house prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-consideration",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "southern-guitar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2      NaN       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.read_csv('Bengaluru_Data.csv')\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dutch-grammar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type          0\n",
       "availability       0\n",
       "location           1\n",
       "size              16\n",
       "society         5502\n",
       "total_sqft         0\n",
       "bath              73\n",
       "balcony          609\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "intellectual-theology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whitefield                            540\n",
       "Sarjapur  Road                        399\n",
       "Electronic City                       302\n",
       "Kanakpura Road                        273\n",
       "Thanisandra                           234\n",
       "                                     ... \n",
       "Garden Layout                           1\n",
       "Chikkanahalli                           1\n",
       "Jagadish Nagar                          1\n",
       "Basvasamithi Layout Vidyaranyapura      1\n",
       "Navodaya Nagar                          1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broad-gabriel",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "conditional-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop(['area_type','availability','society'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ongoing-spokesman",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "miniature-heading",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath  balcony   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0      1.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0      3.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0      3.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0      1.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0      1.0   51.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "competent-hughes",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-6672ea3d6b92>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['bhk'] = data2['size'].apply(lambda x: int(x.split(' ')[0]))\n"
     ]
    }
   ],
   "source": [
    "data2['bhk'] = data2['size'].apply(lambda x: int(x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "applied-lecture",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2Electronic City Phase II</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>43 Bedroom</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location        size total_sqft  bath  balcony  price  \\\n",
       "1718  2Electronic City Phase II      27 BHK       8000  27.0      0.0  230.0   \n",
       "4684                Munnekollal  43 Bedroom       2400  40.0      0.0  660.0   \n",
       "\n",
       "      bhk  \n",
       "1718   27  \n",
       "4684   43  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## access specific column\n",
    "data2[data2.bhk >19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "downtown-france",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sqrt_is_float(x):     ## it return true if its a normal value\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "tracked-wedding",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.760</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 location   size   total_sqft  bath  balcony    price  bhk\n",
       "30              Yelahanka  4 BHK  2100 - 2850   4.0      0.0  186.000    4\n",
       "122                Hebbal  4 BHK  3067 - 8156   4.0      0.0  477.000    4\n",
       "137    8th Phase JP Nagar  2 BHK  1042 - 1105   2.0      0.0   54.005    2\n",
       "165              Sarjapur  2 BHK  1145 - 1340   2.0      0.0   43.490    2\n",
       "188              KR Puram  2 BHK  1015 - 1540   2.0      0.0   56.800    2\n",
       "...                   ...    ...          ...   ...      ...      ...  ...\n",
       "12975          Whitefield  2 BHK   850 - 1060   2.0      0.0   38.190    2\n",
