2_Linear_Model_Chennai_Housing_Price_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABOUT THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nPRT_ID – The Property Transaction ID assigned by ChennaiEstate \\nAREA – The property in which the real estate is located \\nINT_SQFT – The interior Sq. Ft of the propertyDATE_SALE – The date the property was sold \\nDIST_MAINROAD – The distance of the property to the main road \\nN_BEDROOM – The number of Bedrooms \\nN_BATHROOM - The number of bathrooms \\nN_ROOM – Total Number of Rooms \\nSALE_COND – The Sale Condition \\nNormal: Normal Sale \\nAbnormal: Abnormal Sale - trade, foreclosure, short sale \\nAdjLand: Adjoining Land Purchase \\nFamily: Sale between family members \\nPartial: Home was not completed when last assessed \\nPARK_FACIL – Whether parking facility is available \\nDATE_BUILD – The date in which the property was built \\nBUILDTYPE – The type of building \\nHouse \\nCommercial \\nOthers \\nUTILITY_AVAIL \\nAllPub: All public Utilities (E,G,W,& S) \\nNoSewr: Electricity, Gas, and Water (Septic Tank) \\nNoSeWa: Electricity and Gas Only \\nELO: Electricity only \\nSTREET \\nGravel \\nPaved \\nNo Access \\nMZZONE \\nA: Agriculture \\nC: Commercial \\nI: Industrial \\nRH: Residential High Density \\nRL: Residential Low Density \\nRM: Residential Medium Density \\nQS_ROOMS – The quality score assigned for rooms based on buyer reviews \\nQS_BATHROOM – The quality score assigned for bathroom based on buyer reviews \\nQS_BEDROOM – The quality score assigned for bedroom based on buyer reviews \\nQS_OVERALL – The Overall quality score assigned for the property \\nREG_FEE – The registration fee for the property \\nCOMMIS – The Commission paid to the agent \\nSALES_PRICE – The total sale price of the property \\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "PRT_ID – The Property Transaction ID assigned by ChennaiEstate \n",
    "AREA – The property in which the real estate is located \n",
    "INT_SQFT – The interior Sq. Ft of the property\\\n",
    "DATE_SALE – The date the property was sold \n",
    "DIST_MAINROAD – The distance of the property to the main road \n",
    "N_BEDROOM – The number of Bedrooms \n",
    "N_BATHROOM - The number of bathrooms \n",
    "N_ROOM – Total Number of Rooms \n",
    "SALE_COND – The Sale Condition \n",
    "Normal: Normal Sale \n",
    "Abnormal: Abnormal Sale - trade, foreclosure, short sale \n",
    "AdjLand: Adjoining Land Purchase \n",
    "Family: Sale between family members \n",
    "Partial: Home was not completed when last assessed \n",
    "PARK_FACIL – Whether parking facility is available \n",
    "DATE_BUILD – The date in which the property was built \n",
    "BUILDTYPE – The type of building \n",
    "House \n",
    "Commercial \n",
    "Others \n",
    "UTILITY_AVAIL \n",
    "AllPub: All public Utilities (E,G,W,& S) \n",
    "NoSewr: Electricity, Gas, and Water (Septic Tank) \n",
    "NoSeWa: Electricity and Gas Only \n",
    "ELO: Electricity only \n",
    "STREET \n",
    "Gravel \n",
    "Paved \n",
    "No Access \n",
    "MZZONE \n",
    "A: Agriculture \n",
    "C: Commercial \n",
    "I: Industrial \n",
    "RH: Residential High Density \n",
    "RL: Residential Low Density \n",
    "RM: Residential Medium Density \n",
    "QS_ROOMS – The quality score assigned for rooms based on buyer reviews \n",
    "QS_BATHROOM – The quality score assigned for bathroom based on buyer reviews \n",
    "QS_BEDROOM – The quality score assigned for bedroom based on buyer reviews \n",
    "QS_OVERALL – The Overall quality score assigned for the property \n",
    "REG_FEE – The registration fee for the property \n",
    "COMMIS – The Commission paid to the agent \n",
    "SALES_PRICE – The total sale price of the property \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note: This is a real Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let' Import Some Important Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's create a dataframe named prices\n",
    "\n",
    "prices = pd.read_csv(\"chennai_house_price_prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's check out the head of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>PRT_ID</th>\n",
       "      <th>AREA</th>\n",
       "      <th>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>N_BEDROOM</th>\n",
       "      <th>N_BATHROOM</th>\n",
       "      <th>N_ROOM</th>\n",
       "      <th>SALE_COND</th>\n",
       "      <th>PARK_FACIL</th>\n",
       "      <th>BUILDTYPE</th>\n",
       "      <th>UTILITY_AVAIL</th>\n",
       "      <th>STREET</th>\n",
       "      <th>MZZONE</th>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <th>COMMIS</th>\n",
       "      <th>SALES_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P03210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1004</td>\n",
       "      <td>131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Paved</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.330</td>\n",
       "      <td>144400</td>\n",
       "      <td>7600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P09411</td>\n",
       "      <td>Anna Nagar</td>\n",
       "      <td>1986</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>No</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>RH</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.765</td>\n",
       "      <td>304049</td>\n",
       "      <td>21717770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P01812</td>\n",
       "      <td>Adyar</td>\n",
       "      <td>909</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>ELO</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.090</td>\n",
       "      <td>92114</td>\n",
       "      <td>13159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P05346</td>\n",
       "      <td>Velachery</td>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Family</td>\n",
       "      <td>No</td>\n",
       "      <td>Others</td>\n",
       "      <td>NoSewr</td>\n",
       "      <td>Paved</td>\n",
       "      <td>I</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.010</td>\n",
       "      <td>77042</td>\n",
       "      <td>9630290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P06210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1226</td>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Others</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>C</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.290</td>\n",
       "      <td>74063</td>\n",
       "      <td>7406250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRT_ID        AREA  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  \\\n",
       "0  P03210  Karapakkam      1004            131        1.0         1.0       3   \n",
       "1  P09411  Anna Nagar      1986             26        2.0         1.0       5   \n",
       "2  P01812       Adyar       909             70        1.0         1.0       3   \n",
       "3  P05346   Velachery      1855             14        3.0         2.0       5   \n",
       "4  P06210  Karapakkam      1226             84        1.0         1.0       3   \n",
       "\n",
       "  SALE_COND PARK_FACIL   BUILDTYPE UTILITY_AVAIL  STREET MZZONE  QS_ROOMS  \\\n",
       "0  AbNormal        Yes  Commercial        AllPub   Paved      A       4.0   \n",
       "1  AbNormal         No  Commercial        AllPub  Gravel     RH       4.9   \n",
       "2  AbNormal        Yes  Commercial           ELO  Gravel     RL       4.1   \n",
       "3    Family         No      Others       NoSewr    Paved      I       4.7   \n",
       "4  AbNormal        Yes      Others        AllPub  Gravel      C       3.0   \n",
       "\n",
       "   QS_BATHROOM  QS_BEDROOM  QS_OVERALL  COMMIS  SALES_PRICE  \n",
       "0          3.9         4.9       4.330  144400      7600000  \n",
       "1          4.2         2.5       3.765  304049     21717770  \n",
       "2          3.8         2.2       3.090   92114     13159200  \n",
       "3          3.9         3.6       4.010   77042      9630290  \n",
