Model_Train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c27151c",
   "metadata": {},
   "source": [
    "# House Price Prediction Model Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d2ec05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f5d0aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"Bengaluru_House_Data.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a999c377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13320"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1) # so this is quite nice data for model traning "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab5336b",
   "metadata": {},
   "source": [
    "# 1) Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bdeef82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type\n",
       "Built-up  Area          2418\n",
       "Carpet  Area              87\n",
       "Plot  Area              2025\n",
       "Super built-up  Area    8790\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(by='area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b63ef1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Built-up  Area': [2, 13, 20, 27, 34, 39, 40, 49, 52, 56, 63, 64, 77, 78, 79, 81, 85, 90, 98, 99, 101, 106, 107, 108, 114, 115, 123, 125, 126, 130, 135, 140, 160, 163, 168, 169, 179, 187, 190, 197, 202, 204, 208, 212, 216, 222, 226, 233, 238, 239, 251, 254, 256, 265, 272, 280, 281, 283, 286, 318, 330, 336, 343, 350, 363, 366, 368, 371, 381, 384, 386, 387, 389, 394, 395, 396, 398, 415, 419, 424, 430, 447, 455, 463, 464, 472, 476, 480, 493, 497, 502, 504, 511, 519, 520, 526, 529, 539, 541, 542, ...], 'Carpet  Area': [297, 340, 352, 594, 622, 629, 642, 1020, 1033, 1181, 1384, 1420, 1484, 1499, 1605, 1776, 1837, 2087, 2228, 2756, 2816, 2923, 2932, 3249, 3250, 3341, 3412, 3424, 3471, 3738, 4061, 4091, 4190, 4237, 4288, 4585, 4611, 4620, 4853, 4966, 5099, 5153, 5238, 5518, 5709, 6400, 6862, 7200, 7616, 7911, 7915, 7926, 8140, 8153, 8248, 8413, 8527, 8597, 8837, 8890, 9245, 9285, 9319, 9392, 9489, 9734, 9824, 10466, 10541, 10683, 10725, 10791, 10796, 10899, 10907, 10935, 11502, 11543, 12238, 12398, 12531, 12882, 13006, 13183, 13214, 13238, 13305], 'Plot  Area': [1, 9, 11, 14, 22, 43, 45, 48, 58, 60, 62, 68, 70, 89, 93, 96, 102, 119, 129, 131, 133, 149, 159, 175, 176, 177, 185, 192, 193, 209, 227, 241, 245, 248, 255, 258, 260, 273, 277, 282, 296, 299, 315, 322, 323, 325, 332, 339, 346, 349, 355, 362, 382, 402, 405, 406, 409, 413, 417, 418, 420, 425, 434, 439, 440, 451, 453, 469, 470, 481, 482, 483, 486, 491, 500, 510, 513, 514, 518, 544, 545, 554, 555, 559, 566, 573, 577, 579, 592, 603, 610, 614, 615, 630, 641, 676, 677, 679, 681, 685, ...], 'Super built-up  Area': [0, 3, 4, 5, 6, 7, 8, 10, 12, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 41, 42, 44, 46, 47, 50, 51, 53, 54, 55, 57, 59, 61, 65, 66, 67, 69, 71, 72, 73, 74, 75, 76, 80, 82, 83, 84, 86, 87, 88, 91, 92, 94, 95, 97, 100, 103, 104, 105, 109, 110, 111, 112, 113, 116, 117, 118, 120, 121, 122, 124, 127, 128, 132, 134, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 150, 151, 152, 153, ...]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(by='area_type').groups # this returns areatype with indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7d504ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       " Anekal                 1\n",
       " Banaswadi              1\n",
       " Basavangudi            1\n",
       " Bhoganhalli            1\n",
       " Devarabeesana Halli    6\n",
       "                       ..\n",
       "t.c palya               1\n",
       "tc.palya                4\n",
       "vinayakanagar           1\n",
       "white field,kadugodi    1\n",
       "whitefiled              1\n",
       "Name: location, Length: 1305, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(by='location')['location'].agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8889dc3f",
   "metadata": {},
   "source": [
    "# 2) Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4915b145",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop(['area_type','availability','society','balcony'],axis='columns')\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7523ef17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       1\n",
       "size          16\n",
       "total_sqft     0\n",
       "bath          73\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5b23099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.dropna(axis=0)\n",
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1957380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "08f4ff9b",
   "metadata": {},
   "outputs": [
    {
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       "  <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>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>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>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>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>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>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23368798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "398dc500",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10584\\1342993338.py: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",
      "  df3['bhk'] = df3['size'].apply(func = lambda x : int(x.split(' ')[0]) )\n"
     ]
    }
   ],
   "source": [
    "df3['bhk'] = df3['size'].apply(func = lambda x : int(x.split(' ')[0]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "94e33e28",
   "metadata": {},
   "outputs": [
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       "  <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>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</td>\n",
       "      <td>2.0</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</td>\n",
       "      <td>5.0</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</td>\n",
       "      <td>2.0</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</td>\n",
       "      <td>3.0</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</td>\n",
       "      <td>2.0</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   price  bhk\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head() # now we can safely drop 'size' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7d22bdb",
   "metadata": {},
   "outputs": [
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       "<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>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>1056</td>\n",
       "      <td>2.0</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>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II       1056   2.0   39.07    2\n",
       "1          Chikka Tirupathi       2600   5.0  120.00    4\n",
       "2               Uttarahalli       1440   2.0   62.00    3\n",
       "3        Lingadheeranahalli       1521   3.0   95.00    3\n",
