House Price Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <<-----------------------House Price Prediction--------------------->>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "random.seed(100)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\",1000)\n",
    "pd.set_option(\"display.max_columns\",1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>POSTED_BY</th>\n",
       "      <th>UNDER_CONSTRUCTION</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>BHK_OR_RK</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>TARGET(PRICE_IN_LACS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>1300.236407</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ksfc Layout,Bangalore</td>\n",
       "      <td>12.969910</td>\n",
       "      <td>77.597960</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Vishweshwara Nagar,Mysore</td>\n",
       "      <td>12.274538</td>\n",
       "      <td>76.644605</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>933.159722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Jigani,Bangalore</td>\n",
       "      <td>12.778033</td>\n",
       "      <td>77.632191</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>929.921143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sector-1 Vaishali,Ghaziabad</td>\n",
       "      <td>28.642300</td>\n",
       "      <td>77.344500</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BHK</td>\n",
       "      <td>999.009247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New Town,Kolkata</td>\n",
       "      <td>22.592200</td>\n",
       "      <td>88.484911</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  \\\n",
       "0     Owner                   0     0        2       BHK  1300.236407   \n",
       "1    Dealer                   0     0        2       BHK  1275.000000   \n",
       "2     Owner                   0     0        2       BHK   933.159722   \n",
       "3     Owner                   0     1        2       BHK   929.921143   \n",
       "4    Dealer                   1     0        2       BHK   999.009247   \n",
       "\n",
       "   READY_TO_MOVE  RESALE                      ADDRESS  LONGITUDE   LATITUDE  \\\n",
       "0              1       1        Ksfc Layout,Bangalore  12.969910  77.597960   \n",
       "1              1       1    Vishweshwara Nagar,Mysore  12.274538  76.644605   \n",
       "2              1       1             Jigani,Bangalore  12.778033  77.632191   \n",
       "3              1       1  Sector-1 Vaishali,Ghaziabad  28.642300  77.344500   \n",
       "4              0       1             New Town,Kolkata  22.592200  88.484911   \n",
       "\n",
       "   TARGET(PRICE_IN_LACS)  \n",
       "0                   55.0  \n",
       "1                   51.0  \n",
       "2                   43.0  \n",
       "3                   62.5  \n",
       "4                   60.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's check the head of the Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29451, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Let's check the shape of the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POSTED_BY                0\n",
       "UNDER_CONSTRUCTION       0\n",
       "RERA                     0\n",
       "BHK_NO.                  0\n",
       "BHK_OR_RK                0\n",
       "SQUARE_FT                0\n",
       "READY_TO_MOVE            0\n",
       "RESALE                   0\n",
       "ADDRESS                  0\n",
       "LONGITUDE                0\n",
       "LATITUDE                 0\n",
       "TARGET(PRICE_IN_LACS)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's check if any null values exist in our dataset or not\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29451 entries, 0 to 29450\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   POSTED_BY              29451 non-null  object \n",
      " 1   UNDER_CONSTRUCTION     29451 non-null  int64  \n",
      " 2   RERA                   29451 non-null  int64  \n",
      " 3   BHK_NO.                29451 non-null  int64  \n",
      " 4   BHK_OR_RK              29451 non-null  object \n",
      " 5   SQUARE_FT              29451 non-null  float64\n",
      " 6   READY_TO_MOVE          29451 non-null  int64  \n",
      " 7   RESALE                 29451 non-null  int64  \n",
      " 8   ADDRESS                29451 non-null  object \n",
      " 9   LONGITUDE              29451 non-null  float64\n",
      " 10  LATITUDE               29451 non-null  float64\n",
      " 11  TARGET(PRICE_IN_LACS)  29451 non-null  float64\n",
      "dtypes: float64(4), int64(5), object(3)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "##The datasets seems clean\n",
    "#Let's see the information about the dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Perform EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Let's see how many categorical columns are there in our dataset\n",
    "cat_cols=[col for col in df.columns if df[col].dtypes==\"O\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['POSTED_BY', 'BHK_OR_RK', 'ADDRESS']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POSTED_BY</th>\n",
       "      <th>BHK_OR_RK</th>\n",
       "      <th>ADDRESS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Ksfc Layout,Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Vishweshwara Nagar,Mysore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Jigani,Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Sector-1 Vaishali,Ghaziabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>BHK</td>\n",
       "      <td>New Town,Kolkata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29446</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Shamshabad Road,Agra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29447</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>E3-108, Lake View Recidency,,Vapi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29448</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Ajmer Road,Jaipur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29449</th>\n",
       "      <td>Owner</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Sholinganallur,Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29450</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>BHK</td>\n",
       "      <td>Jagatpura,Jaipur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29451 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      POSTED_BY BHK_OR_RK                            ADDRESS\n",
       "0         Owner       BHK              Ksfc Layout,Bangalore\n",
       "1        Dealer       BHK          Vishweshwara Nagar,Mysore\n",
       "2         Owner       BHK                   Jigani,Bangalore\n",
       "3         Owner       BHK        Sector-1 Vaishali,Ghaziabad\n",
       "4        Dealer       BHK                   New Town,Kolkata\n",
       "...         ...       ...                                ...\n",
       "29446     Owner       BHK               Shamshabad Road,Agra\n",
       "29447     Owner       BHK  E3-108, Lake View Recidency,,Vapi\n",
       "29448    Dealer       BHK                  Ajmer Road,Jaipur\n",
       "29449     Owner       BHK             Sholinganallur,Chennai\n",
       "29450    Dealer       BHK                   Jagatpura,Jaipur\n",
       "\n",
       "[29451 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Let's see posted owner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dealer     18291\n",
       "Owner      10538\n",
       "Builder      622\n",
       "Name: POSTED_BY, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"POSTED_BY\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109d9a78c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"POSTED_BY\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POSTED_BY\n",
       "Builder    245.705627\n",
       "Dealer     186.774735\n",
       "Owner       60.674264\n",
       "Name: TARGET(PRICE_IN_LACS), dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's See How Price of the House Varies According the Posted member\n",
    "df.groupby(\"POSTED_BY\")[\"TARGET(PRICE_IN_LACS)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As you can see ,if Builder Posts the project it will be higher price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24157\n",
       "1     5294\n",
       "Name: UNDER_CONSTRUCTION, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## let's see how many buildings are in construction\n",
    "df[\"UNDER_CONSTRUCTION\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109daac0c8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"UNDER_CONSTRUCTION\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    20088\n",
       "1     9363\n",
       "Name: RERA, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's Visulaize the Rera columns\n",
    "##RERA- Rear state regularity Act\n",
    "## It was formed to bring transperency in Real Estate Sector\n",
    "df[\"RERA\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109daf13c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df[\"RERA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109dbb7108>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"RERA\")[\"TARGET(PRICE_IN_LACS)\"].mean().plot(kind=\"bar\",grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## It is ovibous that RERA approved buliding are of Higher Price\n",
    "##Because it aims to reduce project delays and mis-selling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     13324\n",
       "3     10546\n",
       "1      3574\n",
       "4      1723\n",
       "5       190\n",
       "6        52\n",