       "4          2.5         4.1       3.290   74063      7406250  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's get some imfo and description of the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7109 entries, 0 to 7108\n",
      "Data columns (total 19 columns):\n",
      "PRT_ID           7109 non-null object\n",
      "AREA             7109 non-null object\n",
      "INT_SQFT         7109 non-null int64\n",
      "DIST_MAINROAD    7109 non-null int64\n",
      "N_BEDROOM        7108 non-null float64\n",
      "N_BATHROOM       7104 non-null float64\n",
      "N_ROOM           7109 non-null int64\n",
      "SALE_COND        7109 non-null object\n",
      "PARK_FACIL       7109 non-null object\n",
      "BUILDTYPE        7109 non-null object\n",
      "UTILITY_AVAIL    7109 non-null object\n",
      "STREET           7109 non-null object\n",
      "MZZONE           7109 non-null object\n",
      "QS_ROOMS         7109 non-null float64\n",
      "QS_BATHROOM      7109 non-null float64\n",
      "QS_BEDROOM       7109 non-null float64\n",
      "QS_OVERALL       7061 non-null float64\n",
      "COMMIS           7109 non-null int64\n",
      "SALES_PRICE      7109 non-null int64\n",
      "dtypes: float64(6), int64(5), object(8)\n",
      "memory usage: 1.0+ MB\n",
      "None\n",
      "\n",
      " Description \n",
      "\n",
      "          INT_SQFT  DIST_MAINROAD    N_BEDROOM   N_BATHROOM       N_ROOM  \\\n",
      "count  7109.000000    7109.000000  7108.000000  7104.000000  7109.000000   \n",
      "mean   1382.073006      99.603179     1.637029     1.213260     3.688704   \n",
      "std     457.410902      57.403110     0.802902     0.409639     1.019099   \n",
      "min     500.000000       0.000000     1.000000     1.000000     2.000000   \n",
      "25%     993.000000      50.000000     1.000000     1.000000     3.000000   \n",
      "50%    1373.000000      99.000000     1.000000     1.000000     4.000000   \n",
      "75%    1744.000000     148.000000     2.000000     1.000000     4.000000   \n",
      "max    2500.000000     200.000000     4.000000     2.000000     6.000000   \n",
      "\n",
      "          QS_ROOMS  QS_BATHROOM   QS_BEDROOM   QS_OVERALL         COMMIS  \\\n",
      "count  7109.000000  7109.000000  7109.000000  7061.000000    7109.000000   \n",
      "mean      3.517471     3.507244     3.485300     3.503254  141005.726544   \n",
      "std       0.891972     0.897834     0.887266     0.527223   78768.093718   \n",
      "min       2.000000     2.000000     2.000000     2.000000    5055.000000   \n",
      "25%       2.700000     2.700000     2.700000     3.130000   84219.000000   \n",
      "50%       3.500000     3.500000     3.500000     3.500000  127628.000000   \n",
      "75%       4.300000     4.300000     4.300000     3.890000  184506.000000   \n",
      "max       5.000000     5.000000     5.000000     4.970000  495405.000000   \n",
      "\n",
      "        SALES_PRICE  \n",
      "count  7.109000e+03  \n",
      "mean   1.089491e+07  \n",
      "std    3.768603e+06  \n",
      "min    2.156875e+06  \n",
      "25%    8.272100e+06  \n",
      "50%    1.033505e+07  \n",
      "75%    1.299390e+07  \n",
      "max    2.366734e+07  \n"
     ]
    }
   ],
   "source": [
    "print(\"Info \\n\")\n",
    "print(prices.info())\n",
    "print(\"\\n Description \\n\")\n",
    "print(prices.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Size of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7109, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT_ID            0\n",
       "AREA              0\n",
       "INT_SQFT          0\n",
       "DIST_MAINROAD     0\n",
       "N_BEDROOM         1\n",
       "N_BATHROOM        5\n",
       "N_ROOM            0\n",
       "SALE_COND         0\n",
       "PARK_FACIL        0\n",
       "BUILDTYPE         0\n",
       "UTILITY_AVAIL     0\n",
       "STREET            0\n",
       "MZZONE            0\n",
       "QS_ROOMS          0\n",
       "QS_BATHROOM       0\n",
       "QS_BEDROOM        0\n",
       "QS_OVERALL       48\n",
       "COMMIS            0\n",
       "SALES_PRICE       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check out the null objects \n",
    "\n",
    "prices.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so we don't have many missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's start our analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PRT_ID', 'AREA', 'INT_SQFT', 'DIST_MAINROAD', 'N_BEDROOM',\n",
       "       'N_BATHROOM', 'N_ROOM', 'SALE_COND', 'PARK_FACIL', 'BUILDTYPE',\n",
       "       'UTILITY_AVAIL', 'STREET', 'MZZONE', 'QS_ROOMS', 'QS_BATHROOM',\n",
       "       'QS_BEDROOM', 'QS_OVERALL', 'COMMIS', 'SALES_PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.CATEGORICAL VARIABLES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Karapakkam\n",
       "1    Anna Nagar\n",
       "2         Adyar\n",
       "3     Velachery\n",
       "4    Karapakkam\n",
       "Name: AREA, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.AREA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of unique areas area :  17\n"
     ]
    }
   ],
   "source": [
    "#let's check unique areas number\n",
    "\n",
    "print(\"Total no. of unique areas area : \",prices['AREA'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Different Areas Are : \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Chrompet      1681\n",
       "Karapakkam    1363\n",
       "KK Nagar       996\n",
       "Velachery      979\n",
       "Anna Nagar     783\n",
       "Adyar          773\n",
       "T Nagar        496\n",
       "Chrompt          9\n",
       "Chormpet         6\n",
       "Chrmpet          6\n",
       "TNagar           5\n",
       "Karapakam        3\n",
       "Ana Nagar        3\n",
       "Ann Nagar        2\n",
       "Velchery         2\n",
       "KKNagar          1\n",
       "Adyr             1\n",
       "Name: AREA, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check out these areas\n",
    "\n",
    "print(\"Different Areas Are : \\n\")\n",
    "prices['AREA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AREA\n",
       "Adyar         9.185852e+06\n",
       "Adyr          9.044045e+06\n",
       "Ana Nagar     1.855402e+07\n",
       "Ann Nagar     1.348870e+07\n",
       "Anna Nagar    1.515952e+07\n",
       "Chormpet      9.687353e+06\n",
       "Chrmpet       9.423537e+06\n",
       "Chrompet      1.001666e+07\n",
       "Chrompt       1.009225e+07\n",
       "KK Nagar      1.269581e+07\n",
       "KKNagar       1.311702e+07\n",
       "Karapakam     8.009333e+06\n",
       "Karapakkam    7.338627e+06\n",
       "T Nagar       1.562348e+07\n",
       "TNagar        1.494866e+07\n",
       "Velachery     1.104665e+07\n",
       "Velchery      1.138938e+07\n",
       "Name: SALES_PRICE, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's Check the housing mean prices of these areas\n",
    "\n",
    "prices.groupby(\"AREA\")[\"SALES_PRICE\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so there is not any significant differnce in the price based of area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chrompt          9\n",
    "# Chormpet         6\n",
    "# Chrmpet          6\n",
    "# TNagar           5\n",
    "# Karapakam        3\n",
    "# Ana Nagar        3\n",
    "# Ann Nagar        2\n",
    "# Velchery         2\n",
    "# KKNagar          1\n",
    "# Adyr             1\n",
    "\n",
    "# These areas can be put together in a column by the name other\n",
    "\n",
    "prices['AREA'] = prices['AREA'].map({'Chrompet':'Chrompet','Karapakkam':'Karapakkam','KK Nagar':'KK Nagar','Velachery':'Velachery','Anna Nagar':'Anna Nagar','Adyar':'Adyar','T Nagar':'T Nagar','Chrompt':'Other','Chormpet':'Other','Chrmpet':'Other','TNagar':'Other','Karapakam':'Other','Ana Nagar':'Other','Ann Nagar':'Other','Velchery':'Other','KKNagar':'Other','Adyr':'Other'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>PRT_ID</th>\n",
       "      <th>AREA</th>\n",