       "12990      Talaghattapura  3 BHK  1804 - 2273   3.0      0.0  122.000    3\n",
       "13059              Harlur  2 BHK  1200 - 1470   2.0      0.0   72.760    2\n",
       "13265               Hoodi  2 BHK  1133 - 1384   2.0      0.0   59.135    2\n",
       "13299          Whitefield  4 BHK  2830 - 2882   5.0      0.0  154.500    4\n",
       "\n",
       "[186 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[~data2['total_sqft'].apply(sqrt_is_float)]    ##neget function ie opposite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "operational-makeup",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "logical-annotation",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = data2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "polished-memorabilia",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_range(x):        ##value pass must be string\n",
    "    range = x.split('-')\n",
    "    if(len(range)==2):\n",
    "        return (float(range[0])+float(range[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "thorough-anxiety",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1073.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_range('1042 - 1105')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "large-neighborhood",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath  balcony   price  bhk\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0      1.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0      3.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0      3.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0      1.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0      1.0   51.00    2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3['total_sqft'] = data3['total_sqft'].apply(remove_range)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "lasting-angle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Raja Rajeshwari Nagar\n",
       "size                          3 BHK\n",
       "total_sqft                   1693.0\n",
       "bath                            3.0\n",
       "balcony                         3.0\n",
       "price                         57.39\n",
       "bhk                               3\n",
       "Name: 33, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.iloc[30]      ##to call specific row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dominant-blast",
   "metadata": {},
   "source": [
    "## feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "royal-registration",
   "metadata": {},
   "outputs": [],
   "source": [
    "data4 = data3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "seventh-ready",
   "metadata": {},
   "outputs": [],
   "source": [
    "data4.location = data4.location.apply(lambda x: x.strip())    ##strip additional space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "typical-lotus",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath  balcony   price  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0      1.0   39.07   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0      3.0  120.00   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0      3.0   62.00   \n",
       "\n",
       "   bhk  price_per_sqft  \n",
       "0    2     3699.810606  \n",
       "1    4     4615.384615  \n",
       "2    3     4305.555556  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4['price_per_sqft'] =  data4['price']*100000/data4['total_sqft']\n",
    "data4.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "soviet-filename",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield              515\n",
       "Sarjapur  Road          372\n",
       "Electronic City         302\n",
       "Kanakpura Road          261\n",
       "Thanisandra             234\n",
       "                       ... \n",
       "Kanakapura  Rod           1\n",
       "Kanakapura Main Road      1\n",
       "Kanakapura Road           1\n",
       "Kanakapura Road,          1\n",
       "whitefiled                1\n",