       "7        11\n",
       "8        10\n",
       "15        4\n",
       "10        4\n",
       "20        4\n",
       "12        3\n",
       "9         3\n",
       "13        1\n",
       "11        1\n",
       "17        1\n",
       "Name: BHK_NO., dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's See on BHK_NO which represents number of rooms\n",
    "df[\"BHK_NO.\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "##As we can see we number of rooms ranging from 1 to 17\n",
    "#But generally we don't have more than 6 columns as you can see from the distribution\n",
    "## The values that are greater than 6 can be treated as potential outliers\n",
    "## Let's Remove this Values From Our dataset\n",
    "df2=df[df[\"BHK_NO.\"]<7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29409, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.85739024141796"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13324\n",
       "3    10546\n",
       "1     3574\n",
       "4     1723\n",
       "5      190\n",
       "6       52\n",
       "Name: BHK_NO., dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"BHK_NO.\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BHK_NO.\n",
       "1     72.563433\n",
       "2     97.176584\n",
       "3    176.329423\n",
       "4    350.820255\n",
       "5    604.187895\n",
       "6    868.842308\n",
       "Name: TARGET(PRICE_IN_LACS), dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby([\"BHK_NO.\"])[\"TARGET(PRICE_IN_LACS)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As you can see as the number of rooms increases , price of the house decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BHK    29385\n",
       "RK        24\n",
       "Name: BHK_OR_RK, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's See how many are RK and How Many are BHK\n",
    "df2[\"BHK_OR_RK\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## RK means Rooms and Kitchen which mean in a single big room there is kitchen and room attached \n",
    "## These type of housese are rare and used in old days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    24\n",
       "Name: BHK_NO., dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2[\"BHK_OR_RK\"]==\"RK\"][\"BHK_NO.\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As you can see these are only single rooms\n",
    "## Hence it would be better to remove these values.\n",
    "## As after removing this value we have only BHK value left in that column , It would be better to remove this columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2[df2[\"BHK_OR_RK\"]!=\"RK\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.drop(columns=[\"BHK_OR_RK\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>POSTED_BY</th>\n",
       "      <th>UNDER_CONSTRUCTION</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>TARGET(PRICE_IN_LACS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.236407</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ksfc Layout,Bangalore</td>\n",
       "      <td>12.969910</td>\n",
       "      <td>77.597960</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Vishweshwara Nagar,Mysore</td>\n",
       "      <td>12.274538</td>\n",
       "      <td>76.644605</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>933.159722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Jigani,Bangalore</td>\n",
       "      <td>12.778033</td>\n",
       "      <td>77.632191</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>929.921143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sector-1 Vaishali,Ghaziabad</td>\n",
       "      <td>28.642300</td>\n",
       "      <td>77.344500</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>999.009247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New Town,Kolkata</td>\n",
       "      <td>22.592200</td>\n",
       "      <td>88.484911</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO.    SQUARE_FT  READY_TO_MOVE  \\\n",
       "0     Owner                   0     0        2  1300.236407              1   \n",
       "1    Dealer                   0     0        2  1275.000000              1   \n",
       "2     Owner                   0     0        2   933.159722              1   \n",
       "3     Owner                   0     1        2   929.921143              1   \n",
       "4    Dealer                   1     0        2   999.009247              0   \n",
       "\n",
       "   RESALE                      ADDRESS  LONGITUDE   LATITUDE  \\\n",
       "0       1        Ksfc Layout,Bangalore  12.969910  77.597960   \n",
       "1       1    Vishweshwara Nagar,Mysore  12.274538  76.644605   \n",
       "2       1             Jigani,Bangalore  12.778033  77.632191   \n",
       "3       1  Sector-1 Vaishali,Ghaziabad  28.642300  77.344500   \n",
       "4       1             New Town,Kolkata  22.592200  88.484911   \n",
       "\n",
       "   TARGET(PRICE_IN_LACS)  \n",
       "0                   55.0  \n",
       "1                   51.0  \n",
       "2                   43.0  \n",
       "3                   62.5  \n",
       "4                   60.5  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1300.236407\n",
       "1        1275.000000\n",
       "2         933.159722\n",
       "3         929.921143\n",
       "4         999.009247\n",
       "            ...     \n",
       "29446    2500.000000\n",
       "29447     769.230769\n",
       "29448    1022.641509\n",
       "29449     927.079009\n",
       "29450     896.774194\n",
       "Name: SQUARE_FT, Length: 29451, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's See SQUARE_FT columns\n",
    "df[\"SQUARE_FT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-b7e393f3ced2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf3\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"SQUARE_FT\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdisplot\u001b[1;34m(data, x, y, hue, row, col, weights, kind, rug, rug_kws, log_scale, legend, palette, hue_order, hue_norm, color, col_wrap, row_order, col_order, height, aspect, facet_kws, **kwargs)\u001b[0m\n\u001b[0;32m   2222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2223\u001b[0m             \u001b[0m_assign_default_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhist_kws\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_univariate_histogram\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhistplot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2224\u001b[1;33m             \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_univariate_histogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mhist_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2225\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2226\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mplot_univariate_histogram\u001b[1;34m(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)\u001b[0m\n\u001b[0;32m    558\u001b[0m                     \u001b[0mbottom\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m                     \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"edge\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m                     \u001b[1;33m**\u001b[0m\u001b[0martist_kws\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    561\u001b[0m                 )\n\u001b[0;32m    562\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mbar\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1563\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1564\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1565\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2333\u001b[0m             \u001b[0my0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2334\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2335\u001b[1;33m             \u001b[0mheight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_dx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2336\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0myerr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2337\u001b[0m                 \u001b[0myerr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_dx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m_convert_dx\u001b[1;34m(dx, x0, xconv, convert)\u001b[0m\n\u001b[0;32m   2153\u001b[0m                 \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2154\u001b[0m                 \u001b[0mdelist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m             \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mddx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mddx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2156\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdelist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2157\u001b[0m                 \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m   2153\u001b[0m                 \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2154\u001b[0m                 \u001b[0mdelist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2155\u001b[1;33m             \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mddx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mddx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2156\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mdelist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2157\u001b[0m                 \u001b[0mdx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\acer\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mconvert_yunits\u001b[1;34m(self, y)\u001b[0m\n\u001b[0;32m    178\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m         \"\"\"\n\u001b[0;32m    182\u001b[0m         \u001b[0mConvert\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m*\u001b[0m \u001b[0musing\u001b[0m \u001b[0mthe\u001b[0m \u001b[0munit\u001b[0m \u001b[0mtype\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0myaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.displot(df3[\"SQUARE_FT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2109dc7a288>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Let's See the scatter plot between squaers\n",
