real e state price prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7ffdd695",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beb5b758",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"E:\\\\real e state\\\\bengaluru_house_prices.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6850018c",
   "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",
       "    </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",
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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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.drop([\"area_type\",\"availability\",\"society\",\"balcony\"],axis=\"columns\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81bc0393",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b224c686",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.dropna()\n",
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cfed92e",
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"size\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "909b3457",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Krishna Kesharwani\\AppData\\Local\\Temp\\ipykernel_10484\\3179918180.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[\"size_bhk\"]=df2[\"size\"].apply(lambda x:int(x.split(\" \")[0]))\n",
      "C:\\Users\\Krishna Kesharwani\\AppData\\Local\\Temp\\ipykernel_10484\\3179918180.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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.drop(\"size\",axis=\"columns\",inplace=True)\n"
     ]
    },
    {
     "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>size_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  size_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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"size_bhk\"]=df2[\"size\"].apply(lambda x:int(x.split(\" \")[0]))\n",
    "df2.drop(\"size\",axis=\"columns\",inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f871b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"total_sqft\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ff3dce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_range(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a157f32",
   "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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>size_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>186.000</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>477.000</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>54.005</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>43.490</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>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.760</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>154.500</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  size_bhk\n",
       "30              Yelahanka  2100 - 2850   4.0  186.000         4\n",
       "122                Hebbal  3067 - 8156   4.0  477.000         4\n",
       "137    8th Phase JP Nagar  1042 - 1105   2.0   54.005         2\n",
       "165              Sarjapur  1145 - 1340   2.0   43.490         2\n",
       "188              KR Puram  1015 - 1540   2.0   56.800         2\n",
       "...                   ...          ...   ...      ...       ...\n",
       "12975          Whitefield   850 - 1060   2.0   38.190         2\n",
       "12990      Talaghattapura  1804 - 2273   3.0  122.000         3\n",
       "13059              Harlur  1200 - 1470   2.0   72.760         2\n",
       "13265               Hoodi  1133 - 1384   2.0   59.135         2\n",
       "13299          Whitefield  2830 - 2882   5.0  154.500         4\n",
       "\n",
       "[190 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[~df2[\"total_sqft\"].apply(check_range)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57b8766f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_range(x):\n",
    "    t=x.split(\"-\")\n",
    "    if len(t)==2:\n",
    "        return (float(t[0])+float(t[1]))//2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87d37912",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Krishna Kesharwani\\AppData\\Local\\Temp\\ipykernel_10484\\2411576420.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[\"total_sqft\"]=df2[\"total_sqft\"].apply(convert_range)\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>size_bhk</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>39.07</td>\n",
       "      <td>2</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>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",
       "      <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>1521.0</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>1200.0</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  size_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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"total_sqft\"]=df2[\"total_sqft\"].apply(convert_range)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b7a8e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1056., 2600., 1440., ..., 2758.,  774., 4689.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"total_sqft\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "031d35c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>size_bhk</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>39.07</td>\n",
       "      <td>2</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>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",
       "      <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>1521.0</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>1200.0</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  size_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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.copy()\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "391b64e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3[\"price_per_sqft\"]=df3[\"price\"]*100000/df3[\"total_sqft\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e975e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>size_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>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location  total_sqft  bath   price  size_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",
       "   price_per_sqft  \n",
       "0     3699.810606  \n",
       "1     4615.384615  \n",
       "2     4305.555556  \n",
       "3     6245.890861  \n",
       "4     4250.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf7dfe96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1304"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3[\"location\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "337abd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "other                        2896\n",
