Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1af025a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b41a0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Bengaluru_House_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "284e2ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>ArsiaEx</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Mahla T</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>18-Jun</td>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>SollyCl</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>488.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13320 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area_type   availability                  location  \\\n",
       "0      Super built-up  Area         19-Dec  Electronic City Phase II   \n",
       "1                Plot  Area  Ready To Move          Chikka Tirupathi   \n",
       "2            Built-up  Area  Ready To Move               Uttarahalli   \n",
       "3      Super built-up  Area  Ready To Move        Lingadheeranahalli   \n",
       "4      Super built-up  Area  Ready To Move                  Kothanur   \n",
       "...                     ...            ...                       ...   \n",
       "13315        Built-up  Area  Ready To Move                Whitefield   \n",
       "13316  Super built-up  Area  Ready To Move             Richards Town   \n",
       "13317        Built-up  Area  Ready To Move     Raja Rajeshwari Nagar   \n",
       "13318  Super built-up  Area         18-Jun           Padmanabhanagar   \n",
       "13319  Super built-up  Area  Ready To Move              Doddathoguru   \n",
       "\n",
       "            size  society total_sqft  bath  balcony   price  \n",
       "0          2 BHK  Coomee        1056   2.0      1.0   39.07  \n",
       "1      4 Bedroom  Theanmp       2600   5.0      3.0  120.00  \n",
       "2          3 BHK      NaN       1440   2.0      3.0   62.00  \n",
       "3          3 BHK  Soiewre       1521   3.0      1.0   95.00  \n",
       "4          2 BHK      NaN       1200   2.0      1.0   51.00  \n",
       "...          ...      ...        ...   ...      ...     ...  \n",
       "13315  5 Bedroom  ArsiaEx       3453   4.0      0.0  231.00  \n",
       "13316      4 BHK      NaN       3600   5.0      NaN  400.00  \n",
       "13317      2 BHK  Mahla T       1141   2.0      1.0   60.00  \n",
       "13318      4 BHK  SollyCl       4689   4.0      1.0  488.00  \n",
       "13319      1 BHK      NaN        550   1.0      1.0   17.00  \n",
       "\n",
       "[13320 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28542436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area_type\n",
       "Built-up  Area          2418\n",
       "Carpet  Area              87\n",
       "Plot  Area              2025\n",
       "Super built-up  Area    8790\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('area_type')['area_type'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a638be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Super built-up  Area    8790\n",
       "Built-up  Area          2418\n",
       "Plot  Area              2025\n",
       "Carpet  Area              87\n",
       "Name: area_type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['area_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef8eed26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area_type', 'availability', 'location', 'size', 'society',\n",
       "       'total_sqft', 'bath', 'balcony', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03dcd096",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.drop(['area_type','society','balcony','availability'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59b39f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size total_sqft  bath   price\n",
       "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
       "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
       "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
       "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
       "4                  Kothanur      2 BHK       1200   2.0   51.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "659b9e47",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b107b370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location      0\n",
       "size          0\n",
       "total_sqft    0\n",
       "bath          0\n",
       "price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.dropna()\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9df5f147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f627b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
       "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
       "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
       "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
       "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
       "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2c87405",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_14052\\878257637.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['BHK'] = df2['size'].apply(lambda x:str(x).split('\\n')[0][0])\n"
     ]
    }
   ],
   "source": [
    "df2['BHK'] = df2['size'].apply(lambda x:str(x).split('\\n')[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90156dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['BHK'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "953eef52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>231.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>488.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13246 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location       size total_sqft  bath   price BHK\n",
       "0      Electronic City Phase II      2 BHK       1056   2.0   39.07   2\n",
       "1              Chikka Tirupathi  4 Bedroom       2600   5.0  120.00   4\n",
       "2                   Uttarahalli      3 BHK       1440   2.0   62.00   3\n",
       "3            Lingadheeranahalli      3 BHK       1521   3.0   95.00   3\n",
       "4                      Kothanur      2 BHK       1200   2.0   51.00   2\n",
       "...                         ...        ...        ...   ...     ...  ..\n",
       "13315                Whitefield  5 Bedroom       3453   4.0  231.00   5\n",
       "13316             Richards Town      4 BHK       3600   5.0  400.00   4\n",
       "13317     Raja Rajeshwari Nagar      2 BHK       1141   2.0   60.00   2\n",
       "13318           Padmanabhanagar      4 BHK       4689   4.0  488.00   4\n",
       "13319              Doddathoguru      1 BHK        550   1.0   17.00   1\n",
       "\n",
       "[13246 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41cd04fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_14052\\812716007.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['BHK'] = df2['BHK'].astype('int64')\n"
     ]
    }
   ],
   "source": [
    "df2['BHK'] = df2['BHK'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a9e66fc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>231.00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>488.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13246 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location       size total_sqft  bath   price  BHK\n",
       "0      Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