       "Name: location, Length: 1254, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats = data4.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "white-stretch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1017"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])      ##locations with less than 10 houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "boxed-thought",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Kanakapura  Rod         1\n",
       "Kanakapura Main Road    1\n",
       "Kanakapura Road         1\n",
       "Kanakapura Road,        1\n",
       "whitefiled              1\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_10 =  location_stats[location_stats<=10]     ## store all values less than 10\n",
    "location_stats_less_10.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "sweet-citizen",
   "metadata": {},
   "outputs": [],
   "source": [
    "data4.location = data4.location.apply(lambda x: 'others' if x in location_stats_less_10 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "buried-cowboy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data4.location.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floral-finding",
   "metadata": {},
   "source": [
    "## Outlier Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "premium-assignment",
   "metadata": {},
   "outputs": [],
   "source": [
    "data5 = data4.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "chicken-lounge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location          529\n",
       "size              529\n",
       "total_sqft        529\n",
       "bath              529\n",
       "balcony           529\n",
       "price             529\n",
       "bhk               529\n",
       "price_per_sqft    529\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5[data5.total_sqft / data5.bhk < 250].agg('count')     ##outliers bedroom should be atleat 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "worst-combat",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath  balcony   price  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0      1.0   39.07   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0      3.0  120.00   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0      3.0   62.00   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0      1.0   95.00   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0      1.0   51.00   \n",
       "\n",
       "   bhk  price_per_sqft  \n",
       "0    2     3699.810606  \n",
       "1    4     4615.384615  \n",
       "2    3     4305.555556  \n",
       "3    3     6245.890861  \n",
       "4    2     4250.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5 = data5[~(data5.total_sqft / data5.bhk < 250)]\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "welsh-scheduling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12181, 8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "abroad-spouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12139.000000\n",
       "mean       6247.976225\n",
       "std        4025.906065\n",
       "min         267.829813\n",
       "25%        4200.000000\n",
       "50%        5271.084337\n",
       "75%        6874.439629\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "offshore-anthony",
   "metadata": {},
   "outputs": [],
   "source": [
    "## function to remove outliers from set \n",
    "\n",
    "def remove_outliers(df):\n",
    "    df_out = pd.DataFrame()\n",
    "    for key,sub_data in df.groupby('location'):\n",
    "        m =  np.mean(sub_data.price_per_sqft)\n",
    "        sd = np.std(sub_data.price_per_sqft)\n",
    "        reduced_data =  sub_data[(sub_data.price_per_sqft >(m-2*sd)) & (sub_data.price_per_sqft <(m+2*sd))]\n",
    "        df_out = pd.concat([df_out,reduced_data],ignore_index =True)\n",
    "    return df_out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "conscious-barcelona",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11619, 8)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6 = remove_outliers(data5)\n",
    "data6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "mobile-siemens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8196, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_bhk_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for location, location_df in df.groupby('location'):\n",
    "        bhk_stats = {}\n",