       "Whitefield                    534\n",
       "Sarjapur  Road                392\n",
       "Electronic City               302\n",
       "Kanakpura Road                266\n",
       "                             ... \n",
       "HAL 2nd Stage                  11\n",
       "Nehru Nagar                    11\n",
       "Kodigehalli                    11\n",
       "2nd Phase Judicial Layout      11\n",
       "Pattandur Agrahara             11\n",
       "Name: location, Length: 242, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats=df3.groupby(\"location\")[\"location\"].agg('count').sort_values(ascending=False)\n",
    "location_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bbe068f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1063"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location_stats[location_stats<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f83e8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Dodsworth Layout         10\n",
       "1st Block Koramangala    10\n",
       "Nagappa Reddy Layout     10\n",
       "Ganga Nagar              10\n",
       "Dairy Circle             10\n",
       "                         ..\n",
       " Banaswadi                1\n",
       "Kanakadasa Layout         1\n",
       "Kanakapur main road       1\n",
       "Kanakapura  Rod           1\n",
       "whitefiled                1\n",
       "Name: location, Length: 1063, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_stats_less_than_10=location_stats[location_stats<=10]\n",
    "location_stats_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d9a53f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[\"location\"]=df3[\"location\"].apply(lambda x:\"other\" if x in location_stats_less_than_10 else x)\n",
    "len(df3[\"location\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d31f7faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 6)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "198d8208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12502, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=df3[~(df3[\"total_sqft\"]/df3[\"size_bhk\"]<300)]\n",
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c1745492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12456.000000\n",
       "mean       6308.513707\n",
       "std        4168.123545\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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[\"price_per_sqft\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6d951f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_pps(df):\n",
    "    df_out=pd.DataFrame()\n",
    "    for key, subdf in df.groupby('location'):\n",
    "        m=np.mean(subdf.price_per_sqft)\n",
    "        st=np.std(subdf.price_per_sqft)\n",
    "        reduce_df=subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out=pd.concat([df_out,reduce_df],ignore_index=True)\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa232d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10244, 6)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=remove_pps(df4)\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9447f86e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location==location) & (df.size_bhk==2)]\n",
    "    bhk3 = df[(df.location==location) & (df.size_bhk==3)]\n",
    "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 BHK', s=50)\n",
    "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='+', color='green',label='3 BHK', s=50)\n",
    "    plt.xlabel(\"Total Square Feet Area\")\n",
    "    plt.ylabel(\"Price (Lakh Indian Rupees)\")\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df5,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9a825ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df5,\"Hebbal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5cfdaae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7342, 6)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_bhk_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for location, location_df in df.groupby('location'):\n",
    "        bhk_stats = {}\n",
    "        for bhk, bhk_df in location_df.groupby('size_bhk'):\n",
    "            bhk_stats[bhk] = {\n",
    "                'mean': np.mean(bhk_df.price_per_sqft),\n",
    "                'std': np.std(bhk_df.price_per_sqft),\n",
    "                'count': bhk_df.shape[0]\n",
    "            }\n",
    "        for bhk, bhk_df in location_df.groupby('size_bhk'):\n",
    "            stats = bhk_stats.get(bhk-1)\n",
    "            if stats and stats['count']>5:\n",
    "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
    "    return df.drop(exclude_indices,axis='index')\n",
    "df8 = remove_bhk_outliers(df5)\n",
    "df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc6da732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yqxtsMiMiIiKXx4TISa1atQpt27aFUqmEUqlE165d8cMPP1R5zpo1ayCTyaSXj48POnbsiKSkJKNyvXr1wvTp0ys8v0GDBpVuA8DZs2cREhKCmJgYFBYW1vT2iIgcXsGcAul1fdZ1af/1WdeNjlHdYEJUR+p64b6QkBAsWbIER44cwZEjR9CnTx9ER0fj9OnTVZ6nVCqRmZmJzMxMHDt2DFFRURg5ciTOnTtX65gOHz6MHj16ICoqCt988w28vLxqfU0iIkfl7el99+VRfpRZ6YvqBhMiJzVkyBA8/vjjaNmyJVq2bIm3334bPj4+OHToUJXnyWQyqNVqqNVqtGjRAgsXLoSbm1utV6bftWsX+vTpg7///e9ISEiAu7t7ra5HRET2r64rA2qDCZEL0Ol0WLduHTQaDbp27WrWeZ9//jkAoEOHDjV+/+TkZDzxxBN47bXXsGzZshpfh4iIyFo4ysyKbL1w38mTJ9G1a1fcuXMHPj4+SE5ORuvWras8Jzc3Fz4+hpEPWq0WHh4e+Pjjj/HAAw8YlYuPj8enn35qtK+kpAT16tUz2ldQUIAnn3wSc+fOxauvvmqBuyIicj7ent4cTWZjTIisyNYL9z344IM4fvw4bt26hQ0bNmDcuHHYu3dvlUmRr68vjh49CgC4ffs2du7ciYkTJ8Lf3x9DhgyRyo0dOxavvfaa0blJSUlYtGiR0T6FQoGIiAh88sknGD16NFq1amXBOyQiorI0RRrp+6dgTkGd90OydWVATdlNk9nixYshk8mMRi8JITBv3jwEBwdDoVCgV69e5ToFFxYWYurUqWjUqBG8vb0xdOhQ/PHHH3UcvX3y9PRE8+bN0alTJyxevBjt2rXD+++/X+U5bm5uaN68OZo3b462bdsiLi4OvXv3xtKlS43KqVQqqVzpKyCg/No77u7u2LhxIzp27IjevXvjzJkzFr1HIiJX4Eh9cXwW+0iveysAAt8NNDpmb+wiITp8+DA+/vhjtG3b1mj/O++8gxUrVuDDDz/E4cOHoVar0b9/f+Tn50tlpk+fjuTkZKxbtw5paWkoKCjA4MGDodPp6vo2yrG3IZVCiBoNdXd3d4dWq63x+3p5eSEpKQmPPfYYevfujVOnTtX4WkRERNZg8yazgoICjB07Fp988gkWLlwo7RdC4L333sNrr72GmJgYAMDnn3+OwMBAfPXVV5g4cSJyc3ORkJCAL774Av369QMAfPnllwgNDcXOnTsRFRVlk3sqVVl1YF0s3Dd37lwMGjQIoaGhyM/Px7p167Bnzx5s27atyvOEEMjKygJg6EOUkpKC7du344033qhVPJ6entiwYQNGjhyJPn364Mcff0SbNm1qdU0iIjKwp2aqe/9HX1OskWqJrs+6bjS9gL2xeUI0efJkPPHEE+jXr59RQpSRkYGsrCwMGDBA2ufl5YWePXviwIEDmDhxItLT01FcXGxUJjg4GOHh4Thw4EClCVFhYaFRTUleXp4V7sy2rl+/jmeeeQaZmZlQqVRo27Yttm3bhv79+1d5Xl5eHoKCggAYnndYWBgWLFiAV155pdYxeXh44Ouvv8bo0aOlpKhsrSARERmYk+TYus9q2Xgq3F8HlQG1YdOEaN26dTh69CgOHz5c7lhpLUVgoPEfMzAwEJcvX5bKeHp6omHDhuXKlJ5fkcWLF2P+/Pm1Dd+uJSQkmH3O+PHjMX78+GrL7dmzx6TzK7qeh4cHvv32W7NjIyJyNfaU5LgCmyVEV69exbRp07Bjx45yQ7XvJZPJjLaFEOX2lVVdmTlz5iAuLk7azsvLQ2hoqImR1wyHVBIRkbU4ajOVPbFZQpSeno7s7Gx07NhR2qfT6bBv3z58+OGH0lIRWVlZUhMOAGRnZ0u1Rmq1GkVFRcjJyTGqJcrOzka3bt0qfW8vLy8uG0FERHatYE4BNEUaBC43rhGqKMmx12YqR6oMsNkos759++LkyZM4fvy49OrUqRPGjh2L48ePo1mzZlCr1UhJSZHOKSoqwt69e6Vkp2PHjvDw8DAqk5mZiVOnTlWZEBEREdm7ytYy41pn1mGzGiJfX1+Eh4cb7fP29oa/v7+0f/r06Vi0aBFatGiBFi1aYNGiRahfvz7GjBkDwDAXznPPPYeZM2fC398ffn5+mDVrFtq0aSONOiMiInIklXWmLnucyZBl2XyUWVVmz54NrVaLSZMmIScnB507d8aOHTvg6+srlVm5ciXkcjlGjhwJrVaLvn37Ys2aNRZfPFQIx6jyc3R8zkTk6qqbtLC0Ca2ypihHaqayJzLBbyDk5eVBpVIhNzcXSqXS6JhOp8Nvv/2GgIAA+Pv72yhC13Hjxg1kZ2ejZcuWFk9qiYgcgWx+1QOHSjHpqfr721x2XUNkD9zd3dGgQQNkZ2cDAOrXr1/tKDcynxACt2/fRnZ2Nho0aMBkiIhcFkeM2QYTIhOo1WoAkJIisp4GDRpIz5uIyBXZ64gxZ8eEyAQymQxBQUEICAhAcXGxrcNxWh4eHqwZIiIim2BCZAZ3d3d+YRMRETkhJkRERER2iiPG6o7NJmYkIiIishdMiIiIiMjlMSEiIiIil8eEiIiIiFweEyIiIiJyeUyIiIiIyOUxISIiIiKXx4SIiIiIXB4TIiIiInJ5TIiIiIjI5TEhIiIiIpfHhIiIiMjKNEUayObLIJsvg6ZIY+twqAJMiIiIiMjlMSEiIiIilye3dQBERETO6N6mMU1xxb8DgLend53FRJVjQkRERGQFPot9Ktwf+G6g0bZ4U0BTpJHKF8wpYJJkA2wyIyIiIpfHGiIiIiIrKJhTIP2uKdZINUPXZ12HtwdrgOwNEyIiIqJ7WKr5qrLzvD284e3pLfUx0hRp2MfIDjAhIiIisgFz+hiR9bEPEREREbk81hAREZHLs/YQeW9P73I1PexjZF+YEBERkcuzRfNVdX2MqG6xyYyIiIhcHmuIiIjI5bH5ipgQERGRy7N181VFfYyobrHJjIiIiFweEyIiIiJyeWwyIyIiuoc9NV9x0de6wxoiIiIicnlMiIiIiMjlscmMiIjIjlh71myqGBMiIiIiO8JFX22DTWZERETk8lhDREREZEc4a7ZtMCEiIiKyI7aeNdtVscmMiIiIXB4TIiIiInJ5ZjWZ5ebmIjk5Gampqbh06RJu376Nxo0bo3379oiKikK3bt2sFScREZHLsadZs52dSTVEmZmZeOGFFxAUFIQFCxZAo9HgkUceQd++fRESEoLdu3ejf//+aN26NdavX2/tmImIiMhKNEUayObLIJsvM5oTydmZlBC1a9cOKpUKP//8My5evIh169Zh+fLlWLhwIeLj47Fv3z789ddf+Oc//4kVK1bg3XffNenNV61ahbZt20KpVEKpVKJr16744YcfpOPjx4+HTCYzenXp0sXoGoWFhZg6dSoaNWoEb29vDB06FH/88YcZj4CIiIhcnUlNZqdPn0bjxo2rLKNQKDB69GiMHj0af/75p0lvHhISgiVLlqB58+YAgM8//xzR0dE4duwYHn74YQDAwIEDkZiYKJ3j6elpdI3p06djy5YtWLduHfz9/TFz5kwMHjwY6enpcHd3NykOIiIicm0mJUTVJUM1LT9kyBCj7bfffhurVq3CoUOHpITIy8sLarW6wvNzc3ORkJCAL774Av369QMAfPnllwgNDcXOnTsRFRVlVtxERESuiMuF1GCU2eeff47vv/9e2p49ezYaNGiAbt264fLlyzUORKfTYd26ddBoNOjatau0f8+ePQgICEDLli3xwgsvIDs7WzqWnp6O4uJiDBgwQNoXHByM8PBwHDhwoNL3KiwsRF5entGLiIjIVfks9pFe9y4REvhuoNExZ2Z2QrRo0SIoFAoAwMGDB/Hhhx/inXfeQaNGjTBjxgyzAzh58iR8fHzg5eWF2NhYJCcno3Xr1gCAQYMG4d///jd27dqF5cuX4/Dhw+jTpw8KCwsBAFlZWfD09ETDhg2NrhkYGIisrKxK33Px4sVQqVTSKzQ01Oy4iYiIyHmYPVP11atXpT4/GzduxN/+9jf84x//QPfu3dGrVy+zA3jwwQdx/Phx3Lp1Cxs2bMC4ceOwd+9etG7dGqNGjZLKhYeHo1OnTggLC8P333+PmJiYSq8phIBMJqv0+Jw5cxAXFydt5+XlMSkiIiKXxeVCalBD5OPjgxs3bgAAduzYIfXdqVevHrRardkBeHp6onnz5ujUqRMWL16Mdu3a4f3336+wbFBQEMLCwnD+/HkAgFqtRlFREXJycozKZWdnIzAwsKJLADD0Syod2Vb6IiIiclXent53X/ckQKXLhZS+nJnZCVH//v3x/PPP4/nnn8dvv/2GJ554AoBhJFqTJk1qHZAQQmoSK+vGjRu4evUqgoKCAAAdO3aEh4cHUlJSpDKZmZk4deoUJ4kkIiIik5ndZPavf/0Lr7/+Oq5evYoNGzbA398fgKGD8+jRo8261ty5czFo0CCEhoYiPz8f69atw549e7Bt2zYUFBRg3rx5GDFiBIKCgnDp0iXMnTsXjRo1wvDhwwEAKpUKzz33HGbOnAl/f3/4+flh1qxZaNOmjVRzRURERFQdsxOiBg0a4MMPPyy3f/78+Wa/+fXr1/HMM88gMzMTKpUKbdu2xbZt29C/f39otVqcPHkSa9euxa1btxAUFITevXtj/fr18PX1la6xcuVKyOVyjBw5ElqtFn379sWaNWs4BxEREVENuOpyITIhhNl3nZqaio8++ggXL17EN998g/vuuw9ffPEFmjZtioiICGvEaVV5eXlQqVTIzc1lfyIiIiIHYcnvb7P7EG3YsAFRUVFQKBQ4evSo1N8nPz8fixYtqlUwREREzsiR1gdzpFgtyeyEaOHChVi9ejU++eQTeHh4SPu7deuGo0ePWjQ4IiIiorpgdkJ07tw5REZGltuvVCpx69YtS8REREREVKfM7lQdFBSECxculBtin5aWhmbNmlkqLiIiIofmSOuDOVKs1mJ2QjRx4kRMmzYNn332GWQyGa5du4aDBw9i1qxZeOONN6wRIxERkcOpbO2ve9cKA2AXI7ocKVZrMTshmj17NnJzc9G7d2/cuXMHkZGR8PLywqxZszBlyhRrxEhERERkVTUadg8At2/fxpkzZ6DX69G6dWv4+DjuKrgcdk9ERJZWthmqsvXB7KEZypFivZclv7/NriEqde3aNdy4cQORkZFQKBTVLqhKRETkSipLHkrXByulKdJITVYFcwpsknSYGqszM3uU2Y0bN9C3b1+0bNkSjz/+ODIzMwEAzz//PGbOnGnxAImIiIiszeyEaMaMGfDw8MCVK1dQv359af+oUaOwbds2iwZHREREVBfMbjLbsWMHtm/fjpCQEKP9LVq0wOXLly0WGBERkbMouz6YPQ9zd9W1zMxOiDQajVHNUKm//voLXl5eFgmKiIjImXGYu/0xu8ksMjISa9eulbZlMhn0ej2WLVuG3r17WzQ4IiIiorpgdg3RsmXL0KtXLxw5cgRFRUWYPXs2Tp8+jZs3b2L//v3WiJGIiMipFMwpkH6vapg71R2zE6LWrVvjxIkTWLVqFdzd3aHRaBATE4PJkycjKCjIGjESERE5FQ5ztz81modIrVZj/vz5lo6FiIiIyCZqlBDl5OQgISEBZ8+ehUwmQ6tWrfD3v/8dfn5+lo6PiIiIyOrMXrpj7969iI6OhlKpRKdOnQAA6enpuHXrFjZv3oyePXtaJVBr4tIdREREjseS399mJ0Th4eHo1q2b1IcIAHQ6HSZNmoT9+/fj1KlTtQrIFpgQEREROR5Lfn+bPez+999/x8yZM6VkCADc3d0RFxeH33//vVbBEBEREdmC2QlRhw4dcPbs2XL7z549i0ceecQSMRERERHVKbM7Vb/00kuYNm0aLly4gC5dugAADh06hH/9619YsmQJTpw4IZVt27at5SIlIiIishKz+xC5uVVdqSSTySCEgEwmg06nq1VwdYV9iIiIiByPJb+/za4hysjIqNUbEhEREdkbsxOisLAwa8RBREREZDNmJ0T3LuxakWeffbbGwRARERHZgtl9iBo2bGi0XVxcjNu3b8PT0xP169fHzZs3LRpgXWAfIiIiIsdj03mIcnJyjF4FBQU4d+4cIiIi8J///KdWwRARERHZgtkJUUVatGiBJUuWYNq0aZa4HBEREVGdskhCBBhmq7527ZqlLkdERERUZ8zuVL1582ajbSEEMjMz8eGHH6J79+4WC4yIiIiorpidEA0bNsxoWyaToXHjxujTpw+WL19uqbiIiIiI6ozZCZFer7dGHEREREQ2Y7E+RElJSVy7jIiIiBySWQnRJ598gieffBJjxozBoUOHAAC7du1C+/bt8fTTT6Nr165WCZKIiIjImkxOiN59911MnjwZGRkZ2LRpE/r27YtFixZh5MiRGDZsGK5cuYKPPvrImrESERERWYXJfYgSEhKwevVqTJgwAXv27EGfPn2wa9cuXLhwAQ0aNLBiiERERETWZXIN0eXLl9GvXz8AQK9eveDh4YG3336byRARERE5PJMTojt37qBevXrStqenJxo3bmyVoIiIiIjqklnD7j/99FP4+PgAAEpKSrBmzRo0atTIqMxLL71kueiIiIiI6oDJq903adIEMpms6ovJZLh48aJFAqtLXO2eiIjI8Vjy+9vkGqJLly7V6o2IiIiI7JXFJmYkIiIiclRMiIiIiMjlMSEiIiIil8eEiIiIiFyeTROiVatWoW3btlAqlVAqlejatSt++OEH6bgQAvPmzUNwcDAUCgV69eqF06dPG12jsLAQU6dORaNGjeDt7Y2hQ4fijz/+qOtbISIiIgdWo4RIr9fjt99+Q1paGvbt22f0MkdISAiWLFmCI0eO4MiRI+jTpw+io6OlpOedd97BihUr8OGHH+Lw4cNQq9Xo378/8vPzpWtMnz4dycnJWLduHdLS0lBQUIDBgwdDp9PV5NaIiIjIBZk8D1GpQ4cOYcyYMbh8+TLKniqTyWqdiPj5+WHZsmWYMGECgoODMX36dLzyyisADLVBgYGBWLp0KSZOnIjc3Fw0btwYX3zxBUaNGgUAuHbtGkJDQ7F161ZERUWZ9J6ch4iIiMjxWPL72+waotjYWHTq1AmnTp3CzZs3kZOTI71u3rxZ40B0Oh3WrVsHjUaDrl27IiMjA1lZWRgwYIBUxsvLCz179sSBAwcAAOnp6SguLjYqExwcjPDwcKlMRQoLC5GXl2f0IiIiItdl1tIdAHD+/Hl8++23aN68uUUCOHnyJLp27Yo7d+7Ax8cHycnJaN26tZTQBAYGGpUPDAzE5cuXAQBZWVnw9PREw4YNy5XJysqq9D0XL16M+fPnWyR+IiIicnxm1xB17twZFy5csFgADz74II4fP45Dhw7hxRdfxLhx43DmzBnpeNnlQoQQ1S4hUl2ZOXPmIDc3V3pdvXq1djdBREREDs3sGqKpU6di5syZyMrKQps2beDh4WF0vG3btmZdz9PTU6pt6tSpEw4fPoz3339f6jeUlZWFoKAgqXx2drZUa6RWq1FUVIScnByjWqLs7Gx069at0vf08vKCl5eXWXESERGR8zI7IRoxYgQAYMKECdI+mUwm1crUtlO1EAKFhYVo2rQp1Go1UlJS0L59ewBAUVER9u7di6VLlwIAOnbsCA8PD6SkpGDkyJEAgMzMTJw6dQrvvPNOreIgIiIi12F2QpSRkWGxN587dy4GDRqE0NBQ5OfnY926ddizZw+2bdsGmUyG6dOnY9GiRWjRogVatGiBRYsWoX79+hgzZgwAQKVS4bnnnsPMmTPh7+8PPz8/zJo1C23atEG/fv0sFicRERE5N7MTorCwMIu9+fXr1/HMM88gMzMTKpUKbdu2xbZt29C/f38AwOzZs6HVajFp0iTk5OSgc+fO2LFjB3x9faVrrFy5EnK5HCNHjoRWq0Xfvn2xZs0auLu7WyxOIiIicm5mz0NU6syZM7hy5QqKioqM9g8dOtQigdUlzkNERETkeCz5/W12DdHFixcxfPhwnDx5Uuo7BNwdDcYZoomck1YL5OUBSiWgUNg6GiIiyzJ72P20adPQtGlTXL9+HfXr18fp06exb98+dOrUCXv27LFCiERkS2lpQEwM4OMDqNWGnzExwP79to6MiMhyzE6IDh48iAULFqBx48Zwc3ODm5sbIiIisHjxYrz00kvWiJGIbGTVKiAyEtiyBdDrDfv0esN2jx7A6tW2jY+IyFLMToh0Oh18fHwAAI0aNcK1a9cAGDpbnzt3zrLREZHNpKUBkycDQgAlJcbHSkoM+ydNYk0RETkHs/sQhYeH48SJE2jWrBk6d+6Md955B56envj444/RrFkza8RIRDawYgXg7l4+GbqXuzuwciXQvXvdxUVEZA1mJ0Svv/46NBoNAGDhwoUYPHgwevToAX9/f6xfv97iARJR3dNqgU2b7jaTVaakBEhONpRnR2sicmRmJ0RRUVHS782aNcOZM2dw8+ZNNGzYsNo1xojIMeTlVZ8MldLrDeWZEBGRIzO7D1FF/Pz8mAwRORGlEnAz8dPBzc1QnojIkZlUQxQTE4M1a9ZAqVQiJiamyrJJSUkWCYyIbEehAKKjDaPJqupDJJcbyrF2iIgcnUkJkUqlkmqAVCqVVQMiIvsQFwds3Fh1GZ0OmDGjTsIhIrKqGi/d4Uy4dAdRxVavNgytLzvaTC43JEPx8UBsrO3iIyLXZsnvb4v0ISIi5xQbC6SmGprFSvsUubkZtlNTmQwRkfMwqcmsffv2JneaPnr0aK0CIiL70r274cW1zIjImZmUEA0bNkz6/c6dO4iPj0fr1q3RtWtXAMChQ4dw+vRpTJo0ySpBEpHtKRRMhIjIeZmUEL355pvS788//zxeeuklvPXWW+XKXL161bLREREREdUBsztVq1QqHDlyBC1atDDaf/78eXTq1Am5ubkWDbAusFM1UfXYZEZE9samnaoVCgXS0tLK7U9LS0O9evVqFQwR2Z+0NCAmBvDxAdRqw8+YGC7qSkTOxeylO6ZPn44XX3wR6enp6NKlCwBDH6LPPvsMb7zxhsUDJCLbWbXKsOK9u/vdpTz0esOEjRs3ctg9ETmPGs1D9PXXX+P999/H2bNnAQCtWrXCtGnTMHLkSIsHWBfYZEZUXloaEBkJVPUJIZMZht9ztXsisgVLfn9zYkYwISKqSEyM6Ut3fPtt3cVFRFTKkt/fZjeZlSoqKkJ2djb0ZZbEvv/++2sVEBHZnlYLbNpU/Yr3JSVAcrKhPDtaE5EjMzshOn/+PCZMmIADBw4Y7RdCQCaTQafTWSw4IrKNvLzqk6FSer2hPBMiInJkZidE48ePh1wux3fffYegoCCTZ7AmIsehVBqW6DAlKXJzM5QnInJkZidEx48fR3p6Oh566CFrxENEdkChMPQNMrUPEWuHiMjRmT0PUevWrfHXX39ZIxYiqgFNkQay+TLI5sugKdJY7LpxcYYV7aui0wEzZljsLYmIbMbshGjp0qWYPXs29uzZgxs3biAvL8/oRUTOISLCMM+QTGaoCbqXXG7YHx/PIfdE5BzMbjLr168fAKBv375G+9mpmsj5xMYCbdoAK1caRpPp9YY+Q9HRhpohJkNE5CzMToh2795tjTiIyAz3No1piiv+HQC8Pb1r/V7duxteXMuMiJwZJ2YEJ2YkxyObb9roTvGmy//nTUROzCYTM544ccKkcm3btq1xMERERES2YHJC9Mgjj0Amk6GqCiX2ISKqGwVzCqTfNcUaBL4bCAC4Pus6vD1q30xmaWxuIyJ7Z3JClJGRYc04iMgM9/YNMhpqLyzTb8hS0tKAFSvuLgNS2iF75kx2yCYi+2JyQhQWFmbNOIjIyaxaBUyeDLi7353xWq83TPa4caNhyH5srE1DJCKSmD0PERFRddLSDMmQEOVnui4pMeyfNAnYv7/8udaaaJKIqCpMiIgckKZII71w74AzWZljNrJihaFmqCru7ob5jYiI7IHZ8xARke35LPapcH9p5+pSthh2r9Xe7TNUlZISw2SPWi07WhOR7TEhIiKLysurPhkqpdf/r7x73U00SURUESZERA7InofdK5WG0WSmJEVuboby9e24xouIXIPZfYiuX7+OZ555BsHBwZDL5XB3dzd6EZH1eXt6333dkwB5e3gbHbMFhcIwtL7sgrBlyeXA8OFsLiMi+2B2DdH48eNx5coV/POf/0RQUBBkMtOWECAi1xEXZxhaXxWdzrBALGDfNV5E5BrMTojS0tKQmpqKRx55xArhEJEziIgwzDM0aZJhNNm9Q+/lckMyFB9/d3LGymqzSmu8iIiszewms9DQ0CqX7yCiuuXt6Q3xpoB4U9hV8hAbC6SmGprP3P73SVM6U3VqKidlJCL7YnYN0XvvvYdXX30VH330EZo0aWKFkIjIWXTvbnhxLTMisncmJUQNGzY06iuk0WjwwAMPoH79+vDw8DAqe/PmTctGSERV0hRppHmJCuYU2FUtUSmFwvREqLTGi4ioLpmUEL333ntWDoOIyMAREjwicj4mJUTjxo0z6WJardasN1+8eDGSkpLw66+/QqFQoFu3bli6dCkefPBBqcz48ePx+eefG53XuXNnHDp0SNouLCzErFmz8J///AdarRZ9+/ZFfHw8QkJCzIqHiIiIXJPZnaonT55c4X6NRoNBgwaZda29e/di8uTJOHToEFJSUlBSUoIBAwZAozGenXbgwIHIzMyUXlu3bjU6Pn36dCQnJ2PdunVIS0tDQUEBBg8eDJ1OZ97NETmIe9crKzuzsz2sZUZE5GjM7lS9Y8cOvP7661i4cKG0T6PRYODAgWa/+bZt24y2ExMTERAQgPT0dERGRkr7vby8oFarK7xGbm4uEhIS8MUXX6Bfv34AgC+//BKhoaHYuXMnoqKizI6LyN7Z81pmNXFv8salO4jIFmqUEEVERMDf3x8zZsxAfn4+oqKiIJfL8cMPP9QqmNzcXACAn5+f0f49e/YgICAADRo0QM+ePfH2228jICAAAJCeno7i4mIMGDBAKh8cHIzw8HAcOHCgwoSosLAQhYWF0nZeXl6t4iai2nG2BI+IHI/ZCVHTpk2xfft29OrVC25ubli3bh28vLzw/fffw9u75v/3JoRAXFwcIiIiEB4eLu0fNGgQnnzySYSFhSEjIwP//Oc/0adPH6Snp8PLywtZWVnw9PREw4YNja4XGBiIrKysCt9r8eLFmD9/fo1jJbI1zuxMRGRZNVrcNTw8HN999x369euHzp0747vvvoOilpOLTJkyBSdOnEBaWprR/lGjRhm9b6dOnRAWFobvv/8eMTExlV5PCFHpsiJz5sxBXFyctJ2Xl4fQ0NBaxU9Ul+5tOjLqKyQcs1mJCR4R2ZpJCVH79u0rTC68vLxw7do1dC+dfx/A0aNHzQ5i6tSp2Lx5M/bt21ftyLCgoCCEhYXh/PnzAAC1Wo2ioiLk5OQY1RJlZ2ejW7duFV7Dy8sLXl5eZsdJRNbBpTuIyNZMSoiGDRtmlTcXQmDq1KlITk7Gnj170LRp02rPuXHjBq5evYqgoCAAQMeOHeHh4YGUlBSMHDkSAJCZmYlTp07hnXfesUrcRERE5FxMSojefPNNq7z55MmT8dVXX2HTpk3w9fWV+vyoVCooFAoUFBRg3rx5GDFiBIKCgnDp0iXMnTsXjRo1wvDhw6Wyzz33HGbOnAl/f3/4+flh1qxZaNOmjTTqjMjZGDWT3Vt5KzM+xtoVIiLTyIQNV2qtrI9PYmIixo8fD61Wi2HDhuHYsWO4desWgoKC0Lt3b7z11ltGfX7u3LmDl19+GV999ZXRxIym9gvKy8uDSqVCbm4ulEqlRe6NyJpk8yv+b6csjsoiImdmye9vsxMinU6HlStX4uuvv8aVK1dQVFRkdNwR1zJjQkSOxtESIi7uSkTWYMnvb7Nnqp4/fz5WrFiBkSNHIjc3F3FxcYiJiYGbmxvmzZtXq2CIyDQFcwqk18WXLkr7L7500eiYraWlATExgI8PoFYbfsbEAPv32zoyIiJjZidE//73v/HJJ59g1qxZkMvlGD16ND799FO88cYbRuuLEZFrW7UKiIwEtmwB9HrDPr3esN2jB7B6tW3jIyK6l9lNZt7e3jh79izuv/9+BAUF4fvvv0eHDh1w8eJFtG/fXppt2pGwyYwcjb03maWlGZKhqj5dZDIgNRW4Z9YOIiKz2LTJLCQkBJmZmQCA5s2bY8eOHQCAw4cPc24fIgIArFgBuLtXXcbdHVi5sm7iISKqjtkzVQ8fPhw//vgjOnfujGnTpmH06NFISEjAlStXMGPGDGvESERl2PPMzlotsGnT3WayypSUAMnJhvLsaE1EtmZ2QrRkyRLp97/97W8IDQ3F/v370bx5cwwdOtSiwRFRxex5Zue8vOqToVJ6vaE8EyIisjWzm8zK6ty5M+Li4tC5c2csWLDAEjERkQNTKgE3Ez9Z3NwM5YmIbK3WCVGprKwsriBPRFAogOhoQF5N/bNcDgwfztohIrIPNVrtnojsh7ent91MwFgqLg7YuLHqMjodwG6HRGQvLFZDRERUKiICiI83DK0vW1Mklxv2x8dzyD0R2Q8mRERkFbGxhnmGoqPv9ilyczNsp6YajhMR2QuTm8zi4uKqPP7nn3/WOhgiMp+mSAOfxT4ADMPxbT3K7F7duxteXMuMiOydyQnRsWPHqi0TGRlZq2CIyHyaIo3R7/aUEJVSKJgIEZF9Mzkh2r17tzXjICIiIrIZjjIjckBGtULFxr/fe8wea4uIiOyRSQnRkiVLMHXqVHh7V//h+tNPP+Gvv/7CE088UevgyD6w/4f9Ke0zVFazD5oZbdvbcHwiIntl0iizM2fOICwsDC+++CJ++OEHow7UJSUlOHHiBOLj49GtWzc89dRTXDHeSaSlATExgI8PoFYbfsbEAPv32zoyIiIiyzIpIVq7di127doFvV6PsWPHQq1Ww9PTE76+vvDy8kL79u3x2WefYfz48fj111/Ro0cPa8dNVrZqFRAZCWzZcnddKr3esN2jB7B6tW3jIyIisiSZEMKsOnUhBE6cOIFLly5Bq9WiUaNGeOSRR9CoUSNrxWh1eXl5UKlUyM3NZe0WDDVDkZFAVf8yZDLDXDKcWM827u0nlK3JlprKLr50EQHeAdIx9iEiImdmye9vsztVy2QytGvXDu3atavVG5P9WrECcHcHSkoqL+PuDqxcyYTIVowSHU0Vx4iIyCScqZqMaLXApk1VJ0OA4XhysqE8ERGRo2NCREby8u72GaqOXm8oT7bl7eFd4e9ERGQ6zkNERpRKw3pTpiRFbm6G8lT37u1DBBmMfuc8RERE5mNCREYUCsPim1u2VN1sJpcbynFeItuobB6iwHcDjbY5DxERkWlq3GR24cIFbN++Hdr/dSIxc7Aa2bG4OECnq7qMTgfMmFE38RAREVmb2TVEN27cwKhRo7Br1y7IZDKcP38ezZo1w/PPP48GDRpg+fLl1oiT6lBEBBAfD0yaVH60mVxuSIbi4znCzJYK5hRIv2uKNVLN0PVZ19mPiIioBsyuIZoxYwbkcjmuXLmC+vXrS/tHjRqFbdu2WTQ4sp3YWMM8Q9HRhr5CgOFndLRhf2ysbeNzdd6e3ndfZTpV33uMiIhMY3YN0Y4dO7B9+3aEhIQY7W/RogUuX75sscDI9rp3N7y4lhkRETk7sxMijUZjVDNU6q+//oKXl5dFgiL7olAwESIiIudmdpNZZGQk1q5dK23LZDLo9XosW7YMvXv3tmhwRFQ9b09viDcFxJuCzWRERDVkdg3RsmXL0KtXLxw5cgRFRUWYPXs2Tp8+jZs3b2I/l0EnIiIiB2R2DVHr1q1x4sQJPPbYY+jfvz80Gg1iYmJw7NgxPPDAA9aIkYiIiMiqzF7t3hlxtXsiIiLHY8nvb7NriBITE/HNN9+U2//NN9/g888/r1UwRERERLZgdkK0ZMkSNGrUqNz+gIAALFq0yCJBEREREdUlsxOiy5cvo2nTpuX2h4WF4cqVKxYJioiIiKgumZ0QBQQE4MSJE+X2//LLL/D397dIUERERER1yeyE6KmnnsJLL72E3bt3Q6fTQafTYdeuXZg2bRqeeuopa8RIREREZFVmz0O0cOFCXL58GX379oVcbjhdr9fj2WefZR8iIiIickg1Hnb/22+/4ZdffoFCoUCbNm0QFhZm6djqDIfdExEROR5Lfn+bXUNUqmXLlmjZsmWt3pyIiIjIHpiUEMXFxeGtt96Ct7c34uLiqiy7YsUKiwRGREREVFdMSoiOHTuG4uJiAMDRo0chk8kqLFfZfiIiIiJ7xqU7wD5EREREjshmS3eUlJRALpfj1KlTtXpTqpxWC1y/bvhJREREdcOshEgulyMsLAw6nc4ib7548WI8+uij8PX1RUBAAIYNG4Zz584ZlRFCYN68eQgODoZCoUCvXr1w+vRpozKFhYWYOnUqGjVqBG9vbwwdOhR//PGHRWKsK2lpQEwM4OMDqNWGnzExwP79to6MiIjI+Zk9MePrr7+OOXPm4ObNm7V+871792Ly5Mk4dOgQUlJSUFJSggEDBkCj0Uhl3nnnHaxYsQIffvghDh8+DLVajf79+yM/P18qM336dCQnJ2PdunVIS0tDQUEBBg8ebLHEzdpWrQIiI4EtWwC93rBPrzds9+gBrF5t2/iIiIicndl9iNq3b48LFy6guLgYYWFh8Pb2Njp+9OjRGgfz559/IiAgAHv37kVkZCSEEAgODsb06dPxyiuvADDUBgUGBmLp0qWYOHEicnNz0bhxY3zxxRcYNWoUAODatWsIDQ3F1q1bERUVVe372rIPUVqaIRmq6q8gkwGpqUD37nUXFxERkb2z6TxE0dHRVhtNlpubCwDw8/MDAGRkZCArKwsDBgyQynh5eaFnz544cOAAJk6ciPT0dBQXFxuVCQ4ORnh4OA4cOGBSQmRLK1YA7u5ASUnlZdzdgZUrmRARERFZi9kJ0bx586wQhqGvUFxcHCIiIhAeHg4AyMrKAgAEBgYalQ0MDMTly5elMp6enmjYsGG5MqXnl1VYWIjCwkJpOy8vz2L3YQ6tFti06W4zWWVKSoDkZEN5haJuYiMiInIlJvchun37NiZPnoz77rsPAQEBGDNmDP766y+LBTJlyhScOHEC//nPf8odK1sjJYSotpaqqjKLFy+GSqWSXqGhoTUPvBby8qpPhkrp9YbyREREZHkmJ0Rvvvkm1qxZgyeeeAJPPfUUUlJS8OKLL1okiKlTp2Lz5s3YvXs3QkJCpP1qtRoAytX0ZGdnS7VGarUaRUVFyMnJqbRMWXPmzEFubq70unr1qkXuw1xKJeBm4l/Azc1QnoiIiCzP5IQoKSkJCQkJ+Pjjj/HBBx/g+++/x8aNG2s1kksIgSlTpiApKQm7du1C06ZNjY43bdoUarUaKSkp0r6ioiLs3bsX3bp1AwB07NgRHh4eRmUyMzNx6tQpqUxZXl5eUCqVRi9bUCiA6GhAXk3DpVwODB/O5jIiIiJrMbkP0dWrV9GjRw9p+7HHHoNcLpdGdNXE5MmT8dVXX2HTpk3w9fWVaoJUKhUUCgVkMhmmT5+ORYsWoUWLFmjRogUWLVqE+vXrY8yYMVLZ5557DjNnzoS/vz/8/Pwwa9YstGnTBv369atRXHUpLg7YuLHqMjodMGNGnYRDRETkkkxOiHQ6HTw9PY1PlstRUtXwqGqsWrUKANCrVy+j/YmJiRg/fjwAYPbs2dBqtZg0aRJycnLQuXNn7NixA76+vlL5lStXQi6XY+TIkdBqtejbty/WrFkDd3f3GsdWVyIigPh4YNKk8qPN5HJDMhQfzxFmRERE1mTyPERubm4YNGgQvLy8pH1btmxBnz59jOYiSkpKsnyUVmYPa5nt328YWp+cbOhA7eZmaCabMYPJEBERUUVsMg/RuHHjyu17+umna/XmdFf37oaXVmsYTaZUss8QERFRXTE5IUpMTLRmHPQ/CgUTISIiorpm9lpmRERERM6GCRERERG5PCZERERE5PKYEJFT0GqB69cNP4mIiMzFhIgcWloaEBMD+PgAarXhZ0yMYRoDIiIiUzEhIoe1ahUQGQls2XJ3kVy93rDdowewerVt4yMiIsfBhIgcUloaMHkyIITx7N6AYVsIw+zfrCkiIiJTMCEih7RihWGpk6q4uxtm/yYiIqoOEyJyOFotsGlT+ZqhskpKDEuhsKM1ERFVhwkROZy8vLt9hqqj1xvKExERVYUJETkcpdKw+K0p3NwM5YmIiKrChIgcjkIBREcD8mpW4pPLgeHDuTYcERFVjwkROaS4OECnq7qMTgfMmFE38RARkWNjQkQOKSICiI8HZLLyNUVyuWF/fDzQvbtt4iMiIsfChIgcVmwskJpqaD4r7VPk5mbYTk01HCciIjJFNb0wiOxb9+6Gl1ZrGE2mVLLPEBERmY8JETkFhYKJEBER1RybzIiIiMjlMSEiIiIil8eEiOqMVgtcv26dpTSseW0iInJ+TIjI6tLSgJgYwMcHUKsNP2NiLLMSvTWvTUREroMJEVnVqlVAZCSwZcvd9cf0esN2jx7A6tX2eW0iInItMiGEsHUQtpaXlweVSoXc3FwoufCVxaSlGRKWqv6FyWSGOYPMnUDRmtcmIiLHYMnvb9YQkdWsWAG4u1ddxt0dWLnSvq5NRESuhzVEYA2RNWi1hv48pU1ZVXFzAwoKTJ9HyJrXJiIix8EaIrJ7eXmmJSyAoVxenn1cm4iIXBMTIrIKpfLu+mLVcXMzlLeHaxMRkWtiQmRFrjw3jkJhWGS17Er0ZcnlwPDh5jVplV67uqTIzc38axMRkWtiQmQFnBvHIC4O0OmqLqPTATNmmH/tfv2qbzbT64G+fc2/NhERuR4mRBbGuXHuiogA4uMNw9/L1hTJ5Yb98fE1Gxa/c6dpNUQ//mj+tYmIyPVwlBks10udc+NUbP9+w/D35GRDcljalDVjRs2eA0eZERERYNlRZtX08CBzlM6NU1JSeZnSuXFcKSHq3t3w0moNI76UytolKDUZZcaEiIiIqsKEyEK0WmDTpuq/qEtKDDUlWq3rfUkrFJa559JRZqbWEHGUGRERVYd9iCyEc+PUHWuOYCMiItfEhMhCODdO3bLmCDYiInI9TIgshLUWdcuaI9iIiMj1MCGyINZa1K3YWMOIvXsnaXRzM2ynphqOExERmYKdqi2otNZi0qTyo83kckMyxFoLy7L0CDYiInJNrCGyMNZa2IZCAQQGMhkiIqKaYQ2RFbDWgoiIyLEwIbIiS827Q0RERNbFJjMiIiJyeUyI7IxWC1y/bvhJREREdYMJkZ1ISwNiYgyLlqrVhp8xMYaFUYmIiMi6bJoQ7du3D0OGDEFwcDBkMhk2btxodHz8+PGQyWRGry5duhiVKSwsxNSpU9GoUSN4e3tj6NCh+OOPP+rwLmpv1SogMhLYsuXu8h96vWG7Rw9g9WrbxkdEROTsbJoQaTQatGvXDh9++GGlZQYOHIjMzEzptXXrVqPj06dPR3JyMtatW4e0tDQUFBRg8ODB0FU3Q6KdSEsDJk8GhDCetwgwbAthmNeINUVERETWY9NRZoMGDcKgQYOqLOPl5QW1Wl3hsdzcXCQkJOCLL75Av379AABffvklQkNDsXPnTkRFRVk8ZktbsaL8JI5lubsDK1dyQkciIiJrsfs+RHv27EFAQABatmyJF154AdnZ2dKx9PR0FBcXY8CAAdK+4OBghIeH48CBA5Ves7CwEHl5eUYvW9BqgU2bqk6GAMPx5GR2tCYiIrIWu06IBg0ahH//+9/YtWsXli9fjsOHD6NPnz4oLCwEAGRlZcHT0xMNGzY0Oi8wMBBZWVmVXnfx4sVQqVTSKzQ01Kr3UZm8vLt9hqqj1xvKExERkeXZ9cSMo0aNkn4PDw9Hp06dEBYWhu+//x4xMTGVnieEgEwmq/T4nDlzEBcXJ23n5eXZJClSKg3LepiSFLm5GcoTERGR5dl1DVFZQUFBCAsLw/nz5wEAarUaRUVFyMnJMSqXnZ2NwMDASq/j5eUFpVJp9LIFhcKwxpm8mrRULgeGD+es10RERNbiUAnRjRs3cPXqVQQFBQEAOnbsCA8PD6SkpEhlMjMzcerUKXTr1s1WYZolLg6obkCcTgfMmFE38RAREbkimzaZFRQU4MKFC9J2RkYGjh8/Dj8/P/j5+WHevHkYMWIEgoKCcOnSJcydOxeNGjXC8OHDAQAqlQrPPfccZs6cCX9/f/j5+WHWrFlo06aNNOrM3kVEAPHxhqH1ZUebyeWGZCg+niPMiIiIrMmmCdGRI0fQu3dvabu0X8+4ceOwatUqnDx5EmvXrsWtW7cQFBSE3r17Y/369fD19ZXOWblyJeRyOUaOHAmtVou+fftizZo1cHd3r/P7qanYWKBNG8PQ+uRkQ58iNzdDc9qMGUyGiIiIrE0mhBC2DsLW8vLyoFKpkJuba7P+RKW0WsNoMqWSfYaIiIiqYsnvb7seZeaKFAomQkRERHXNoTpVExEREVkDEyIiIiJyeUyIiIiIyOUxISIiIiKXx4SIiIiIXB4TIiIiInJ5TIiIiIjI5TEhIiIiIpfHhIiIiIhcHhMishqtFrh+3fCTiIjInjEhIotLSwNiYgAfH0CtNvyMiQH277d1ZERERBVjQkQWtWoVEBkJbNkC6PWGfXq9YbtHD2D1atvGR0REVBEmRC6iLpqv0tKAyZMBIYCSEuNjJSWG/ZMmsaaIiIjsDxMiJ1eXzVcrVgDu7lWXcXcHVq60/HsTERHVhkwIIWwdhK3l5eVBpVIhNzcXSqXS1uFYzKpVhhobd3fjGhu5HNDpgPh4IDbWMu+l1RqSrdJmsqq4uQEFBYBCYZn3JiIi12TJ72/WEDmpum6+ysszLRkCDOXy8izzvkRERJbAhMhJ1XXzlVJpqPkxhZuboTwREZG9YELkhLRaYNOm8jVDZZWUAMnJlulorVAA0dGG5riqyOXA8OFsLiMiIvvChMgJ2ar5Ki7O0DepKjodMGOGZd6PiIjIUpgQOSFbNV9FRBg6astk5WuK5HLD/vh4oHt3y7wfERGRpTAhckK2bL6KjQVSUw3vX5qUubkZtlNTLTeqjYiIyJI47B7OOew+Lc0wY3RVf12ZzJCkWKvGRqs1NMcplewzRERElsdh91Qte2i+UiiAwEAmQ0REZP+YEDkxNl8RERGZpppeJuTounc3vNh8RUREVDkmRC5CoWAiREREVBk2mREREZHLY0JERERELo8JEREREbk8JkRERETk8pgQERERkctjQkREREQujwkRERERuTwmREREROTyODEjgNL1bfPy8mwcCREREZmq9HvbEuvUMyECkJ+fDwAIDQ21cSRERERkrvz8fKhUqlpdQyYskVY5OL1ej2vXrsHX1xcymcymseTl5SE0NBRXr16FUqm0aSy24urPwNXvH+AzcPX7B/gMXP3+AdOegRAC+fn5CA4Ohptb7XoBsYYIgJubG0JCQmwdhhGlUumy/xGUcvVn4Or3D/AZuPr9A3wGrn7/QPXPoLY1Q6XYqZqIiIhcHhMiIiIicnlMiOyMl5cX3nzzTXh5edk6FJtx9Wfg6vcP8Bm4+v0DfAaufv9A3T8DdqomIiIil8caIiIiInJ5TIiIiIjI5TEhIiIiIpfHhIiIiIhcHhMiK9i3bx+GDBmC4OBgyGQybNy4UTpWXFyMV155BW3atIG3tzeCg4Px7LPP4tq1a0bXKCwsxNSpU9GoUSN4e3tj6NCh+OOPP4zK5OTk4JlnnoFKpYJKpcIzzzyDW7du1cEdVq+qZ1DWxIkTIZPJ8N577xntd+RnYMr9nz17FkOHDoVKpYKvry+6dOmCK1euSMcd+f6B6p9BQUEBpkyZgpCQECgUCrRq1QqrVq0yKuOoz2Dx4sV49NFH4evri4CAAAwbNgznzp0zKiOEwLx58xAcHAyFQoFevXrh9OnTRmUc9f6B6p+Bs38WmvJv4F7O+Dlo6jOwm89CQRa3detW8dprr4kNGzYIACI5OVk6duvWLdGvXz+xfv168euvv4qDBw+Kzp07i44dOxpdIzY2Vtx3330iJSVFHD16VPTu3Vu0a9dOlJSUSGUGDhwowsPDxYEDB8SBAwdEeHi4GDx4cF3dZpWqegb3Sk5OFu3atRPBwcFi5cqVRscc+RlUd/8XLlwQfn5+4uWXXxZHjx4Vv//+u/juu+/E9evXpTKOfP9CVP8Mnn/+efHAAw+I3bt3i4yMDPHRRx8Jd3d3sXHjRqmMoz6DqKgokZiYKE6dOiWOHz8unnjiCXH//feLgoICqcySJUuEr6+v2LBhgzh58qQYNWqUCAoKEnl5eVIZR71/Iap/Bs7+WWjKv4FSzvo5aMozsKfPQiZEVlZVMlDq559/FgDE5cuXhRCGDwoPDw+xbt06qcx///tf4ebmJrZt2yaEEOLMmTMCgDh06JBU5uDBgwKA+PXXXy1/I7VQ2TP4448/xH333SdOnTolwsLCjD4InOkZVHT/o0aNEk8//XSl5zjT/QtR8TN4+OGHxYIFC4z2dejQQbz++utCCOd6BtnZ2QKA2Lt3rxBCCL1eL9RqtViyZIlU5s6dO0KlUonVq1cLIZzr/oUo/wwq4syfhZXdv6t8DgpR8TOwp89CNpnZgdzcXMhkMjRo0AAAkJ6ejuLiYgwYMEAqExwcjPDwcBw4cAAAcPDgQahUKnTu3Fkq06VLF6hUKqmMPdPr9XjmmWfw8ssv4+GHHy533JmfgV6vx/fff4+WLVsiKioKAQEB6Ny5s1GTkjPff6mIiAhs3rwZ//3vfyGEwO7du/Hbb78hKioKgHM9g9zcXACAn58fACAjIwNZWVlG9+bl5YWePXtKcTvT/QPln0FlZZz1s7Ci+3e1z8Gyz8DePguZENnYnTt38Oqrr2LMmDHS4nVZWVnw9PREw4YNjcoGBgYiKytLKhMQEFDuegEBAVIZe7Z06VLI5XK89NJLFR535meQnZ2NgoICLFmyBAMHDsSOHTswfPhwxMTEYO/evQCc+/5LffDBB2jdujVCQkLg6emJgQMHIj4+HhEREQCc5xkIIRAXF4eIiAiEh4cDgBRbYGCgUdmy9+YM9w9U/AzKcubPwsru35U+Byt6Bvb2WcjV7m2ouLgYTz31FPR6PeLj46stL4SATCaTtu/9vbIy9ig9PR3vv/8+jh49anaszvAM9Ho9ACA6OhozZswAADzyyCM4cOAAVq9ejZ49e1Z6rjPcf6kPPvgAhw4dwubNmxEWFoZ9+/Zh0qRJCAoKQr9+/So9z9GewZQpU3DixAmkpaWVO1Y2RlPidrT7B6p+BoDzfxZWdP+u9jlY0TOwt89C1hDZSHFxMUaOHImMjAykpKRI/0cEAGq1GkVFRcjJyTE6Jzs7W/o/SrVajevXr5e77p9//lnu/zrtTWpqKrKzs3H//fdDLpdDLpfj8uXLmDlzJpo0aQLAuZ9Bo0aNIJfL0bp1a6P9rVq1kkZWOPP9A4BWq8XcuXOxYsUKDBkyBG3btsWUKVMwatQovPvuuwCc4xlMnToVmzdvxu7duxESEiLtV6vVAFDu/17L3puj3z9Q+TMo5eyfhZXdvyt9Dlb2DOzus9Dk3kZUI6igM2lRUZEYNmyYePjhh0V2dna5c0o7ka1fv17ad+3atQo7kf30009SmUOHDtllR7qyz+Cvv/4SJ0+eNHoFBweLV155RYrdmZ5BRf8GunbtWq4j4bBhw8To0aOFEM51/0KUfwa5ubkCgNi6datRuX/84x+if//+QgjHfgZ6vV5MnjxZBAcHi99++63C42q1WixdulTaV1hYWGGnake8fyGqfwZCOPdnYXX37wqfg6b8G7Cnz0ImRFaQn58vjh07Jo4dOyYAiBUrVohjx46Jy5cvi+LiYjF06FAREhIijh8/LjIzM6VXYWGhdI3Y2FgREhIidu7cKY4ePSr69OlT4TDDtm3bioMHD4qDBw+KNm3a2MVQSyGqfgYVKTu6QgjHfgbV3X9SUpLw8PAQH3/8sTh//rz4v//7P+Hu7i5SU1Olazjy/QtR/TPo2bOnePjhh8Xu3bvFxYsXRWJioqhXr56Ij4+XruGoz+DFF18UKpVK7Nmzx+i/8du3b0tllixZIlQqlUhKShInT54Uo0ePrnDYvSPevxDVPwNn/yw05d9AWc72OWjKM7Cnz0ImRFawe/duAaDca9y4cSIjI6PCYwDE7t27pWtotVoxZcoU4efnJxQKhRg8eLC4cuWK0fvcuHFDjB07Vvj6+gpfX18xduxYkZOTU7c3W4mqnkFFKvogcORnYMr9JyQkiObNm4t69eqJdu3aGc2/I4Rj378Q1T+DzMxMMX78eBEcHCzq1asnHnzwQbF8+XKh1+ulazjqM6jsv/HExESpjF6vF2+++aZQq9XCy8tLREZGipMnTxpdx1HvX4jqn4Gzfxaa8m+gLGf7HDT1GdjLZ6Hsf0ETERERuSx2qiYiIiKXx4SIiIiIXB4TIiIiInJ5TIiIiIjI5TEhIiIiIpfHhIiIiIhcHhMiIiIicnlMiIioWk2aNMF7771n6zCIiKyGCRGRA5HJZFW+xo8fX+35GzdutHhcGo0Gr7zyCpo1a4Z69eqhcePG6NWrF7777juLv1dduXTpUoXP+Omnn7bYe5j79/jHP/4Bd3d3rFu3zmIxEJGB3NYBEJHpMjMzpd/Xr1+PN954A+fOnZP2KRQKW4SF2NhY/Pzzz/jwww/RunVr3LhxAwcOHMCNGzdsEs+9ioqK4OnpWePzd+7ciYcffljattUzvn37NtavX4+XX34ZCQkJeOqpp6osX9v7JnI55q9OQkT2IDExUahUKqN98fHxolmzZsLDw0O0bNlSrF27VjoWFhZmtJ5QWFiYEEKICxcuiKFDh4qAgADh7e0tOnXqJFJSUoyuW9EaS/dSqVRizZo1VcZ7/fp1MXjwYFGvXj3RpEkT8eWXXxpdt3Rtq2PHjknn5OTkGK1tVVJSIiZMmCCaNGki6tWrJ1q2bCnee+89o/cZN26ciI6OFosWLRJBQUHSff7xxx9i5MiRokGDBsLPz08MHTpUZGRkVBpvRfHc69atW+KFF14QjRs3Fr6+vqJ3797i+PHjRmU2b94sOnToILy8vETTpk3FvHnzRHFxsRCi8r9HZdasWSO6dOkibt26JRQKRbnYa3rfP//8s+jXr5/w9/cXSqVSREZGivT09CpjIXJGbDIjchLJycmYNm0aZs6ciVOnTmHixIn4+9//jt27dwMADh8+DABITExEZmamtF1QUIDHH38cO3fuxLFjxxAVFYUhQ4bgypUrJr+3Wq3G1q1bkZ+fX2mZ8ePH49KlS9i1axe+/fZbxMfHIzs726x71Ov1CAkJwddff40zZ87gjTfewNy5c/H1118blfvxxx9x9uxZpKSk4LvvvsPt27fRu3dv+Pj4YN++fUhLS4OPjw8GDhyIoqIis2IAACEEnnjiCWRlZWHr1q1IT09Hhw4d0LdvX9y8eRMAsH37djz99NN46aWXcObMGXz00UdYs2YN3n77bQCV/z0qk5CQgKeffhoqlQqPP/44EhMTy5WpyX3n5+dj3LhxSE1NxaFDh9CiRQs8/vjjVf4tiZySrTMyIqqZsjVE3bp1Ey+88IJRmSeffFI8/vjj0jYAkZycXO21W7duLf7v//5P2q6uhmjv3r0iJCREeHh4iE6dOonp06eLtLQ06fi5c+cEAHHo0CFp39mzZwUAs2qIKjJp0iQxYsQIaXvcuHEiMDBQFBYWSvsSEhLEgw8+KPR6vbSvsLBQKBQKsX379gqvWxqPQqEQ3t7e0uvo0aPixx9/FEqlUty5c8fonAceeEB89NFHQgghevToIRYtWmR0/IsvvhBBQUHStql/j99++014eHiIP//8UwghRHJysggNDRU6nc7i911SUiJ8fX3Fli1bqo2LyJmwhojISZw9exbdu3c32te9e3ecPXu2yvM0Gg1mz56N1q1bo0GDBvDx8cGvv/5qVg1RZGQkLl68iB9//BEjRozA6dOn0aNHD7z11ltSbHK5HJ06dZLOeeihh9CgQQPTb/B/Vq9ejU6dOqFx48bw8fHBJ598Ui7WNm3aGPWfSU9Px4ULF+Dr6wsfHx/4+PjAz88Pd+7cwe+//17l+61fvx7Hjx+XXq1bt0Z6ejoKCgrg7+8vXc/HxwcZGRnS9dLT07FgwQKj4y+88AIyMzNx+/Zts+45ISEBUVFRaNSoEQDg8ccfh0ajwc6dO2t939nZ2YiNjUXLli2hUqmgUqlQUFBg1t+fyBmwUzWRE5HJZEbbQohy+8p6+eWXsX37drz77rto3rw5FAoF/va3v5ndlOTh4YEePXqgR48eePXVV7Fw4UIsWLAAr7zyCoQQFcZ3Lzc3NynmUsXFxUZlvv76a8yYMQPLly9H165d4evri2XLluGnn34yKuft7W20rdfr0bFjR/z73/8u976NGzeu8r5CQ0PRvHnzctcLCgrCnj17ypUvTfL0ej3mz5+PmJiYcmXq1atX5XveS6fTYe3atcjKyoJcLjfan5CQgAEDBkj7anLf48ePx59//on33nsPYWFh8PLyQteuXWvUlEjkyJgQETmJVq1aIS0tDc8++6y078CBA2jVqpW07eHhAZ1OZ3Reamoqxo8fj+HDhwMw9Cm6dOlSreNp3bo1SkpKcOfOHbRq1QolJSU4cuQIHnvsMQDAuXPncOvWLal86Rd0ZmYm2rdvDwA4fvx4uVi7deuGSZMmSfuqq+EBgA4dOmD9+vUICAiAUqms5Z0ZrleaoDRp0qTSMufOnSuXTN2ror9HWaV9s44dOwZ3d3dp/6+//oqxY8fixo0b8Pf3rzSG6u47NTUV8fHxePzxxwEAV69exV9//VVlTETOiE1mRE7i5Zdfxpo1a7B69WqcP38eK1asQFJSEmbNmiWVadKkCX788UdkZWUhJycHANC8eXMkJSXh+PHj+OWXXzBmzBjo9Xqz3rtXr1746KOPkJ6ejkuXLmHr1q2YO3cuevfuDaVSiQcffBADBw7ECy+8gJ9++gnp6el4/vnnjYawKxQKdOnSBUuWLMGZM2ewb98+vP7660bv07x5cxw5cgTbt2/Hb7/9hn/+85/VdkYGgLFjx6JRo0aIjo5GamoqMjIysHfvXkybNg1//PGHWfcKAP369UPXrl0xbNgwbN++HZcuXcKBAwfw+uuv48iRIwCAN954A2vXrsW8efNw+vRpnD17FuvXrze6p4r+HmUlJCTgiSeeQLt27RAeHi69RowYgcaNG+PLL7+s1X03b94cX3zxBc6ePYuffvoJY8eOtdnUAkS2xISIyEkMGzYM77//PpYtW4aHH34YH330ERITE9GrVy+pzPLly5GSkoLQ0FCpFmblypVo2LAhunXrhiFDhiAqKgodOnQw672joqLw+eefY8CAAWjVqhWmTp2KqKgoo9FfiYmJCA0NRc+ePRETE4N//OMfCAgIMLrOZ599huLiYnTq1AnTpk3DwoULjY7HxsYiJiYGo0aNQufOnXHjxg2j2qLK1K9fH/v27cP999+PmJgYtGrVChMmTIBWq61RjZFMJsPWrVsRGRmJCRMmoGXLlnjqqadw6dIlBAYGSs/ku+++Q0pKCh599FF06dIFK1asQFhYmHSdiv4e97p+/Tq+//57jBgxosIYYmJikJCQUKv7/uyzz5CTk4P27dvjmWeewUsvvVTu70LkCmTi3gZ7IqI61KRJE0yfPh3Tp0+3dShE5OJYQ0REREQujwkRERERuTw2mREREZHLYw0RERERuTwmREREROTymBARERGRy2NCRERERC6PCRERERG5PCZERERE5PKYEBEREZHLY0JERERELo8JEREREbm8/wcr8EUQdNEy+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df8,\"Rajaji Nagar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f49c55a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>size_bhk</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>Chikkabanavar</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3252.032520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5232</th>\n",
       "      <td>Nagasandra</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6428.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6702</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>3</td>\n",
       "      <td>6423.034330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8405</th>\n",
       "      <td>other</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>6</td>\n",
       "      <td>8819.897689</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "           location  total_sqft  bath   price  size_bhk  price_per_sqft\n",
       "1637  Chikkabanavar      2460.0   7.0    80.0         4     3252.032520\n",
       "5232     Nagasandra      7000.0   8.0   450.0         4     6428.571429\n",
       "6702    Thanisandra      1806.0   6.0   116.0         3     6423.034330\n",
       "8405          other     11338.0   9.0  1000.0         6     8819.897689"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8[df8.bath>df8.size_bhk+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ae85c79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7264, 6)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8[df8.bath<df8.size_bhk+2]\n",
    "df9.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6a7217a2",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Devarachikkanahalli</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "               location  total_sqft  bath  price  size_bhk\n",
       "1   Devarachikkanahalli      1250.0   2.0   40.0         2\n",
       "2   Devarachikkanahalli      1200.0   2.0   83.0         2\n",
       "3   Devarachikkanahalli      1170.0   2.0   40.0         2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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    }
   ],
   "source": [
    "df10 = df9.drop('price_per_sqft',axis='columns')\n",
    "df10.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "53d1f1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies=pd.get_dummies(df10.location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "41250f80",
   "metadata": {},
   "outputs": [
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       "               location  total_sqft  bath  price  size_bhk  \\\n",
       "1   Devarachikkanahalli      1250.0   2.0   40.0         2   \n",
       "2   Devarachikkanahalli      1200.0   2.0   83.0         2   \n",
       "3   Devarachikkanahalli      1170.0   2.0   40.0         2   \n",
       "4   Devarachikkanahalli      1425.0   2.0   65.0         3   \n",