       "      <th>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>N_BEDROOM</th>\n",
       "      <th>N_BATHROOM</th>\n",
       "      <th>N_ROOM</th>\n",
       "      <th>SALE_COND</th>\n",
       "      <th>PARK_FACIL</th>\n",
       "      <th>BUILDTYPE</th>\n",
       "      <th>UTILITY_AVAIL</th>\n",
       "      <th>STREET</th>\n",
       "      <th>MZZONE</th>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <th>COMMIS</th>\n",
       "      <th>SALES_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P03210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1004</td>\n",
       "      <td>131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Paved</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.330</td>\n",
       "      <td>144400</td>\n",
       "      <td>7600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P09411</td>\n",
       "      <td>Anna Nagar</td>\n",
       "      <td>1986</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>No</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>RH</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.765</td>\n",
       "      <td>304049</td>\n",
       "      <td>21717770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P01812</td>\n",
       "      <td>Adyar</td>\n",
       "      <td>909</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>ELO</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>RL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.090</td>\n",
       "      <td>92114</td>\n",
       "      <td>13159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P05346</td>\n",
       "      <td>Velachery</td>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Family</td>\n",
       "      <td>No</td>\n",
       "      <td>Others</td>\n",
       "      <td>NoSewr</td>\n",
       "      <td>Paved</td>\n",
       "      <td>I</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.010</td>\n",
       "      <td>77042</td>\n",
       "      <td>9630290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P06210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1226</td>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Others</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>C</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.290</td>\n",
       "      <td>74063</td>\n",
       "      <td>7406250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRT_ID        AREA  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  \\\n",
       "0  P03210  Karapakkam      1004            131        1.0         1.0       3   \n",
       "1  P09411  Anna Nagar      1986             26        2.0         1.0       5   \n",
       "2  P01812       Adyar       909             70        1.0         1.0       3   \n",
       "3  P05346   Velachery      1855             14        3.0         2.0       5   \n",
       "4  P06210  Karapakkam      1226             84        1.0         1.0       3   \n",
       "\n",
       "  SALE_COND PARK_FACIL   BUILDTYPE UTILITY_AVAIL  STREET MZZONE  QS_ROOMS  \\\n",
       "0  AbNormal        Yes  Commercial        AllPub   Paved      A       4.0   \n",
       "1  AbNormal         No  Commercial        AllPub  Gravel     RH       4.9   \n",
       "2  AbNormal        Yes  Commercial           ELO  Gravel     RL       4.1   \n",
       "3    Family         No      Others       NoSewr    Paved      I       4.7   \n",
       "4  AbNormal        Yes      Others        AllPub  Gravel      C       3.0   \n",
       "\n",
       "   QS_BATHROOM  QS_BEDROOM  QS_OVERALL  COMMIS  SALES_PRICE  \n",
       "0          3.9         4.9       4.330  144400      7600000  \n",
       "1          4.2         2.5       3.765  304049     21717770  \n",
       "2          3.8         2.2       3.090   92114     13159200  \n",
       "3          3.9         3.6       4.010   77042      9630290  \n",
       "4          2.5         4.1       3.290   74063      7406250  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's see the head of the dataframe\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Karapakkam', 'Anna Nagar', 'Adyar', 'Velachery', 'Chrompet',\n",
       "       'KK Nagar', 'Other', 'T Nagar'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['AREA'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N_BEDROOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['N_BEDROOM'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so we have one data missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    3795\n",
       "2.0    2352\n",
       "3.0     707\n",
       "4.0     254\n",
       "Name: N_BEDROOM, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check the values counts of bedrooms\n",
    "prices['N_BEDROOM'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so let's fill the unavailabel data set with mode(1)\n",
    "prices['N_BEDROOM'].fillna(1.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['N_BEDROOM'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we have all the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N_BATHROOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['N_BEDROOM'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so we have all the data available "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N_ROOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['N_ROOM'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so we have all the data available "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SALE_COND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['SALE_COND'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so no data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique variables are :  9\n"
     ]
    }
   ],
   "source": [
    "# let's check out the unique variables\n",
    "\n",
    "print(\"No. of unique variables are : \",prices['SALE_COND'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdjLand        1433\n",
       "Partial        1429\n",
       "Normal Sale    1423\n",
       "AbNormal       1406\n",
       "Family         1403\n",
       "Adj Land          6\n",
       "Ab Normal         5\n",
       "Partiall          3\n",
       "PartiaLl          1\n",
       "Name: SALE_COND, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check these out \n",
    "prices['SALE_COND'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#looks like there could be a typo so let's just include them in the remaining Data\n",
    "\n",
    "prices['SALE_COND'] = prices['SALE_COND'].map({'AdjLand':'AdjLand','Partial':'Partial','Normal Sale':'Normal Sale','AbNormal':'AbNormal','Family':'Family','Adj Land':'AdjLand','Ab Normal':'AbNormal','Partiall':'Partial','PartiaLl':'Partial'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdjLand        1439\n",
       "Partial        1433\n",
       "Normal Sale    1423\n",
       "AbNormal       1411\n",
       "Family         1403\n",
       "Name: SALE_COND, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check these out now\n",
    "prices['SALE_COND'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ok "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PARK_FACIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['PARK_FACIL'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so no data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique variables are :  3\n"
     ]
    }
   ],
   "source": [
    "# let's check out the unique variables\n",
    "\n",
    "print(\"No. of unique variables are : \",prices['PARK_FACIL'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    3587\n",
       "No     3520\n",
       "Noo       2\n",
       "Name: PARK_FACIL, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check these out \n",
    "prices['PARK_FACIL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#again a case of typo\n",
    "\n",
    "prices['PARK_FACIL'] = prices['PARK_FACIL'].map({'Yes':'Yes','No':'No','Noo':'No'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    3587\n",
       "No     3522\n",