       "4                  Kothanur       1200   2.0   51.00    2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.drop(labels=['size'],axis='columns')\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55b04d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now going to price column \n",
    "df4['price'] = df4['price'] * 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15cb2dee",
   "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>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>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3907000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6200000.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9500000.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5100000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location total_sqft  bath       price  bhk\n",
       "0  Electronic City Phase II       1056   2.0   3907000.0    2\n",
       "1          Chikka Tirupathi       2600   5.0  12000000.0    4\n",
       "2               Uttarahalli       1440   2.0   6200000.0    3\n",
       "3        Lingadheeranahalli       1521   3.0   9500000.0    3\n",
       "4                  Kothanur       1200   2.0   5100000.0    2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ea9c943",
   "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>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>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23000000.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>66000000.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location total_sqft  bath       price  bhk\n",
       "1718  2Electronic City Phase II       8000  27.0  23000000.0   27\n",
       "4684                Munnekollal       2400  40.0  66000000.0   43"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[df4.bhk>20] # we need to clear this type of error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "75125563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.total_sqft.unique() # here there is range of sqft so we can filter this by mean operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dd9d9dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x) :\n",
    "    try :\n",
    "        float(x)\n",
    "    except :\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6e1e808a",
   "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>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18600000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47700000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5400500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4349000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5680000.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3819000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12200000.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7276000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5913500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15450000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 location   total_sqft  bath       price  bhk\n",
       "30              Yelahanka  2100 - 2850   4.0  18600000.0    4\n",
       "122                Hebbal  3067 - 8156   4.0  47700000.0    4\n",
       "137    8th Phase JP Nagar  1042 - 1105   2.0   5400500.0    2\n",
       "165              Sarjapur  1145 - 1340   2.0   4349000.0    2\n",
       "188              KR Puram  1015 - 1540   2.0   5680000.0    2\n",
       "...                   ...          ...   ...         ...  ...\n",
       "12975          Whitefield   850 - 1060   2.0   3819000.0    2\n",
       "12990      Talaghattapura  1804 - 2273   3.0  12200000.0    3\n",
       "13059              Harlur  1200 - 1470   2.0   7276000.0    2\n",
       "13265               Hoodi  1133 - 1384   2.0   5913500.0    2\n",
       "13299          Whitefield  2830 - 2882   5.0  15450000.0    4\n",
       "\n",
       "[190 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[~df4.total_sqft.apply(func=is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e499b22f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['1', '1.25Acres', '1.26Acres', '1000', '1000 - 1285', '10000', '1000Sq. Meter', '1001', '1002', '1003', '10030', '1004', '1004 - 1204', '1005', '1005.03 - 1252.49', '1006', '1007', '1008', '1009', '1010', '1010 - 1300', '1011', '1012', '1013', '1014', '1015', '1015 - 1540', '1016', '1017', '1018', '1019', '1020', '1020.07', '10200', '1021', '1022', '1023', '1024', '1025', '1026', '1027', '1028', '1030', '1031', '1032', '1033', '1034', '1035', '1036', '1037', '1039', '1040', '1041', '1042 - 1105', '1043', '1044', '1045', '1046', '1047', '1048', '1049', '1050', '1051', '1052', '1052 - 1322', '1053', '1053.4', '1054', '1055', '1056', '1057', '1058', '1059', '1060', '1061', '1062', '10624', '1063', '1065', '1066', '1067', '1068', '1069', '1070', '1070 - 1315', '1071', '1072', '1073', '1074', '1075', '1076', '1076 - 1199', '1077', '1078', '1079', '1079 - 1183', '1080', '1081', '1082', '1083', '1084', '1085', '1086', '1087', '1088', '1089', '1090', '1091', '1092', '1093', '1094', '1095', '1096', '10961', '1097', '1098', '1099', '11', '1100', '11000', '1100Sq. Meter', '1100Sq. Yards', '1101', '1102', '1103', '1104', '1105', '1106', '1107', '1107.83', '1108', '1109', '1110', '1111', '1112', '1113', '1113.12', '1113.27', '1114', '1115', '1115 - 1130', '1116', '1117', '1118', '1119', '1120', '1120 - 1145', '1121', '1123', '1124', '1125', '1125 - 1500', '1126', '1127', '1128', '1129', '1130', '1132', '1133', '1133 - 1384', '11338', '1134', '1135', '1136', '1137', '1138', '1139', '1139.7', '1140', '1140 - 1250', '1141', '1142', '1143', '1144', '1145', '1145 - 1340', '1146', '1147', '1148', '1149', '1150', '1150 - 1194', '1151', '1152', '1153', '1154', '1155', '1156', '1157', '1158', '1159', '1160', '1160 - 1195', '1160 - 1315', '1161', '1162', '1163', '1164', '1165', '1166', '1167', '1168', '1169', '1170', '1171', '1172', '1173', '1174', '1175', '1176', '1178', '1179', '117Sq. Yards', '1180', '1180 - 1630', '1181', '1181.7', '1182', '1183', '1184', '1185', '1186', '1187', '1188', '1189', '11890', '1190', '1191', '1192', '1193', '1194', '1195', '1195 - 1440', '1196', '1197', '1198', '1199', '1200', '1200 - 1470', '12000', '1201', '1202', '1203', '1204', '1205', '1205.47', '1206', '1207', '1208', '1208.51', '1209', '120Sq. Yards', '1210', '1210 - 1477', '1211', '1212', '1213', '1214', '1215', '1215 - 1495', '1216', '1217', '1218', '1219', '1220', '1221', '1222', '1223', '1224', '1225', '1226', '1227', '1229', '122Sq. Yards', '1230', '1230 - 1290', '1230 - 1490', '1231', '1232', '1233', '1234', '1234.6', '1235', '1235 - 1410', '1236', '1237', '1238', '1239', '1240', '1241', '1242', '1243', '1244', '1245', '1246', '1247', '1248', '1248.52', '1249', '1250', '1250 - 1305', '1251', '1252', '1253', '1254', '1255', '1255 - 1350', '1255 - 1375', '1255 - 