    "plt.figure(figsize=(20,16))\n",
    "plt.scatter(x=df3[\"SQUARE_FT\"],y=df3[\"TARGET(PRICE_IN_LACS)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109dcaec08>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bTFlVPSgr/yF9zxjjb9cZYu9gsuatD/vHfNs52nwiyROq6vFVdUGSFyb54JoxH0yyb/aK7s9M8o0xxlc2e6Kwyeaujap6ZFXV7O2nZ+VYcXzTZwr92DdgHfYNpmr2df9nSe4YY7ztDMPsHUzSIuvD/jHftr08aoxxoqpemeTvk5yf5F1jjNur6hWz+/8kyYeTXJHkSJJ7k7x0q+YLm2XBtfELSX61qk4k+U6SF44xnMbLtldVNyXZk2RXVd2V5A1JHpTYN5i2BdaGfYOpenaSX05yuKpum932+iSPTewdTN4i68P+MUf59wAAAADoZztfHgUAAADwPUu0AQAAAGhItAEAAABoSLQBAAAAaEi0AQAAAHgAqupdVfXVqvrMAmMfW1UHqupfq+rTVXXFvMeINgAAAAAPzPVJ9i449pok7x1jPCXJC5O8c94DRBsAYFNU1dVVdfvsJ0u3VdUzquqCqvrDqvp8VR2pqg9V1WNn45fW/tSqqt5YVa9d9f6Oqrq7qn5nzbhbq+rOqvpUVX2iqp686r6jVXV4NofbquodZ5nz9VX1hVVj37Dq7f+qqv9c9f4F5+5fCwD4XjDG+GiSr62+rap+uKqWq+pQVX2sqn701PAkD5u9/YNJvjzv+Xec09kCAKyjqp6V5PlJnjrG+G5V7UpyQZLfTvLQJD8yxjhZVS9N8oGqetqCT/2zSe5M8ktV9foxxlh134vHGAdnz/nWJJevuu85Y4y7F/wYrxtj/M2q9980+5zemOSeMcbvLfg8AMA0XJfkFWOMz1XVM7JyRs3PJHljko9U1VVJLkxy2bwncqYNALAZHpXk7jHGd5NkFky+nuSlSV49xjg5u/3dSe7JAt/EzLwoyduTfCnJM88w5uNJHv3Apw4AsJiqekiSn0zy11V1W5I/zcr3QcnK9y3XjzEek+SKJH9eVWftMqINALAZPpLk4qr6t6p6Z1X9dJJLknxpjPHNNWMPJvmxeU9YVT+Q5LlJPpTkpqx8I7SevUnev+a2A6sua3r1nA/11lVjL503LwBg0s5L8vUxxpNX/XnS7L6XJXlvkowxPp7k+5PsmvdkAAAbaoxxT5KnJXl5kmNJ/irJc7JybfdadephZ3q62d/PT3JgjHFvkvcl+fmqOn/VuPdU1V1JfiPJ/jXP8ZxV30j9wZzpv27V2MNzxgIAEzb7YdQXquoXk6RW/MTs7i9l5QdOqaonZSXaHDvb84k2AMCmGGOcHGPcOsZ4Q5JXZiW6PK6qHrpm6FOzcrbN8SQ/tOa+RyQ59Vo0L0pyWVUdTXIoyc6shKBTXpzk8Un+Ism15/BTAQBIklTVTVm5FPuJVXVXVb0sK9+DvKyqPpXk9iQvmA1/TZJfmd1+U5KXrHk9vtN4IWIAYMNV1ROT3DfG+Nzspidn5QWEDyd5W1W9YvZCxPuS/HeSfxpjjKr6SlU9d4zxD1X1iKxc6vT2qnpYkp9KcvGp18mZveDwi5LccurjjjH+t6quSfL5qnrSGOOOzfqcAYDtb4xxtsuz1479bJJn35/nF20AgM3wkCT7q+rhSU4kOZKVS6W+lZXf7HTn7DVqjiV51qqfOu1Lcm1V/f7s/TeNMT5fVS9J8o+ngs3MB5L8blV93+oPPMb4zuzxr83KteTJymvanJy9/ekxxr5z+ckCAJwLNedMHACATVFVj0yynOSdY4zrtno+AABbTbQBAAAAaMjlUQDA5FXVtTn9GvO3jzHevRXzAQBInGkDAAAA0JJf+Q0AAADQkGgDAAAA0JBoAwAAANCQaAMAAADQkGgDAAAA0ND/AfgjSBYKchYRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(df3[\"SQUARE_FT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>POSTED_BY</th>\n",
       "      <th>UNDER_CONSTRUCTION</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>TARGET(PRICE_IN_LACS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.333333e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Mulanthuruthy,Kochi</td>\n",
       "      <td>9.893479</td>\n",
       "      <td>76.408774</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10745</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.545455e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Malur,Bangalore</td>\n",
       "      <td>13.021000</td>\n",
       "      <td>77.938000</td>\n",
       "      <td>28000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11238</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.875000e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>R.T. Nagar,Bangalore</td>\n",
       "      <td>13.018900</td>\n",
       "      <td>77.596300</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15598</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.928571e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sainik Colony,Jammu</td>\n",
       "      <td>32.674044</td>\n",
       "      <td>74.907348</td>\n",
       "      <td>55.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15724</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.064516e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lakkasandra,Bangalore</td>\n",
       "      <td>12.795926</td>\n",
       "      <td>77.331535</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22155</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.400000e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sayajipura,Vadodara</td>\n",
       "      <td>22.323406</td>\n",
       "      <td>73.240395</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25241</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.300000e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>New Kumaran Nagar,Chennai</td>\n",
       "      <td>10.810426</td>\n",
       "      <td>78.668914</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28591</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.750000e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ajit Singh Nagar,Vijayawada</td>\n",
       "      <td>30.466084</td>\n",
       "      <td>74.501902</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO.     SQUARE_FT  \\\n",
       "373       Owner                   0     0        4  4.333333e+05   \n",
       "10745     Owner                   0     0        3  2.545455e+08   \n",
       "11238    Dealer                   0     0        3  1.875000e+08   \n",
       "15598     Owner                   0     0        3  7.928571e+05   \n",
       "15724     Owner                   0     0        2  8.064516e+07   \n",
       "22155     Owner                   0     0        1  3.400000e+05   \n",
       "25241     Owner                   0     0        3  4.300000e+06   \n",
       "28591     Owner                   0     0        2  8.750000e+05   \n",
       "\n",
       "       READY_TO_MOVE  RESALE                      ADDRESS  LONGITUDE  \\\n",
       "373                1       1          Mulanthuruthy,Kochi   9.893479   \n",
       "10745              1       1              Malur,Bangalore  13.021000   \n",
       "11238              1       1         R.T. Nagar,Bangalore  13.018900   \n",
       "15598              1       1          Sainik Colony,Jammu  32.674044   \n",
       "15724              1       1        Lakkasandra,Bangalore  12.795926   \n",
       "22155              1       1          Sayajipura,Vadodara  22.323406   \n",
       "25241              1       1    New Kumaran Nagar,Chennai  10.810426   \n",
       "28591              1       1  Ajit Singh Nagar,Vijayawada  30.466084   \n",
       "\n",
       "        LATITUDE  TARGET(PRICE_IN_LACS)  \n",
       "373    76.408774                   65.0  \n",
       "10745  77.938000                28000.0  \n",
       "11238  77.596300                30000.0  \n",
       "15598  74.907348                   55.5  \n",
       "15724  77.331535                25000.0  \n",
       "22155  73.240395                   17.0  \n",
       "25241  78.668914                   43.0  \n",
       "28591  74.501902                   35.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3[\"SQUARE_FT\"]>3*1e5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "## These Eight values are potential Outliers.As square foot size is extremely high\n",
    "##hence drop these values\n",
    "df4=df3[df3[\"SQUARE_FT\"]<3*1e5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109dd13548>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "sns.boxplot(df4[\"SQUARE_FT\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>POSTED_BY</th>\n",
       "      <th>UNDER_CONSTRUCTION</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>TARGET(PRICE_IN_LACS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.236407</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ksfc Layout,Bangalore</td>\n",
       "      <td>12.969910</td>\n",
       "      <td>77.597960</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Vishweshwara Nagar,Mysore</td>\n",
       "      <td>12.274538</td>\n",
       "      <td>76.644605</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>933.159722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Jigani,Bangalore</td>\n",
       "      <td>12.778033</td>\n",
       "      <td>77.632191</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>929.921143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sector-1 Vaishali,Ghaziabad</td>\n",
       "      <td>28.642300</td>\n",
       "      <td>77.344500</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dealer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>999.009247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>New Town,Kolkata</td>\n",
       "      <td>22.592200</td>\n",
       "      <td>88.484911</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO.    SQUARE_FT  READY_TO_MOVE  \\\n",
       "0     Owner                   0     0        2  1300.236407              1   \n",
       "1    Dealer                   0     0        2  1275.000000              1   \n",
       "2     Owner                   0     0        2   933.159722              1   \n",
       "3     Owner                   0     1        2   929.921143              1   \n",
       "4    Dealer                   1     0        2   999.009247              0   \n",
       "\n",
       "   RESALE                      ADDRESS  LONGITUDE   LATITUDE  \\\n",
       "0       1        Ksfc Layout,Bangalore  12.969910  77.597960   \n",
       "1       1    Vishweshwara Nagar,Mysore  12.274538  76.644605   \n",
       "2       1             Jigani,Bangalore  12.778033  77.632191   \n",
       "3       1  Sector-1 Vaishali,Ghaziabad  28.642300  77.344500   \n",
       "4       1             New Town,Kolkata  22.592200  88.484911   \n",
       "\n",
       "   TARGET(PRICE_IN_LACS)  \n",