       "1              Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
       "2                   Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
       "3            Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
       "4                      Kothanur      2 BHK       1200   2.0   51.00    2\n",
       "...                         ...        ...        ...   ...     ...  ...\n",
       "13315                Whitefield  5 Bedroom       3453   4.0  231.00    5\n",
       "13316             Richards Town      4 BHK       3600   5.0  400.00    4\n",
       "13317     Raja Rajeshwari Nagar      2 BHK       1141   2.0   60.00    2\n",
       "13318           Padmanabhanagar      4 BHK       4689   4.0  488.00    4\n",
       "13319              Doddathoguru      1 BHK        550   1.0   17.00    1\n",
       "\n",
       "[13246 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d1d1988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['BHK'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83b36be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 3, 6, 1, 8, 7, 5, 9], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['BHK'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d2ecb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['total_sqft'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a37536f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_float(x):\n",
    "    try:\n",
    "        float(x)\n",
    "    except:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "187359b9",
   "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>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2100 - 2850</td>\n",
       "      <td>4.0</td>\n",
       "      <td>186.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hebbal</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3067 - 8156</td>\n",
       "      <td>4.0</td>\n",
       "      <td>477.000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8th Phase JP Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1042 - 1105</td>\n",
       "      <td>2.0</td>\n",
       "      <td>54.005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sarjapur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1145 - 1340</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.490</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>KR Puram</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1015 - 1540</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.800</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>850 - 1060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>Talaghattapura</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1804 - 2273</td>\n",
       "      <td>3.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13059</th>\n",
       "      <td>Harlur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200 - 1470</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.760</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13265</th>\n",
       "      <td>Hoodi</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1133 - 1384</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.135</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13299</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2830 - 2882</td>\n",
       "      <td>5.0</td>\n",
       "      <td>154.500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 location   size   total_sqft  bath    price  BHK\n",
       "30              Yelahanka  4 BHK  2100 - 2850   4.0  186.000    4\n",
       "122                Hebbal  4 BHK  3067 - 8156   4.0  477.000    4\n",
       "137    8th Phase JP Nagar  2 BHK  1042 - 1105   2.0   54.005    2\n",
       "165              Sarjapur  2 BHK  1145 - 1340   2.0   43.490    2\n",
       "188              KR Puram  2 BHK  1015 - 1540   2.0   56.800    2\n",
       "...                   ...    ...          ...   ...      ...  ...\n",
       "12975          Whitefield  2 BHK   850 - 1060   2.0   38.190    2\n",
       "12990      Talaghattapura  3 BHK  1804 - 2273   3.0  122.000    3\n",
       "13059              Harlur  2 BHK  1200 - 1470   2.0   72.760    2\n",
       "13265               Hoodi  2 BHK  1133 - 1384   2.0   59.135    2\n",
       "13299          Whitefield  4 BHK  2830 - 2882   5.0  154.500    4\n",
       "\n",
       "[190 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[~df2['total_sqft'].apply(is_float)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9ba1ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_sqft_to_num(x):\n",
    "    val = x.split('-')\n",
    "    if len(val) == 2:\n",
    "        return (float(val[0])+float(val[1]))/2\n",
    "    try:\n",
    "        return float(x)\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f949bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "57b623ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  BHK\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_sqft'] = df3['total_sqft'].apply(convert_sqft_to_num)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a4ba9426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0    843\n",
       "1100.0    221\n",
       "1500.0    204\n",
       "2400.0    195\n",
       "600.0     180\n",
       "         ... \n",
       "904.0       1\n",
       "396.0       1\n",
       "4273.0      1\n",
       "3329.0      1\n",
       "4689.0      1\n",
       "Name: total_sqft, Length: 1972, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['total_sqft'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a19b16a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d25855f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['price_per_sqft'] = df4['price']*100000/df4['total_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "255fcf0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  BHK  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "\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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "74b7c709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1306"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.location.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "644c3d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['location'] = df4.location.apply(lambda x:x.strip())\n",
    "\n",
    "loc_desc_order = df4['location'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4fcc6d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1052"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(loc_desc_order[loc_desc_order<=10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7b68f885",
   "metadata": {},
   "outputs": [],
   "source": [
    "loc_less_than_10 = loc_desc_order[loc_desc_order<=10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f50b2af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ganga Nagar                      10\n",
       "Nagadevanahalli                  10\n",
       "Dodsworth Layout                 10\n",
       "1st Block Koramangala            10\n",
       "Kalkere                          10\n",
       "                                 ..\n",
       "Old Mangammanapalya Road          1\n",
       "HAL Layout                        1\n",
       "Gubbi Cross, Hennur Main Road     1\n",
       "Jeevanhalli                       1\n",
       "Abshot Layout                     1\n",