       "5   Devarachikkanahalli       947.0   2.0   43.0         2   \n",
       "\n",
       "    Devarachikkanahalli  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
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       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  ...  Vishveshwarya Layout  \\\n",
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       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
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       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "5                   0            0           0               0          0   \n",
       "\n",
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     },
     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "df11=pd.concat([df10,dummies],axis=\"columns\")\n",
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "35aef913",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "               location  total_sqft  bath  price  size_bhk  \\\n",
       "1   Devarachikkanahalli      1250.0   2.0   40.0         2   \n",
       "2   Devarachikkanahalli      1200.0   2.0   83.0         2   \n",
       "3   Devarachikkanahalli      1170.0   2.0   40.0         2   \n",
       "4   Devarachikkanahalli      1425.0   2.0   65.0         3   \n",
       "5   Devarachikkanahalli       947.0   2.0   43.0         2   \n",
       "\n",
       "    Devarachikkanahalli  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "1                     1                    0                   0   \n",
       "2                     1                    0                   0   \n",
       "3                     1                    0                   0   \n",
       "4                     1                    0                   0   \n",
       "5                     1                    0                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  ...  Vijayanagar  \\\n",
       "1                          0                     0  ...            0   \n",
       "2                          0                     0  ...            0   \n",
       "3                          0                     0  ...            0   \n",
       "4                          0                     0  ...            0   \n",
       "5                          0                     0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\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",
       "5                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \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",
       "5               0          0                   0            0             0  \n",
       "\n",
       "[5 rows x 246 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.drop(\"other\",axis=\"columns\",inplace=True)\n",
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d77ff3de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>size_bhk</th>\n",
       "      <th>Devarachikkanahalli</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>...</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>1</th>\n",
       "      <td>1250.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  price  size_bhk   Devarachikkanahalli  \\\n",
       "1      1250.0   2.0   40.0         2                     1   \n",
       "2      1200.0   2.0   83.0         2                     1   \n",
       "3      1170.0   2.0   40.0         2                     1   \n",
       "\n",
       "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "1                    0                   0                          0   \n",
       "2                    0                   0                          0   \n",
       "3                    0                   0                          0   \n",
       "\n",
       "   2nd Stage Nagarbhavi  5th Block Hbr Layout  ...  Vijayanagar  \\\n",
       "1                     0                     0  ...            0   \n",
       "2                     0                     0  ...            0   \n",
       "3                     0                     0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "1               0          0                   0            0             0  \n",
       "2               0          0                   0            0             0  \n",
       "3               0          0                   0            0             0  \n",
       "\n",
       "[3 rows x 245 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.drop(\"location\",axis=\"columns\",inplace=True)\n",
    "df11.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e4988baf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>size_bhk</th>\n",
       "      <th>Devarachikkanahalli</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>1</th>\n",
       "      <td>1250.0</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200.0</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  size_bhk   Devarachikkanahalli  1st Block Jayanagar  \\\n",
       "1      1250.0   2.0         2                     1                    0   \n",
       "2      1200.0   2.0         2                     1                    0   \n",
       "3      1170.0   2.0         2                     1                    0   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "3                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  5th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "1                     0                   0  ...            0   \n",
       "2                     0                   0  ...            0   \n",
       "3                     0                   0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
       "1                     0                   0            0           0   \n",
       "2                     0                   0            0           0   \n",
       "3                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "1               0          0                   0            0             0  \n",
       "2               0          0                   0            0             0  \n",
       "3               0          0                   0            0             0  \n",
       "\n",
       "[3 rows x 244 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df11.price\n",
    "x=df11.drop(\"price\",axis=\"columns\")\n",
    "x.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "06944b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    40.0\n",
       "2    83.0\n",
       "3    40.0\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "00ba9a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9d5b095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.881695034702102"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2)\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,y_train)\n",