       "0                   55.0  \n",
       "1                   51.0  \n",
       "2                   43.0  \n",
       "3                   62.5  \n",
       "4                   60.5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's see How many building are ready to move"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    24098\n",
       "0     5279\n",
       "Name: READY_TO_MOVE, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"READY_TO_MOVE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "READY_TO_MOVE\n",
       "0    221.023724\n",
       "1    121.468453\n",
       "Name: TARGET(PRICE_IN_LACS), dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.groupby(\"READY_TO_MOVE\")[\"TARGET(PRICE_IN_LACS)\"].mean()\n",
    "## There is no much more difference in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    27311\n",
       "0     2066\n",
       "Name: RESALE, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"RESALE\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESALE\n",
       "0    640.193311\n",
       "1    101.471628\n",
       "Name: TARGET(PRICE_IN_LACS), dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## As you can see most of the houses are resold\n",
    "df4.groupby(\"RESALE\")[\"TARGET(PRICE_IN_LACS)\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "## It is clearly seen that resold houses are relatively lower price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's see the address columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Zirakpur,Chandigarh              509\n",
       "Whitefield,Bangalore             230\n",
       "Raj Nagar Extension,Ghaziabad    215\n",
       "Sector-137 Noida,Noida           139\n",
       "New Town,Kolkata                 131\n",
       "                                ... \n",
       "Surathkal,Mangalore                1\n",
       "Tikrapara,Bilaspur                 1\n",
       "shiyapura,Vadodara                 1\n",
       "Shankarapuram,Bangalore            1\n",
       "Sector-42 Noida,Noida              1\n",
       "Name: ADDRESS, Length: 6868, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"ADDRESS\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As we can see in the address , at the end of each address there is a prominent city or state name\n",
    "##Let's extract these citis and states from these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Bangalore\n",
       "1           Mysore\n",
       "2        Bangalore\n",
       "3        Ghaziabad\n",
       "4          Kolkata\n",
       "           ...    \n",
       "29446         Agra\n",
       "29447         Vapi\n",
       "29448       Jaipur\n",
       "29449      Chennai\n",
       "29450       Jaipur\n",
       "Name: ADDRESS, Length: 29377, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"ADDRESS\"].apply(lambda x : x.split(\",\")[-1].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## Let's store these values in a new column\n",
    "df4[\"LOCATION\"]=df4[\"ADDRESS\"].apply(lambda x : x.split(\",\")[-1].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bangalore         4326\n",
       "Lalitpur          2966\n",
       "Mumbai            2022\n",
       "Pune              1989\n",
       "Noida             1767\n",
       "Kolkata           1707\n",
       "Maharashtra       1578\n",
       "Chennai           1253\n",
       "Ghaziabad         1087\n",
       "Jaipur             960\n",
       "Chandigarh         696\n",
       "Faridabad          648\n",
       "Mohali             556\n",
       "Vadodara           509\n",
       "Gurgaon            430\n",
       "Surat              423\n",
       "Nagpur             336\n",
       "Lucknow            326\n",
       "Indore             306\n",
       "Bhubaneswar        234\n",
       "Bhopal             218\n",
       "Kochi              204\n",
       "Visakhapatnam      178\n",
       "Bhiwadi            161\n",
       "Goa                149\n",
       "Coimbatore         147\n",
       "Dehradun           131\n",
       "Ranchi             124\n",
       "Mangalore          121\n",
       "Gandhinagar        118\n",
       "Sonipat            117\n",
       "Secunderabad       108\n",
       "Palghar            107\n",
       "Kanpur              92\n",
       "Raipur              89\n",
       "Guwahati            89\n",
       "Jamshedpur          86\n",
       "Siliguri            83\n",
       "Rajkot              83\n",
       "Agra                83\n",
       "Patna               82\n",
       "Panchkula           82\n",
       "Vijayawada          79\n",
       "Jamnagar            66\n",
       "Aurangabad          66\n",
       "Raigad              65\n",
       "Dharuhera           63\n",
       "Thrissur            57\n",
       "Durgapur            55\n",
       "Gwalior             53\n",
       "Meerut              51\n",
       "Allahabad           49\n",
       "Bahadurgarh         49\n",
       "Anand               48\n",
       "Ernakulam           46\n",
       "Kota                44\n",
       "Mysore              43\n",
       "Bharuch             43\n",
       "Valsad              42\n",
       "Varanasi            42\n",
       "Haridwar            40\n",
       "Ratnagiri           40\n",
       "Bhilai              39\n",
       "Jalandhar           38\n",
       "Vapi                37\n",
       "Udaipur             35\n",
       "Asansol             35\n",
       "Neemrana            35\n",
       "Jabalpur            34\n",
       "Udupi               34\n",
       "Kolhapur            33\n",
       "Madurai             32\n",
       "Panaji              32\n",
       "Guntur              32\n",
       "Bhavnagar           31\n",
       "Ludhiana            31\n",
       "Hubli               30\n",
       "Dhanbad             30\n",
       "Amravati            28\n",
       "Jodhpur             27\n",
       "Ahmednagar          25\n",
       "Palakkad            24\n",
       "Aligarh             22\n",
       "Sindhudurg          22\n",
       "Solapur             22\n",
       "Nellore             22\n",
       "Ajmer               21\n",
       "Navsari             21\n",
       "Bilaspur            21\n",
       "Shimla              21\n",
       "Karjat              20\n",
       "Margao              19\n",
       "Belgaum             19\n",
       "Vizianagaram        18\n",
       "Rudrapur            18\n",
       "Junagadh            18\n",
       "Kottayam            18\n",
       "Jalgaon             17\n",
       "Pondicherry         17\n",
       "Tirupati            16\n",
       "Mathura             16\n",
       "Solan               15\n",
       "Karad               14\n",
       "Nainital            13\n",
       "Kakinada            12\n",
       "Kannur              12\n",
       "Chandrapur          12\n",
       "Alwar               11\n",
       "Durg                11\n",
       "Amritsar            11\n",
       "Wardha              10\n",
       "Latur               10\n",
       "Puri                10\n",
       "Kanchipuram         10\n",
       "Karnal              10\n",
       "Akola                9\n",
       "Bareilly             9\n",
       "Gandhidham           9\n",
       "Hazaribagh           9\n",
       "Tirunelveli          8\n",
       "Tenali               8\n",
       "Cuttack              8\n",
       "Bardhaman            8\n",
       "Moradabad            8\n",
       "Rohtak               8\n",
       "Ongole               7\n",
       "Rewari               7\n",
       "Jhansi               7\n",
       "Berhampur            7\n",
       "Eluru                7\n",
       "Morbi                6\n",
       "Kollam               6\n",
       "Haldwani             6\n",
       "Muzaffarpur          6\n",
       "Palwal               6\n",
       "Shirdi               5\n",
       "Bhimavaram           5\n",
       "Raigarh              5\n",
       "Bokaro               5\n",
       "Anantapur            5\n",
       "Dharwad              5\n",
       "Gaya                 5\n",
       "Nadiad               5\n",
       "Ujjain               5\n",
       "Gulbarga             5\n",
       "Panipat              4\n",
       "Jammu                4\n",
       "Ambala               4\n",
       "Kharagpur            4\n",
       "Thanjavur            4\n",
       "Warangal             4\n",
       "Nanded               4\n",
       "Godhra               4\n",
       "Hosur                4\n",
       "Dhule                4\n",
       "Bhagalpur            4\n",
       "Sikar                3\n",
       "Rishikesh            3\n",
       "Porbandar            3\n",
       "Hapur                3\n",
       "Jalna                3\n",
       "Gadhinglaj           3\n",
       "Erode                3\n",
       "Thoothukudi          3\n",
       "Bhandara             3\n",
       "Hoshangabad          3\n",
       "Dalhousie            3\n",
       "Satna                3\n",
       "Pathanamthitta       3\n",
       "Salem                3\n",
       "Roorkee              3\n",
       "Srikakulam           3\n",
       "Rourkela             2\n",
       "Bankura              2\n",
       "Baddi                2\n",
       "Jind                 2\n",
       "Hoshiarpur           2\n",
       "Bhiwani              2\n",
       "Bikaner              2\n",
       "Barabanki            2\n",
       "Arrah                2\n",
       "Ooty                 2\n",
       "Karwar               2\n",
       "Gurdaspur            2\n",
       "Kotdwara             2\n",
       "Idukki               2\n",
       "Haldia               2\n",
       "Beed                 2\n",
