supportvectormachine3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "896f0af0",
   "metadata": {},
   "source": [
    "## Bengluru House Price With Support Vector Regression\n",
    "\n",
    "Dataset: https://www.kaggle.com/datasets/amitabhajoy/bengaluru-house-price-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "26112c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"]=(20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "80804830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv(\"C:/Users/Susheel Yadav/Desktop/Bengaluru_House_Data.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "53306e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13320, 9)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c8b74340",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cfbf14c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "</table>\n",
       "</div>"
      ],
      "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df.drop(['area_type','society','balcony','availability'],axis='columns')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7945a88e",
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "83333fe3",
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.dropna()\n",
    "\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0ee9b0ab",
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3201f7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susheel Yadav\\AppData\\Local\\Temp\\ipykernel_9592\\3716419620.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",
      "  df2['bhk']=df2['size'].apply(lambda x:int(x.split(' ')[0]))\n"
     ]
    }
   ],
   "source": [
    "df2['bhk']=df2['size'].apply(lambda x:int(x.split(' ')[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de57ff7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "458f9cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  3,  6,  1,  8,  7,  5, 11,  9, 27, 10, 19, 16, 43, 14, 12,\n",
       "       13, 18], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['bhk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1b90cd60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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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>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>2Electronic City Phase II</td>\n",
       "      <td>27 BHK</td>\n",
       "      <td>8000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>43 Bedroom</td>\n",
       "      <td>2400</td>\n",
       "      <td>40.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location        size total_sqft  bath  price  bhk\n",
       "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
       "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.bhk>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8ca59c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.total_sqft.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3dd85a54",
   "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": 62,
   "id": "e39dd86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>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>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Kengeri</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>34.46Sq. Meter</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Hennur Road</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1195 - 1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.770</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Arekere</td>\n",
       "      <td>9 Bedroom</td>\n",
       "      <td>4125Perch</td>\n",
       "      <td>9.0</td>\n",
       "      <td>265.000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1120 - 1145</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.130</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>Bettahalsoor</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>3090 - 5002</td>\n",
       "      <td>4.0</td>\n",
       "      <td>445.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size      total_sqft  bath    price  bhk\n",
       "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
       "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
       "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
       "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
       "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
       "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
       "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
       "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
       "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
       "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[~df2['total_sqft'].apply(is_float)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d254b667",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sqft_to_num(x):\n",
    "    tokens = x.split('-')\n",
    "    if len(tokens) == 2:\n",
    "        return (float(tokens[0])+float(tokens[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "332a74ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_sqft_to_num('2000 - 6000')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "261cd245",
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_sqft_to_num('34.46Sq. Meter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "05c976fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "bhk           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c26e1c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gandhi Bazar</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</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      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2\n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2\n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4\n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4\n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3\n",
       "9              Gandhi Bazar  6 Bedroom      1020.0   6.0  370.00    6"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.copy()\n",
    "df3['total_sqft'] = df2['total_sqft'].apply(convert_sqft_to_num)\n",
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b2e4acda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "bhk           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df3.dropna()\n",
    "\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eedd5816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Yelahanka\n",
       "size              4 BHK\n",
       "total_sqft       2475.0\n",
       "bath                4.0\n",
       "price             186.0\n",
       "bhk                   4\n",
       "Name: 30, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "083ba0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      Tharabanahalli\n",
       "size               3 Bedroom\n",
       "total_sqft            4395.0\n",
       "bath                     3.0\n",
       "price                  240.0\n",
       "bhk                        3\n",
       "Name: 60, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7bc210cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1298"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "70f357ab",
   "metadata": {},