1863', '1256', '1257', '1258', '1259', '1260', '1261', '1262', '1263', '1264', '1265', '1266', '1266.67', '1267', '1268', '1269', '1269.72', '1270', '1270 - 1275', '1272', '1273', '1274', '1275', '1276', '1277', '1278', '1279', '1280', '1281', '1282', '1283', '1284', '1285', '1286', '1287', '1288', '1289', '1290', '1291', '1293', '1294', '1295', '1296', '1297', '1298', '1299', '1300', '1300 - 1405', '1301', '1302', '1303', '1304', '1305', '1306', '1307', '1308', '1309', '1310', '1311', '1312', '1313', '1314', '1315', '1316', '1317', '1318', '1319', '1320', '1321', '1322', '1323', '1324', '1325', '1326', '1327', '1328', '1329', '132Sq. Yards', '133.3Sq. Yards', '1330', '1330.74', '1331', '1331.95', '1332', '1333', '1334', '1335', '1336', '1337', '1338', '1339', '1340', '1341', '1342', '1343', '1344', '1345', '1346', '1347', '1348', '1349', '1349 - 3324', '1350', '1351', '1352', '1353', '1354', '1355', '1356', '1357', '1358', '1359', '1360', '1361', '1362', '1363', '1364', '1365', '1365 - 1700', '1366', '1367', '1368', '1369', '1369.1', '1370', '1370.07', '1371', '1372', '1373', '1374', '1375', '1376', '1377', '1378', '1379', '1380', '1381', '1382', '1383', '1384', '1385', '1386', '1388', '1389', '1390', '1391', '1392', '1393', '1394', '1395', '1396', '1397', '1398', '1400', '1400 - 1421', '14000', '1401', '1402', '1403', '1404', '1405', '1407', '1408', '1408 - 1455', '1409', '1410', '1411', '1412', '1413', '1414', '1415', '1416', '1417', '1418', '1419', '1419.59', '142.61Sq. Meter', '142.84Sq. Meter', '1420', '1421', '1423', '1424', '1425', '1426', '1427', '1428', '1429', '1430', '1431', '1432', '1434', '1435', '1436', '1437', '1437 - 1629', '1438', '1439', '1440', '1441', '1442', '1443', '1444', '1445', '1445 - 1455', '1447', '1448', '1449', '1450', '1450 - 1950', '1451', '1451.5', '1452', '1452.19', '1452.55', '1453', '1454', '1455', '1457', '1458', '1459', '1460', '1462', '1463', '1464', '1465', '1466', '1467', '1468', '1469', '1469 - 1766', '1470', '1471', '1473', '1475', '1476', '1477', '1478', '1480', '1482', '1483', '1484', '1485', '1486', '1488', '1489', '1490', '1491', '1492', '1493', '1494', '1495', '1496', '1498', '1499', '15', '1500', '1500Cents', '1500Sq. Meter', '1502', '1503', '1504', '1505', '1506', '1507', '1508', '151.11Sq. Yards', '1510', '1510 - 1670', '1511', '1512', '1513', '1515', '1516', '1517', '1518', '1519', '1520', '1520 - 1759', '1521', '1522', '1523', '1524', '1525', '1525.84', '1527', '1529', '1530', '1531', '1532', '1533', '1535', '1536', '1537', '1538', '1539', '1540', '1541', '1542', '1542.14', '1544', '1545', '1546', '1547', '1548', '1548.3', '1549', '1550', '1550 - 1590', '1551', '1552', '1553', '1554', '1554.3', '1555', '1556', '1557', '1558', '1558.67', '1559', '1560', '1561', '1562', '1563', '1563.05', '1564', '1565', '1565 - 1595', '1566', '1567', '1567.2', '1568', '1569', '1570', '1571', '1573', '1574', '1574Sq. Yards', '1575', '1576', '1577', '1578', '1580', '1581', '1583', '1584', '1584.01', '1585', '1586', '1587', '1588', '1589', '1590', '1591', '1592', '1593', '1594', '1595', '1596', '1597', '1599', '15Acres', '1600', '1601', '1602', '1603', '1604', '1605', '1606', '1607', '1608', '1609', '1610', '1611', '1612', '1613', '1614', '1615', '1616', '1617', '1618', '1620', '1621', '1622', '1623', '1623.29', '1625', '1626', '1626.6', '1627.86', '1628', '1629', '1630', '1631', '1632', '1633', '16335', '1634', '1635', '1636', '1637', '1639', '1640', '1641', '1642', '1643', '1644', '1645', '1646', '1648', '1649', '1650', '1651', '1652', '1652.5', '1653', '1654', '1655', '1656', '1659', '1660', '1660 - 1805', '1660.4', '1662', '1664', '1665', '1666', '1667', '1668', '1669', '1670', '1672', '1673', '1674', '1675', '1676', '1677', '1678', '1679', '167Sq. Meter', '1680', '1681', '1682', '1683', '1684', '1685', '1686', '1687', '1688', '1688.12', '1689', '1689.28', '1690', '1691', '1691.2', '1692', '1693', '1694', '1695', '1697', '1698', '1699', '1700', '1701', '1702', '1703', '1704', '1705', '1706', '1707', '1708', '1709', '1710', '1711', '1712', '1713', '1715', '1716', '1717', '1718', '1719', '1719.3', '1720', '1721', '1722', '1723', '1724', '1725', '1726', '1728', '1730', '1731', '1732', '1732.46', '1733', '1733.5', '1734', '1735', '1736', '1737', '1738', '1739', '1740', '1741', '1743', '1744', '1745', '1746', '1747', '1748', '1749', '1750', '1751', '1752', '1752.12', '1753', '1754', '1755', '1756', '1757', '1758', '1760', '1761', '1762', '1763', '1763.25', '1765', '1766', '1767', '1768', '1769', '1770', '1775', '1776', '1776.42', '1777', '1777.26', '1778', '1779', '1780', '1783', '1783 - 1878', '1785', '1786', '1787', '1788', '1789', '1790', '1792', '1793', '1795', '1797', '1798', '1799', '1800', '1801', '1804', '1804 - 2273', '1805', '1806', '1808', '1810', '1814', '1815', '1816', '1817', '1818', '1819', '1819.18', '1820', '1823', '1824', '1825', '1826', '1827', '1828', '1829', '1830', '1831', '1832', '1835', '1836', '1837', '1839', '1840', '1842', '1843', '1844', '1845', '1846', '1847', '1850', '1852', '1853', '1855', '1856', '1857', '1858', '1859', '1860', '1861', '1862', '1863', '1864', '1865', '1866', '1867', '1868', '1870', '1872', '1874', '1875', '1876', '1877', '1879', '188.89Sq. Yards', '1880', '1881', '1884', '1885', '1886', '1888', '1890', '1891', '1892', '1893', '1894', '1896', '1897', '1900', '1901', '1902', '1903', '1904', '1905', '1907', '1909', '1910', '1914', '1915', '1916', '1917', '1918', '1919', '1920', '1921', '1922', '1925', '1929', '1930', '1932', '1932.47', '1933', '1934', '1935', '1936', '1937', '1938', '1939', '1940', '1942', '1945', '1948', '1949', '1950', '1950.2', '1960', '1962', '1965', '1970', '1974', '1975', '1976', '1980', '1981', '1984', '1985', '1987', '1989', '1990', '1991', '1995', '1996', '1998', '1999', '1Grounds', '2.09Acres', '2000', '20000', '2002', '2003', '2005', '2006', '2006.8', '2008', '2010', '2015', '2017', '2019', '2020', '2023', '2023.71', '2024', '2025', '2026', '2028', '2030', '2031', '2035', '2039', '2040', '2041', '2041 - 2090', '2043', '2045', '2047', '2048', '204Sq. Meter', '2050', '2051', '2059', '2060', '2061', '2062', '2064', '2065', '2070', '2072', '2075', '2077', '2079', '2080', '2082', '2086', '2087', '2087.01', '2088', '2089', '2090', '2093', '2095', '2098', '2099', '2100', '2100 - 2850', '2105', '2106', '2107', '2108', '2110', '2111', '2112.95', '2113', '2118', '2119', '2120', '2121', '2122', '2125', '2127', '2130', '2132', '2134', '2135', '2137', '2138', '2140', '2144', '2144.6', '2145', '2150', '2150 - 2225', '2153', '2155', '2159', '2160', '2162', '2162.03', '2167', '2168', '2169', '2170', '2171', '2171.66', '2172', '2172.65', '2173', '2176', '2180', '2181', '2182', '2185', '2195', '2197', '2200', '2204', '2206', '2210', '2214', '2215', '2220', '2223', '2225', '2230', '2232', '2236', '2238', '2240', '2245', '2246', '2247', '2249.81', '2249.81 - 