       "Kurnool              2\n",
       "Patiala              2\n",
       "Rewa                 2\n",
       "Yavatmal             2\n",
       "Deoghar              2\n",
       "Kadapa               2\n",
       "Kadi                 2\n",
       "Phagwara             1\n",
       "Silchar              1\n",
       "Rajpura              1\n",
       "Kangra               1\n",
       "Pudukkottai          1\n",
       "Jhunjhunu            1\n",
       "Harda                1\n",
       "Jharsuguda           1\n",
       "Bharatpur            1\n",
       "Darbhanga            1\n",
       "Nagaur               1\n",
       "Malappuram           1\n",
       "Jajpur               1\n",
       "Sabarkantha          1\n",
       "Darjeeling           1\n",
       "Banswara             1\n",
       "Kurukshetra          1\n",
       "Rajnandgaon          1\n",
       "Hathras              1\n",
       "Midnapore            1\n",
       "Srinagar             1\n",
       "Ottapalam            1\n",
       "Sambalpur            1\n",
       "Angul                1\n",
       "Pali                 1\n",
       "Nagapattinam         1\n",
       "Agartala             1\n",
       "Chittoor             1\n",
       "Hassan               1\n",
       "Nagaon               1\n",
       "Hajipur              1\n",
       "Raisen               1\n",
       "Vellore              1\n",
       "Chhindwara           1\n",
       "Betul                1\n",
       "Amroha               1\n",
       "Gadarwara            1\n",
       "Raichur              1\n",
       "Surendranagar        1\n",
       "Jagdalpur            1\n",
       "Siddipet             1\n",
       "Korba                1\n",
       "Machilipatnam        1\n",
       "Gondia               1\n",
       "Karaikudi            1\n",
       "Visnagar             1\n",
       "Washim               1\n",
       "Osmanabad            1\n",
       "Cuddalore            1\n",
       "Dahod                1\n",
       "Bhuj                 1\n",
       "Siwan                1\n",
       "Balasore             1\n",
       "Dibrugarh            1\n",
       "Rayagada             1\n",
       "Palanpur             1\n",
       "Faizabad             1\n",
       "Name: LOCATION, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"LOCATION\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's make the value of the loaction \"Others\" if that value is less frequent i.e less than 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_values=list(df4[\"LOCATION\"].value_counts()[df4[\"LOCATION\"].value_counts()<15].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Karad',\n",
       " 'Nainital',\n",
       " 'Kakinada',\n",
       " 'Kannur',\n",
       " 'Chandrapur',\n",
       " 'Alwar',\n",
       " 'Durg',\n",
       " 'Amritsar',\n",
       " 'Wardha',\n",
       " 'Latur',\n",
       " 'Puri',\n",
       " 'Kanchipuram',\n",
       " 'Karnal',\n",
       " 'Akola',\n",
       " 'Bareilly',\n",
       " 'Gandhidham',\n",
       " 'Hazaribagh',\n",
       " 'Tirunelveli',\n",
       " 'Tenali',\n",
       " 'Cuttack',\n",
       " 'Bardhaman',\n",
       " 'Moradabad',\n",
       " 'Rohtak',\n",
       " 'Ongole',\n",
       " 'Rewari',\n",
       " 'Jhansi',\n",
       " 'Berhampur',\n",
       " 'Eluru',\n",
       " 'Morbi',\n",
       " 'Kollam',\n",
       " 'Haldwani',\n",
       " 'Muzaffarpur',\n",
       " 'Palwal',\n",
       " 'Shirdi',\n",
       " 'Bhimavaram',\n",
       " 'Raigarh',\n",
       " 'Bokaro',\n",
       " 'Anantapur',\n",
       " 'Dharwad',\n",
       " 'Gaya',\n",
       " 'Nadiad',\n",
       " 'Ujjain',\n",
       " 'Gulbarga',\n",
       " 'Panipat',\n",
       " 'Jammu',\n",
       " 'Ambala',\n",
       " 'Kharagpur',\n",
       " 'Thanjavur',\n",
       " 'Warangal',\n",
       " 'Nanded',\n",
       " 'Godhra',\n",
       " 'Hosur',\n",
       " 'Dhule',\n",
       " 'Bhagalpur',\n",
       " 'Sikar',\n",
       " 'Rishikesh',\n",
       " 'Porbandar',\n",
       " 'Hapur',\n",
       " 'Jalna',\n",
       " 'Gadhinglaj',\n",
       " 'Erode',\n",
       " 'Thoothukudi',\n",
       " 'Bhandara',\n",
       " 'Hoshangabad',\n",
       " 'Dalhousie',\n",
       " 'Satna',\n",
       " 'Pathanamthitta',\n",
       " 'Salem',\n",
       " 'Roorkee',\n",
       " 'Srikakulam',\n",
       " 'Rourkela',\n",
       " 'Bankura',\n",
       " 'Baddi',\n",
       " 'Jind',\n",
       " 'Hoshiarpur',\n",
       " 'Bhiwani',\n",
       " 'Bikaner',\n",
       " 'Barabanki',\n",
       " 'Arrah',\n",
       " 'Ooty',\n",
       " 'Karwar',\n",
       " 'Gurdaspur',\n",
       " 'Kotdwara',\n",
       " 'Idukki',\n",
       " 'Haldia',\n",
       " 'Beed',\n",
       " 'Kurnool',\n",
       " 'Patiala',\n",
       " 'Rewa',\n",
       " 'Yavatmal',\n",
       " 'Deoghar',\n",
       " 'Kadapa',\n",
       " 'Kadi',\n",
       " 'Phagwara',\n",
       " 'Silchar',\n",
       " 'Rajpura',\n",
       " 'Kangra',\n",
       " 'Pudukkottai',\n",
       " 'Jhunjhunu',\n",
       " 'Harda',\n",
       " 'Jharsuguda',\n",
       " 'Bharatpur',\n",
       " 'Darbhanga',\n",
       " 'Nagaur',\n",
       " 'Malappuram',\n",
       " 'Jajpur',\n",
       " 'Sabarkantha',\n",
       " 'Darjeeling',\n",
       " 'Banswara',\n",
       " 'Kurukshetra',\n",
       " 'Rajnandgaon',\n",
       " 'Hathras',\n",
       " 'Midnapore',\n",
       " 'Srinagar',\n",
       " 'Ottapalam',\n",
       " 'Sambalpur',\n",
       " 'Angul',\n",
       " 'Pali',\n",
       " 'Nagapattinam',\n",
       " 'Agartala',\n",
       " 'Chittoor',\n",
       " 'Hassan',\n",
       " 'Nagaon',\n",
       " 'Hajipur',\n",
       " 'Raisen',\n",
       " 'Vellore',\n",
       " 'Chhindwara',\n",
       " 'Betul',\n",
       " 'Amroha',\n",
       " 'Gadarwara',\n",
       " 'Raichur',\n",
       " 'Surendranagar',\n",
       " 'Jagdalpur',\n",
       " 'Siddipet',\n",
       " 'Korba',\n",
       " 'Machilipatnam',\n",
       " 'Gondia',\n",
       " 'Karaikudi',\n",
       " 'Visnagar',\n",
       " 'Washim',\n",
       " 'Osmanabad',\n",
       " 'Cuddalore',\n",
       " 'Dahod',\n",
       " 'Bhuj',\n",
       " 'Siwan',\n",
       " 'Balasore',\n",
       " 'Dibrugarh',\n",
       " 'Rayagada',\n",
       " 'Palanpur',\n",
       " 'Faizabad']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4[\"LOCATION\"].replace(rare_values,\"Others\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bangalore        4326\n",
       "Lalitpur         2966\n",
       "Mumbai           2022\n",
       "Pune             1989\n",
       "Noida            1767\n",
       "Kolkata          1707\n",
       "Maharashtra      1578\n",
       "Chennai          1253\n",
       "Ghaziabad        1087\n",
       "Jaipur            960\n",
       "Chandigarh        696\n",
       "Faridabad         648\n",
       "Mohali            556\n",
       "Others            540\n",
       "Vadodara          509\n",
       "Gurgaon           430\n",
       "Surat             423\n",
       "Nagpur            336\n",
       "Lucknow           326\n",
       "Indore            306\n",
       "Bhubaneswar       234\n",
       "Bhopal            218\n",
       "Kochi             204\n",
       "Visakhapatnam     178\n",
       "Bhiwadi           161\n",
       "Goa               149\n",
       "Coimbatore        147\n",
       "Dehradun          131\n",
       "Ranchi            124\n",
       "Mangalore         121\n",
       "Gandhinagar       118\n",
       "Sonipat           117\n",
       "Secunderabad      108\n",
       "Palghar           107\n",
       "Kanpur             92\n",
       "Raipur             89\n",
       "Guwahati           89\n",
       "Jamshedpur         86\n",
       "Rajkot             83\n",
       "Agra               83\n",
       "Siliguri           83\n",
       "Panchkula          82\n",
       "Patna              82\n",
       "Vijayawada         79\n",
       "Aurangabad         66\n",
       "Jamnagar           66\n",
       "Raigad             65\n",
       "Dharuhera          63\n",
       "Thrissur           57\n",
       "Durgapur           55\n",
       "Gwalior            53\n",
       "Meerut             51\n",
       "Allahabad          49\n",
       "Bahadurgarh        49\n",
       "Anand              48\n",
       "Ernakulam          46\n",
       "Kota               44\n",
       "Mysore             43\n",
       "Bharuch            43\n",
       "Valsad             42\n",
       "Varanasi           42\n",
       "Haridwar           40\n",
       "Ratnagiri          40\n",
       "Bhilai             39\n",
       "Jalandhar          38\n",
       "Vapi               37\n",
       "Neemrana           35\n",
       "Asansol            35\n",
       "Udaipur            35\n",
       "Udupi              34\n",
       "Jabalpur           34\n",
       "Kolhapur           33\n",
       "Panaji             32\n",
       "Madurai            32\n",
       "Guntur             32\n",
       "Bhavnagar          31\n",
       "Ludhiana           31\n",
       "Dhanbad            30\n",
       "Hubli              30\n",
       "Amravati           28\n",
       "Jodhpur            27\n",
       "Ahmednagar         25\n",
       "Palakkad           24\n",
       "Aligarh            22\n",
       "Nellore            22\n",
       "Sindhudurg         22\n",
       "Solapur            22\n",
       "Navsari            21\n",
       "Shimla             21\n",
       "Bilaspur           21\n",
       "Ajmer              21\n",
       "Karjat             20\n",
       "Belgaum            19\n",
       "Margao             19\n",
       "Junagadh           18\n",