   "outputs": [
    {
     "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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.location = df3.location.apply(lambda x:x.strip()) #remove extra space\n",
    "#here location_stats is series\n",
    "\n",
    "location_stats = df3.groupby('location')['location'].agg('count').sort_values(ascending = False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "95e871fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1047"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats <= 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1987c553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Sadashiva Nagar          10\n",
       "Naganathapura            10\n",
       "Basapura                 10\n",
       "Nagadevanahalli          10\n",
       "Kalkere                  10\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: 1047, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_lessthan_10 = location_stats[location_stats <= 10]\n",
    "location_stats_lessthan_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "42a54e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1287"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9412e33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.location = df3.location.apply(lambda x: 'Other' if x in location_stats_lessthan_10 else x)\n",
    "\n",
    "#transfering all location which have less number of rows to other\n",
    "\n",
    "len(df3.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7d341d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</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      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2\n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2\n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4\n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4\n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3\n",
       "9                     Other  6 Bedroom      1020.0   6.0  370.00    6"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8f37ee5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.drop([\"size\"], axis = \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2a972b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
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       "      <th>bhk</th>\n",
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       "  <tbody>\n",
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       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</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>1440.0</td>\n",
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       "      <td>62.00</td>\n",
       "      <td>3</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>95.00</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
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      "text/plain": [
       "                   location  total_sqft  bath   price  bhk\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1a5e5258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13200, 5)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0891c96d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5d8ae44d",
   "metadata": {},
   "outputs": [
    {
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       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial 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",
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       "[5 rows x 241 columns]"
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     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies= pd.get_dummies(df3.location) #one-hot-encoding\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "866300e7",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                   location  total_sqft  bath   price  bhk  \\\n",
       "0  Electronic City Phase II      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      1200.0   2.0   51.00    2   \n",
       "\n",
       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
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       "1                    0                   0                          0   \n",
       "2                    0                   0                          0   \n",
       "3                    0                   0                          0   \n",
       "4                    0                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  ...  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 245 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.concat([df3,dummies.drop('Other',axis ='columns')],axis = 'columns')\n",
    "# as in dummies we must drop one column, hence other column is dropped\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d3479dc7",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</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>...</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>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</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>1</th>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</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>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</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>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</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>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</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 × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath   price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      1056.0   2.0   39.07    2                    0                   0   \n",
       "1      2600.0   5.0  120.00    4                    0                   0   \n",
       "2      1440.0   2.0   62.00    3                    0                   0   \n",
       "3      1521.0   3.0   95.00    3                    0                   0   \n",
       "4      1200.0   2.0   51.00    2                    0                   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  ...  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 244 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5= df4.drop('location',axis='columns')\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "181addc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13200, 244)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1bb3f871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1952"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df5.price.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a40313f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6)) \n",
    "plt.scatter(range(len(df5['price'])), df5['price'], alpha=0.5)\n",
    "plt.title('Scatter Plot of Price')\n",
    "plt.xlabel('Data Point Index')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "51f40868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(x = df5['price'])\n",
    "plt.title('Box Plot of Price')\n",
    "plt.xlabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "47351349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   total_sqft  bath   price  bhk  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