4112.19', '2250', '2251', '2254', '2257', '2259', '2260', '2264', '2265', '2266', '2268', '2270', '2273', '2274.24', '2275', '2280', '2282', '2283', '2285', '2289', '2292', '2293', '2295', '2297', '2300', '2302', '2312', '2316', '2317', '2319', '2320', '2321', '2325', '2328', '2330', '2337', '2340', '2342', '2350', '2357', '2360', '2363', '2365', '2367', '2370', '2372', '2375', '2376', '2378', '2380', '2383', '2384', '2386', '2388', '2390', '2392', '2395', '2396', '24', '2400', '2400 - 2600', '2401', '2403', '2404', '2405', '2406', '2408', '2409', '2415', '2416', '2420', '2422', '2423', '2424', '2425', '2429', '2430', '2431', '2435', '2437', '2439', '2440', '2444', '2448', '2450', '2453', '2456', '2460', '2461', '2462', '2462 - 2467', '2465', '2470', '2470 - 2790', '2475', '2476', '2479.13', '2480', '2483', '2485', '2489', '2493', '2495', '2496', '2497', '24Guntha', '24Sq. Meter', '250', '2500', '2501', '2502', '2503', '2504', '2505', '2507', '2511', '2515', '2519', '2520', '2524', '2526', '2528 - 3188', '2533', '2540', '2546', '2550', '2556', '2557', '2559', '2560', '2563 - 2733', '2569', '2570', '2572', '2580 - 2591', '2582', '2592', '2597', '2600', '2601', '2610', '2611', '26136', '2615', '2625', '2631', '2640', '2646', '2648', '2650', '2650 - 2990', '2651', '2658', '2663', '2665', '2666', '2675', '2679', '2689', '2690', '2695', '2700', '2704', '2710', '2710 - 3360', '2720', '2721', '2725 - 3250', '2732', '2733', '2735', '2736', '2750', '2758', '276', '2760', '2770', '2774', '2775', '2776', '2777.29', '2779', '2780', '2785', '2787', '2790', '2791', '2792', '2795', '2800', '2800 - 2870', '2801.25', '2805', '2806 - 3019', '2820', '2825', '2826', '2830', '2830 - 2882', '284', '2842', '2845', '2849', '2850', '2863', '2872', '2875', '2880', '2882', '2885', '2894', '2900', '2901', '2912', '2916', '2920', '2925', '2928', '2932', '2940', '2940Sq. Yards', '2950', '2955', '2956', '296', '2968', '2970', '2980', '2990', '2996', '2999.97', '2Acres', '300', '3000', '30000', '3004', '3005', '3009', '300Sq. Yards', '3010', '3012', '3016', '3019', '302', '3024', '3025', '3027', '3033', '3035', '3040', '30400', '3040Sq. Meter', '3042', '3044', '3045', '3050', '3056', '3060', '3067', '3067 - 8156', '3071', '3073', '3075', '3080', '3090', '3090 - 5002', '3092', '3095', '30Acres', '3100', '3117', '3122', '3124', '3125', '3126', '3131', '3144', '315', '3150', '3155', '315Sq. Yards', '3160', '3161', '3170', '3175', '3179', '3190', '3197', '3198', '3200', '3204', '3205', '3206', '3210', '3216', '3227', '3230', '3235', '3245', '3250', '3252', '3259', '3260', '3262', '3270', '3280', '3290', '3293', '3295', '3297', '3300', '3300 - 3335', '3301.8', '3307 - 3464', '3309', '3329', '3335', '3339', '3350', '3356', '3360', '3362', '3366', '3369 - 3464', '3381', '3385', '34.46Sq. Meter', '340', '3400', '3401', '3405.1', '3408', '3410', '3418', '3420', '3425', '3425 - 3435', '3435', '3436', '3445', '3450', '3453', '3463', '3467.86', '3480', '3489', '3496', '350', '3500', '35000', '3504', '3508', '3508 - 4201', '351', '3515', '3516', '3520', '3522', '3526', '3530', '3532', '3535', '3537', '3554', '3555', '3560', '3563', '3565', '3569', '3580', '3584', '3589', '3595', '3596', '360', '3600', '36000', '3602', '3606', '361.33Sq. Yards', '3621', '3626', '3628', '3630', '3630 - 3800', '3633', '3640', '3657', '3664', '3670', '3671', '3675', '3680', '3692', '3700', '3729', '3730', '3734', '3746', '375', '3750', '3754', '3758', '3760', '3761', '3770', '3785', '3800', '381', '381 - 535', '3811', '3815', '3820', '385 - 440', '3850', '3855', '3860', '3870', '3875', '3876', '3884', '3895', '38Guntha', '3900', '3905', '3913', '3930', '3940', '395', '3950', '3951', '396', '3960', '3968', '3978', '3990', '3Cents', '400', '4000', '4000 - 4450', '4000 - 5249', '4003', '4007', '4025', '4040', '4041', '4050', '4051', '4075', '4097', '410', '4100', '4104', '4110', '4111', '4125Perch', '4144', '416', '4166', '4170', '418', '4190', '420', '4200', '42000', '4201', '4209', '4235', '4239', '425', '4250', '4260', '4273', '4278', '4290', '4300', '4303', '4304', '432', '4320', '4346', '435', '4350', '4355', '4356', '4360', '4382', '4395', '440', '4400', '4408', '4428', '4446', '445', '4450', '4460', '4470', '4482', '45', '45.06Sq. Meter', '450', '4500', '4500 - 5540', '451', '4510', '4550', '456', '4560', '45Sq. Yards', '460', '4600', '461.82', '462', '4634', '4640', '4689', '469', '4694', '470', '4700', '4723', '475', '4750', '4772', '480', '4800', '4818', '4827', '4830', '485', '4850', '4856', '488', '4900', '4900 - 4940', '492', '4920', '4960', '497', '499', '5', '5.31Acres', '500', '5000', '500Sq. Yards', '502', '505', '5080', '509', '510', '5100', '5108', '515', '5150', '516', '520', '520 - 645', '5200', '52272', '5230', '524', '524 - 894', '525', '527', '527 - 639', '5270', '530', '532', '534', '535', '5384', '539', '540', '540 - 565', '540 - 670', '540 - 740', '5400', '5422', '5425', '545', '547.34 - 827.31', '5480', '550', '5500', '551', '5515', '552', '5530', '555', '560', '5600', '565', '5656', '5665.84', '5666 - 5669', '567', '570', '5700', '5720', '575', '580', '580 - 650', '5800', '581', '581.91', '583', '585', '588', '589', '590', '5924', '595', '596', '596 - 804', '596 - 861', '5965', '598 - 958', '5985', '60', '600', '6000', '602', '6040', '605', '605 - 624', '606', '607', '610', '610 - 615', '612', '613', '613 - 648', '6136', '614', '615', '615 - 985', '6150', '616', '620', '620 - 934', '6200', '621', '623', '624', '625', '627', '628', '629 - 1026', '630', '633', '633 - 666', '635', '638', '640', '644', '645', '645 - 936', '646', '648', '650', '650 - 665', '650 - 760', '6500', '651', '654', '655', '655 - 742', '656', '658', '660', '660 - 700', '660 - 780', '6600', '661', '6613', '662', '663', '664', '664 - 722', '665', '6652', '666', '667', '668', '6688', '669', '670', '670 - 980', '671', '672', '6729', '673', '674', '675', '680', '682', '6830', '684 - 810', '685', '686', '688', '690', '691', '693', '694', '695', '697', '697Sq. Meter', '6Acres', '700', '7000', '702', '703', '704', '704 - 730', '705', '706', '706 - 716', '708', '710', '711', '712', '712 - 938', '714', '715', '7150', '716Sq. Meter', '717', '720', '7200', '722', '725', '726', '727', '730', '734', '735', '740', '7400', '745', '747', '750', '750 - 800', '7500', '7514', '753', '754', '755', '755 - 770', '760', '763 - 805', '764', '765', '766', '769', '770', '770 - 841', '772', '774', '775', '777.4', '78.03Sq. Meter', '780', '7800', '782', '783 - 943', '784', '785', '787', '790', '792', '793', '794', '795', '797', '799', '799 - 803', '800', '8000', '804.1', '805', '806', '808', '810', '812', '813', '814', '815', '817', '818', '820', '823', '824', '825', '826', '827', '829', '830', '832', '8321', '833', '834', '835', '84.53Sq. Meter', '840', '840 - 1010', '8400', '842', '845', '849', '850', '850 - 1060', '850 - 1093', '8500', '854 - 960', '855', '856', '857', '858', '86.72Sq. Meter', '860', '861', '864', '865', '866', '866.28', '869', '870', '870 - 1080', '871', '873', '875', '876', '877', '880', '881', '882', '883', '884', '884 - 1116', '8840', '885', '886', '888', '888 - 1290', '890', '891', '892', '893', '896', '896.9', '897', '900', '9000', '901', '902', '904', '905', '906', '907', '908', '909', '910', '910.2', '911', '912', '913', '914', '915', '916', '918', '919', '920', '9200', '921', '922', '923', '924', '925', '927', '929', '929 - 1078', '930', '933', '934', '934 - 1437', '935', '936', '937', '938', '939', '940', '941', '942 - 1117', '943', '943 - 1220', '944', '945', '946', '947', '947.55', '948', '950', '951', '952', '953', '955', '956', '957', '958', '959', '960', '9600', '961', '963', '964', '965', '966', '967', '970', '971', '972', '973', '975', '976', '977', '978', '980', '980 - 1030', '981', '981 - 1249', '982', '983', '984', '985', '986', '987', '989', '990', '991', '992', '993', '994', '995', '996', '997', '998', '999'])\n"