       "Name: location, Length: 1052, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc_less_than_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0cf6f730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1293"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4['location'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ab85050f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4['location'] = df4['location'].apply(lambda x: 'other' if x in loc_less_than_10 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "880f9f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electronic City Phase II', 'Chikka Tirupathi', 'Uttarahalli',\n",
       "       'Lingadheeranahalli', 'Kothanur', 'Whitefield', 'Old Airport Road',\n",
       "       'Rajaji Nagar', 'Marathahalli', 'other', '7th Phase JP Nagar',\n",
       "       'Gottigere', 'Sarjapur', 'Mysore Road', 'Bisuvanahalli',\n",
       "       'Raja Rajeshwari Nagar', 'Kengeri', 'Binny Pete', 'Thanisandra',\n",
       "       'Bellandur', 'Electronic City', 'Ramagondanahalli', 'Yelahanka',\n",
       "       'Hebbal', 'Kasturi Nagar', 'Kanakpura Road',\n",
       "       'Electronics City Phase 1', 'Kundalahalli', 'Chikkalasandra',\n",
       "       'Murugeshpalya', 'Sarjapur  Road', 'HSR Layout', 'Doddathoguru',\n",
       "       'KR Puram', 'Bhoganhalli', 'Lakshminarayana Pura', 'Begur Road',\n",
       "       'Varthur', 'Bommanahalli', 'Gunjur', 'Devarachikkanahalli',\n",
       "       'Hegde Nagar', 'Haralur Road', 'Hennur Road', 'Kothannur',\n",
       "       'Kalena Agrahara', 'Kaval Byrasandra', 'ISRO Layout',\n",
       "       'Garudachar Palya', 'EPIP Zone', 'Dasanapura', 'Kasavanhalli',\n",
       "       'Sanjay nagar', 'Domlur', 'Sarjapura - Attibele Road',\n",
       "       'Yeshwanthpur', 'Chandapura', 'Nagarbhavi', 'Devanahalli',\n",
       "       'Ramamurthy Nagar', 'Malleshwaram', 'Akshaya Nagar', 'Shampura',\n",
       "       'Kadugodi', 'LB Shastri Nagar', 'Hormavu', 'Vishwapriya Layout',\n",
       "       'Kudlu Gate', '8th Phase JP Nagar', 'Bommasandra Industrial Area',\n",
       "       'Anandapura', 'Vishveshwarya Layout', 'Kengeri Satellite Town',\n",
       "       'Kannamangala', 'Hulimavu', 'Mahalakshmi Layout', 'Hosa Road',\n",
       "       'Attibele', 'CV Raman Nagar', 'Kumaraswami Layout', 'Nagavara',\n",
       "       'Hebbal Kempapura', 'Vijayanagar', 'Pattandur Agrahara',\n",
       "       'Nagasandra', 'Kogilu', 'Panathur', 'Padmanabhanagar',\n",
       "       '1st Block Jayanagar', 'Kammasandra', 'Dasarahalli', 'Magadi Road',\n",
       "       'Koramangala', 'Dommasandra', 'Budigere', 'Kalyan nagar',\n",
       "       'OMBR Layout', 'Horamavu Agara', 'Ambedkar Nagar',\n",
       "       'Talaghattapura', 'Balagere', 'Jigani', 'Gollarapalya Hosahalli',\n",
       "       'Old Madras Road', 'Kaggadasapura', '9th Phase JP Nagar', 'Jakkur',\n",
       "       'TC Palaya', 'Giri Nagar', 'Singasandra', 'AECS Layout',\n",
       "       'Mallasandra', 'Begur', 'JP Nagar', 'Malleshpalya', 'Munnekollal',\n",
       "       'Kaggalipura', '6th Phase JP Nagar', 'Ulsoor', 'Thigalarapalya',\n",
       "       'Somasundara Palya', 'Basaveshwara Nagar', 'Bommasandra',\n",
       "       'Ardendale', 'Harlur', 'Kodihalli', 'Narayanapura',\n",
       "       'Bannerghatta Road', 'Hennur', '5th Phase JP Nagar', 'Kodigehaali',\n",
       "       'Billekahalli', 'Jalahalli', 'Mahadevpura', 'Anekal', 'Sompura',\n",
       "       'Dodda Nekkundi', 'Hosur Road', 'Battarahalli', 'Sultan Palaya',\n",
       "       'Ambalipura', 'Hoodi', 'Brookefield', 'Yelenahalli', 'Vittasandra',\n",
       "       '2nd Stage Nagarbhavi', 'Vidyaranyapura', 'Amruthahalli',\n",
       "       'Kodigehalli', 'Subramanyapura', 'Basavangudi', 'Kenchenahalli',\n",
       "       'Banjara Layout', 'Kereguddadahalli', 'Kambipura',\n",
       "       'Banashankari Stage III', 'Sector 7 HSR Layout', 'Rajiv Nagar',\n",
       "       'Arekere', 'Mico Layout', 'Kammanahalli', 'Banashankari',\n",
       "       'Chikkabanavar', 'HRBR Layout', 'Nehru Nagar', 'Kanakapura',\n",
       "       'Konanakunte', 'Margondanahalli', 'R.T. Nagar', 'Tumkur Road',\n",
       "       'Vasanthapura', 'GM Palaya', 'Jalahalli East', 'Hosakerehalli',\n",
       "       'Indira Nagar', 'Kodichikkanahalli', 'Varthur Road', 'Anjanapura',\n",
       "       'Abbigere', 'Tindlu', 'Gubbalala', 'Parappana Agrahara',\n",
       "       'Cunningham Road', 'Kudlu', 'Banashankari Stage VI', 'Cox Town',\n",
       "       'Kathriguppe', 'HBR Layout', 'Yelahanka New Town',\n",
       "       'Sahakara Nagar', 'Rachenahalli', 'Yelachenahalli',\n",
       "       'Green Glen Layout', 'Thubarahalli', 'Horamavu Banaswadi',\n",
       "       '1st Phase JP Nagar', 'NGR Layout', 'Seegehalli', 'BEML Layout',\n",
       "       'NRI Layout', 'ITPL', 'Babusapalaya', 'Iblur Village',\n",
       "       'Ananth Nagar', 'Channasandra', 'Choodasandra', 'Kaikondrahalli',\n",
       "       'Neeladri Nagar', 'Frazer Town', 'Cooke Town', 'Doddakallasandra',\n",
       "       'Chamrajpet', 'Rayasandra', '5th Block Hbr Layout', 'Pai Layout',\n",
       "       'Banashankari Stage V', 'Sonnenahalli', 'Benson Town',\n",
       "       '2nd Phase Judicial Layout', 'Poorna Pragna Layout',\n",
       "       'Judicial Layout', 'Banashankari Stage II', 'Karuna Nagar',\n",
       "       'Bannerghatta', 'Marsur', 'Bommenahalli', 'Laggere',\n",
       "       'Prithvi Layout', 'Banaswadi', 'Sector 2 HSR Layout',\n",
       "       'Shivaji Nagar', 'Badavala Nagar', 'Nagavarapalya', 'BTM Layout',\n",
       "       'BTM 2nd Stage', 'Hoskote', 'Doddaballapur', 'Sarakki Nagar',\n",
       "       'Thyagaraja Nagar', 'Bharathi Nagar', 'HAL 2nd Stage',\n",
       "       'Kadubeesanahalli'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "90b24784",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df4['location'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "15dc3634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Whitefield</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2</td>\n",
       "      <td>3247.863248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old Airport Road</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2732.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>204.00</td>\n",
       "      <td>4</td>\n",
       "      <td>7467.057101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rajaji Nagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>4</td>\n",
       "      <td>18181.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marathahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.25</td>\n",
       "      <td>3</td>\n",
       "      <td>4828.244275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.00</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  BHK  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "5                Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
       "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4   \n",
       "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4   \n",
       "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3   \n",
       "9                     other  6 Bedroom      1020.0   6.0  370.00    6   \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  \n",
       "5     3247.863248  \n",