      "0      1056.0   2.0   39.07    2                    0                   0   \n",
      "1      2600.0   5.0  120.00    4                    0                   0   \n",
      "2      1440.0   2.0   62.00    3                    0                   0   \n",
      "3      1521.0   3.0   95.00    3                    0                   0   \n",
      "4      1200.0   2.0   51.00    2                    0                   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  ...  Vishveshwarya Layout  Vishwapriya 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",
      "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
      "0            0           0               0          0                   0   \n",
      "1            0           0               0          0                   0   \n",
      "2            0           0               0          0                   0   \n",
      "3            0           0               0          0                   0   \n",
      "4            0           0               0          0                   0   \n",
      "\n",
      "   Yelenahalli  Yeshwanthpur  log_price  \n",
      "0            0             0   3.665355  \n",
      "1            0             0   4.787492  \n",
      "2            0             0   4.127134  \n",
      "3            0             0   4.553877  \n",
      "4            0             0   3.931826  \n",
      "\n",
      "[5 rows x 245 columns]\n"
     ]
    }
   ],
   "source": [
    "df5['log_price'] = np.log(df5['price']+ 1e-6)\n",
    "\n",
    "print(df5.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa045ae3",
   "metadata": {},
   "source": [
    "### In order to predict house price based on several characteristics, such as location, square footage, number of bedrooms, etc., you are developing an SVM regression model. Which regression metric in this situation would be the best to employ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "10763f60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.01\n",
      "RMSE: 0.09\n",
      "MAE: 0.09\n",
      "R-squared (R²): 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "X = df5.drop('log_price', axis = 'columns')\n",
    "y = df5.price\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "model = SVR(kernel = 'linear')\n",
    "\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "predictions = model.predict(X_test_scaled)\n",
    "\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "r_squared = r2_score(y_test, predictions)\n",
    "\n",
    "print(f'MSE: {mse:.2f}')\n",
    "print(f'RMSE: {rmse:.2f}')\n",
    "print(f'MAE: {mae:.2f}')\n",
    "print(f'R-squared (R²): {r_squared:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "739506e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99999962, 0.99999948, 0.99999967, 0.99999954, 0.99999957])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits = 5, test_size = 0.2, random_state = 0)\n",
    "cross_val_score(model, X, y, cv = cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1401fa43",
   "metadata": {},
   "source": [
    "All of these metrics are relevant and should be employed to evaluate our SVM regression model comprehensively. However, the choice of the \"best\" metric depends on our specific goals and what you want to emphasize in your evaluation:\n",
    "\n",
    "If we want to emphasize prediction accuracy and assess how far off your model's predictions are from the actual house prices, MSE, RMSE, and MAE are suitable metrics.\n",
    "\n",
    "If we want to understand how much of the variance in house prices our model can explain, R-squared provides that information.\n",
    "\n",
    "In practice, it's often a good idea to use a combination of these metrics to gain a well-rounded view of our model's performance. For example, we might use MSE and R-squared together to assess both prediction accuracy and explanatory power. Additionally, consider using visualizations and domain knowledge to interpret and communicate the results effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee661225",
   "metadata": {},
   "source": [
    "### You have built an SVM regression model and are trying to decide between using MSE or R-squared as your evaluation metric. Which metric would be more appropriate if your goal is to predict the actual price of a house as accurately as possible?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713b06d8",
   "metadata": {},
   "source": [
    "If our primary goal is to predict the actual price of a house as accurately as possible, then Mean Squared Error (MSE) would be the more appropriate evaluation metric.\n",
    "\n",
    "Here's why:\n",
    "\n",
    "1. **MSE Measures Prediction Accuracy:** MSE directly quantifies the average squared difference between model's predictions and the actual house prices. It penalizes larger prediction errors more heavily, which is often a desirable property when main objective is to minimize prediction errors. In other words, it prioritizes accuracy.\n",
    "\n",
    "2. **Focus on Prediction Error Magnitude:** MSE provides a clear indication of how far off, on average, your model's predictions are from the true prices. For applications where we need to minimize the financial impact of prediction errors (such as in real estate), knowing the magnitude of these errors is crucial.\n",
    "\n",
    "3. **Easier Interpretation in Units of the Target Variable:** MSE is measured in the same units as the target variable (house prices in this case), making it easy to interpret. For example, if the MSE is 1000, it means your model's predictions are off by an average of 1000 units (in the currency or measurement unit of house prices).\n",
    "\n",
    "While R-squared (R²) is a valuable metric for understanding the proportion of variance in the target variable explained by our model, it doesn't directly measure prediction accuracy in the same way MSE does. R² focuses more on the goodness of fit and the proportion of variance explained, which might be more relevant when you want to understand the model's overall performance or the percentage of variability in house prices that your model captures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0775e0ab",
   "metadata": {},
   "source": [
    "###  You have a dataset with a significant number of outliers and are trying to select an appropriate regression metric to use with your SVM model. Which metric would be the most appropriate in this scenario?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9806b48e",
   "metadata": {},
   "source": [
    "When dealing with a dataset that has a significant number of outliers and using an SVM regression model, the most appropriate regression metric to consider is the **Median Absolute Error (MedAE)**.\n",
    "\n",
    "Here's why MedAE is a suitable choice in this scenario:\n",
    "\n",
    "1. **Robustness to Outliers:** MedAE is less sensitive to outliers compared to metrics like Mean Squared Error (MSE) or Mean Absolute Error (MAE). Instead of averaging the absolute errors, it computes the median of the absolute errors. The median is less affected by extreme values because it's not influenced by the magnitude of outliers, making it robust in the presence of outliers.\n",
