",
       "...                    ...  ...                    ...                ...   \n",
       "1254                     0  ...                      0                  0   \n",
       "1255                     0  ...                      0                  0   \n",
       "1256                     0  ...                      0                  0   \n",
       "1257                     0  ...                      0                  0   \n",
       "1258                     0  ...                      0                  0   \n",
       "\n",
       "      Locality_Sheikh Sarai  Locality_Sultanpur  Locality_Uttam Nagar  \\\n",
       "0                         0                   0                     0   \n",
       "1                         0                   0                     0   \n",
       "2                         0                   0                     0   \n",
       "3                         0                   0                     0   \n",
       "4                         0                   0                     0   \n",
       "...                     ...                 ...                   ...   \n",
       "1254                      0                   0                     0   \n",
       "1255                      0                   0                     0   \n",
       "1256                      0                   0                     0   \n",
       "1257                      0                   0                     0   \n",
       "1258                      0                   0                     0   \n",
       "\n",
       "      Locality_Vasant Kunj  Locality_Vasundhara  Status_Ready_to_move  \\\n",
       "0                        0                    0                     1   \n",
       "1                        0                    0                     1   \n",
       "2                        0                    0                     1   \n",
       "3                        0                    0                     1   \n",
       "4                        0                    0                     1   \n",
       "...                    ...                  ...                   ...   \n",
       "1254                     0                    0                     1   \n",
       "1255                     0                    0                     1   \n",
       "1256                     0                    0                     1   \n",
       "1257                     0                    0                     1   \n",
       "1258                     0                    0                     1   \n",
       "\n",
       "      Transaction_Resale  Type_Builder_Floor  \n",
       "0                      0                   1  \n",
       "1                      0                   0  \n",
       "2                      1                   0  \n",
       "3                      1                   1  \n",
       "4                      0                   1  \n",
       "...                  ...                 ...  \n",
       "1254                   0                   1  \n",
       "1255                   1                   1  \n",
       "1256                   0                   1  \n",
       "1257                   1                   1  \n",
       "1258                   0                   1  \n",
       "\n",
       "[1240 rows x 44 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "demo.to_csv('final_with_dummies.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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