    "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
    "            bhk_stats[bhk] = {\n",
    "                'mean': np.mean(bhk_df.price_per_sqft),\n",
    "                'std': np.std(bhk_df.price_per_sqft),\n",
    "                'count': bhk_df.shape[0]\n",
    "            }\n",
    "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
    "            stats = bhk_stats.get(bhk-1)\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "\n",
    "data7 = remove_bhk_outliers(data6)\n",
    "\n",
    "data7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "coral-helicopter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QAAAAAFvL/j5Fbk9/ksR9mQAAAADY73sw/WrWnhqXJEcl+YYk16xqUgAAAABsHft7D6afXLf8YJI7u/uuFcwHAAAAgC1mvy6R6+7fTPL7SZ6Q5Ngkf7bKSQEAAACwdexXYKqqFyZ5X5IXJHlhkvdW1fNXOTEAAAAAtob9vUTuPyT51u6+J0mqanuS/5XkrauaGAAAAABbw/4+Re5Ru+PS4jMH8F4AAAAADmP7ewbTO6vqXUl+cXn9PUmuX82UAAAAANhKHjYwVdVTkhzf3T9WVf8kydOXVb+T5C2rnhwAAAAAh75HOoPpp5O8Ikm6+21J3pYkVfV3lnX/eIVzAwAAAGALeKT7KB3f3R/cc3AZO3klMwIAAABgS3mkwHTMw6x7/AbOAwAAAIAt6pEC086q+sE9B6vqB5LcspopAQAAALCVPNI9mH44ydur6vvy5aB0RpLHJPnuFc4LAAAAgC3iYQNTd386ybdX1Xcm+aZl+Ne6+90rnxkAAAAAW8IjncGUJOnum5LctOK5AAAAALAFPdI9mAAAAADgYQlMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIysLDBV1UlVdVNV3V5Vt1XVS5fx46rqhqq6Y/l+7DJeVfWGqtpVVR+oqqeu29eOZfs7qmrHquYMAAAAwIFb5RlMDyb50e4+LcnZSS6qqtOSvDzJjd19apIbl9dJ8pwkpy5fFya5NFkLUkkuTnJWkjOTXLw7SgEAAACw+VYWmLr7k939u8vyHyf5cJITkpyX5MplsyuTPG9ZPi/JVb3mPUmOqaonJ3l2khu6+77uvj/JDUnOXdW8AQAAADgwB+UeTFV1cpJvSfLeJMd39yeXVZ9KcvyyfEKST6x7213L2L7GAQAAADgErDwwVdXRSX4lyQ939+fWr+vuTtIb9DkXVtXOqtp57733bsQuAQAAANgPKw1MVfXorMWlt3T325bhTy+XvmX5fs8yfneSk9a9/cRlbF/jf0l3X9bdZ3T3Gdu3b9/YHwQAAACAfVrlU+QqyeVJPtzdP7Vu1XVJdj8JbkeSa9eNv2h5mtzZSR5YLqV7V5JzqurY5ebe5yxjAAAAABwCtq1w309L8v1JPlhVty5jP57ktUmuqaoLktyZ5IXLuuuTPDfJriRfSPLiJOnu+6rqVUluXrZ7ZXfft8J5AwAAAHAAVhaYuvt/J6l9rH7WXrbvJBftY19XJLli42YHAAAAwEY5KE+RAwAAAODwJTABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADCybbMnAMCRpy6pzZ7CYaEv7s2eAgAAJHEGEwAAAABDAhMAAAAAIwITAAAAACMCEwAAAAAjAhMAAAAAIysLTFV1RVXdU1UfWjd2XFXdUFV3LN+PXcarqt5QVbuq6gNV9dR179mxbH9HVe1Y1XwBAAAA+Mqs8gymNyU5d4+xlye5sbtPTXLj8jpJnpPk1OXrwiSXJmtBKsnFSc5KcmaSi3dHKQAAAAAODSsLTN39W0nu22P4vCRXLstXJnneuvGres17khxTVU9O8uwkN3T3fd19f5Ib8lejFQAAAACb6GDfg+n47v7ksvypJMcvyyck+cS67e5axvY1DgAAAMAhYtNu8t3dnaQ3an9VdWFV7ayqnffee+9G7RYAAACAR3CwA9Onl0vfsny/Zxm/O8lJ67Y7cRnb1/hf0d2XdfcZ3X3G9u3bN3ziAAAAAOzdwQ5M1yXZ/SS4HUmuXTf+ouVpcmcneWC5lO5dSc6pqmOXm3ufs4wBAAAAcIjYtqodV9UvJnlGkidV1V1Zexrca5NcU1UXJLkzyQuXza9P8twku5J8IcmLk6S776uqVyW5ednuld29543DAQAAANhEKwtM3f29+1j1rL1s20ku2sd+rkhyxQZODQAAAIANtGk3+QYAAADg8CAwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADAiMAEAAAAwIjABAAAAMCIwAQAAADCybbMnAAAcOuqS2uwpHDb64t7sKQAAHDTOYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgZNtmTwAAgP1Tl9RmT+Gw0Bf3Zk8BAA47zmACAAAAYERgAgAAAGBEYAIAAABgxD2YDnHutbBx3G8BAAAAVkNgAgCAIf8ouHH8oyDA1uQSOQAAAABGBCYAAAAARgQmAAAAAEYEJgAAAABGBCYAAAAARgQmAAAAAEYEJgAAAABGtm32BAAAAFapLqnNnsJhoy/uzZ4CcIhyBhMAAAAAIwITAAAAACMCEwAAAAAjWyYwVdW5VfWRqtpVVS/f7PkAAAAAsGZLBKaqOirJzyZ5TpLTknxvVZ22ubMCAAAAINk6T5E7M8mu7v5oklTV1UnOS3L7ps4KAACAr5gn/G0cT/hjs22VwHRCkk+se31XkrM2aS4AAABw2BMAN86REACr+9D/Iavq+UnO7e4fWF5/f5Kzuvsl67a5MMmFy8u/neQjB3maT0ryRwf5M+FI4ziD1XOcweo5zmD1HGewGn+zu7fvbcVWOYPp7iQnrXt94jL2F7r7siSXHcxJrVdVO7v7jM36fDgSOM5g9RxnsHqOM1g9xxkcfFviJt9Jbk5yalWdUlWPSXJ+kus2eU4AAAAAZIucwdTdD1bVS5K8K8lRSa7o7ts2eVoAAAAAZIsEpiTp7uuTXL/Z83gYm3Z5HhxBHGeweo4zWD3HGaye4wwOsi1xk28AAAAADl1b5R5MAAAAAByiBKYNUFXnVtVHqmpXVb18s+cDW0lVfbyqPlhVt1bVzmXsuKq6oaruWL4fu4xXVb1hOdY+UFVPXbefHcv2d1TVjs36eeBQUVVXVNU9VfWhdWMbdmxV1d9bjt1dy3vr4P6EsPn2cZz9RFXdvfxeu7Wqnrtu3SuWY+YjVfXsdeN7/VtyecDNe5fxX1oedgNHlKo6qapuqqrbq+q2qnrpMu53GhxiBKahqjoqyc8meU6S05J8b1Wdtrmzgi3nO7v79HWPkn15khu7+9QkNy6vk7Xj7NTl68IklyZrf2AkuTjJWUnOTHLx7j8y4Aj2piTn7jG2kcfWpUl+cN379vwsOBK8KXv/7/7rl99rpy/3Ec3y9+H5Sb5xec8bq+qoR/hb8nXLvp6S5P4kF6z0p4FD04NJfrS7T0tydpKLlmPE7zQ4xAhMc2cm2dXdH+3uP0tydZLzNnlOsNWdl+TKZfnKJM9bN35Vr3lPkmOq6slJnp3khu6+r7vvT3JD/GHAEa67fyvJfXsMb8ixtaz76939nl67meNV6/YFR4x9HGf7cl6Sq7v7S939sSS7svZ35F7/llzOoHhmkrcu719/zMIRo7s/2d2/uyz/cZIPJzkhfqfBIUdgmjshySfWvb5rGQP2Tyf59aq6paouXMaO7+5PLsufSnL8sryv481xCPtno46tE5blPceBNS9ZLs25Yt0ZEgd6nD0xyWe7+8E9xuGIVVUnJ/mWJO+N32lwyBGYgM329O5+atZOZ76oqr5j/crlX5I87hI2mGMLVubSJF+X5PQkn0zyXzZ1NnCYqKqjk/xKkh/u7s+tX+d3GhwaBKa5u5OctO71icsYsB+6++7l+z1J3p61SwU+vZyunOX7Pcvm+zreHIewfzbq2Lp7Wd5zHI543f3p7n6ou/88yX/P2u+15MCPs89k7dKebXuMwxGnqh6dtbj0lu5+2zLsdxocYgSmuZuTnLo85eMxWbt543WbPCfYEqrqq6rqCbuXk5yT5ENZO4Z2P9ljR5Jrl+XrkrxoeTrI2UkeWE6NfleSc6rq2OVShHOWMeAv25Bja1n3uao6e7lPzIvW7QuOaLv/D+/iu7P2ey1ZO87Or6rHVtUpWbuR8Puyj78llzMybkry/OX9649ZOGIsv2cuT/Lh7v6pdav8ToNDzLZH3oSH090PVtVLsvY/WEcluaK7b9vkacFWcXySty9Pgt2W5Be6+51VdXOSa6rqgiR3Jnnhsv31SZ6btRujfiHJi5Oku++rqldl7Y/0JHlld+/vTVfhsFRVv5jkGUmeVFV3Ze3JOa/Nxh1bP5S1J2g9Psn/XL7giLKP4+wZVXV61i7X+XiSf5kk3X1bVV2T5PasPRXrou5+aNnPvv6WfFmSq6vq1Unen7X/kw1Hmqcl+f4kH6yqW5exH4/faXDIqbV/HAEAAACAr4xL5AAAAAAYEZgAAAAAGBGYAAAAABgRmAAAAAAYEZgAAAAAGBGYAIAjWlU9VFW3VtWHquqXq+qv7WO7/3uw57bRquo/V9Vty/d/XlV/Y7PnBAAcHqq7N3sOAACbpqo+391HL8tvSXJLd//UuvXbuvvBTZvgl+dRWfvb7c8H+3ggyXHd/VBV/UaSf9fdOzdqjgDAkcsZTAAAX/bbSZ5SVc+oqt+uquuS3J6shajdG1XVy6rqg1X1e1X12mXs66rqnVV1y/Ler99z51X1E1X15qr6naq6o6p+cN26H6uqm6vqA1V1yTJ2clV9pKquSvKhJCftsb/XVtXty3t+chk7Zdn/B6vq1bvnvfwsRye5paq+J8kZSd6ynL31+I38DxEAOPJs2+wJAAAcCqpqW5LnJHnnMvTUJN/U3R/bY7vnJDkvyVnd/YWqOm5ZdVmSf9Xdd1TVWUnemOSZe/mov5vk7CRfleT9VfVrSb4pyalJzkxSSa6rqu9I8gfL+I7ufs8e83hiku9O8vXd3VV1zLLqZ5Jc2t1XVdVFu7fv7u9aztY6fXn/v44zmACADSIwAQBHusdX1a3L8m8nuTzJtyd5355xafH3k/yP7v5CknT3fVV19PKeX167ki1J8th9fN613f3FJF+sqpuyFpWenuScJO9ftjk6a2HpD5LcuWdcWjyQ5E+TXF5V70jyjmX8aUn+6bL85iSve5ifHQBgQwhMAMCR7ou7z+rZbYlEf3IA+3hUks/uuZ992PMGmJ21s5b+U3f/tz3mcfK+5tHdD1bVmUmeleT5SV6SL58x5SabAMBB5R5MAAAH5oYkL979tLmqOq67P5fkY1X1gmWsquqb9/H+86rqccslbs9IcnOSdyX5F8uZUKmqE6rqax5uEsu2X93d1yf5t0l2f97/SXL+svx9D7OLP07yhIf/UQEA9o8zmAAADkB3v7OqTk+ys6r+LMn1SX48azHn0qr6j0keneTqJL+3l118IMlNSZ6U5FXd/YdJ/rCqviHJ7yxnT30+yT9L8tDDTOUJSa6tqsdl7QyoH1nGX5rkF6rqZUmufZj3vynJz1XVF5N823LZHgDAV6S6nUENAHAwVNVPJPl8d//kQfzMz3f30Qfr8wCAI5NL5AAAAAAYcQYTAAAAACPOYAIAAABgRGACAAAAYERgAgAAAGBEYAIAAABgRGACAAAAYERgAgAAAGDk/wMS08CXwsV03wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(data6.price_per_sqft,rwidth=0.8,color = 'green')      ##rwidth is width of bar in histogram\n",
    "plt.xlabel('Price per sqft')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "intensive-survivor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  3,  2,  1,  5,  6,  7,  8,  9, 10, 11, 27, 13], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7.bhk.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "automated-concentration",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9536</th>\n",
       "      <td>others</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "      <td>2875.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10988</th>\n",
       "      <td>others</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location    size  total_sqft  bath  balcony  price  bhk  price_per_sqft\n",
       "9536    others  27 BHK      8000.0  27.0      0.0  230.0   27     2875.000000\n",
       "10988   others  13 BHK      5425.0  13.0      0.0  275.0   13     5069.124424"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7[data7.bath>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "hungarian-auckland",
   "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>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>Chamrajpet</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6</td>\n",
       "      <td>15333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>Chikkabanavar</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3252.032520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7853</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6423.034330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location       size  total_sqft  bath  balcony  price  bhk  \\\n",
       "1704     Chamrajpet  6 Bedroom      1500.0   9.0      3.0  230.0    6   \n",
       "1852  Chikkabanavar  4 Bedroom      2460.0   7.0      2.0   80.0    4   \n",
       "7853    Thanisandra      3 BHK      1806.0   6.0      2.0  116.0    3   \n",
       "\n",
       "      price_per_sqft  \n",
       "1704    15333.333333  \n",
       "1852     3252.032520  \n",