       "Name: PARK_FACIL, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['PARK_FACIL'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BUILDTYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "House         2444\n",
       "Commercial    2325\n",
       "Others        2310\n",
       "Other           26\n",
       "Comercial        4\n",
       "Name: BUILDTYPE, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['BUILDTYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['BUILDTYPE'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#again there is case of typo\n",
    "\n",
    "prices['BUILDTYPE'] = prices['BUILDTYPE'].map({'House':'House','Commercial':'Commercial','Others':'Others','Other':'Others','Comercial':'Commercial'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "House         2444\n",
       "Others        2336\n",
       "Commercial    2329\n",
       "Name: BUILDTYPE, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['BUILDTYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UTILITY_AVAIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['UTILITY_AVAIL'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so no data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AllPub     1886\n",
       "NoSeWa     1871\n",
       "NoSewr     1829\n",
       "ELO        1522\n",
       "All Pub       1\n",
       "Name: UTILITY_AVAIL, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['UTILITY_AVAIL'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again a case of typo \n",
    "prices['UTILITY_AVAIL'] = prices['UTILITY_AVAIL'].map({'AllPub':'AllPub','NoSeWa':'NoSeWa','NoSeWr':'NoSeWr','ELO':'ELO','All Pub':'AllPub'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AllPub    1887\n",
       "NoSeWa    1871\n",
       "ELO       1522\n",
       "Name: UTILITY_AVAIL, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['UTILITY_AVAIL'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STREET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['STREET'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no values are null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paved        2560\n",
       "Gravel       2520\n",
       "No Access    2010\n",
       "Pavd           12\n",
       "NoAccess        7\n",
       "Name: STREET, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['STREET'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's remove the typos\n",
    "prices['STREET'] = prices['STREET'].map({'Paved':'Paved','Gravel':'Gravel','No Access':'No Access','Pavd':'Paved','NoAccess':'No Access'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paved        2572\n",
       "Gravel       2520\n",
       "No Access    2017\n",
       "Name: STREET, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['STREET'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MZZONE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['MZZONE'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RL    1858\n",
       "RH    1822\n",
       "RM    1817\n",
       "C      550\n",
       "A      537\n",
       "I      525\n",
       "Name: MZZONE, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices['MZZONE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So We Are Done With The Univariate Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#**\"let's Generate the Dummies for the given DataSet\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "dum = pd.get_dummies(prices[['AREA','SALE_COND', 'PARK_FACIL','BUILDTYPE','UTILITY_AVAIL','STREET','MZZONE']],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>AREA_Anna Nagar</th>\n",
       "      <th>AREA_Chrompet</th>\n",
       "      <th>AREA_KK Nagar</th>\n",
       "      <th>AREA_Karapakkam</th>\n",
       "      <th>AREA_Other</th>\n",
       "      <th>AREA_T Nagar</th>\n",
       "      <th>AREA_Velachery</th>\n",
       "      <th>SALE_COND_AdjLand</th>\n",
       "      <th>SALE_COND_Family</th>\n",
       "      <th>SALE_COND_Normal Sale</th>\n",
       "      <th>...</th>\n",
       "      <th>BUILDTYPE_Others</th>\n",
       "      <th>UTILITY_AVAIL_ELO</th>\n",
       "      <th>UTILITY_AVAIL_NoSeWa</th>\n",
       "      <th>STREET_No Access</th>\n",
       "      <th>STREET_Paved</th>\n",
       "      <th>MZZONE_C</th>\n",
       "      <th>MZZONE_I</th>\n",
       "      <th>MZZONE_RH</th>\n",
       "      <th>MZZONE_RL</th>\n",
       "      <th>MZZONE_RM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AREA_Anna Nagar  AREA_Chrompet  AREA_KK Nagar  AREA_Karapakkam  AREA_Other  \\\n",
       "0                0              0              0                1           0   \n",
       "1                1              0              0                0           0   \n",
       "2                0              0              0                0           0   \n",
       "3                0              0              0                0           0   \n",
       "4                0              0              0                1           0   \n",
       "\n",
       "   AREA_T Nagar  AREA_Velachery  SALE_COND_AdjLand  SALE_COND_Family  \\\n",
       "0             0               0                  0                 0   \n",
       "1             0               0                  0                 0   \n",
       "2             0               0                  0                 0   \n",
       "3             0               1                  0                 1   \n",
       "4             0               0                  0                 0   \n",
       "\n",
       "   SALE_COND_Normal Sale  ...  BUILDTYPE_Others  UTILITY_AVAIL_ELO  \\\n",
       "0                      0  ...                 0                  0   \n",
       "1                      0  ...                 0                  0   \n",
       "2                      0  ...                 0                  1   \n",
       "3                      0  ...                 1                  0   \n",
       "4                      0  ...                 1                  0   \n",
       "\n",
       "   UTILITY_AVAIL_NoSeWa  STREET_No Access  STREET_Paved  MZZONE_C  MZZONE_I  \\\n",
       "0                     0                 0             1         0         0   \n",
       "1                     0                 0             0         0         0   \n",
       "2                     0                 0             0         0         0   \n",
       "3                     0                 0             1         0         1   \n",
       "4                     0                 0             0         1         0   \n",
       "\n",
       "   MZZONE_RH  MZZONE_RL  MZZONE_RM  \n",
       "0          0          0          0  \n",
       "1          1          0          0  \n",
       "2          0          1          0  \n",
       "3          0          0          0  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_new = pd.concat([prices,dum],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRT_ID</th>\n",
       "      <th>AREA</th>\n",
       "      <th>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>N_BEDROOM</th>\n",
       "      <th>N_BATHROOM</th>\n",
       "      <th>N_ROOM</th>\n",
       "      <th>SALE_COND</th>\n",
       "      <th>PARK_FACIL</th>\n",
       "      <th>BUILDTYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>BUILDTYPE_Others</th>\n",
       "      <th>UTILITY_AVAIL_ELO</th>\n",
       "      <th>UTILITY_AVAIL_NoSeWa</th>\n",
       "      <th>STREET_No Access</th>\n",
       "      <th>STREET_Paved</th>\n",
       "      <th>MZZONE_C</th>\n",
       "      <th>MZZONE_I</th>\n",
       "      <th>MZZONE_RH</th>\n",
       "      <th>MZZONE_RL</th>\n",
       "      <th>MZZONE_RM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P03210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1004</td>\n",
       "      <td>131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P09411</td>\n",
       "      <td>Anna Nagar</td>\n",
       "      <td>1986</td>\n",
       "      <td>26</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>No</td>\n",