       "6     7467.057101  \n",
       "7    18181.818182  \n",
       "8     4828.244275  \n",
       "9    36274.509804  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "24ccd296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other              2881\n",
       "Whitefield          535\n",
       "Sarjapur  Road      392\n",
       "Electronic City     304\n",
       "Kanakpura Road      266\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['location'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "15a59ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1555.3027829545454"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['total_sqft'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d54fb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>other</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>6</td>\n",
       "      <td>36274.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HSR Layout</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>600.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>8</td>\n",
       "      <td>33333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Murugeshpalya</td>\n",
       "      <td>6 Bedroom</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>6</td>\n",
       "      <td>10660.980810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Devarachikkanahalli</td>\n",
       "      <td>8 Bedroom</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>8</td>\n",
       "      <td>6296.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>other</td>\n",
       "      <td>3 Bedroom</td>\n",
       "      <td>500.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location       size  total_sqft  bath  price  BHK  \\\n",
       "9                 other  6 Bedroom      1020.0   6.0  370.0    6   \n",
       "45           HSR Layout  8 Bedroom       600.0   9.0  200.0    8   \n",
       "58        Murugeshpalya  6 Bedroom      1407.0   4.0  150.0    6   \n",
       "68  Devarachikkanahalli  8 Bedroom      1350.0   7.0   85.0    8   \n",
       "70                other  3 Bedroom       500.0   3.0  100.0    3   \n",
       "\n",
       "    price_per_sqft  \n",
       "9     36274.509804  \n",
       "45    33333.333333  \n",
       "58    10660.980810  \n",
       "68     6296.296296  \n",
       "70    20000.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[df4['total_sqft']/df4['BHK']<300].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2e7c27f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d400eebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='BHK', ylabel='total_sqft'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='BHK',y='total_sqft', data=df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7702ce49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13246, 7)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8376fdf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4[~(df4['total_sqft']/df4['BHK']<300)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b7a7adc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>39.07</td>\n",
       "      <td>2</td>\n",
       "      <td>3699.810606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>4</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>3</td>\n",
       "      <td>4305.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6245.890861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>2</td>\n",
       "      <td>4250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   location       size  total_sqft  bath   price  BHK  \\\n",
       "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
       "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
       "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
       "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
       "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
       "\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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0d7263d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12518, 7)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cece9788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     12472.000000\n",
       "mean       6321.224286\n",
       "std        4188.395933\n",
       "min         267.829813\n",
       "25%        4210.526316\n",
       "50%        5295.544522\n",
       "75%        6929.522602\n",
       "max      176470.588235\n",
       "Name: price_per_sqft, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['price_per_sqft'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6d6f147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10255, 7)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_pps_outliers(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",
    "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
    "        df_out = pd.concat([df_out, reduced_df], ignore_index=True)\n",
    "    return df_out\n",
    "df6 = remove_pps_outliers(df5)\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "398463ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10255, 7)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = remove_pps_outliers(df5)\n",
    "df6.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7573a592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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crnYR1dbW1pbm5ua0tramaYM5mS+++GKeeuqp7Lbbbn12vByIK69Mzjqr7+6gdXWVUG3hwuTMM7f0HYwdW/rnDQAAALAlNpUTbciItWF25pnJkiWVaaHr11yrqam8XrJEqAYAAAAwWtkVdCuYPbtydHZWdv9sahqeHUABAAAA2HoEa1tRfb1ADQAAAGCsMBUUAAAAYLzo6EhKpcrR0VHtakY9wRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKsCsoAAAAwFjU3+YEL2/b2OYFDQ3DU88YJFjbWjo6ksbGyvft7R5SAAAAYHitzyE2ZurU/tvL5aGvZYwyFXSMuuKKK7L33nunqakpTU1N2W+//fKDH/xgk9dcc801KZVKPUdjY2P22WefLFq0qFe/gw46KOedd16/12+33XYbfZ0kTz75ZHbZZZfMmzcvq1evLvr2AAAAAKpOsDZG7bLLLvnMZz6TBx98MA8++GAOOeSQzJ07Nz/72c82eV1TU1OWLVuWZcuW5ZFHHsmRRx6ZE044Ib/4xS+2uKYHHnggBxxwQI488sh897vfzaRJk7b4ngAAAMBGtLf3PVaseOn8ihX992HABGtj1LHHHpt3vOMd2XPPPbPnnnvm05/+dBobG3P//fdv8rpSqZSWlpa0tLRkjz32yKc+9anU1NTkscce26J67rzzzhxyyCF5//vfn6uuuiq1tbVbdD8AAABgMxoa+j8Gep7NEqyNA11dXbn++uvT0dGR/fbbb1DXXXvttUmSN73pTYV//uLFi/POd74zH/3oR/P5z3++8H0AAAAARhKbFwyHEbLrxuOPP5799tsvL774YhobG7N48eK8/vWv3+Q1ra2tafzz4oadnZ2ZMGFCvva1r+XVr351r34LFy7MN77xjV5t69atyzbbbNOrrb29Pf/tv/23XHzxxfnIRz4yBO8KAAAAYGQQrA2HEbLrxmte85o8+uij+dOf/pQbbrghp556au6+++5NhmuTJ0/Oww8/nCR54YUXcscdd+SMM87IjjvumGOPPban33vf+9589KMf7XXtokWLcumll/Zqq6+vz5w5c/L1r389J510Ul73utcN4TsEAAAAqB7B2hg2ceLE7L777kmSfffdNw888EC+/OUv5x//8R83ek1NTU3PNUmy995757bbbstnP/vZXsFac3Nzr35JMmXKlD73q62tzY033ph3v/vdOfjgg3PnnXdudtQcAAAAwGhgjbXhMEJ33SiXy1m9evWgr6utrU1nZ2fhnztp0qQsWrQob3nLW3LwwQfniSeeKHwvAAAAYAs0NFRmzJXLNioYAkasDYfNPZhbYZeNiy++OEcffXRmzJiR559/Ptdff33uuuuu3HrrrZu8rlwuZ/ny5Ukqa6zdfvvt+eEPf5iPf/zjW1TPxIkTc8MNN+SEE07IIYcckn//93/PrFmztuieAAAAANUkWBujVqxYkVNOOSXLli1Lc3Nz9t5779x66605/PDDN3ldW1tbpk2blqQy0mzXXXfNJz/5yXz4wx/e4pomTJiQf/mXf8lJJ53UE67tvffeW3xfAAAAgGoolctDvGL+KNTW1pbm5ua0tramqamp17kXX3wxTz31VHbbbbc+O14OSkfHS5satLcbbrkRQ/bnDQAAAFDApnKiDVljDQAAAAAKEKwBAAAAQAHWWNta1u+6AQAAAMCYYMQaAAAAABQgWBsgezxsHf6cAQAAgNFCsLYZEyZMSJK88MILVa5kfFj/57z+zx0AAIARrqMjKZUqR0dHtauBrcoaa5tRW1ub7bbbLitXrkySbLvttimVSlWuauwpl8t54YUXsnLlymy33Xapra2tdkkAAAAAmyRYG4CWlpYk6QnXGD7bbbddz583AAAAwEgmWBuAUqmUadOmZcqUKVm7dm21yxmzJkyYYKQaAAAAMGoI1gahtrZW8AMAAABAEsEaAAAAMFD9bU7w8raNbV7Q0DA89UCVCdYAAACAgWls3PT5qVP7by+Xh74WGAFqql