       "7853     6423.034330  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7[data7.bath> data7.bhk+2]     ##no of bath room should be <= 2+bhk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "continuing-clarity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8193, 8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data8 =  data7[data7.bath<=data7.bhk+2]\n",
    "data8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "attached-context",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data8.bhk,color = 'red')      \n",
    "plt.xlabel('Bedroom')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ready-receptor",
   "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>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  balcony  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0      1.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0      2.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0      3.0  235.0    3\n",
       "3  1st Block Jayanagar      2400.0   4.0      2.0  450.0    4\n",
       "4  1st Block Jayanagar      1200.0   2.0      0.0  130.0    3"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data9 = data8.copy()\n",
    "data9.drop(['size','price_per_sqft'],axis=1,inplace=True)\n",
    "data9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "reported-obligation",
   "metadata": {},
   "outputs": [],
   "source": [
    "data9.to_pickle('house_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "systematic-maximum",
   "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>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  balcony  price  bhk\n",
       "0  1st Block Jayanagar      2850.0   4.0      1.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0      2.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0      3.0  235.0    3\n",
       "3  1st Block Jayanagar      2400.0   4.0      2.0  450.0    4\n",
       "4  1st Block Jayanagar      1200.0   2.0      0.0  130.0    3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data10 = pd.read_pickle('house_model')\n",
    "data10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "operating-candy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data10.location.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "extra-trash",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
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       "      <th>AECS Layout</th>\n",
       "      <th>Abbigere</th>\n",
       "      <th>...</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "      <th>others</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 237 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                   0                          0                     0   \n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "3                   0                          0                     0   \n",
       "4                   0                          0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  6th Phase JP Nagar  7th Phase JP Nagar  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   8th Phase JP Nagar  9th Phase JP Nagar  AECS Layout  Abbigere  ...  \\\n",
       "0                   0                   0            0         0  ...   \n",
       "1                   0                   0            0         0  ...   \n",
       "2                   0                   0            0         0  ...   \n",
       "3                   0                   0            0         0  ...   \n",
       "4                   0                   0            0         0  ...   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "0                     0                   0            0           0   \n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "4                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \\\n",
       "0               0          0                   0            0             0   \n",
       "1               0          0                   0            0             0   \n",
       "2               0          0                   0            0             0   \n",
       "3               0          0                   0            0             0   \n",
       "4               0          0                   0            0             0   \n",
       "\n",
       "   others  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "\n",