       "      <td>Commercial</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>P01812</td>\n",
       "      <td>Adyar</td>\n",
       "      <td>909</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P05346</td>\n",
       "      <td>Velachery</td>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Family</td>\n",
       "      <td>No</td>\n",
       "      <td>Others</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P06210</td>\n",
       "      <td>Karapakkam</td>\n",
       "      <td>1226</td>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Others</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRT_ID        AREA  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  \\\n",
       "0  P03210  Karapakkam      1004            131        1.0         1.0       3   \n",
       "1  P09411  Anna Nagar      1986             26        2.0         1.0       5   \n",
       "2  P01812       Adyar       909             70        1.0         1.0       3   \n",
       "3  P05346   Velachery      1855             14        3.0         2.0       5   \n",
       "4  P06210  Karapakkam      1226             84        1.0         1.0       3   \n",
       "\n",
       "  SALE_COND PARK_FACIL   BUILDTYPE  ... BUILDTYPE_Others UTILITY_AVAIL_ELO  \\\n",
       "0  AbNormal        Yes  Commercial  ...                0                 0   \n",
       "1  AbNormal         No  Commercial  ...                0                 0   \n",
       "2  AbNormal        Yes  Commercial  ...                0                 1   \n",
       "3    Family         No      Others  ...                1                 0   \n",
       "4  AbNormal        Yes      Others  ...                1                 0   \n",
       "\n",
       "  UTILITY_AVAIL_NoSeWa  STREET_No Access  STREET_Paved  MZZONE_C  MZZONE_I  \\\n",
       "0                    0                 0             1         0         0   \n",
       "1                    0                 0             0         0         0   \n",
       "2                    0                 0             0         0         0   \n",
       "3                    0                 0             1         0         1   \n",
       "4                    0                 0             0         1         0   \n",
       "\n",
       "   MZZONE_RH  MZZONE_RL  MZZONE_RM  \n",
       "0          0          0          0  \n",
       "1          1          0          0  \n",
       "2          0          1          0  \n",
       "3          0          0          0  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's remove the earlier categorical columns\n",
    "\n",
    "prices_new.drop(['AREA','SALE_COND', 'PARK_FACIL','BUILDTYPE','UTILITY_AVAIL','STREET','MZZONE'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7109, 35)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRT_ID</th>\n",
       "      <th>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>N_BEDROOM</th>\n",
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       "      <th>STREET_Paved</th>\n",
       "      <th>MZZONE_C</th>\n",
       "      <th>MZZONE_I</th>\n",
       "      <th>MZZONE_RH</th>\n",
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       "      <th>MZZONE_RM</th>\n",
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       "      <th>0</th>\n",
       "      <td>P03210</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>4.2</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
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       "      <td>2.2</td>\n",
       "      <td>3.090</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P05346</td>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7</td>\n",
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       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRT_ID  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  QS_ROOMS  \\\n",
       "0  P03210      1004            131        1.0         1.0       3       4.0   \n",
       "1  P09411      1986             26        2.0         1.0       5       4.9   \n",
       "2  P01812       909             70        1.0         1.0       3       4.1   \n",
       "3  P05346      1855             14        3.0         2.0       5       4.7   \n",
       "4  P06210      1226             84        1.0         1.0       3       3.0   \n",
       "\n",
       "   QS_BATHROOM  QS_BEDROOM  QS_OVERALL  ...  BUILDTYPE_Others  \\\n",
       "0          3.9         4.9       4.330  ...                 0   \n",
       "1          4.2         2.5       3.765  ...                 0   \n",
       "2          3.8         2.2       3.090  ...                 0   \n",
       "3          3.9         3.6       4.010  ...                 1   \n",
       "4          2.5         4.1       3.290  ...                 1   \n",
       "\n",
       "   UTILITY_AVAIL_ELO  UTILITY_AVAIL_NoSeWa  STREET_No Access  STREET_Paved  \\\n",
       "0                  0                     0                 0             1   \n",
       "1                  0                     0                 0             0   \n",
       "2                  1                     0                 0             0   \n",
       "3                  0                     0                 0             1   \n",
       "4                  0                     0                 0             0   \n",
       "\n",
       "   MZZONE_C  MZZONE_I  MZZONE_RH  MZZONE_RL  MZZONE_RM  \n",
       "0         0         0          0          0          0  \n",
       "1         0         0          1          0          0  \n",
       "2         0         0          0          1          0  \n",
       "3         0         1          0          0          0  \n",
       "4         1         0          0          0          0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>P09411</td>\n",
       "      <td>Anna Nagar</td>\n",
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       "      <td>AllPub</td>\n",
       "      <td>Gravel</td>\n",
       "      <td>RH</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.765</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>AbNormal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Commercial</td>\n",
       "      <td>ELO</td>\n",
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       "      <td>RL</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.090</td>\n",
       "      <td>92114</td>\n",
       "      <td>13159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P05346</td>\n",
       "      <td>Velachery</td>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
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       "      <td>2.5</td>\n",
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       "      <td>3.290</td>\n",
       "      <td>74063</td>\n",
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      ],
      "text/plain": [
       "   PRT_ID        AREA  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  \\\n",
       "0  P03210  Karapakkam      1004            131        1.0         1.0       3   \n",
       "1  P09411  Anna Nagar      1986             26        2.0         1.0       5   \n",
       "2  P01812       Adyar       909             70        1.0         1.0       3   \n",
       "3  P05346   Velachery      1855             14        3.0         2.0       5   \n",
       "4  P06210  Karapakkam      1226             84        1.0         1.0       3   \n",
       "\n",
       "  SALE_COND PARK_FACIL   BUILDTYPE UTILITY_AVAIL  STREET MZZONE  QS_ROOMS  \\\n",
       "0  AbNormal        Yes  Commercial        AllPub   Paved      A       4.0   \n",
       "1  AbNormal         No  Commercial        AllPub  Gravel     RH       4.9   \n",
       "2  AbNormal        Yes  Commercial           ELO  Gravel     RL       4.1   \n",
       "3    Family         No      Others           NaN   Paved      I       4.7   \n",
       "4  AbNormal        Yes      Others        AllPub  Gravel      C       3.0   \n",
       "\n",
       "   QS_BATHROOM  QS_BEDROOM  QS_OVERALL  COMMIS  SALES_PRICE  \n",