     ]
    }
   ],
   "source": [
    "L = dict(df4.groupby(by='total_sqft')['total_sqft'].agg('count'))\n",
    "print(L.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "624a2d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2475.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Convert_sqft_range_to_num(x) :\n",
    "    \n",
    "    # first split with '-'\n",
    "    L = x.split('-')\n",
    "    \n",
    "    if len(L) == 2 :\n",
    "        return (float(L[0]) + float(L[1])) / 2\n",
    "    else :\n",
    "        try :\n",
    "            return float(x)\n",
    "        except :\n",
    "            return None\n",
    "        # here pending feature is unit conversation\n",
    "\n",
    "Convert_sqft_range_to_num('2100 - 2850')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f4075c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['total_sqft'] = df4['total_sqft'].apply(func=Convert_sqft_range_to_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dd4a9aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       Yelahanka\n",
       "total_sqft        2475.0\n",
       "bath                 4.0\n",
       "price         18600000.0\n",
       "bhk                    4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.loc[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28f8ba1a",
   "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>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [location, total_sqft, bath, price, bhk]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[~df4.total_sqft.apply(func=is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5f71c712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location       0\n",
       "total_sqft    46\n",
       "bath           0\n",
       "price          0\n",
       "bhk            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8f36e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "bhk           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here total_sqft has na values because of 'Convert_sqft_range_to_num' function's design\n",
    "df5 = df4.dropna()\n",
    "df5.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "90eee8fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10584\\1053376915.py:2: 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",
      "  df5['price_per_sqft'] = df5['price']/df5['total_sqft']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</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>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3907000.0</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>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12000000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6200000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9500000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5100000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath       price  bhk  price_per_sqft\n",
       "0  Electronic City Phase II      1056.0   2.0   3907000.0    2     3699.810606\n",
       "1          Chikka Tirupathi      2600.0   5.0  12000000.0    4     4615.384615\n",
       "2               Uttarahalli      1440.0   2.0   6200000.0    3     4305.555556\n",
       "3        Lingadheeranahalli      1521.0   3.0   9500000.0    3     6245.890861\n",
       "4                  Kothanur      1200.0   2.0   5100000.0    2     4250.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for outlier removal price_per_sqft is useful\n",
    "df5['price_per_sqft'] = df5['price']/df5['total_sqft']\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "57226614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1298"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "236a2b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10584\\1810253599.py: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",
      "  df5.location = df5.location.apply(func= lambda x : x.strip())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "location\n",
       "Whitefield               533\n",
       "Sarjapur  Road           392\n",
       "Electronic City          304\n",
       "Kanakpura Road           264\n",
       "Thanisandra              235\n",
       "                        ... \n",
       "1 Giri Nagar               1\n",
       "Kanakapura Road,           1\n",
       "Kanakapura main  Road      1\n",
       "Kannur                     1\n",
       "whitefiled                 1\n",
       "Name: location, Length: 1287, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.location = df5.location.apply(func= lambda x : x.strip())\n",
    "location_stats = df5.groupby(by='location')['location'].agg('count').sort_values(ascending=False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f0ccbe01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1047"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d9c53265",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_stats_less10 = location_stats[location_stats<=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78820540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'other'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Rename_location(x) :\n",
    "    if x in location_stats_less10 :\n",
    "        return \"other\"\n",
    "    return x\n",
    "\n",
    "Rename_location('Kannur')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7d4840b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_10584\\2653242938.py: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",
      "  df5['location'] = df5['location'].apply(func=Rename_location)\n"
     ]
    }
   ],
   "source": [
    "df5['location'] = df5['location'].apply(func=Rename_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f89861a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "other                 2872\n",
       "Whitefield             533\n",
       "Sarjapur  Road         392\n",
       "Electronic City        304\n",
       "Kanakpura Road         264\n",
       "                      ... \n",
       "Pattandur Agrahara      11\n",
       "Banjara Layout          11\n",
       "HAL 2nd Stage           11\n",