    "lr.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d6d0ff35",
   "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_parms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.845404</td>\n",
       "      <td>{'positive': False}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.709512</td>\n",
       "      <td>{'alpha': 1, 'selection': 'cyclic'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score                           best_parms\n",
       "0  linear_regression    0.845404                  {'positive': False}\n",
       "1              lasso    0.709512  {'alpha': 1, 'selection': 'cyclic'}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "def find_best_model(x,y):\n",
    "    algo={\n",
    "        'linear_regression':{\n",
    "            'model':LinearRegression(),\n",
    "            'parms':{\n",
    "                'positive':[True,False]\n",
    "            }\n",
    "        },\n",
    "        'lasso':{\n",
    "            'model':Lasso(),\n",
    "            'parms':{\n",
    "                'alpha':[1,2],\n",
    "                'selection':['random','cyclic']\n",
    "            }\n",
    "        }\n",
    "        \n",
    "    }\n",
    "    scores=[]\n",
    "    cv=ShuffleSplit(n_splits=5,test_size=0.2,random_state=0)\n",
    "    for algo_name,config in algo.items():\n",
    "        gs=GridSearchCV(config['model'],config['parms'],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_parms':gs.best_params_\n",
    "        })\n",
    "    return pd.DataFrame(scores,columns=['model','best_score','best_parms'])\n",
    "find_best_model(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dc198f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_price(location,sqft,bath,bhk):\n",
    "    loc_index=np.where(x.columns==location)[0]\n",
    "    X=np.zeros(len(x.columns))\n",
    "    X[0]=sqft\n",
    "    X[1]=bath\n",
    "    X[2]=bhk\n",
    "    if loc_index>=0:\n",
    "        X[loc_index]=1\n",
    "    return lr.predict([X])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a51e7e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>size_bhk</th>\n",
       "      <th>Devarachikkanahalli</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>1</th>\n",
       "      <td>1250.0</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200.0</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1170.0</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",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1425.0</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>5</th>\n",
       "      <td>947.0</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 × 244 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  size_bhk   Devarachikkanahalli  1st Block Jayanagar  \\\n",
       "1      1250.0   2.0         2                     1                    0   \n",
       "2      1200.0   2.0         2                     1                    0   \n",
       "3      1170.0   2.0         2                     1                    0   \n",
       "4      1425.0   2.0         3                     1                    0   \n",
       "5       947.0   2.0         2                     1                    0   \n",
       "\n",
       "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
       "1                   0                          0                     0   \n",
       "2                   0                          0                     0   \n",
       "3                   0                          0                     0   \n",
       "4                   0                          0                     0   \n",
       "5                   0                          0                     0   \n",
       "\n",
       "   5th Block Hbr Layout  5th Phase JP Nagar  ...  Vijayanagar  \\\n",
       "1                     0                   0  ...            0   \n",
       "2                     0                   0  ...            0   \n",
       "3                     0                   0  ...            0   \n",
       "4                     0                   0  ...            0   \n",
       "5                     0                   0  ...            0   \n",
       "\n",
       "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\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",
       "5                     0                   0            0           0   \n",
       "\n",
       "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \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",
       "5               0          0                   0            0             0  \n",
       "\n",
       "[5 rows x 244 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7c4d1574",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Krishna Kesharwani\\AppData\\Local\\Temp\\ipykernel_10484\\554128759.py:7: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if loc_index>=0:\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.29634838342744"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price(\"Devarachikkanahalli\",1000,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "84417d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "158.06838509334858"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price(\"1st Block Jayanagar\",1000,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1dc49f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "159.89230074114568"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_price(\"1st Block Jayanagar\",1000,3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "30ed95a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='e_state_prediction.sav'\n",
    "pickle.dump(lr,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f3c9f483",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_model=pickle.load(open('e_state_prediction.sav','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "50db3ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def price_predict(location,sqft,bath,bhk):\n",
    "    loc_index=np.where(x.columns==location)[0]\n",
    "    X=np.zeros(len(x.columns))\n",
    "    X[0]=sqft\n",
    "    X[1]=bath\n",
    "    X[2]=bhk\n",
    "    if loc_index>=0:\n",
    "        X[loc_index]=1\n",
    "    return load_model.predict([X])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e1fe08f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "159.89230074114568"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_predict(\"1st Block Jayanagar\",1000,3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cd7ffdd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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