       "0          3.9         4.9       4.330  144400      7600000  \n",
       "1          4.2         2.5       3.765  304049     21717770  \n",
       "2          3.8         2.2       3.090   92114     13159200  \n",
       "3          3.9         3.6       4.010   77042      9630290  \n",
       "4          2.5         4.1       3.290   74063      7406250  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's remove the columns from here too so that we are left only with the continous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.drop(['AREA','SALE_COND', 'PARK_FACIL','BUILDTYPE','UTILITY_AVAIL','STREET','MZZONE'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.010</td>\n",
       "      <td>77042</td>\n",
       "      <td>9630290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P06210</td>\n",
       "      <td>1226</td>\n",
       "      <td>84</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.290</td>\n",
       "      <td>74063</td>\n",
       "      <td>7406250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRT_ID  INT_SQFT  DIST_MAINROAD  N_BEDROOM  N_BATHROOM  N_ROOM  QS_ROOMS  \\\n",
       "0  P03210      1004            131        1.0         1.0       3       4.0   \n",
       "1  P09411      1986             26        2.0         1.0       5       4.9   \n",
       "2  P01812       909             70        1.0         1.0       3       4.1   \n",
       "3  P05346      1855             14        3.0         2.0       5       4.7   \n",
       "4  P06210      1226             84        1.0         1.0       3       3.0   \n",
       "\n",
       "   QS_BATHROOM  QS_BEDROOM  QS_OVERALL  COMMIS  SALES_PRICE  \n",
       "0          3.9         4.9       4.330  144400      7600000  \n",
       "1          4.2         2.5       3.765  304049     21717770  \n",
       "2          3.8         2.2       3.090   92114     13159200  \n",
       "3          3.9         3.6       4.010   77042      9630290  \n",
       "4          2.5         4.1       3.290   74063      7406250  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT_ID                    0\n",
       "INT_SQFT                  0\n",
       "DIST_MAINROAD             0\n",
       "N_BEDROOM                 0\n",
       "N_BATHROOM                5\n",
       "N_ROOM                    0\n",
       "QS_ROOMS                  0\n",
       "QS_BATHROOM               0\n",
       "QS_BEDROOM                0\n",
       "QS_OVERALL               48\n",
       "COMMIS                    0\n",
       "SALES_PRICE               0\n",
       "AREA_Anna Nagar           0\n",
       "AREA_Chrompet             0\n",
       "AREA_KK Nagar             0\n",
       "AREA_Karapakkam           0\n",
       "AREA_Other                0\n",
       "AREA_T Nagar              0\n",
       "AREA_Velachery            0\n",
       "SALE_COND_AdjLand         0\n",
       "SALE_COND_Family          0\n",
       "SALE_COND_Normal Sale     0\n",
       "SALE_COND_Partial         0\n",
       "PARK_FACIL_Yes            0\n",
       "BUILDTYPE_House           0\n",
       "BUILDTYPE_Others          0\n",
       "UTILITY_AVAIL_ELO         0\n",
       "UTILITY_AVAIL_NoSeWa      0\n",
       "STREET_No Access          0\n",
       "STREET_Paved              0\n",
       "MZZONE_C                  0\n",
       "MZZONE_I                  0\n",
       "MZZONE_RH                 0\n",
       "MZZONE_RL                 0\n",
       "MZZONE_RM                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check the null values\n",
    "\n",
    "prices_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's fill the missing Data with the mean rating\n",
    "\n",
    "prices_new['QS_OVERALL'].fillna(prices_new['QS_OVERALL'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT_ID                   0\n",
       "INT_SQFT                 0\n",
       "DIST_MAINROAD            0\n",
       "N_BEDROOM                0\n",
       "N_BATHROOM               5\n",
       "N_ROOM                   0\n",
       "QS_ROOMS                 0\n",
       "QS_BATHROOM              0\n",
       "QS_BEDROOM               0\n",
       "QS_OVERALL               0\n",
       "COMMIS                   0\n",
       "SALES_PRICE              0\n",
       "AREA_Anna Nagar          0\n",
       "AREA_Chrompet            0\n",
       "AREA_KK Nagar            0\n",
       "AREA_Karapakkam          0\n",
       "AREA_Other               0\n",
       "AREA_T Nagar             0\n",
       "AREA_Velachery           0\n",
       "SALE_COND_AdjLand        0\n",
       "SALE_COND_Family         0\n",
       "SALE_COND_Normal Sale    0\n",
       "SALE_COND_Partial        0\n",
       "PARK_FACIL_Yes           0\n",
       "BUILDTYPE_House          0\n",
       "BUILDTYPE_Others         0\n",
       "UTILITY_AVAIL_ELO        0\n",
       "UTILITY_AVAIL_NoSeWa     0\n",
       "STREET_No Access         0\n",
       "STREET_Paved             0\n",
       "MZZONE_C                 0\n",
       "MZZONE_I                 0\n",
       "MZZONE_RH                0\n",
       "MZZONE_RL                0\n",
       "MZZONE_RM                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Still there are 3 categorical data remaining\n",
    "\n",
    "#N_BEDROOM        \n",
    "#N_BATHROOM       \n",
    "#N_ROOM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_new['N_BATHROOM'].fillna(1.0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT_ID                   0\n",
       "INT_SQFT                 0\n",
       "DIST_MAINROAD            0\n",
       "N_BEDROOM                0\n",
       "N_BATHROOM               0\n",
       "N_ROOM                   0\n",
       "QS_ROOMS                 0\n",
       "QS_BATHROOM              0\n",
       "QS_BEDROOM               0\n",
       "QS_OVERALL               0\n",
       "COMMIS                   0\n",
       "SALES_PRICE              0\n",
       "AREA_Anna Nagar          0\n",
       "AREA_Chrompet            0\n",
       "AREA_KK Nagar            0\n",
       "AREA_Karapakkam          0\n",
       "AREA_Other               0\n",
       "AREA_T Nagar             0\n",
       "AREA_Velachery           0\n",
       "SALE_COND_AdjLand        0\n",
       "SALE_COND_Family         0\n",
       "SALE_COND_Normal Sale    0\n",
       "SALE_COND_Partial        0\n",
       "PARK_FACIL_Yes           0\n",
       "BUILDTYPE_House          0\n",
       "BUILDTYPE_Others         0\n",
       "UTILITY_AVAIL_ELO        0\n",
       "UTILITY_AVAIL_NoSeWa     0\n",
       "STREET_No Access         0\n",
       "STREET_Paved             0\n",
       "MZZONE_C                 0\n",
       "MZZONE_I                 0\n",
       "MZZONE_RH                0\n",
       "MZZONE_RL                0\n",
       "MZZONE_RM                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's drop the categorical variables\n",
    "\n",
    "prices.drop(['PRT_ID','N_BEDROOM','N_BATHROOM','N_ROOM'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <th>COMMIS</th>\n",
       "      <th>SALES_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1004</td>\n",
       "      <td>131</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.330</td>\n",
       "      <td>144400</td>\n",
       "      <td>7600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>26</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.765</td>\n",
       "      <td>304049</td>\n",
       "      <td>21717770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>909</td>\n",
       "      <td>70</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.090</td>\n",
       "      <td>92114</td>\n",
       "      <td>13159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1855</td>\n",
       "      <td>14</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.010</td>\n",
       "      <td>77042</td>\n",
       "      <td>9630290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1226</td>\n",
       "      <td>84</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.290</td>\n",
       "      <td>74063</td>\n",