       "Doddaballapur           11\n",
       "Kodigehalli             11\n",
       "Name: location, Length: 241, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.groupby(by='location')['location'].agg('count').sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6aa3fc5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqft_bhk_thresold = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aaeea2ec",
   "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>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37000000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HSR Layout</td>\n",
       "      <td>600.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>8</td>\n",
       "      <td>33333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Murugeshpalya</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10660.980810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Devarachikkanahalli</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8500000.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6296.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>other</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Kaval Byrasandra</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2200000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4782.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>710.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>22535.211268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Hennur Road</td>\n",
       "      <td>276.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Vishwapriya Layout</td>\n",
       "      <td>950.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11500000.0</td>\n",
       "      <td>7</td>\n",
       "      <td>12105.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>other</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18500000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>17891.682785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                location  total_sqft  bath       price  bhk  price_per_sqft\n",
       "9                  other      1020.0   6.0  37000000.0    6    36274.509804\n",
       "45            HSR Layout       600.0   9.0  20000000.0    8    33333.333333\n",
       "58         Murugeshpalya      1407.0   4.0  15000000.0    6    10660.980810\n",
       "68   Devarachikkanahalli      1350.0   7.0   8500000.0    8     6296.296296\n",
       "70                 other       500.0   3.0  10000000.0    3    20000.000000\n",
       "78      Kaval Byrasandra       460.0   1.0   2200000.0    2     4782.608696\n",
       "89          Rajaji Nagar       710.0   6.0  16000000.0    6    22535.211268\n",
       "119          Hennur Road       276.0   3.0   2300000.0    2     8333.333333\n",
       "129   Vishwapriya Layout       950.0   7.0  11500000.0    7    12105.263158\n",
       "149                other      1034.0   5.0  18500000.0    6    17891.682785"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5[df5.total_sqft/df5.bhk < sqft_bhk_thresold].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ce03e0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13200, 6)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d62d8f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12456, 6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df5[~ (df5.total_sqft/df5.bhk < sqft_bhk_thresold)]\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "008e58cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12456.000000\n",
       "mean       6308.502826\n",
       "std        4168.127339\n",
       "min         267.829813\n",
       "25%        4210.526316\n",
       "50%        5294.117647\n",
       "75%        6916.666667\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statics per column\n",
    "df6.price_per_sqft.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ebcf9dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Remove_Price_location_Outliers(x) :\n",
    "    \n",
    "    df_out = pd.DataFrame()\n",
    "    \n",
    "    for key , subdf in df6.groupby(by='location') :\n",
    "        \n",
    "        m = np.mean(subdf.price_per_sqft)\n",
    "        sd = np.std(subdf.price_per_sqft)\n",
    "        \n",
    "        reduced_df = subdf[((subdf.price_per_sqft <= (m+sd)) & (subdf.price_per_sqft > (m-sd)))]\n",
    "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
    "    \n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0c51e5c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7 = Remove_Price_location_Outliers(df6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "22b61c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10242, 6)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a4fe9318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Price_bhk_Outliers(df,location) :\n",
    "    bhk2 = df[(df.location==location) & (df.bhk==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.bhk==3)]\n",
    "    \n",
    "    matplotlib.rcParams['figure.figsize'] = (18,12)\n",
    "    \n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price_per_sqft,color=\"blue\",label='2 BHK',s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price_per_sqft,color=\"green\",label='3 BHK',s=50)\n",
    "    \n",
    "    plt.xlabel(\"Total Sqft\")\n",
    "    plt.ylabel(\"Price Per Sqft\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "\n",
    "Price_bhk_Outliers(df7,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "700a4948",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom above graph we can see that for some sqft value price of 2 bhk flats are higher than 3 bhk flats with having same location\\nand we ignore this type of data for simplicity purpose.\\n'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "from above graph we can see that for some sqft value price of 2 bhk flats are higher than 3 bhk flats with having same location\n",
    "and we ignore this type of data for simplicity purpose.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c8bcb477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7317, 6)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Remove_bhk_outliers(df) :\n",
    "    \n",
    "    execlude_indices = np.array([])\n",
    "    \n",
    "    # first we need to traverse all location df's\n",
    "    \n",
    "    for location , location_df in df.groupby(by='location') :\n",
    "        \n",
    "        # here i need to create stats which have mean , std , count based on bhk\n",
    "        bhk_stats = dict()\n",
    "        \n",
    "        for bhk , bhk_df in location_df.groupby(by='bhk') :\n",
    "            \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",
    "            \n",
    "        # removing outliers\n",
    "        for bhk , bhk_df in location_df.groupby(by='bhk') :\n",
    "            \n",
    "            state = bhk_stats.get(bhk-1)\n",
    "            \n",
    "            if state and state['count'] > 5 :\n",