       "Vizianagaram       18\n",
       "Rudrapur           18\n",
       "Kottayam           18\n",
       "Jalgaon            17\n",
       "Pondicherry        17\n",
       "Tirupati           16\n",
       "Mathura            16\n",
       "Solan              15\n",
       "Name: LOCATION, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"LOCATION\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's drop ADDRESS column\n",
    "## As Latitude and Longitude are Specific to that address let's drop this also\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5=df4.drop(columns=[\"ADDRESS\",\"LATITUDE\",\"LONGITUDE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations_values=list(df5[\"LOCATION\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bangalore',\n",
       " 'Mysore',\n",
       " 'Ghaziabad',\n",
       " 'Kolkata',\n",
       " 'Kochi',\n",
       " 'Jaipur',\n",
       " 'Mohali',\n",
       " 'Chennai',\n",
       " 'Siliguri',\n",
       " 'Noida',\n",
       " 'Raigad',\n",
       " 'Bhubaneswar',\n",
       " 'Others',\n",
       " 'Pune',\n",
       " 'Mumbai',\n",
       " 'Nagpur',\n",
       " 'Bhiwadi',\n",
       " 'Faridabad',\n",
       " 'Lalitpur',\n",
       " 'Maharashtra',\n",
       " 'Vadodara',\n",
       " 'Visakhapatnam',\n",
       " 'Vapi',\n",
       " 'Mangalore',\n",
       " 'Aurangabad',\n",
       " 'Vijayawada',\n",
       " 'Belgaum',\n",
       " 'Bhopal',\n",
       " 'Lucknow',\n",
       " 'Kanpur',\n",
       " 'Gandhinagar',\n",
       " 'Pondicherry',\n",
       " 'Agra',\n",
       " 'Ranchi',\n",
       " 'Gurgaon',\n",
       " 'Udupi',\n",
       " 'Indore',\n",
       " 'Jodhpur',\n",
       " 'Coimbatore',\n",
       " 'Valsad',\n",
       " 'Palghar',\n",
       " 'Surat',\n",
       " 'Varanasi',\n",
       " 'Guwahati',\n",
       " 'Amravati',\n",
       " 'Anand',\n",
       " 'Tirupati',\n",
       " 'Secunderabad',\n",
       " 'Raipur',\n",
       " 'Vizianagaram',\n",
       " 'Thrissur',\n",
       " 'Madurai',\n",
       " 'Chandigarh',\n",
       " 'Shimla',\n",
       " 'Gwalior',\n",
       " 'Rajkot',\n",
       " 'Sonipat',\n",
       " 'Allahabad',\n",
       " 'Dharuhera',\n",
       " 'Durgapur',\n",
       " 'Panchkula',\n",
       " 'Solapur',\n",
       " 'Goa',\n",
       " 'Jamshedpur',\n",
       " 'Jabalpur',\n",
       " 'Hubli',\n",
       " 'Patna',\n",
       " 'Bilaspur',\n",
       " 'Ratnagiri',\n",
       " 'Meerut',\n",
       " 'Jalandhar',\n",
       " 'Ludhiana',\n",
       " 'Kota',\n",
       " 'Panaji',\n",
       " 'Kolhapur',\n",
       " 'Ernakulam',\n",
       " 'Bhavnagar',\n",
       " 'Bharuch',\n",
       " 'Asansol',\n",
       " 'Margao',\n",
       " 'Bhilai',\n",
       " 'Dehradun',\n",
       " 'Guntur',\n",
       " 'Jalgaon',\n",
       " 'Udaipur',\n",
       " 'Neemrana',\n",
       " 'Sindhudurg',\n",
       " 'Kottayam',\n",
       " 'Dhanbad',\n",
       " 'Navsari',\n",
       " 'Bahadurgarh',\n",
       " 'Nellore',\n",
       " 'Haridwar',\n",
       " 'Jamnagar',\n",
       " 'Junagadh',\n",
       " 'Ahmednagar',\n",
       " 'Palakkad',\n",
       " 'Karjat',\n",
       " 'Ajmer',\n",
       " 'Aligarh',\n",
       " 'Rudrapur',\n",
       " 'Solan',\n",
       " 'Mathura']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col=[col for col in df5.columns if df5[col].dtypes ==\"O\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['POSTED_BY', 'LOCATION']"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's use label encoding to fill the value of categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Owner', 'Dealer', 'Builder'], dtype=object)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[\"POSTED_BY\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "posted_map={\"Owner\":0,\"Dealer\":1,\"Builder\":2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_loc=list(df5.groupby(\"LOCATION\")[\"TARGET(PRICE_IN_LACS)\"].mean().sort_values().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "location_map={loc:i for i,loc in enumerate(all_loc)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Karjat': 0,\n",
       " 'Bhavnagar': 1,\n",
       " 'Rudrapur': 2,\n",
       " 'Palghar': 3,\n",
       " 'Junagadh': 4,\n",
       " 'Durgapur': 5,\n",
       " 'Ratnagiri': 6,\n",
       " 'Bharuch': 7,\n",
       " 'Vapi': 8,\n",
       " 'Neemrana': 9,\n",
       " 'Bhiwadi': 10,\n",
       " 'Valsad': 11,\n",
       " 'Bhilai': 12,\n",
       " 'Navsari': 13,\n",
       " 'Asansol': 14,\n",
       " 'Vizianagaram': 15,\n",
       " 'Jamnagar': 16,\n",
       " 'Haridwar': 17,\n",
       " 'Mathura': 18,\n",
       " 'Raigad': 19,\n",
       " 'Meerut': 20,\n",
       " 'Sindhudurg': 21,\n",
       " 'Bilaspur': 22,\n",
       " 'Solan': 23,\n",
       " 'Dhanbad': 24,\n",
       " 'Bhopal': 25,\n",
       " 'Aurangabad': 26,\n",
       " 'Nellore': 27,\n",
       " 'Hubli': 28,\n",
       " 'Raipur': 29,\n",
       " 'Amravati': 30,\n",
       " 'Ajmer': 31,\n",
       " 'Dharuhera': 32,\n",
       " 'Solapur': 33,\n",
       " 'Kolhapur': 34,\n",
       " 'Siliguri': 35,\n",
       " 'Gwalior': 36,\n",
       " 'Others': 37,\n",
       " 'Ahmednagar': 38,\n",
       " 'Agra': 39,\n",
       " 'Udupi': 40,\n",
       " 'Aligarh': 41,\n",
       " 'Jodhpur': 42,\n",
       " 'Gandhinagar': 43,\n",
       " 'Guntur': 44,\n",
       " 'Anand': 45,\n",
       " 'Bahadurgarh': 46,\n",
       " 'Belgaum': 47,\n",
       " 'Indore': 48,\n",
       " 'Jamshedpur': 49,\n",
       " 'Margao': 50,\n",
       " 'Rajkot': 51,\n",
       " 'Palakkad': 52,\n",
       " 'Madurai': 53,\n",
       " 'Sonipat': 54,\n",
       " 'Kota': 55,\n",
       " 'Vijayawada': 56,\n",
       " 'Jabalpur': 57,\n",
       " 'Pondicherry': 58,\n",
       " 'Guwahati': 59,\n",
       " 'Jalandhar': 60,\n",
       " 'Allahabad': 61,\n",
       " 'Tirupati': 62,\n",
       " 'Udaipur': 63,\n",
       " 'Secunderabad': 64,\n",
       " 'Vadodara': 65,\n",
       " 'Visakhapatnam': 66,\n",
       " 'Ghaziabad': 67,\n",
       " 'Jaipur': 68,\n",
       " 'Thrissur': 69,\n",
       " 'Patna': 70,\n",
       " 'Faridabad': 71,\n",
       " 'Bhubaneswar': 72,\n",
       " 'Surat': 73,\n",
       " 'Shimla': 74,\n",
       " 'Varanasi': 75,\n",
       " 'Mysore': 76,\n",
       " 'Mangalore': 77,\n",
       " 'Dehradun': 78,\n",
       " 'Nagpur': 79,\n",
       " 'Coimbatore': 80,\n",
       " 'Ernakulam': 81,\n",
       " 'Ludhiana': 82,\n",
       " 'Panchkula': 83,\n",
       " 'Lucknow': 84,\n",
       " 'Chandigarh': 85,\n",
       " 'Kolkata': 86,\n",
       " 'Kanpur': 87,\n",
       " 'Kottayam': 88,\n",
       " 'Panaji': 89,\n",
       " 'Jalgaon': 90,\n",
       " 'Mohali': 91,\n",
       " 'Pune': 92,\n",
       " 'Kochi': 93,\n",
       " 'Ranchi': 94,\n",
       " 'Noida': 95,\n",
       " 'Chennai': 96,\n",
       " 'Bangalore': 97,\n",
       " 'Goa': 98,\n",
       " 'Lalitpur': 99,\n",
       " 'Mumbai': 100,\n",
       " 'Maharashtra': 101,\n",
       " 'Gurgaon': 102}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5[\"LOCATION\"]=df5[\"LOCATION\"].map(location_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5[\"POSTED_BY\"]=df5[\"POSTED_BY\"].map(posted_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 29377 entries, 0 to 29450\n",
      "Data columns (total 9 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   POSTED_BY              29377 non-null  int64  \n",
      " 1   UNDER_CONSTRUCTION     29377 non-null  int64  \n",
      " 2   RERA                   29377 non-null  int64  \n",
      " 3   BHK_NO.                29377 non-null  int64  \n",
      " 4   SQUARE_FT              29377 non-null  float64\n",
      " 5   READY_TO_MOVE          29377 non-null  int64  \n",
      " 6   RESALE                 29377 non-null  int64  \n",
      " 7   TARGET(PRICE_IN_LACS)  29377 non-null  float64\n",
      " 8   LOCATION               29377 non-null  int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 2.2 MB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see correlation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Let's plot correlation between output and input features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a7a278d88>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "df5.drop(columns=[\"TARGET(PRICE_IN_LACS)\"]).corrwith(df[\"TARGET(PRICE_IN_LACS)\"]).plot(kind=\"bar\",color=\"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As you can see the square foot column is more correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's see correlation betweem input features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15a7f2bca48>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(df5.drop(columns=[\"TARGET(PRICE_IN_LACS)\"]).corr(),cmap=\"rainbow\",fmt=\".2f\",annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As I can see there is high correlation betwwen under construction and ready to move. The realtion is clear that as the house is under cnstruction it is not ready to use hence drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.drop(columns=[\"UNDER_CONSTRUCTION\"],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df5.drop(columns=[\"TARGET(PRICE_IN_LACS)\"])\n",
    "y=df5[\"TARGET(PRICE_IN_LACS)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>POSTED_BY</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>LOCATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.236407</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>933.159722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>929.921143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>999.009247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   POSTED_BY  RERA  BHK_NO.    SQUARE_FT  READY_TO_MOVE  RESALE  LOCATION\n",