       "      <td>7406250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INT_SQFT  DIST_MAINROAD  QS_ROOMS  QS_BATHROOM  QS_BEDROOM  QS_OVERALL  \\\n",
       "0      1004            131       4.0          3.9         4.9       4.330   \n",
       "1      1986             26       4.9          4.2         2.5       3.765   \n",
       "2       909             70       4.1          3.8         2.2       3.090   \n",
       "3      1855             14       4.7          3.9         3.6       4.010   \n",
       "4      1226             84       3.0          2.5         4.1       3.290   \n",
       "\n",
       "   COMMIS  SALES_PRICE  \n",
       "0  144400      7600000  \n",
       "1  304049     21717770  \n",
       "2   92114     13159200  \n",
       "3   77042      9630290  \n",
       "4   74063      7406250  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's normalize the Data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mms = MinMaxScaler()\n",
    "\n",
    "x_transformed = mms.fit_transform(prices.drop('SALES_PRICE',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7109, 7)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_ = ['INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM', 'QS_BEDROOM',\n",
    "       'QS_OVERALL', 'COMMIS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_ = pd.DataFrame(x_transformed,columns=columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <th>COMMIS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2520</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.784512</td>\n",
       "      <td>0.284175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7430</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.594276</td>\n",
       "      <td>0.609756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2045</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.367003</td>\n",
       "      <td>0.177545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6775</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.146807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.434343</td>\n",
       "      <td>0.140732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INT_SQFT  DIST_MAINROAD  QS_ROOMS  QS_BATHROOM  QS_BEDROOM  QS_OVERALL  \\\n",
       "0    0.2520          0.655  0.666667     0.633333    0.966667    0.784512   \n",
       "1    0.7430          0.130  0.966667     0.733333    0.166667    0.594276   \n",
       "2    0.2045          0.350  0.700000     0.600000    0.066667    0.367003   \n",
       "3    0.6775          0.070  0.900000     0.633333    0.533333    0.676768   \n",
       "4    0.3630          0.420  0.333333     0.166667    0.700000    0.434343   \n",
       "\n",
       "     COMMIS  \n",
       "0  0.284175  \n",
       "1  0.609756  \n",
       "2  0.177545  \n",
       "3  0.146807  \n",
       "4  0.140732  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_['SALES_PRICE'] = prices['SALES_PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INT_SQFT</th>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <th>COMMIS</th>\n",
       "      <th>SALES_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2520</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.784512</td>\n",
       "      <td>0.284175</td>\n",
       "      <td>7600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7430</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.594276</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>21717770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2045</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.367003</td>\n",
       "      <td>0.177545</td>\n",
       "      <td>13159200</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6775</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.146807</td>\n",
       "      <td>9630290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.434343</td>\n",
       "      <td>0.140732</td>\n",
       "      <td>7406250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   INT_SQFT  DIST_MAINROAD  QS_ROOMS  QS_BATHROOM  QS_BEDROOM  QS_OVERALL  \\\n",
       "0    0.2520          0.655  0.666667     0.633333    0.966667    0.784512   \n",
       "1    0.7430          0.130  0.966667     0.733333    0.166667    0.594276   \n",
       "2    0.2045          0.350  0.700000     0.600000    0.066667    0.367003   \n",
       "3    0.6775          0.070  0.900000     0.633333    0.533333    0.676768   \n",
       "4    0.3630          0.420  0.333333     0.166667    0.700000    0.434343   \n",
       "\n",
       "     COMMIS  SALES_PRICE  \n",
       "0  0.284175      7600000  \n",
       "1  0.609756     21717770  \n",
       "2  0.177545     13159200  \n",
       "3  0.146807      9630290  \n",
       "4  0.140732      7406250  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOW we can see that all the Data is Normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM', 'QS_BEDROOM',\n",
       "       'QS_OVERALL', 'COMMIS', 'SALES_PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's remove these values from Old DataFrame\n",
    "prices_new.drop(['INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM', 'QS_BEDROOM',\n",
    "       'QS_OVERALL', 'COMMIS', 'SALES_PRICE'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_new1 = pd.concat([prices_new,prices_],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PRT_ID', 'N_BEDROOM', 'N_BATHROOM', 'N_ROOM', 'AREA_Anna Nagar',\n",
       "       'AREA_Chrompet', 'AREA_KK Nagar', 'AREA_Karapakkam', 'AREA_Other',\n",
       "       'AREA_T Nagar', 'AREA_Velachery', 'SALE_COND_AdjLand',\n",
       "       'SALE_COND_Family', 'SALE_COND_Normal Sale', 'SALE_COND_Partial',\n",
       "       'PARK_FACIL_Yes', 'BUILDTYPE_House', 'BUILDTYPE_Others',\n",
       "       'UTILITY_AVAIL_ELO', 'UTILITY_AVAIL_NoSeWa', 'STREET_No Access',\n",
       "       'STREET_Paved', 'MZZONE_C', 'MZZONE_I', 'MZZONE_RH', 'MZZONE_RL',\n",
       "       'MZZONE_RM', 'INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM',\n",
       "       'QS_BEDROOM', 'QS_OVERALL', 'COMMIS', 'SALES_PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRT_ID                   0\n",
       "N_BEDROOM                0\n",
       "N_BATHROOM               0\n",
       "N_ROOM                   0\n",
       "AREA_Anna Nagar          0\n",
       "AREA_Chrompet            0\n",
       "AREA_KK Nagar            0\n",
       "AREA_Karapakkam          0\n",
       "AREA_Other               0\n",
       "AREA_T Nagar             0\n",
       "AREA_Velachery           0\n",
       "SALE_COND_AdjLand        0\n",
       "SALE_COND_Family         0\n",
       "SALE_COND_Normal Sale    0\n",
       "SALE_COND_Partial        0\n",
       "PARK_FACIL_Yes           0\n",
       "BUILDTYPE_House          0\n",
       "BUILDTYPE_Others         0\n",
       "UTILITY_AVAIL_ELO        0\n",
       "UTILITY_AVAIL_NoSeWa     0\n",
       "STREET_No Access         0\n",
       "STREET_Paved             0\n",
       "MZZONE_C                 0\n",
       "MZZONE_I                 0\n",
       "MZZONE_RH                0\n",
       "MZZONE_RL                0\n",
       "MZZONE_RM                0\n",
       "INT_SQFT                 0\n",
       "DIST_MAINROAD            0\n",
       "QS_ROOMS                 0\n",
       "QS_BATHROOM              0\n",
       "QS_BEDROOM               0\n",
       "QS_OVERALL               0\n",
       "COMMIS                   0\n",
       "SALES_PRICE              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#So we have our complete data ready"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's start the training of our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = prices_new1.drop(['PRT_ID','SALES_PRICE'],axis=1)\n",
    "y = prices_new1['SALES_PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's predict the values for the remaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'predictions')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let;s check our predictions \n",
    "sns.scatterplot(pred,y_test)\n",