    "                execlude_indices = np.append(execlude_indices, bhk_df[bhk_df.price_per_sqft<=(state['mean'])].index.values)\n",
    "        \n",
    "    return df.drop(execlude_indices,axis='index')\n",
    "\n",
    "df8 = Remove_bhk_outliers(df7)\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5d2f6545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Price_bhk_Outliers(df8,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "26739129",
   "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>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>1st Block Jayanagar</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42800000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</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>19400000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</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>23500000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13000000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14800000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location  total_sqft  bath       price  bhk  price_per_sqft\n",
       "0  1st Block Jayanagar      2850.0   4.0  42800000.0    4    15017.543860\n",
       "1  1st Block Jayanagar      1630.0   3.0  19400000.0    3    11901.840491\n",
       "2  1st Block Jayanagar      1875.0   2.0  23500000.0    3    12533.333333\n",
       "3  1st Block Jayanagar      1200.0   2.0  13000000.0    3    10833.333333\n",
       "4  1st Block Jayanagar      1235.0   2.0  14800000.0    2    11983.805668"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4fcc21c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "152c7fbd",
   "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>price</th>\n",
       "      <th>bhk</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5277</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16000000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8483</th>\n",
       "      <td>other</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>52500000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8572</th>\n",
       "      <td>other</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>55000000.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9306</th>\n",
       "      <td>other</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15000000.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9637</th>\n",
       "      <td>other</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27500000.0</td>\n",
       "      <td>13</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location  total_sqft  bath       price  bhk  price_per_sqft\n",
       "5277  Neeladri Nagar      4000.0  12.0  16000000.0   10     4000.000000\n",
       "8483           other     12000.0  12.0  52500000.0   10     4375.000000\n",
       "8572           other     10000.0  16.0  55000000.0   16     5500.000000\n",
       "9306           other      6000.0  12.0  15000000.0   11     2500.000000\n",
       "9637           other      5425.0  13.0  27500000.0   13     5069.124424"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>9] # so we need to fix some function for number of bathroom per bhk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "60054094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7239, 6)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[(df8.bath <= df8.bhk + 1)]    \n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0eedba87",
   "metadata": {},
   "outputs": [
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       "       1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
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       "       2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
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       "       6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
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       "       9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
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       "[7239 rows x 241 columns]"
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     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df9.location)\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "874ef041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7239, 245)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = pd.concat([df9.drop(['location'],axis='columns'),dummies.drop(['other'],axis='columns')],axis='columns')\n",
    "df10.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "99ee2a80",
   "metadata": {
    "scrolled": true
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14800000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "      <td>1</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>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath       price  bhk  price_per_sqft  1st Block Jayanagar  \\\n",
       "0      2850.0   4.0  42800000.0    4    15017.543860                    1   \n",
       "1      1630.0   3.0  19400000.0    3    11901.840491                    1   \n",
       "2      1875.0   2.0  23500000.0    3    12533.333333                    1   \n",
       "3      1200.0   2.0  13000000.0    3    10833.333333                    1   \n",
       "4      1235.0   2.0  14800000.0    2    11983.805668                    1   \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",
       "3                   0                          0                     0   \n",
       "4                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\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",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\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",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \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",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4fa37c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_sqft',\n",
       " 'bath',\n",
       " 'price',\n",
       " 'bhk',\n",
       " 'price_per_sqft',\n",
       " '1st Block Jayanagar',\n",
       " '1st Phase JP Nagar',\n",
       " '2nd Phase Judicial Layout',\n",
       " '2nd Stage Nagarbhavi',\n",
       " '5th Block Hbr Layout']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df10.columns[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3dc03325",
   "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",
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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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>bhk</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>6th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</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>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>5 rows × 243 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0    4                    1                   0   \n",