       "[5 rows x 237 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Location = pd.get_dummies(data10.location,drop_first=True)\n",
    "Location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "neither-movement",
   "metadata": {},
   "outputs": [],
   "source": [
    "data10 = pd.concat([data10,Location],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "favorite-disorder",
   "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>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "      <th>others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 243 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath  balcony  price  bhk  \\\n",
       "0  1st Block Jayanagar      2850.0   4.0      1.0  428.0    4   \n",
       "1  1st Block Jayanagar      1630.0   3.0      2.0  194.0    3   \n",
       "2  1st Block Jayanagar      1875.0   2.0      3.0  235.0    3   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "0                   0                          0                     0   \n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                   0  ...                     0                   0   \n",
       "1                   0  ...                     0                   0   \n",
       "2                   0  ...                     0                   0   \n",
       "\n",
       "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0            0           0               0          0                   0   \n",
       "1            0           0               0          0                   0   \n",
       "2            0           0               0          0                   0   \n",
       "\n",
       "   Yelenahalli  Yeshwanthpur  others  \n",
       "0            0             0       0  \n",
       "1            0             0       0  \n",
       "2            0             0       0  \n",
       "\n",
       "[3 rows x 243 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "compact-springfield",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data10.drop('location',axis=1)\n",
    "y = data10.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "humanitarian-teaching",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "welsh-petite",
   "metadata": {},
   "source": [
    "## Model Testing And Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "global-revolution",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size =0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "attempted-jesus",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "modelLR = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "periodic-reunion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelLR.fit(x_train,y_train)\n",
    "y_pred = modelLR.predict(x_test)\n",
    "modelLR.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "constitutional-wholesale",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "signed-fault",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "curious-mixer",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "occasional-literature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9974120133768488"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "modelT = DecisionTreeRegressor()\n",
    "modelT.fit(x_train,y_train)\n",
    "modelT.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "latter-electron",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cheap-venice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4758694567192032"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr_model = SVR()\n",
    "svr_model.fit(x_train,y_train)\n",
    "svr_model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "solar-firewall",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "typical-caribbean",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9981110918423777"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "modelRF = RandomForestRegressor()\n",
    "modelRF.fit(x_train,y_train)\n",
    "modelRF.score(x_test,y_test)"
   ]
  }
 ],
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  "kernelspec": {
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   "codemirror_mode": {
    "name": "ipython",
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