    "\n",
    "2. **Median Interpretation:** The MedAE is straightforward to interpret. It represents the median magnitude of prediction errors in the original units of the target variable. For example, if the MedAE is 10, it means that, on average, model's predictions are off by 10 units (in the same measurement as your target variable).\n",
    "\n",
    "3. **Handling Outliers:** Since SVM models are sensitive to outliers, using a robust metric like MedAE helps focus on the typical prediction error while downplaying the influence of outliers. This can lead to a more accurate representation of the model's performance, especially when the dataset contains significant outliers.\n",
    "\n",
    "4. **Balance Between Bias and Variance:** MedAE balances the trade-off between bias and variance in the presence of outliers. While it may not be as efficient at capturing the full variability as metrics like R-squared (R²) or MSE, it provides a more robust assessment of prediction error.\n",
    "\n",
    "5. **Visualization:** MedAE can be helpful in visualizing the spread of prediction errors, particularly when creating residual plots or diagnostic plots. It allows you to assess the central tendency of errors while revealing the presence of outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385d51f3",
   "metadata": {},
   "source": [
    "### You have built an SVM regression model using a polynomial kernel and are trying to select the best metric to evaluate its performance. You have calculated both MSE and RMSE and found that both values are very close. Which metric should you choose to use in this case?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b35a1166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 18721.750601664367\n",
      "Root Mean Squared Error (RMSE): 136.8274482757914\n"
     ]
    }
   ],
   "source": [
    "model1 = SVR(kernel ='poly', degree = 3)\n",
    "\n",
    "model1.fit(X_train_scaled, y_train)\n",
    "\n",
    "predictions = model1.predict(X_test_scaled)\n",
    "\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a8b395",
   "metadata": {},
   "source": [
    "When we have built an SVM regression model using a polynomial kernel and find that both Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are very close, it's generally appropriate to choose the **RMSE** as the preferred evaluation metric for the following reasons:\n",
    "\n",
    "1. **Interpretability:** RMSE is a more interpretable metric compared to MSE because it's measured in the same units as the target variable. In the context of our regression problem, the RMSE value directly represents the average prediction error in the original units of the target variable, making it easier to communicate and understand.\n",
    "\n",
    "2. **Root Transformation:** The RMSE inherently incorporates a square root transformation, which balances the emphasis on larger errors and smaller errors. This is valuable because it gives more weight to larger errors, which can be more consequential in practice.\n",
    "\n",
    "3. **Comparability:** RMSE is more directly comparable to the scale of our data, which can be helpful when discussing the magnitude of prediction errors with stakeholders or when comparing model performance across different datasets or scenarios.\n",
    "\n",
    "4. **Visual Assessment:** RMSE is particularly useful when creating residual plots or diagnostic plots because it provides a more meaningful y-axis scale, making it easier to visualize the spread of prediction errors and identify patterns in residuals.\n",
    "\n",
    "While MSE and RMSE are closely related and often yield similar results, RMSE has the advantage of being more interpretable and aligning better with the typical use case of regression evaluation. However, keep in mind that both metrics can be valuable, and it's a good practice to report both when assessing model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d391e9",
   "metadata": {},
   "source": [
    "### You are comparing the performance of different SVM regression models using different kernels (linear, polynomial, and RBF) and are trying to select the best evaluation metric. Which metric would be most appropriate if your goal is to measure how well the model explains the variance in the target variable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "157c71b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Kernel  R-squared\n",
      "0      Linear   1.000000\n",
      "1  Polynomial   0.022168\n",
      "2         RBF   0.192208\n"
     ]
    }
   ],
   "source": [
    "kernel_names = [\"Linear\", \"Polynomial\", \"RBF\"]\n",
    "kernel_models = {\"Linear\": SVR(kernel = 'linear'),\n",
    "                 \"Polynomial\": SVR(kernel= 'poly', degree = 3), \n",
    "                 \"RBF\": SVR(kernel = 'rbf')}\n",
    "\n",
    "results = {\"Kernel\": [], \"R-squared\": []}\n",
    "\n",
    "for kernel_name, model2 in kernel_models.items():\n",
    "    model2.fit(X_train_scaled, y_train)\n",
    "\n",
    "    y_pred = model2.predict(X_test_scaled)\n",
    "\n",
    "    r_squared = r2_score(y_test, y_pred)\n",
    "\n",
    "    results[\"Kernel\"].append(kernel_name)\n",
    "    results[\"R-squared\"].append(r_squared)\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db2afd1c",
   "metadata": {},
   "source": [
    "If our goal is to measure how well different SVM regression models explain the variance in the target variable and we are comparing models with different kernels (linear, polynomial, and RBF), then the most appropriate evaluation metric to consider is the **R-squared (R²) score**.\n",
    "\n",
    "Here's why R-squared is suitable for this purpose:\n",
    "\n",
    "1. **Explained Variance:** R-squared quantifies the proportion of the variance in the target variable that is explained by our regression model. In other words, it measures the goodness of fit of the model to the data, indicating how well the model captures the underlying patterns and variability in the target variable.\n",
    "\n",
    "2. **Comparability:** R-squared is a standardized metric that ranges from 0 to 1. A higher R-squared value indicates that a larger portion of the variance is explained by the model. This makes it easy to compare different models with various kernels and assess their relative abilities to capture variance.\n",
    "\n",
    "3. **Model Selection:** When we're comparing models with different kernels (linear, polynomial, RBF), R-squared provides a consistent measure to evaluate and choose the model that best fits our data. Higher R-squared values generally indicate better fit.\n",
    "\n",
    "4. **Interpretability:** R-squared has a straightforward interpretation. An R² of 0 means that the model does not explain any variance in the target variable, while an R² of 1 means that the model explains all of the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32448813",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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