    "plt.xlabel('predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so the predictions are good "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.93850559e+05, -4.12421560e+05,  1.92896045e+05,  1.37523808e+06,\n",
       "       -4.74234926e+05, -2.18986795e+06, -2.46423620e+06, -1.76055093e+04,\n",
       "        1.54101188e+06, -1.96041406e+06,  3.19332207e+05, -1.15187724e+05,\n",
       "        3.43555010e+04, -2.68525237e+05,  9.73059104e+05, -4.52757909e+06,\n",
       "       -3.86674434e+06, -2.15196901e+05, -1.94787570e+05, -1.13242125e+06,\n",
       "       -4.98144475e+05,  5.84945075e+05,  1.12537021e+06,  1.63923791e+06,\n",
       "        2.27433028e+06,  2.89541584e+06,  7.63142672e+06, -5.87241466e+03,\n",
       "       -2.82772682e+05, -3.16730903e+05, -3.12723411e+05,  1.08256985e+06,\n",
       "        1.27815008e+06])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PRT_ID', 'N_BEDROOM', 'N_BATHROOM', 'N_ROOM', 'AREA_Anna Nagar',\n",
       "       'AREA_Chrompet', 'AREA_KK Nagar', 'AREA_Karapakkam', 'AREA_Other',\n",
       "       'AREA_T Nagar', 'AREA_Velachery', 'SALE_COND_AdjLand',\n",
       "       'SALE_COND_Family', 'SALE_COND_Normal Sale', 'SALE_COND_Partial',\n",
       "       'PARK_FACIL_Yes', 'BUILDTYPE_House', 'BUILDTYPE_Others',\n",
       "       'UTILITY_AVAIL_ELO', 'UTILITY_AVAIL_NoSeWa', 'STREET_No Access',\n",
       "       'STREET_Paved', 'MZZONE_C', 'MZZONE_I', 'MZZONE_RH', 'MZZONE_RL',\n",
       "       'MZZONE_RM', 'INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM',\n",
       "       'QS_BEDROOM', 'QS_OVERALL', 'COMMIS', 'SALES_PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_new1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let;'s  make a table of the coef_\n",
    "\n",
    "coef = pd.DataFrame(lr.coef_,index=['N_BEDROOM', 'N_BATHROOM', 'N_ROOM', 'AREA_Anna Nagar',\n",
    "       'AREA_Chrompet', 'AREA_KK Nagar', 'AREA_Karapakkam', 'AREA_Other',\n",
    "       'AREA_T Nagar', 'AREA_Velachery', 'SALE_COND_AdjLand',\n",
    "       'SALE_COND_Family', 'SALE_COND_Normal Sale', 'SALE_COND_Partial',\n",
    "       'PARK_FACIL_Yes', 'BUILDTYPE_House', 'BUILDTYPE_Others',\n",
    "       'UTILITY_AVAIL_ELO', 'UTILITY_AVAIL_NoSeWa', 'STREET_No Access',\n",
    "       'STREET_Paved', 'MZZONE_C', 'MZZONE_I', 'MZZONE_RH', 'MZZONE_RL',\n",
    "       'MZZONE_RM', 'INT_SQFT', 'DIST_MAINROAD', 'QS_ROOMS', 'QS_BATHROOM',\n",
    "       'QS_BEDROOM', 'QS_OVERALL', 'COMMIS'],columns=['Coef_'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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>Coef_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N_BEDROOM</th>\n",
       "      <td>1.938506e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_BATHROOM</th>\n",
       "      <td>-4.124216e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_ROOM</th>\n",
       "      <td>1.928960e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_Anna Nagar</th>\n",
       "      <td>1.375238e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_Chrompet</th>\n",
       "      <td>-4.742349e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_KK Nagar</th>\n",
       "      <td>-2.189868e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_Karapakkam</th>\n",
       "      <td>-2.464236e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_Other</th>\n",
       "      <td>-1.760551e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_T Nagar</th>\n",
       "      <td>1.541012e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AREA_Velachery</th>\n",
       "      <td>-1.960414e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_COND_AdjLand</th>\n",
       "      <td>3.193322e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_COND_Family</th>\n",
       "      <td>-1.151877e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_COND_Normal Sale</th>\n",
       "      <td>3.435550e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALE_COND_Partial</th>\n",
       "      <td>-2.685252e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARK_FACIL_Yes</th>\n",
       "      <td>9.730591e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUILDTYPE_House</th>\n",
       "      <td>-4.527579e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUILDTYPE_Others</th>\n",
       "      <td>-3.866744e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTILITY_AVAIL_ELO</th>\n",
       "      <td>-2.151969e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTILITY_AVAIL_NoSeWa</th>\n",
       "      <td>-1.947876e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STREET_No Access</th>\n",
       "      <td>-1.132421e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STREET_Paved</th>\n",
       "      <td>-4.981445e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZZONE_C</th>\n",
       "      <td>5.849451e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZZONE_I</th>\n",
       "      <td>1.125370e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZZONE_RH</th>\n",
       "      <td>1.639238e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZZONE_RL</th>\n",
       "      <td>2.274330e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MZZONE_RM</th>\n",
       "      <td>2.895416e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INT_SQFT</th>\n",
       "      <td>7.631427e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIST_MAINROAD</th>\n",
       "      <td>-5.872415e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QS_ROOMS</th>\n",
       "      <td>-2.827727e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QS_BATHROOM</th>\n",
       "      <td>-3.167309e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QS_BEDROOM</th>\n",
       "      <td>-3.127234e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QS_OVERALL</th>\n",
       "      <td>1.082570e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMIS</th>\n",
       "      <td>1.278150e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Coef_\n",
       "N_BEDROOM              1.938506e+05\n",
       "N_BATHROOM            -4.124216e+05\n",
       "N_ROOM                 1.928960e+05\n",
       "AREA_Anna Nagar        1.375238e+06\n",
       "AREA_Chrompet         -4.742349e+05\n",
       "AREA_KK Nagar         -2.189868e+06\n",
       "AREA_Karapakkam       -2.464236e+06\n",
       "AREA_Other            -1.760551e+04\n",
       "AREA_T Nagar           1.541012e+06\n",
       "AREA_Velachery        -1.960414e+06\n",
       "SALE_COND_AdjLand      3.193322e+05\n",
       "SALE_COND_Family      -1.151877e+05\n",
       "SALE_COND_Normal Sale  3.435550e+04\n",
       "SALE_COND_Partial     -2.685252e+05\n",
       "PARK_FACIL_Yes         9.730591e+05\n",
       "BUILDTYPE_House       -4.527579e+06\n",
       "BUILDTYPE_Others      -3.866744e+06\n",
       "UTILITY_AVAIL_ELO     -2.151969e+05\n",
       "UTILITY_AVAIL_NoSeWa  -1.947876e+05\n",
       "STREET_No Access      -1.132421e+06\n",
       "STREET_Paved          -4.981445e+05\n",
       "MZZONE_C               5.849451e+05\n",
       "MZZONE_I               1.125370e+06\n",
       "MZZONE_RH              1.639238e+06\n",
       "MZZONE_RL              2.274330e+06\n",
       "MZZONE_RM              2.895416e+06\n",
       "INT_SQFT               7.631427e+06\n",
       "DIST_MAINROAD         -5.872415e+03\n",
       "QS_ROOMS              -2.827727e+05\n",
       "QS_BATHROOM           -3.167309e+05\n",
       "QS_BEDROOM            -3.127234e+05\n",
       "QS_OVERALL             1.082570e+06\n",
       "COMMIS                 1.278150e+06"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE: these coef indicate how the prices changes with a unit change in the coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's calculate the error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root mean squared error is  794856.4923377759\n"
     ]
    }
   ],
   "source": [
    "print(\"root mean squared error is \", np.sqrt(mean_squared_error(pred,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Done"
   ]
  }
 ],
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