       "0          0     0        2  1300.236407              1       1        97\n",
       "1          1     0        2  1275.000000              1       1        76\n",
       "2          0     0        2   933.159722              1       1        97\n",
       "3          0     1        2   929.921143              1       1        67\n",
       "4          1     0        2   999.009247              0       1        86"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29377, 7)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score,RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X,test_X,train_y,test_y=train_test_split(X,y,test_size=0.15,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "train_X=sc.fit_transform(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X=sc.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_test_score(model):\n",
    "    pred=model.predict(test_X)\n",
    "    print(\"R2 score is:\", r2_score(test_y,pred))\n",
    "    print(\"The mean squared error is :\",mean_squared_error(test_y,pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LinearRegression()\n",
    "lr.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.7945177179324279\n",
      "The mean squared error is : 70028.41458409226\n"
     ]
    }
   ],
   "source": [
    "check_test_score(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(random_state=100)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso=Lasso(random_state=100)\n",
    "lasso.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.7942948719180659\n",
      "The mean squared error is : 70104.36056310868\n"
     ]
    }
   ],
   "source": [
    "check_test_score(lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(random_state=100)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge=Ridge(random_state=100)\n",
    "ridge.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.7945110126119608\n",
      "The mean squared error is : 70030.6997590322\n"
     ]
    }
   ],
   "source": [
    "check_test_score(ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(kernel='linear')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv_regressor=SVR(kernel=\"linear\")\n",
    "sv_regressor.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.7666366257093578\n",
      "The mean squared error is : 79530.297985467\n"
     ]
    }
   ],
   "source": [
    "check_test_score(sv_regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sv_regressor=SVR(kernel=\"rbf\")\n",
    "sv_regressor.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.010398217852529146\n",
      "The mean squared error is : 337256.54190754256\n"
     ]
    }
   ],
   "source": [
    "check_test_score(sv_regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeRegressor()\n",
    "dt.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9314820321358683\n",
      "The mean squared error is : 23350.941072724945\n"
     ]
    }
   ],
   "source": [
    "check_test_score(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9602519859120132\n",
      "The mean squared error is : 13546.133425423675\n"
     ]
    }
   ],
   "source": [
    "check_test_score(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_bosting=GradientBoostingRegressor()\n",
    "gradient_bosting.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9632687153803534\n",
      "The mean squared error is : 12518.031246630959\n"
     ]
    }
   ],
   "source": [
    "check_test_score(gradient_bosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.069206\n",
      "0:\tlearn: 560.5714310\ttotal: 239ms\tremaining: 3m 58s\n",
      "1:\tlearn: 528.3960133\ttotal: 259ms\tremaining: 2m 9s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x15a3d831fc8>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_boost=CatBoostRegressor()\n",
    "cat_boost.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9570339014142635\n",
      "The mean squared error is : 14642.857450033032\n"
     ]
    }
   ],
   "source": [
    "check_test_score(cat_boost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_boost=XGBRegressor()\n",
    "xg_boost.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9605888764856125\n",
      "The mean squared error is : 13431.321031283911\n"
     ]
    }
   ],
   "source": [
    "check_test_score(xg_boost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light_gbm=LGBMRegressor()\n",
    "light_gbm.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.9512236081595057\n",
      "The mean squared error is : 16623.006885815186\n"
     ]
    }
   ],
   "source": [
    "check_test_score(light_gbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Among all models Gradient Boosting Techniques Outperforms all these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "X=sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:   33.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.90892751, 0.94892841, 0.96794133, 0.97426299, 0.94754754,\n",
       "       0.92531688, 0.94760952, 0.93234461, 0.89320119, 0.95741009])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(GradientBoostingRegressor(),X,y,cv=10,scoring=\"r2\",verbose=2,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9693990346965493"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "## As you can see it is performimg well\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"my_model.pkl\",\"wb\") as f:\n",
    "    pickle.dump(model,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"my_scalar.pkl\",\"wb\") as f:\n",
    "    pickle.dump(sc,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>POSTED_BY</th>\n",
       "      <th>RERA</th>\n",
       "      <th>BHK_NO.</th>\n",
       "      <th>SQUARE_FT</th>\n",
       "      <th>READY_TO_MOVE</th>\n",
       "      <th>RESALE</th>\n",
       "      <th>TARGET(PRICE_IN_LACS)</th>\n",
       "      <th>LOCATION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1300.236407</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1275.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>933.159722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>929.921143</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62.5</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>999.009247</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.5</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29446</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29447</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>769.230769</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29448</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1022.641509</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29449</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>927.079009</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29450</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>896.774194</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27.8</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29377 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       POSTED_BY  RERA  BHK_NO.    SQUARE_FT  READY_TO_MOVE  RESALE  \\\n",
       "0              0     0        2  1300.236407              1       1   \n",
       "1              1     0        2  1275.000000              1       1   \n",
       "2              0     0        2   933.159722              1       1   \n",
       "3              0     1        2   929.921143              1       1   \n",
       "4              1     0        2   999.009247              0       1   \n",
       "...          ...   ...      ...          ...            ...     ...   \n",
       "29446          0     0        3  2500.000000              1       1   \n",
       "29447          0     0        2   769.230769              1       1   \n",
       "29448          1     0        2  1022.641509              1       1   \n",
       "29449          0     0        2   927.079009              1       1   \n",
       "29450          1     1        2   896.774194              1       1   \n",
       "\n",
       "       TARGET(PRICE_IN_LACS)  LOCATION  \n",
       "0                       55.0        97  \n",
       "1                       51.0        76  \n",
       "2                       43.0        97  \n",
       "3                       62.5        67  \n",
       "4                       60.5        86  \n",
       "...                      ...       ...  \n",
       "29446                   45.0        39  \n",
       "29447                   16.0         8  \n",
       "29448                   27.1        68  \n",
       "29449                   67.0        96  \n",
       "29450                   27.8        68  \n",
       "\n",
       "[29377 rows x 8 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Owner': 0, 'Dealer': 1, 'Builder': 2}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posted_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "yes_map={\"Yes\":1,\"No\":0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POSTED_BYOwner\n",
      "RERA approved or notYes\n",
      "Number of Rooms4\n",
      "Square foot700\n",
      "Ready to MoveYes\n",
      "ResaleYes\n",
      "LocationMumbai\n"
     ]
    }
   ],
   "source": [
    "Pause=True\n",
    "while Pause:\n",
    "    values=[]\n",
    "    a=input(\"POSTED_BY\")\n",
    "    values.append(posted_map[a])\n",
    "    a=input(\"RERA approved or not\")\n",
    "    values.append(yes_map[a])\n",
    "    a=int(input(\"Number of Rooms\"))\n",
    "    values.append(a)\n",
    "    a=int(input(\"Square foot\"))\n",
    "    values.append(a)\n",