       "1      1630.0   3.0    3                    1                   0   \n",
       "2      1875.0   2.0    3                    1                   0   \n",
       "3      1200.0   2.0    3                    1                   0   \n",
       "4      1235.0   2.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\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  ...  Vijayanagar  \\\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",
       "   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",
       "[5 rows x 243 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df10.drop(['price','price_per_sqft'],axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ed5697d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7239"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df10.price\n",
    "len(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3deb46",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bac5a0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train , X_test , Y_train , Y_test = train_test_split(X,Y,test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "af34055e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8629132245229444"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train,Y_train)\n",
    "lr_model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1708911c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86291322 0.85632943 0.87669838 0.80558377 0.79969281 0.84376698\n",
      " 0.84889945 0.84310649 0.88460721 0.81623699 0.80134043 0.83136808\n",
      " 0.86334329 0.8123263  0.85900367 0.84571306 0.81850563 0.8567117\n",
      " 0.84863457 0.85479015]\n",
      "\n",
      "Mean : 0.8414785802571272\n"
     ]
    }
   ],
   "source": [
    "# let's apply K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score , ShuffleSplit\n",
    "\n",
    "cv = ShuffleSplit(n_splits=20,test_size=0.2,random_state=10)\n",
    "# n_splits : devide total data into 20 blocks and after 10 random shuffling and use train data as 16 and test data as 4 block at one iteration\n",
    "\n",
    "# for evaluting this CV\n",
    "cv_scores = cross_val_score(estimator= LinearRegression(),X=X,y=Y,cv=cv)\n",
    "\n",
    "print(cv_scores,f\"Mean : {np.mean(cv_scores)}\",sep=\"\\n\\n\")\n",
    "# so we get nearly 84% accuracy through Linear Reg. But let's look at Other algorithms also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "936bb250",
   "metadata": {},
   "outputs": [],
   "source": [
    "# models\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4168e2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyper-parameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b5e15a59",
   "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.838696</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.838698</td>\n",
       "      <td>{'alpha': 2, 'selection': 'cyclic'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.688815</td>\n",
       "      <td>{'criterion': 'squared_error', 'splitter': 'be...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.838696   \n",
       "1              lasso    0.838698   \n",
       "2      decision_tree    0.688815   \n",
       "\n",
       "                                         best_params  \n",
       "0                                                 {}  \n",
       "1                {'alpha': 2, 'selection': 'cyclic'}  \n",
       "2  {'criterion': 'squared_error', 'splitter': 'be...  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_best_model_using_gridsearchcv(X,y):\n",
    "    algos = {\n",
    "        'linear_regression' : {\n",
    "            'model': LinearRegression(),\n",
    "            'params': {}\n",
    "        },\n",
    "        'lasso': {\n",
    "            'model': Lasso(),\n",
    "            'params': {\n",
    "                'alpha': [1,2],\n",
    "                'selection': ['random', 'cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree': {\n",
    "            'model': DecisionTreeRegressor(),\n",
    "            'params': {\n",
    "                'criterion' : ['squared_error'],\n",
    "                'splitter': ['best','random']\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    \n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model': algo_name,\n",
    "            'best_score': gs.best_score_,\n",
    "            'best_params': gs.best_params_\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "\n",
    "find_best_model_using_gridsearchcv(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "347be0e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8629132245229444"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so our final model is Linear_regression model\n",
    "lr_model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "dc02361c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking our model\n",
    "def Predict(location,bhk,bath,total_sqft) :\n",
    "    x = np.zeros(len(X.columns))\n",
    "    \n",
    "    # for location we need to find index for it\n",
    "    index = np.where(X.columns == location)[0][0]\n",
    "    \n",
    "    x[0] = total_sqft\n",
    "    x[1] = bath\n",
    "    x[2] = bhk\n",
    "    if index >= 0 :\n",
    "        x[index] = 1\n",
    "    \n",
    "    print(x,index)\n",
    "    return lr_model.predict([x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "2c4d6f9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1000.    2.    2.    0.    1.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.] 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8386570.258312229"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predict(location=\"1st Phase JP Nagar\",bhk=2,bath=2,total_sqft=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "90ccb40e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1000.    2.    3.    0.    1.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
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      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.\n",
      "    0.    0.    0.] 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8236367.614310257"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predict(location=\"1st Phase JP Nagar\",bhk=3,bath=2,total_sqft=1000)\n",
    "#https://stackoverflow.com/questions/69326639/sklearn-warning-valid-feature-names-in-version-1-0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "95d1f1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('model.pkl','wb') as f :\n",
    "    pickle.dump(lr_model,file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "206e4028",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "columns = {\n",
    "    'columns' : [ str(col).lower() for col in X.columns]\n",
    "}\n",
    "\n",
    "with open('columns.json','w') as f :\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a3891b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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