    "    a=input(\"Ready to Move\")\n",
    "    values.append(yes_map[a])\n",
    "    a=input(\"Resale\")\n",
    "    values.append(yes_map[a])\n",
    "    a=input(\"Location\")\n",
    "    values.append(location_map[a])\n",
    "    Pause=False\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=sc.transform([values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([241.66430223])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<option value=\"Bangalore\">Bangalore</option>\n",
      "<option value=\"Mysore\">Mysore</option>\n",
      "<option value=\"Ghaziabad\">Ghaziabad</option>\n",
      "<option value=\"Kolkata\">Kolkata</option>\n",
      "<option value=\"Kochi\">Kochi</option>\n",
      "<option value=\"Jaipur\">Jaipur</option>\n",
      "<option value=\"Mohali\">Mohali</option>\n",
      "<option value=\"Chennai\">Chennai</option>\n",
      "<option value=\"Siliguri\">Siliguri</option>\n",
      "<option value=\"Noida\">Noida</option>\n",
      "<option value=\"Raigad\">Raigad</option>\n",
      "<option value=\"Bhubaneswar\">Bhubaneswar</option>\n",
      "<option value=\"Others\">Others</option>\n",
      "<option value=\"Pune\">Pune</option>\n",
      "<option value=\"Mumbai\">Mumbai</option>\n",
      "<option value=\"Nagpur\">Nagpur</option>\n",
      "<option value=\"Bhiwadi\">Bhiwadi</option>\n",
      "<option value=\"Faridabad\">Faridabad</option>\n",
      "<option value=\"Lalitpur\">Lalitpur</option>\n",
      "<option value=\"Maharashtra\">Maharashtra</option>\n",
      "<option value=\"Vadodara\">Vadodara</option>\n",
      "<option value=\"Visakhapatnam\">Visakhapatnam</option>\n",
      "<option value=\"Vapi\">Vapi</option>\n",
      "<option value=\"Mangalore\">Mangalore</option>\n",
      "<option value=\"Aurangabad\">Aurangabad</option>\n",
      "<option value=\"Vijayawada\">Vijayawada</option>\n",
      "<option value=\"Belgaum\">Belgaum</option>\n",
      "<option value=\"Bhopal\">Bhopal</option>\n",
      "<option value=\"Lucknow\">Lucknow</option>\n",
      "<option value=\"Kanpur\">Kanpur</option>\n",
      "<option value=\"Gandhinagar\">Gandhinagar</option>\n",
      "<option value=\"Pondicherry\">Pondicherry</option>\n",
      "<option value=\"Agra\">Agra</option>\n",
      "<option value=\"Ranchi\">Ranchi</option>\n",
      "<option value=\"Gurgaon\">Gurgaon</option>\n",
      "<option value=\"Udupi\">Udupi</option>\n",
      "<option value=\"Indore\">Indore</option>\n",
      "<option value=\"Jodhpur\">Jodhpur</option>\n",
      "<option value=\"Coimbatore\">Coimbatore</option>\n",
      "<option value=\"Valsad\">Valsad</option>\n",
      "<option value=\"Palghar\">Palghar</option>\n",
      "<option value=\"Surat\">Surat</option>\n",
      "<option value=\"Varanasi\">Varanasi</option>\n",
      "<option value=\"Guwahati\">Guwahati</option>\n",
      "<option value=\"Amravati\">Amravati</option>\n",
      "<option value=\"Anand\">Anand</option>\n",
      "<option value=\"Tirupati\">Tirupati</option>\n",
      "<option value=\"Secunderabad\">Secunderabad</option>\n",
      "<option value=\"Raipur\">Raipur</option>\n",
      "<option value=\"Vizianagaram\">Vizianagaram</option>\n",
      "<option value=\"Thrissur\">Thrissur</option>\n",
      "<option value=\"Madurai\">Madurai</option>\n",
      "<option value=\"Chandigarh\">Chandigarh</option>\n",
      "<option value=\"Shimla\">Shimla</option>\n",
      "<option value=\"Gwalior\">Gwalior</option>\n",
      "<option value=\"Rajkot\">Rajkot</option>\n",
      "<option value=\"Sonipat\">Sonipat</option>\n",
      "<option value=\"Allahabad\">Allahabad</option>\n",
      "<option value=\"Dharuhera\">Dharuhera</option>\n",
      "<option value=\"Durgapur\">Durgapur</option>\n",
      "<option value=\"Panchkula\">Panchkula</option>\n",
      "<option value=\"Solapur\">Solapur</option>\n",
      "<option value=\"Goa\">Goa</option>\n",
      "<option value=\"Jamshedpur\">Jamshedpur</option>\n",
      "<option value=\"Jabalpur\">Jabalpur</option>\n",
      "<option value=\"Hubli\">Hubli</option>\n",
      "<option value=\"Patna\">Patna</option>\n",
      "<option value=\"Bilaspur\">Bilaspur</option>\n",
      "<option value=\"Ratnagiri\">Ratnagiri</option>\n",
      "<option value=\"Meerut\">Meerut</option>\n",
      "<option value=\"Jalandhar\">Jalandhar</option>\n",
      "<option value=\"Ludhiana\">Ludhiana</option>\n",
      "<option value=\"Kota\">Kota</option>\n",
      "<option value=\"Panaji\">Panaji</option>\n",
      "<option value=\"Kolhapur\">Kolhapur</option>\n",
      "<option value=\"Ernakulam\">Ernakulam</option>\n",
      "<option value=\"Bhavnagar\">Bhavnagar</option>\n",
      "<option value=\"Bharuch\">Bharuch</option>\n",
      "<option value=\"Asansol\">Asansol</option>\n",
      "<option value=\"Margao\">Margao</option>\n",
      "<option value=\"Bhilai\">Bhilai</option>\n",
      "<option value=\"Dehradun\">Dehradun</option>\n",
      "<option value=\"Guntur\">Guntur</option>\n",
      "<option value=\"Jalgaon\">Jalgaon</option>\n",
      "<option value=\"Udaipur\">Udaipur</option>\n",
      "<option value=\"Neemrana\">Neemrana</option>\n",
      "<option value=\"Sindhudurg\">Sindhudurg</option>\n",
      "<option value=\"Kottayam\">Kottayam</option>\n",
      "<option value=\"Dhanbad\">Dhanbad</option>\n",
      "<option value=\"Navsari\">Navsari</option>\n",
      "<option value=\"Bahadurgarh\">Bahadurgarh</option>\n",
      "<option value=\"Nellore\">Nellore</option>\n",
      "<option value=\"Haridwar\">Haridwar</option>\n",
      "<option value=\"Jamnagar\">Jamnagar</option>\n",
      "<option value=\"Junagadh\">Junagadh</option>\n",
      "<option value=\"Ahmednagar\">Ahmednagar</option>\n",
      "<option value=\"Palakkad\">Palakkad</option>\n",
      "<option value=\"Karjat\">Karjat</option>\n",
      "<option value=\"Ajmer\">Ajmer</option>\n",
      "<option value=\"Aligarh\">Aligarh</option>\n",
      "<option value=\"Rudrapur\">Rudrapur</option>\n",
      "<option value=\"Solan\">Solan</option>\n",
      "<option value=\"Mathura\">Mathura</option>\n"
     ]
    }
   ],
   "source": [
    "for loc in locations_values:\n",
    "    print(f\"\"\"<option value=\"{loc}\">{loc}</option>\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(locations_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "posted_map={'Owner': 0, 'Dealer': 1, 'Builder': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Owner': 0, 'Dealer': 1, 'Builder': 2}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posted_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Karjat': 0,\n",
       " 'Bhavnagar': 1,\n",
       " 'Rudrapur': 2,\n",
       " 'Palghar': 3,\n",
       " 'Junagadh': 4,\n",
       " 'Durgapur': 5,\n",
       " 'Ratnagiri': 6,\n",
       " 'Bharuch': 7,\n",
       " 'Vapi': 8,\n",
       " 'Neemrana': 9,\n",
       " 'Bhiwadi': 10,\n",
       " 'Valsad': 11,\n",
       " 'Bhilai': 12,\n",
       " 'Navsari': 13,\n",
       " 'Asansol': 14,\n",
       " 'Vizianagaram': 15,\n",
       " 'Jamnagar': 16,\n",
       " 'Haridwar': 17,\n",
       " 'Mathura': 18,\n",
       " 'Raigad': 19,\n",
       " 'Meerut': 20,\n",
       " 'Sindhudurg': 21,\n",
       " 'Bilaspur': 22,\n",
       " 'Solan': 23,\n",
       " 'Dhanbad': 24,\n",
       " 'Bhopal': 25,\n",
       " 'Aurangabad': 26,\n",
       " 'Nellore': 27,\n",
       " 'Hubli': 28,\n",
       " 'Raipur': 29,\n",
       " 'Amravati': 30,\n",
       " 'Ajmer': 31,\n",
       " 'Dharuhera': 32,\n",
       " 'Solapur': 33,\n",
       " 'Kolhapur': 34,\n",
       " 'Siliguri': 35,\n",
       " 'Gwalior': 36,\n",
       " 'Others': 37,\n",
       " 'Ahmednagar': 38,\n",
       " 'Agra': 39,\n",
       " 'Udupi': 40,\n",
       " 'Aligarh': 41,\n",
       " 'Jodhpur': 42,\n",
       " 'Gandhinagar': 43,\n",
       " 'Guntur': 44,\n",
       " 'Anand': 45,\n",
       " 'Bahadurgarh': 46,\n",
       " 'Belgaum': 47,\n",
       " 'Indore': 48,\n",
       " 'Jamshedpur': 49,\n",
       " 'Margao': 50,\n",
       " 'Rajkot': 51,\n",
       " 'Palakkad': 52,\n",
       " 'Madurai': 53,\n",
       " 'Sonipat': 54,\n",
       " 'Kota': 55,\n",
       " 'Vijayawada': 56,\n",
       " 'Jabalpur': 57,\n",
       " 'Pondicherry': 58,\n",
       " 'Guwahati': 59,\n",
       " 'Jalandhar': 60,\n",
       " 'Allahabad': 61,\n",
       " 'Tirupati': 62,\n",
       " 'Udaipur': 63,\n",
       " 'Secunderabad': 64,\n",
       " 'Vadodara': 65,\n",
       " 'Visakhapatnam': 66,\n",
       " 'Ghaziabad': 67,\n",
       " 'Jaipur': 68,\n",
       " 'Thrissur': 69,\n",
       " 'Patna': 70,\n",
       " 'Faridabad': 71,\n",
       " 'Bhubaneswar': 72,\n",
       " 'Surat': 73,\n",
       " 'Shimla': 74,\n",
       " 'Varanasi': 75,\n",
       " 'Mysore': 76,\n",
       " 'Mangalore': 77,\n",
       " 'Dehradun': 78,\n",
       " 'Nagpur': 79,\n",
       " 'Coimbatore': 80,\n",
       " 'Ernakulam': 81,\n",
       " 'Ludhiana': 82,\n",
       " 'Panchkula': 83,\n",
       " 'Lucknow': 84,\n",
       " 'Chandigarh': 85,\n",
       " 'Kolkata': 86,\n",
       " 'Kanpur': 87,\n",
       " 'Kottayam': 88,\n",
       " 'Panaji': 89,\n",
       " 'Jalgaon': 90,\n",
       " 'Mohali': 91,\n",
       " 'Pune': 92,\n",
       " 'Kochi': 93,\n",
       " 'Ranchi': 94,\n",
       " 'Noida': 95,\n",
       " 'Chennai': 96,\n",
       " 'Bangalore': 97,\n",
       " 'Goa': 98,\n",
       " 'Lalitpur': 99,\n",
       " 'Mumbai': 100,\n",
       " 'Maharashtra': 101,\n",
       " 'Gurgaon': 102}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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 },
 "nbformat": 4,
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