0AAAAAAIxGRqwBAAAAA9Pe3reto+OlkWorVpj2ybgiWAMAAAAGZnOhWUODYI1xxVRQAAAAAChAsAYAAAAABQjWAAAAgOI6Ovr/HsYBwRoAAAAAFCBYAwAAAIp7+WYFNi5gnBGsAQAAAEABgjUAAAAAKKCu2gUAAAAAo0R/mxMMZPMCU0QZowRrAAAAwMA0Nm76/NSp/beXy0NfC4wApoICAAAAQAFGrAEAAAAD097et62j46WRaitWmPbJuCJYAwAAAAZmc6FZQ4NgjXHFVFAAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUYPMCAAAAoLiGhqRcrnYVUBVGrAEAAABAAYI1AAAAAIrp6EhKpcrR0VHtarY6wRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKqKt2AQAAAACMAv1tTvDyto1tXtDQMDz1jACCNQAAAAA2r7Fx0+enTu2/vVwe+lpGCFNBAQAAgPGjoyMplSrHxkZYwQAZsQYAAADA5rW3923r6HhppNqKFWN62md/BGsAAAAAbN7mQrOGhnEXrJkKCgAAAIwMpmkyyoyYYO2yyy5LqVTKeeed19NWLpdzySWXZPr06amvr89BBx2Un/3sZ72uW716dc4999zstNNOaWhoyHHHHZdnn312K1cPAAAAwHgzIoK1Bx54IF/72tey995792r/3Oc+lwULFuSrX/1qHnjggbS0tOTwww/P888/39PnvPPOy+LFi3P99ddn6dKlaW9vzzHHHJOurq6t/TYAAACAkaSjo/9joOdhM6oerLW3t+e9731vvv71r2f77bfvaS+Xy/nSl76Uj370o5k3b15mzpyZa6+9Ni+88EK+/e1vJ0laW1tz1VVX5Ytf/GIOO+ywvPGNb8y3vvWtPP7447njjjs2+jNXr16dtra2XgcAAAAwxjQ29j3WL7SfVL7vrw8MUNWDtbPPPjvvfOc7c9hhh/Vqf+qpp7J8+fIcccQRPW2TJk3K29/+9tx3331Jkoceeihr167t1Wf69OmZOXNmT5/+XHbZZWlubu45ZsyYMcTvCgAAAGAcaGhIyuXKMc42LkiqvCvo9ddfn4cffjgPPPBAn3PLly9Pkkx9eZL859dPP/10T5+JEyf2Gum2vs/66/tz0UUXZf78+T2v29rahGsAAACwNfU35XLDaZr9GUx4097e/89YnzWsWDEuwyCGTtWCtd/+9rf54Ac/mNtuuy3bbLPNRvuVSqVer8vlcp+2DW2uz6RJkzJp0qTBFQwAAAAMnc1NudxgoE2PcnngP2NzoVlDg2CNLVK1qaAPPfRQVq5cmX322Sd1dXWpq6vL3Xffna985Supq6vrGam24cizlStX9pxraWnJmjVrsmrVqo32AQAAAIDhULVg7dBDD83jjz+eRx99tOfYd9998973vjePPvpoXvWqV6WlpSW33357zzVr1qzJ3Xffnf333z9Jss8++2TChAm9+ixbtixPPPFETx8AAABgBGpv73usWPHS+RUr+u8DI0jVpoJOnjw5M2fO7NXW0NCQHXfcsaf9vPPOy6WXXpo99tgje+yxRy699NJsu+22Ofnkk5Mkzc3NOf3003PBBRdkxx13zA477JALL7wws2bN6rMZAgAAADDCrJ8O2t7ed0qmaZqMAlXdvGBzPvShD6WzszNnnXVWVq1albe+9a257bbbMnny5J4+l19+eerq6nLCCSeks7Mzhx56aK655prU1tZWsXIAAAAAxrpSuTyYVf/Gpra2tjQ3N6e1tTVNTU3VLgcAAADGvo6OviPW+muDrWwwOVHV1lgDAAAAgNFMsAYAAAAABYzoNdYAAACAMaCjY9NtL//ezp+MIoI1AAAAYHitXzdtY6ZO7b/dsvCMcKaCAgAAAEABRqwBAAAAw6u/6Z0dHS+NVFuxwg6gjEqCNQAAAGB4bS40a2gQrDEqmQoKAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAmxeAAAAAGx9DQ1JuVztKmCLGLEGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAONVR0dSKlWOjo5qVwOjjmANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABdRVuwAAAABgK+hvc4KXt21s84KGhuGpB8YAwRoAAACMB42Nmz4/dWr/7eXy0NcCY4SpoAAAAEBxHR1JqVQ5NjbqDcYoI9YAAABgPGhv79vW0fHSSLUVK0z7hEESrAEAAMB4sLnQrKFBsAaDZCooAAAAABQgWAMAAACAAkwFBQAAAAamv80JXt62sc0LTDFljBKsAQAAAAPT2Ljp8+s3QthQuTz0tcAIYCooAAAADEZHR1IqVY6NjdAaLRoaKqFXuWxUGRRgxBoAAAAwMO3tfds6Ol4aqbZihYCOcUWwBgAAAAzM5kKzhgbBGuOKqaAAAAAAUIBgDQAAAAAKMBUUAAAANqa/zQle3raxzQtMh4RxQbAGAAAAG9PYuOnz6xft31C5PPS1ACOOYA0AAAAorqFBkMi4JVgDAACAjWlv79vW0fHSSLUVK0z7hHFMsAYAAAAbs7nQrKFBsAbjmF1BAQAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIDNCwAAAGAwGhqScrnaVQAjgBFrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAALD1dXQkpVLl6OiodjVQiGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABdRVuwAAAABgjOtvc4KXt21s84KGhuGpB4aIYA0AAAAYXo2Nmz4/dWr/7eXy0NcCQ8hUUAAAAAAowIg1AAAAYHi1t/dt6+h4aaTaihWmfTIqCdYAAACA4bW50KyhQbDGqGQqKAAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAqweQEAAACw9TU0JOVytauALWLEGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAABgJOjqSUqlydHRUuxoGQLAGAAAAAAUI1gAAAACggKoGa1dccUX23nvvNDU1pampKfvtt19+8IMf9Jw/7bTTUiqVeh1ve9vbet1j9erVOffcc7PTTjuloaEhxx13XJ599tmt/VYAAAAAGGeqGqztsssu+cxnPpMHH3wwDz74YA455JDMnTs3P/vZz3r6HHXUUVm2bFnPccstt/S6x3nnnZfFixfn+uuvz9KlS9Pe3p5jjjkmXV1dW/vtAAAAADCO1FXzhx977LG9Xn/605/OFVdckfvvvz977bVXkmTSpElpaWnp9/rW1tZcddVV+eY3v5nDDjssSfKtb30rM2bMyB133JEjjzxyeN8AAAAAQBH9bU7w8raNbV7Q0DA89VDIiFljraurK9dff306Ojqy33779bTfddddmTJlSvbcc8/89V//dVauXNlz7qGHHsratWtzxBFH9LRNnz49M2fOzH333bfRn7V69eq0tbX1OgAAAAC2msbGvsfUqS+dnzq1/z6MKFUP1h5//PE0NjZm0qRJOfPMM7N48eK8/vWvT5IcffTR+ad/+qfceeed+eIXv5gHHngghxxySFavXp0kWb58eSZOnJjtt9++1z2nTp2a5cuXb/RnXnbZZWlubu45ZsyYMXxvEAAAAIAxqapTQZPkNa95TR599NH86U9/yg033JBTTz01d999d17/+tfnxBNP7Ok3c+bM7Lvvvtl1113zb//2b5k3b95G71kul1MqlTZ6/qKLLsr8+fN7Xre1tQnXAAAAgK2nvb1vW0fHS6PWVqww7XMUqHqwNnHixOy+++5Jkn333TcPPPBAvvzlL+cf//Ef+/SdNm1adt111/zqV79KkrS0tGTNmjVZtWpVr1FrK1euzP7777/Rnzlp0qRMmjRpiN8JAAAAwABtLjRraBCsjQJVnwq6oXK53DPVc0N/+MMf8tvf/jbTpk1Lkuyzzz6ZMGFCbr/99p4+y5YtyxNPPLHJYA0AAAAAtlRVR6xdfPHFOfroozNjxow8//zzuf7663PXXXfl1ltvTXt7ey655JK8+93vzrRp0/Kb3/wmF198cXbaaaccf/zxSZLm5uacfvrpueCCC7Ljjjtmhx12yIUXXphZs2b17BIKAAAAAMOhqsHaihUrcsopp2TZsmVpbm7O3nvvnVtvvTWHH354Ojs78/jjj+e6667Ln/70p0ybNi0HH3xwvvOd72Ty5Mk997j88stTV1eXE044IZ2dnTn00ENzzTXXpLa2torvDAAAAICxrlQul8vVLqLa2tra0tzcnNbW1jQ1NVW7HAAAAGA86uhIGhsr37e3W2OtSgaTE1V98wIAAAAAUgnSjH8aVUbc5gUAAAAAMBoI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAONVR0dSKlWOjo5qV4O/j1FHsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIAC6qpdAAAAALAV9LcY/svbNrZYfkPD8NQz3vn7GBMEawAAADAeNDZu+vzUqf23l8tDXwv+PsYIU0EBAAAAoAAj1gAAAGA8aG/v29bR8dLIqBUrTDPcmvx9jAmCNQAAABgPNhfSNDQIcrYmfx9jgqmgAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKMDmBQAAADBeNTQk5fLgr+voSBobK9+3t1tkf6gU/fugaoxYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAE2LwAAAAA2rqNj0239nU9saMC4IFgDAAAANm797p8bM3Vq/+12t2QcMBUUAAAAAAowYg0AAADYuPb2vm0dHS+NVFuxwrRPxi3BGgAAALBxmwvNGhoEa4xbpoICAAAA40tHR1IqVY6Nbb4AAyBYAwAAAIACBGsAAAAAUIBgDQAAAAAKsHkBAAAAMDgNDUm5XO0qoOoEawAAAMDY1d/mBC9v29jmBXY6ZQAEawAAAMDY1di46fNTp/bfbkQeA2CNNQAAAAAowIg1AAAAYOxqb+/b1tHx0ki1FStM+6QwwRoAAAAwdm0uNGtoEKxRmKmgAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKMDmBQAAAMD40tCQlMvVroIxwIg1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAABivOjqSUqlydHRUuxoYdQRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKKCu2gUAAAAAW0F/mxO8vG1jmxc0NAxPPTAGCNYAAABgPGhs3PT5qVP7by+Xh74WGCNMBQUAAACAAoxYAwAAgPGgvb1vW0fHSyPVVqww7RMGSbAGAAAA48HmQrOGBsEaDJKpoAAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAACjA5gUAAAAwXjU0JOVytauAUcuINQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoICqBmtXXHFF9t577zQ1NaWpqSn77bdffvCDH/ScL5fLueSSSzJ9+vTU19fnoIMOys9+9rNe91i9enXOPffc7LTTTmloaMhxxx2XZ599dmu/FQAAAADGmaoGa7vssks+85nP5MEHH8yDDz6YQw45JHPnzu0Jzz73uc9lwYIF+epXv5oHHnggLS0tOfzww/P888/33OO8887L4sWLc/3112fp0qVpb2/PMccck66urmq9LQAAAADGgVK5XC5Xu4iX22GHHfL5z38+H/jABzJ9+vScd955+fCHP5ykMjpt6tSp+exnP5szzjgjra2tecUrXpFvfvObOfHEE5Mkv/vd7zJjxozccsstOfLIIwf0M9va2tLc3JzW1tY0NTUN23sDAAAAYGQbTE40YtZY6+rqyvXXX5+Ojo7st99+eeqpp7J8+fIcccQRPX0mTZqUt7/97bnvvvuSJA899FDWrl3bq8/06dMzc+bMnj79Wb16ddra2nodAAAAADAYVQ/WHn/88TQ2NmbSpEk588wzs3jx4rz+9a/P8uXLkyRTp07t1X/q1Kk955YvX56JEydm++2332if/lx22WVpbm7uOWbMmDHE7woAAACAsW7Qwdp1112X1atX92lfs2ZNrrvuukEX8JrXvCaPPvpo7r///vzN3/xNTj311Py///f/es6XSqVe/cvlcp+2DW2uz0UXXZTW1tae47e//e2g6wYAAABgfBt0sPb+978/ra2tfdqff/75vP/97x90ARMnTszuu++efffdN5dddlne8IY35Mtf/nJaWlqSpM/Is5UrV/aMYmtpacmaNWuyatWqjfbpz6RJk3p2Il1/AAAAAMBgDDpY29hosGeffTbNzc1bXFC5XM7q1auz2267paWlJbfffnvPuTVr1uTuu+/O/vvvnyTZZ599MmHChF59li1blieeeKKnDwAAAAAMh7qBdnzjG9+YUqmUUqmUQw89NHV1L13a1dWVp556KkcdddSgfvjFF1+co48+OjNmzMjzzz+f66+/PnfddVduvfXWlEqlnHfeebn00kuzxx57ZI899sill16abbfdNieffHKSpLm5OaeffnouuOCC7Ljjjtlhhx1y4YUXZtasWTnssMMGVQsAAAAADMaAg7V3vetdSZJHH300Rx55ZBobG3vOTZw4Ma985Svz7ne/e1A/fMWKFTnllFOybNmyNDc3Z++9986tt96aww8/PEnyoQ99KJ2dnTnrrLOyatWqvPWtb81tt92WyZMn99zj8ssvT11dXU444YR0dnbm0EMPzTXXXJPa2tpB1QIAAAAAg1Eql8vlwVxw7bXX5sQTT8w222wzXDVtdW1tbWlubk5ra6v11gAAAADGscHkRINeY+3UU0/Niy++mG984xu56KKL8sc//jFJ8vDDD+e5554rVjEAAAAAjDIDngq63mOPPZbDDjsszc3N+c1vfpO//uu/zg477JDFixfn6aefznXXXTccdQIAAADAiDLoEWvnn39+TjvttPzqV7/qNR306KOPzj333DOkxQEAAADASDXoEWsPPvhgvva1r/Vp33nnnbN8+fIhKQoAAAAARrpBj1jbZptt0tbW1qf9F7/4RV7xilcMSVEAAAAAMNINOlibO3duPvnJT2bt2rVJklKplGeeeSYf+chH8u53v3vICwQAAACAkWjQwdoXvvCF/P73v8+UKVPS2dmZt7/97dl9990zefLkfPrTnx6OGgEAAABgxBn0GmtNTU1ZunRp7rzzzjz88MPp7u7Om970phx22GHDUR8AAAAAjEiDDtbWO+SQQ7L//vtn0qRJKZVKQ1kTAAAAAIx4g54K2t3dnX/4h3/IzjvvnMbGxjz11FNJko997GO56qqrhrxAAAAAABiJBh2sfepTn8o111yTz33uc5k4cWJP+6xZs/KNb3xjSIsDAAAAgJFq0MHaddddl6997Wt573vfm9ra2p72vffeOz//+c+HtDgAAAAAGKkGHaw999xz2X333fu0d3d3Z+3atUNSFAAAAACMdIMO1vbaa68sWbKkT/t3v/vdvPGNbxySogAAAABgpBv0rqCf+MQncsopp+S5555Ld3d3Fi1alF/84he57rrr8q//+q/DUSMAAAAAjDiDHrF27LHH5jvf+U5uueWWlEqlfPzjH8+TTz6Zm2++OYcffvhw1AgAAAAAI86ARqx95Stfyf/4H/8j22yzTZ555pkcccQROfLII4e7NgBgDOjsTNrakqampL6+2tUAAMDQGdCItfnz56etrS1Jsttuu+X3v//9sBYFAIx+S5cm8+YljY1JS0vl67x5yb33VrsyAAAYGgMasTZ9+vTccMMNecc73pFyuZxnn302L774Yr99/+Iv/mJICwQARp8rrkjOPjuprU26uytt3d3JzTcnN96YLFyYnHlmVUsEAIAtViqXy+XNdfra176Wc889N+vWrdton3K5nFKplK6uriEtcGtoa2tLc3NzWltb09TUVO1yAGBUW7o0OfDAZFOfMEqlZMmSZPbsrVcXAAAMxGByogGNWPsf/+N/5KSTTsrTTz+dvffeO3fccUd23HHHISkWABhbFiyojFTbxO/jUlubXH65YA0AgNFtQCPWXu7aa6/Ne97znkyaNGm4atrqjFgDgKHR2VlZS2399M9NqalJ2tttaAAAwMgy5CPWXu7UU08tXBgAMLa1tQ0sVEsq/draBGsAAIxeA9oVFABgIJqaKiPRBqKmptIfAABGK8EaADBk6uuTuXOTus2Mia+rS44/3mg1AABGN8EaADCk5s9PNrdJeFdXcv75W6ceAAAYLoI1AGBIzZmTLFyYlEp9R67V1VXaFy60IygAAKPfoDcvmD9//oD7LliwYLC3BwDGgDPPTGbNSi6/PFm8uLJRQU1NZZro+ecL1QAAGBsGHaw98sgjefjhh7Nu3bq85jWvSZL88pe/TG1tbd70pjf19CuVSkNXJQAw6syeXTk6Oyu7fzY1WVMNAICxZdDB2rHHHpvJkyfn2muvzfbbb58kWbVqVd7//vfngAMOyAUXXDDkRQIAo1d9vUANAICxqVQul8uDuWDnnXfObbfdlr322qtX+xNPPJEjjjgiv/vd74a0wK2hra0tzc3NaW1tTVNTU7XLAQAAAKBKBpMTDXrzgra2tqxYsaJP+8qVK/P8888P9nYAAAAAMCoNOlg7/vjj8/73vz/f+9738uyzz+bZZ5/N9773vZx++umZN2/ecNQIAAAAACPOoNdYu/LKK3PhhRfmfe97X9auXVu5SV1dTj/99Hz+858f8gIBAAAAYCQa9Bpr63V0dOTXv/51yuVydt999zQ0NAx1bVuNNdYAAAAASIZ5jbX1Ghoasvfee2e33XbL7bffnp///OdFbwUAAAAAo86gg7UTTjghX/3qV5MknZ2d2XfffXPCCSdk1qxZueGGG4a8QAAAAAAYiQYdrN1zzz054IADkiSLFy9OuVzOn/70p3zlK1/Jpz71qSEvEAAAAABGokEHa62trdlhhx2SJLfeemve/e53Z9ttt8073/nO/OpXvxryAgEAAABgJBp0sDZjxoz8+Mc/TkdHR2699dYcccQRSZJVq1Zlm222GfICAQAAAGAkqhvsBeedd17e+973prGxMbvuumsOOuigJJUporNmzRrq+gAAAABgRBp0sHbWWWflrW99a5555pkcfvjhqampDHp71ateZY01AAAAAMaNUrlcLle7iGpra2tLc3NzWltb09TUVO1yAAAAAKiSweREg15jDQAAAAAQrAEAAABAIYI1AAAAAChgUMHaunXr8vd///f57W9/O1z1AABjTGdnsmJF5SsAAIwlgwrW6urq8vnPfz5dXV3DVQ8AMEYsXZrMm5c0NiYtLZWv8+Yl995b7coAAGBoDHoq6GGHHZa77rprGEoBAMaKK65IDjwwufnmpLu70tbdXXl9wAHJlVdWtz4AABgKdYO94Oijj85FF12UJ554Ivvss08aGhp6nT/uuOOGrDgAYPRZujQ5++ykXE7Wret9bv3rs85KZs1KZs/e+vUBAMBQKZXL5fJgLqip2fggt1KpNCqniba1taW5uTmtra1pamqqdjkAMKrNm1cZmbZhqPZydXXJ3LnJ97639eoCAICBGExONOgRa93r53MAAGygszO56aaXpn9uzLp1yeLFlf719VunNgAAGGqDXmPt5V588cWhqgMAGAPa2jYfqq3X3V3pDwAAo9Wgg7Wurq78wz/8Q3beeec0NjbmP//zP5MkH/vYx3LVVVcNeYEAwOjR1JRsYtWIXmpqKv0BAGC0GnSw9ulPfzrXXHNNPve5z2XixIk97bNmzco3vvGNIS0OABhd6usra6fVbWaxibq65PjjTQMFAGB0G3Swdt111+VrX/ta3vve96a2tranfe+9987Pf/7zIS0OABh95s9PNreXUVdXcv75W6ceAAAYLoMO1p577rnsvvvufdq7u7uzdu3aISkKABi95sxJFi5MSqW+I9fq6irtCxcms2dXpz4AABgqgw7W9tprryxZsqRP+3e/+9288Y1vHJKiAIDR7cwzkyVLKtNC16+5VlNTeb1kSeU8AACMdptZAaWvT3ziEznllFPy3HPPpbu7O4sWLcovfvGLXHfddfnXf/3X4agRABiFZs+uHJ2dld0/m5qsqQYAwNgy6BFrxx57bL7zne/klltuSalUysc//vE8+eSTufnmm3P44YcPR40AwChWX59MnSpUAwBg7CmVy+VytYuotra2tjQ3N6e1tTVNTU3VLgcAAACAKhlMTjToqaDrPfjgg3nyySdTKpXyute9Lvvss0/RWwEAAADAqDPoYO3ZZ5/NSSedlHvvvTfbbbddkuRPf/pT9t9///zzP/9zZsyYMdQ1AsDY0tGRNDZWvm9vTxoaqlsPAABQyKDXWPvABz6QtWvX5sknn8wf//jH/PGPf8yTTz6Zcrmc008/fThqBAAAAIARZ9Aj1pYsWZL77rsvr3nNa3raXvOa1+R//a//ldmzZw9pcQAAAAAwUg16xNpf/MVfZO3atX3a161bl5133nlIigIAAACAkW7QwdrnPve5nHvuuXnwwQezfkPRBx98MB/84AfzhS98YcgLBAAAAICRqFRen44N0Pbbb58XXngh69atS11dZSbp+u8bNlh8+Y9//OPQVTqMBrONKgAMSkdH/21Tp1a+X7Gi/80LbGgAAABVMZicaNBrrH3pS18qWhcAjD/rd//cmPUB24YG93svAACgCgYdrJ166qnDUQcAAAAAjCqDDtYAgEFob+/bNpCpoAAAwIgnWAOA4bS50KyhQbAGAACj1KB3BQUAAAAABGsAAAAAUEjhYO0//uM/8sMf/jCdnZ1JkrLdywAAAAAYRwYdrP3hD3/IYYcdlj333DPveMc7smzZsiTJf//v/z0XXHDBkBcIAAAAACPRoIO1888/P3V1dXnmmWey7bbb9rSfeOKJufXWW4e0OAAAAAAYqQa9K+htt92WH/7wh9lll116te+xxx55+umnh6wwAICh1NmZtLUlTU1JfX21qwEAYCwY9Ii1jo6OXiPV1vuv//qvTJo0aUiKAgAYKkuXJvPmJY2NSUtL5eu8ecm991a7MgAARrtBB2sHHnhgrrvuup7XpVIp3d3d+fznP5+DDz54SIsDANgSV1yRHHhgcvPNSXd3pa27u/L6gAOSK6+sbn0AAIxug54K+vnPfz4HHXRQHnzwwaxZsyYf+tCH8rOf/Sx//OMfc69f/QIAI8TSpcnZZyflcrJuXe9z61+fdVYya1Yye/YQ//COjsrQuCRpb08aGob4BwAAMBIMesTa61//+jz22GN5y1veksMPPzwdHR2ZN29eHnnkkbz61a8ejhoBYPTq6Oj/GOh5CluwIKmt3XSf2trk8su3Tj0AAIw9pXK5XK52EdXW1taW5ubmtLa2pqmpqdrlADCWlErFrvM/z1uks7MyYGz99M9NqampDCob0g0NjFgDABi1BpMTDXrE2tVXX53vfve7fdq/+93v5tprrx3s7QAAhlxb28BCtaTSr61teOsBAGBsGnSw9pnPfCY77bRTn/YpU6bk0ksvHZKiAGDMaG/ve6xY8dL5FSv678MWaWqqjEQbiJqaSn8AABisQW9e8PTTT2e33Xbr077rrrvmmWeeGZKiAGDM2NwUwIYG0wSHQX19MnduZffPDTcueLm6ukq/LZoG2t+aeBuuo9cff+8AAKPeoIO1KVOm5LHHHssrX/nKXu0//elPs+OOOw5VXQAAW2T+/OTGGzfdp6srOf/8LfxB69dS25ipU/tvt44eAMCoN+ipoO95z3vyt3/7t/nRj36Urq6udHV15c4778wHP/jBvOc97xmOGgEABm3OnGThwsr+EXUb/Cqxrq7SvnBhMnt2deoDAGD0G/SItU996lN5+umnc+ihh6buz59Su7u781d/9VfWWAMARpQzz0xmzUouvzxZvLiyUUFNTWX65/nnD1Go1t+aeB0dL41UW7HCtE8AgDGqVC4Xm4fwy1/+Mj/96U9TX1+fWbNmZddddx3q2raawWyjCgBbrKPjpemD7e1Cl62ks7Oy+2dT0xauqTYQ/o4BAEatweREgx6xtt6ee+6ZPffcs+jlADB+NTRYX6sK6uu3QqAGAMC4MqBgbf78+fmHf/iHNDQ0ZP78+Zvsu2DBgiEpDAAAAABGsgEFa4888kjWrl2bJHn44YdTKpX67bexdgAAAAAYawYUrP3oRz/q+f6uu+4arloAAAAAYNSoGUzndevWpa6uLk888cRw1QMAMPqtX0evXLZxAQDAGDaoYK2uri677rprurq6hqseAAAAABgVBhWsJcnf/d3f5aKLLsof//jH4agHAAAAAEaFAa2x9nJf+cpX8h//8R+ZPn16dt111zRsML3h4YcfHrLiAAAAAGCkGnSwNnfuXLt/AgAAADDulcrlcrnaRVRbW1tbmpub09ramqampmqXAwAAAECVDCYnGvAaay+88ELOPvvs7LzzzpkyZUpOPvnk/Nd//dcWFwsAAAAAo9GAg7VPfOITueaaa/LOd74z73nPe3L77bfnb/7mb4azNgAYmzo6klKpcnR0VLsaAACgoAGvsbZo0aJcddVVec973pMked/73pfZs2enq6srtbW1w1YgAAAAAIxEAx6x9tvf/jYHHHBAz+u3vOUtqaury+9+97thKQwAAAAARrIBB2tdXV2ZOHFir7a6urqsW7duyIsCABjVTPcFABgXBjwVtFwu57TTTsukSZN62l588cWceeaZaWho6GlbtGjR0FYIAAAAACPQgIO1U089tU/b+973viEtBgDGnP5GK728bWOjmV72SysAAGBkGnCwdvXVVw/5D7/sssuyaNGi/PznP099fX3233//fPazn81rXvOanj6nnXZarr322l7XvfWtb83999/f83r16tW58MIL88///M/p7OzMoYcemoULF2aXXXYZ8poBYFAaGzd9furU/tvL5aGvBQAAGFIDXmNtONx99905++yzc//99+f222/PunXrcsQRR6Rjg9/eH3XUUVm2bFnPccstt/Q6f95552Xx4sW5/vrrs3Tp0rS3t+eYY45JV1fX1nw7AAAAAIwjAx6xNhxuvfXWXq+vvvrqTJkyJQ899FAOPPDAnvZJkyalpaWl33u0trbmqquuyje/+c0cdthhSZJvfetbmTFjRu64444ceeSRw/cGAGBz2tv7tnV0vDRSbcUK0z5HO9N9AQDGraoGaxtqbW1Nkuywww692u+6665MmTIl2223Xd7+9rfn05/+dKZMmZIkeeihh7J27docccQRPf2nT5+emTNn5r777us3WFu9enVWr17d87qtrW043g4AbD48aWgQsIx2pvsCAIxbVZ0K+nLlcjnz58/PnDlzMnPmzJ72o48+Ov/0T/+UO++8M1/84hfzwAMP5JBDDukJxpYvX56JEydm++2373W/qVOnZvny5f3+rMsuuyzNzc09x4wZM4bvjQEAAAAwJo2YEWvnnHNOHnvssSxdurRX+4knntjz/cyZM7Pvvvtm1113zb/9279l3rx5G71fuVxOqVTq99xFF12U+fPn97xua2sTrgEAxZjuCwAwbo2IYO3cc8/N97///dxzzz2b3clz2rRp2XXXXfOrX/0qSdLS0pI1a9Zk1apVvUatrVy5Mvvvv3+/95g0aVImTZo0dG8AABi/TPcFABi3qjoVtFwu55xzzsmiRYty5513ZrfddtvsNX/4wx/y29/+NtOmTUuS7LPPPpkwYUJuv/32nj7Lli3LE088sdFgDQAAAAC2VFVHrJ199tn59re/nZtuuimTJ0/uWROtubk59fX1aW9vzyWXXJJ3v/vdmTZtWn7zm9/k4osvzk477ZTjjz++p+/pp5+eCy64IDvuuGN22GGHXHjhhZk1a1bPLqEAMKI0NFi4HgAAxoCqBmtXXHFFkuSggw7q1X711VfntNNOS21tbR5//PFcd911+dOf/pRp06bl4IMPzne+851Mnjy5p//ll1+eurq6nHDCCens7Myhhx6aa665JrW1tVvz7QDAwHR0vLSTZHu7aYIAADBKlcplvzJva2tLc3NzWltb09TUVO1yABjrBGtjn79jAIBRazA50YjYvAAAYEwx3RcAYFyo6uYFAAAAADBaCdYAAAAAoABTQQFgOHV0bLqtv/OJNbkAAGAUEKwBwHBav4D9xkyd2n+79bkAAGDEMxUUAAAAAAowYg0AhlN7e9+2jo6XRqqtWGHaJwAAjFKCNQAYTv2FZhuuqyZYAwCAUclUUAAAAAAoQLAGADDUOjqSUqlybGznVwBGL//OA38mWAMAxoXOzsqSdp2d1a4EAICxQrAGAIxpS5cm8+YljY1JS0vl67x5yb33VrsyAABGO8EaAAynjo6+R5E+FHLFFcmBByY335x0d1faursrrw84ILnyyurWBwDA6GZXUAAYTo2Nmz4/dWr/7eXy0Ncyzixdmpx9duWPct263ufWvz7rrGTWrGT27K1fHwAAo59gDQAYkxYsSGpr+4ZqL1dbm1x++RYGaxsbhbip80nS0LAFPxSArca/88AmlMplvxJva2tLc3NzWltb09TUVO1yABhLNvZhfP1ItRUr+v/g7cP4FunsrAwWXD/9c1NqapL29qS+vuAPK5WKXecjGMDo4N95GHcGkxMZsQYAw2lzAVlDgxBtGLS1DSxUSyr92tq2IFgDAGDcEqwBAGNOU1NlJNpAR6xt0YD19va+bQMZlQjA6ODfeWATBGsAwJhTX5/MnVvZ/XNTa6zV1VX6bdFoNaMSAcY2/84Dm1BT7QIAAIbD/PlJV9em+3R1Jeefv3XqAQBg7BGsAQBj0pw5ycKFlTWn6zYYo19XV2lfuHALdwQFAGBcE6wBwNbW0FDZKaxcNnVkmJ15ZrJkSWW6Z82fP/XU1FReL1lSOQ8AAEVZYw0AGNNmz64cnZ2V3T+bmuwACgDA0BCsAQDjQn39VgzU1o9KBGBs8u888GemggIAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQDY2jo6klKpcnR0VLsaAACgIMEaAAAAABQgWAOAre3lo9SMWAMAgFFLsAYAAAAABQjWAAAAAKCAumoXAABjWn9TPTecCtpfn4aG4asJAAAYEoI1ABhOjY2bPv+qV/XfXi4PfS0AAMCQMhUUAAAAAAoQrAEAAABAAYI1AAAAACjAGmsAMJza2/u2rVz50tpq//mfyZQpW7cmAABgSAjWAGA49be758vbGhrsAAoAAKOUqaAAAAAAUIBgDUaBzs5kxYrKVwAAAGBkEKzBCLZ0aTJvXtLYmLS0VL7Om5fce2+1KwO2yIZTQceDjo6kVKocHR3VrgYAAIaEYA1GqCuuSA48MLn55qS7u9LW3V15fcAByZVXVrc+AAAAGO8EazACLV2anH12Ui4n69b1PrduXaX9rLOMXAMAAIBqEqzBCLRgQVJbu+k+tbXJ5ZdvnXoAAACAvgRrMMJ0diY33dR3pNqG1q1LFi+2oQEAAABUS121CwB6a2t7aU21zenurvSvrx/emgAGpb/NCV7etrHNC8bLRg4AAIwZgjUYYZqakpqagYVrNTWV/gAjSmPjps9Pndp/e7k89LUAAMAwEqzBCFNfn8ydW9n9c1PTQevqKv2MVoMRzugtAAAYswRrMALNn5/ceOOm+3R1Jeefv1XKAbbEeBy91d7et62j46X3umKF4BAAgDHB5gUwAs2ZkyxcmJRKlZFpL1dXV2lfuDCZPbs69QFsUkND/8dAzwMAwCghWIMR6swzkyVLKtM9a/78X2pNTeX1kiWV88Ao0N7e93jiiZfOP/FE/30AAIARz1RQGMFmz64cnZ2V3T+bmqypBqNOfyOxtt229/dGawEAwKgkWINRoL5eoAYAAAAjjamgAAAAAFCAEWsAMJxWruzb9l//1fv7/qaCTpkyfDVVQ0PD6N7pFAAA+iFYA4DhNHXqps+/5S39twuhAABgxDMVFAAAAAAKMGINAIbTihV9237/+2TmzMr3TzyRvOIVW7cmAABgSAjWAGA4bW6ttFe8YuytpwYAAOOEqaAAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAowOYFALC1TZmSlMvVrgIAANhCRqwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAD09GRlEqVo6Nj5NwLAACqRLDGkOrsTFasqHwFAAAAGMsEawyJpUuTefOSxsakpaXydd685N57q10ZAAAAwPCoarB22WWX5c1vfnMmT56cKVOm5F3veld+8Ytf9OpTLpdzySWXZPr06amvr89BBx2Un/3sZ736rF69Oueee2522mmnNDQ05Ljjjsuzzz67Nd/KuHbFFcmBByY335x0d1faursrrw84ILnyyurWBwAAADAcqhqs3X333Tn77LNz//335/bbb8+6detyxBFHpONla6187nOfy4IFC/LVr341DzzwQFpaWnL44Yfn+eef7+lz3nnnZfHixbn++uuzdOnStLe355hjjklXV1c13ta4snRpcvbZSbmcrFvX+9y6dZX2s84ycg0AAAAYe0rlcrlc7SLW+/3vf58pU6bk7rvvzoEHHphyuZzp06fnvPPOy4c//OEkldFpU6dOzWc/+9mcccYZaW1tzSte8Yp885vfzIknnpgk+d3vfpcZM2bklltuyZFHHrnZn9vW1pbm5ua0tramqalpWN/jWDNvXmVk2oah2svV1SVz5ybf+97WqwuALdTfhgIdHcnUqZXvV6xIGhr69umvbSjvBQAAw2wwOVHdVqppQFpbW5MkO+ywQ5LkqaeeyvLly3PEEUf09Jk0aVLe/va357777ssZZ5yRhx56KGvXru3VZ/r06Zk5c2buu+++foO11atXZ/Xq1T2v29rahustjWmdnclNN700/XNj1q1LFi+u9K+v3zq1AbCFGhs3fX59KLah/n5fN5T3AgCAEWTEbF5QLpczf/78zJkzJzNnzkySLF++PEkydYMP3FOnTu05t3z58kycODHbb7/9Rvts6LLLLktzc3PPMWPGjKF+O+NCW9vmQ7X1ursr/QEAAADGihEzYu2cc87JY489lqVLl/Y5VyqVer0ul8t92ja0qT4XXXRR5s+f3/O6ra1NuFZAU1NSUzOwcK2mptIfgFGivb1v20Cmbw73vQAAYAQZEcHaueeem+9///u55557sssuu/S0t7S0JKmMSps2bVpP+8qVK3tGsbW0tGTNmjVZtWpVr1FrK1euzP7779/vz5s0aVImTZo0HG9lXKmvr6ydNtA11kwDBRhFNhd0NTQMPAwbynsBAMAIUtWpoOVyOeecc04WLVqUO++8M7vttluv87vttltaWlpy++2397StWbMmd999d09ots8++2TChAm9+ixbtixPPPHERoM1hs78+cnmNl/t6krOP3/r1AMAAACwtVR1xNrZZ5+db3/727npppsyefLknjXRmpubU19fn1KplPPOOy+XXnpp9thjj+yxxx659NJLs+222+bkk0/u6Xv66afnggsuyI477pgddtghF154YWbNmpXDDjusmm9vXJgzJ1m4MDnrrKS2tvfItbq6Sqi2cGEye3b1agQAAAAYDlUN1q644ookyUEHHdSr/eqrr85pp52WJPnQhz6Uzs7OnHXWWVm1alXe+ta35rbbbsvkyZN7+l9++eWpq6vLCSeckM7Ozhx66KG55pprUltbu7Xeyrh25pnJrFnJ5ZdXdv/s7q6sqTZ3bmWkmlANAAAAGItK5bK97Nva2tLc3JzW1tY0WWF/i3R2Vnb/bGqyphrAmNPRkTQ2Vr5vb9+yddGG8l4AADCEBpMTjYjNCxg76usFagBjVkNDMlS/jxvKewEAQJVUdfMCAAAAABitBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAADrdXQkpVLl6OiodjUAAIxwgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUUFftAgAAqqK/zQle3raxzQsaGoanHgAARh3BGgAwPjU2bvr81Kn9t5fLQ18LAACjkqmgAAAAAFCAEWsAwPjU3t63raPjpZFqK1aY9gkAwCYJ1gCA8WlzoVlDg2ANAIBNMhUUAAAAAAoQrAEAAABAAYI1gE3o7Kwss9TZWe1KAAAAGGkEawD9WLo0mTcvaWxMWloqX+fNS+69t9qVAQAAMFII1gA2cMUVyYEHJjffnHR3V9q6uyuvDzggufLK6tYHDKOGhqRcrhw2LgAAYDMEawAvs3RpcvbZlf9PvW5d73Pr1lXazzrLyDUAAAAEawC9LFiQ1NZuuk9tbXL55VunHgAAAEYuwRrAn3V2Jjfd1Hek2obWrUsWL7ahAQAAwHgnWAP4s7a2l9ZU25zu7kp/AAAAxi/BGsCfNTUlNQP8V7GmptIfAACA8UuwBvBn9fXJ3LlJXd2m+9XVJccfX+kPAADA+CVYA3iZ+fOTrq5N9+nqSs4/f+vUAwAAwMglWAN4mTlzkoULk1Kp78i1urpK+8KFyezZ1akPAACAkUOwBrCBM89MliypTAtdv+ZaTU3l9ZIllfMAAACwmZWEAMan2bMrR2dnZffPpiZrqgEAANCbYA1gE+rrBWoAAAD0z1RQAAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAPR0ZGUSpWjo6Pa1QAAACOAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAF1FW7AAAYcfrbnODlbRvbvKChYXjqAQAARiTBGgBsqLFx0+enTu2/vVwe+loAAIARy1RQAAAAACjAiDUA2FB7e9+2jo6XRqqtWGHaJwAAIFgDgD42F5o1NAjWAAAAU0EBAAAAoAjBGgAAAAAUIFgDAAAAgAIEa8CQ6eysrOne2VntSobOWHxPAAAADA3BGrDFli5N5s1LGhuTlpbK13nzknvvrXZlxY3F98QWamhIyuXKYeMCAAAggjVgC11xRXLggcnNNyfd3ZW27u7K6wMOSK68srr1FTEW3xMAAABDr1Qul8vVLqLa2tra0tzcnNbW1jQ1NVW7HBg1li6tBFCb+lekVEqWLElmz956dW2JsfieAAAAGLjB5ERGrAGFLViQ1NZuuk9tbXL55VunnqEwFt8TAAAAw8OItRixBkV0dlbWHVs/VXJTamqS9vakvn7469oSY/E9AQAAMDhGrAHDrq1tYAFUUunX1ja89QyFsfieAAAAGD6CNaCQpqbKqK2BqKmp9B/pxuJ7AgAAYPgI1oBC6uuTuXOTurpN96urS44/fnRMmRyL7wkAAIDhI1gDCps/P+nq2nSfrq7k/PO3Tj1DYSy+JwAAAIaHYA0obM6cZOHCpFTqO8qrrq7SvnBhMnt2deorYiy+JwAAAIaHYA3YImeemSxZUplCuX59spqayuslSyrnR5uXv6dSqdJWKo3u9wQAAMDQ28xKQgCbN3t25ejsrOyU2dQ0+tcfK5crO3+WSpXvS6WB7xgKAADA+GDEGjBk6uuTqVNHf6h2xRXJgQcmN9/8UpjW3V15fcAByZVXVrc+AAAARgbBGsDLLF2anH12ZZTaunW9z61bV2k/66zk3nurUx8AAAAjh2AN4GUWLEhqazfdp7Y2ufzyrVMPAAAAI5dgbYzq7ExWrKh8BQamszO56aa+I9U2tG5dsnix/74AAADGO8HaGLN0aTJvXtLYmLS0VL7Om2faGgxEW9vANyjo7q70BwAAYPwSrI0hFlyHLdPUlNQM8F/FmppKfwAAAMYvwdoYYcF12HL19cncuUld3ab71dUlxx8/+nc/BQAAYMsI1sYIC67D0Jg/P+nq2nSfrq7k/PO3Tj0AAACMXIK1McCC6zB05sxJFi5MSqW+I9fq6irtCxcms2dXpz4AAABGDsHaGGDBdRhaZ56ZLFlSmRa6fs21mprK6yVLKucBAABgMysJMRqsX3B9IOGaBddhYGbPrhydnZUwuqnJmmoAAAD0ZsTaGGDBdRg+9fXJ1Kn+uwEAAKAvwdoYYcF1AAAAgK1LsDZGWHAdAAAAYOsSrI0hFlwHAAAA2HpsXjDGWHAdAAAAYOsQrI1R9fUCNQAAAIDhZCooAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAbE5HR1IqVY6OjmpXAwAAjBCCNYZUZ2eyYkXlKwAAAMBYJlhjSCxdmsyblzQ2Ji0tla/z5iX33lvtygAAAACGR1WDtXvuuSfHHntspk+fnlKplBtvvLHX+dNOOy2lUqnX8ba3va1Xn9WrV+fcc8/NTjvtlIaGhhx33HF59tlnt+K74IorkgMPTG6+OenurrR1d1deH3BAcuWV1a0PAAAAYDhUNVjr6OjIG97whnz1q1/daJ+jjjoqy5Yt6zluueWWXufPO++8LF68ONdff32WLl2a9vb2HHPMMenq6hru8kllpNrZZyflcrJuXe9z69ZV2s86y8g1AAAAYOypq+YPP/roo3P00Udvss+kSZPS0tLS77nW1tZcddVV+eY3v5nDDjssSfKtb30rM2bMyB133JEjjzxyyGumtwULktravqHay9XWJpdfnsyevfXqAiisv80JXt62sc0LGhqGpx4AAGDEGvFrrN11112ZMmVK9txzz/z1X/91Vq5c2XPuoYceytq1a3PEEUf0tE2fPj0zZ87Mfffdt9F7rl69Om1tbb0OBq+zM7nppk2Haknl/OLFNjQARonGxr7H1KkvnZ86tf8+AADAuDOig7Wjjz46//RP/5Q777wzX/ziF/PAAw/kkEMOyerVq5Mky5cvz8SJE7P99tv3um7q1KlZvnz5Ru972WWXpbm5ueeYMWPGsL6Psaqt7aU11Tanu7vSHwAAAGCsqOpU0M058cQTe76fOXNm9t133+y66675t3/7t8ybN2+j15XL5ZRKpY2ev+iiizJ//vye121tbcK1ApqakpqagYVrNTWV/gAjXnt737aOjpdGra1YYdonAACQZIQHaxuaNm1adt111/zqV79KkrS0tGTNmjVZtWpVr1FrK1euzP7777/R+0yaNCmTJk0a9nrHuvr6ZO7cyu6fm5oOWldX6Vdfv/VqAyhsc6FZQ4NgDQAASDLCp4Ju6A9/+EN++9vfZtq0aUmSffbZJxMmTMjtt9/e02fZsmV54oknNhmsMXTmz082twFrV1dy/vlbpx4AAACAraWqI9ba29vzH//xHz2vn3rqqTz66KPZYYcdssMOO+SSSy7Ju9/97kybNi2/+c1vcvHFF2ennXbK8ccfnyRpbm7O6aefngsuuCA77rhjdthhh1x44YWZNWtWzy6hDK85c5KFC5Ozzuq7O2hdXSVUW7jQjqAAAADA2FPVYO3BBx/MwQcf3PN6/bpnp556aq644oo8/vjjue666/KnP/0p06ZNy8EHH5zvfOc7mTx5cs81l19+eerq6nLCCSeks7Mzhx56aK655prU1tZu9fczXp15ZjJrVnL55ZXdP7u7K2uqzZ1bGakmVAMAAADGolK5XC5Xu4hqa2trS3Nzc1pbW9Nkhf0t0tlZ2f2zqcmaasAY0tGRNDZWvm9vt8YaAACMYYPJiUbV5gWMfPX1AjVgDGpoSPweCgAA2MCo2rwAAAAAAEYKwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1oCto6MjKZUqR0dHtasBAACALSZYAwakszNZsaLyFQAAABCsAZuxdGkyb17S2Ji0tFS+zpuX3HtvtSsDAACA6hKsARt1xRXJgQcmN9+cdHdX2rq7K68POCC58srq1gcAAADVJFgD+rV0aXL22Um5nKxb1/vcunWV9rPOMnINAACA8auu2gUAI9OCBUltbd9Q7eVqa5PLL09mz97gRH+bE7y8bWObFzQ0DLpOAAAAqJZSuVwuV7uIamtra0tzc3NaW1vT1NRU7XKg6jo7K2uprZ/+uSk1NUl7e1Jf/7LGUqnYD/bPEQAAAFU2mJzIVFCgj7a2gYVqSaVfW9vw1gMAAAAjkamgsBGdnZXAqKlpg9FY40BTU2Uk2kBHrPUJ8Nvb+3bs6EimTq18v2KFaZ8AAACMekaswQaWLk3mzatMhWxpqXydN298LdJfX5/MnZvUbSZ6r6tLjj++n+CxoaH/Y6DnAQAAYBQQrMHLXHFFcuCByc03vzRaq7u78vqAA5Irr6xufVvT/PlJV9em+3R1Jeefv3XqAQAAgJFGsAZ/tnRpcvbZlfXzN9wJc926SvtZZ42fkWtz5iQLF1b2Idhw5FpdXaV94cJ+dgQFAACAcUKwBn+2YEFSW7vpPrW1yeWXb516RoIzz0yWLKlMC635878WNTWV10uWVM4DAADAeFUql8vlahdRbYPZRpWxqbOzspbaQBfrb28ffxsabPFmDh0dlT/kpPIHaE01AAAARqDB5ER2BYVUAqOBhGpJpV9b2/gL1urrt/A9NzRU5tMCAADAGGEqKKQyCqtmgP811NRU+gMAAADjm2ANUhmJNXdu30X6N1RXlxx//PgbrQYAAAD0JViDP5s/P+nq2nSfrq7k/PO3Tj0AAADAyCZYgz+bMydZuDAplfqOXKurq7QvXJjMnl2d+gAAAICRRbAGL3PmmcmSJZVpoevXXKupqbxesqRyHgAAACCxKyj0MXt25ejsrOz+2dRkTTUAAACgL8EabER9vUANAAAA2DhTQQEAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABRQV+0CRoJyuZwkaWtrq3IlAAAAAFTT+nxofV60KYK1JM8//3ySZMaMGVWuBAAAAICR4Pnnn09zc/Mm+5TKA4nfxrju7u787ne/y+TJk1MqlapdzqjW1taWGTNm5Le//W2ampqqXQ5jiGeL4eLZYrh4thguni2Gi2eL4eLZYjgNx/NVLpfz/PPPZ/r06amp2fQqakasJampqckuu+xS7TLGlKamJv9gMiw8WwwXzxbDxbPFcPFsMVw8WwwXzxbDaaifr82NVFvP5gUAAAAAUIBgDQAAAAAKEKwxpCZNmpRPfOITmTRpUrVLYYzxbDFcPFsMF88Ww8WzxXDxbDFcPFsMp2o/XzYvAAAAAIACjFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1+rjnnnty7LHHZvr06SmVSrnxxht7zq1duzYf/vCHM2vWrDQ0NGT69On5q7/6q/zud7/rdY/Vq1fn3HPPzU477ZSGhoYcd9xxefbZZ3v1WbVqVU455ZQ0Nzenubk5p5xySv70pz9thXdItWzq2drQGWeckVKplC996Uu92j1b9Gcgz9aTTz6Z4447Ls3NzZk8eXLe9ra35Zlnnuk579miP5t7ttrb23POOedkl112SX19fV73utfliiuu6NXHs0V/Lrvssrz5zW/O5MmTM2XKlLzrXe/KL37xi159yuVyLrnkkkyfPj319fU56KCD8rOf/axXH88XG9rcs+XzPEUN5N+tl/N5noEa6LM1Uj/PC9boo6OjI294wxvy1a9+tc+5F154IQ8//HA+9rGP5eGHH86iRYvyy1/+Mscdd1yvfuedd14WL16c66+/PkuXLk17e3uOOeaYdHV19fQ5+eST8+ijj+bWW2/NrbfemkcffTSnnHLKsL8/qmdTz9bL3Xjjjfm///f/Zvr06X3Oebboz+aerV//+teZM2dOXvva1+auu+7KT3/603zsYx/LNtts09PHs0V/NvdsnX/++bn11lvzrW99K08++WTOP//8nHvuubnpppt6+ni26M/dd9+ds88+O/fff39uv/32rFu3LkcccUQ6Ojp6+nzuc5/LggUL8tWvfjUPPPBAWlpacvjhh+f555/v6eP5YkObe7Z8nqeogfy7tZ7P8wzGQJ6tEf15vgybkKS8ePHiTfb5yU9+Uk5Sfvrpp8vlcrn8pz/9qTxhwoTy9ddf39PnueeeK9fU1JRvvfXWcrlcLv+///f/yknK999/f0+fH//4x+Uk5Z///OdD/0YYcTb2bD377LPlnXfeufzEE0+Ud9111/Lll1/ec86zxUD092ydeOKJ5fe9730bvcazxUD092zttdde5U9+8pO92t70pjeV/+7v/q5cLnu2GLiVK1eWk5TvvvvucrlcLnd3d5dbWlrKn/nMZ3r6vPjii+Xm5ubylVdeWS6XPV8MzIbPVn98nqeIjT1bPs+zpfp7tkby53kj1thira2tKZVK2W677ZIkDz30UNauXZsjjjiip8/06dMzc+bM3HfffUmSH//4x2lubs5b3/rWnj5ve9vb0tzc3NOH8ae7uzunnHJK/uf//J/Za6+9+pz3bFFEd3d3/u3f/i177rlnjjzyyEyZMiVvfetbe03p82xR1Jw5c/L9738/zz33XMrlcn70ox/ll7/8ZY488sgkni0GrrW1NUmyww47JEmeeuqpLF++vNezM2nSpLz97W/veS48XwzEhs/Wxvr4PM9g9fds+TzPUNjw2Rrpn+cFa2yRF198MR/5yEdy8sknp6mpKUmyfPnyTJw4Mdtvv32vvlOnTs3y5ct7+kyZMqXP/aZMmdLTh/Hns5/9bOrq6vK3f/u3/Z73bFHEypUr097ens985jM56qijctttt+X444/PvHnzcvfddyfxbFHcV77ylbz+9a/PLrvskokTJ+aoo47KwoULM2fOnCSeLQamXC5n/vz5mTNnTmbOnJkkPX/3U6dO7dV3w2fH88Wm9PdsbcjneYrY2LPl8zxbqr9na6R/nq8rfCXj3tq1a/Oe97wn3d3dWbhw4Wb7l8vllEqlntcv/35jfRg/HnrooXz5y1/Oww8/POhnwLPFpnR3dydJ5s6dm/PPPz9J8pd/+Ze57777cuWVV+btb3/7Rq/1bLE5X/nKV3L//ffn+9//fnbdddfcc889OeusszJt2rQcdthhG73Os8XLnXPOOXnssceydOnSPuc2fAYG8lx4vlhvU89W4vM8xfX3bPk8z1Do79ka6Z/njVijkLVr1+aEE07IU089ldtvv73nt1tJ0tLSkjVr1mTVqlW9rlm5cmXPb11bWlqyYsWKPvf9/e9/3+c3s4wPS5YsycqVK/MXf/EXqaurS11dXZ5++ulccMEFeeUrX5nEs0UxO+20U+rq6vL617++V/vrXve6nl2EPFsU0dnZmYsvvjgLFizIsccem7333jvnnHNOTjzxxHzhC19I4tli884999x8//vfz49+9KPssssuPe0tLS1J0uc36Bs+O54vNmZjz9Z6Ps9T1MaeLZ/n2VIbe7ZG+ud5wRqDtv5/hH/1q1/ljjvuyI477tjr/D777JMJEybk9ttv72lbtmxZnnjiiey///5Jkv322y+tra35yU9+0tPn//7f/5vW1taePowvp5xySh577LE8+uijPcf06dPzP//n/8wPf/jDJJ4tipk4cWLe/OY399my+5e//GV23XXXJJ4tilm7dm3Wrl2bmpreH6dqa2t7frPq2WJjyuVyzjnnnCxatCh33nlndtttt17nd9ttt7S0tPR6dtasWZO7776757nwfNGfzT1bic/zFLO5Z8vneYra3LM14j/PF972gDHr+eefLz/yyCPlRx55pJykvGDBgvIjjzxSfvrpp8tr164tH3fcceVddtml/Oijj5aXLVvWc6xevbrnHmeeeWZ5l112Kd9xxx3lhx9+uHzIIYeU3/CGN5TXrVvX0+eoo44q77333uUf//jH5R//+MflWbNmlY855phqvGW2kk09W/3ZcBehctmzRf8292wtWrSoPGHChPLXvva18q9+9avy//pf/6tcW1tbXrJkSc89PFv0Z3PP1tvf/vbyXnvtVf7Rj35U/s///M/y1VdfXd5mm23KCxcu7LmHZ4v+/M3f/E25ubm5fNddd/X6PPXCCy/09PnMZz5Tbm5uLi9atKj8+OOPl0866aTytGnTym1tbT19PF9saHPPls/zFDWQf7c25PM8AzGQZ2skf54XrNHHj370o3KSPsepp55afuqpp/o9l6T8ox/9qOcenZ2d5XPOOae8ww47lOvr68vHHHNM+Zlnnun1c/7whz+U3/ve95YnT55cnjx5cvm9731vedWqVVv3zbJVberZ6k9//0Ps2aI/A3m2rrrqqvLuu+9e3mabbcpveMMbyjfeeGOve3i26M/mnq1ly5aVTzvttPL06dPL22yzTfk1r3lN+Ytf/GK5u7u75x6eLfqzsc9TV199dU+f7u7u8ic+8YlyS0tLedKkSeUDDzyw/Pjjj/e6j+eLDW3u2fJ5nqIG8u/WhnyeZyAG+myN1M/zpT+/CQAAAABgEKyxBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAMMK88pWvzJe+9KWq/OxLLrkkU6dOTalUyo033liVGgAARgvBGgDARpRKpU0ep5122mavH45wqqOjIx/+8Ifzqle9Kttss01e8YpX5KCDDsq//uu/btF9n3zyyfz93/99/vEf/zHLli3L0UcfPST1nnbaaXnXu941JPcCABhJ6qpdAADASLVs2bKe77/zne/k4x//eH7xi1/0tNXX11ejrJx55pn5yU9+kq9+9at5/etfnz/84Q+577778oc//GGL7vvrX/86STJ37tyUSqWhKHXYrV27NhMmTKh2GQDAOGXEGgDARrS0tPQczc3NKZVKvdq+/e1v59WvfnUmTpyY17zmNfnmN7/Zc+0rX/nKJMnxxx+fUqnU8/rXv/515s6dm6lTp6axsTFvfvObc8cddwyqrptvvjkXX3xx3vGOd+SVr3xl9tlnn5x77rk59dRTe/qsXLkyxx57bOrr67Pbbrvln/7pnzY5xfSSSy7JsccemySpqanpFaxdffXVed3rXpdtttkmr33ta7Nw4cJe1z733HM58cQTs/3222fHHXfM3Llz85vf/Kbnvtdee21uuummnpF+d911V7813HrrrZkzZ06222677LjjjjnmmGN6wr4k+c1vfpNSqZR/+Zd/yUEHHZRtttkm3/rWtwZU44c//OHsueee2XbbbfOqV70qH/vYx7J27doB/XkDAGyMYA0AoIDFixfngx/8YC644II88cQTOeOMM/L+978/P/rRj5IkDzzwQJJK4LNs2bKe1+3t7XnHO96RO+64I4888kiOPPLIHHvssXnmmWcG/LNbWlpyyy235Pnnn99on9NOOy2/+c1vcuedd+Z73/teFi5cmJUrV260/4UXXpirr746SWWk3vrRel//+tfz0Y9+NJ/+9Kfz5JNP5tJLL83HPvaxXHvttUmSF154IQcffHAaGxtzzz33ZOnSpWlsbMxRRx2VNWvW5MILL8wJJ5yQo446que++++/f781dHR0ZP78+XnggQfy7//+76mpqcnxxx+f7u7uXv0+/OEP52//9m/z5JNP5sgjj9xsjcn/3979hES5hXEc/1VOMaWjJto/Jy0EGzOocSrSJo2gqUAs+0MlVFS2qhiDKSmEUleZmLRSi5kWhUWMi2gh4aIaxYWuIiehjGxXRG0iDfPcxaWX/NO1mRtcuHw/MItz3nPe87xnNTyc93mlpKQkhUIhDQwMqLm5WW1tbWpqavq9DQcAAPgVAwAAgBkFg0GTnJxstQsLC01lZeWEMfv37ze7du2y2pJMR0fHjPfOy8szN27csNpZWVmmqanpl+OfPHliMjMzjc1mMx6Px/j9fhOJRKzrg4ODRpLp7e21+qLRqJH0j/ft6Ogwk/8eOp1Oc/fu3Ql9dXV1ZtOmTcYYY27dumVyc3PN+Pi4dX10dNTY7XbT2dlpjDHm6NGjpqys7Jfr/sr79++NJPP8+XNjjDFv3rwxksz169djinE6V69eNQUFBTHHBAAA8DNOrAEAAMQhGo2qqKhoQl9RUZGi0eg/zvvy5YvOnz+vvLw8paSkKDExUS9fvozpxNqWLVs0NDSkrq4u7d27Vy9evJDX61VdXZ0VW0JCgjwejzVn1apVSklJ+f0HlPThwwe9e/dOJ06cUGJiovWrr6+3XtHs7+/Xq1evlJSUZF1fuHChRkZGJrzG+Ttev36tw4cPa+XKlXI4HFqxYoUkTdmbn5/rd2KUpAcPHmjz5s1avHixEhMTVVNTE9OeAwAATIePFwAAAMRpcoF/Y8yMRf8DgYA6Ozt17do15eTkyG63a9++ffr27VtMa9tsNnm9Xnm9XlVXV6u+vl61tbW6cOGCjDHTxherH69gtrW1aePGjROuzZkzxxpTUFCgO3fuTJmfnp4e03qlpaVyOp1qa2vT0qVLNT4+rvz8/Cl7s2DBgphi7O3t1cGDB3XlyhX5fD4lJyervb1djY2NMcUHAAAwGYk1AACAOLhcLkUiER05csTq6+npkcvlsto2m03fv3+fMO/Zs2c6duyY9uzZI+nvmms/Cv3/G3l5eRobG9PIyIhcLpfGxsbU19enDRs2SJIGBwf1+fPnmO65aNEiLVu2TENDQ6qoqJh2jNvt1r1795SRkSGHwzHtmLlz507Zh8k+fvyoaDSqlpYWeb1eSVIkEvkjMXZ3dysrK0uXLl2y+t6+fTvjvQEAAGZCYg0AACAOgUBABw4ckNvt1rZt2/Tw4UOFw+EJX/jMzs5WV1eXioqKNG/ePKWmpionJ0fhcFilpaWaNWuWampqphTnn0lJSYkOHTokj8ejtLQ0DQwM6OLFi9q6dascDoccDod27NihyspKtba2KiEhQX6/X3a7PebnvHz5ss6ePSuHw6GdO3dqdHRUfX19+vTpk86dO6eKigo1NDSorKxMtbW1yszM1PDwsMLhsAKBgDIzM5Wdna3Ozk4NDg4qLS1NycnJstlsE9b58UXR1tZWLVmyRMPDw6qurv4jMebk5Gh4eFjt7e1av369Hj16pI6Ojpj3AgAAYDJqrAEAAMRh9+7dam5uVkNDg1avXq2WlhYFg0GVlJRYYxobG/X48WM5nU6tW7dOktTU1KTU1FQVFhaqtLRUPp9Pbrc7prV9Pp9u376t7du3y+Vy6cyZM/L5fLp//741JhgMyul0qri4WOXl5Tp16pQyMjJifs6TJ0/q5s2bCoVCWrNmjYqLixUKhaz6Z/Pnz9fTp0+1fPlylZeXy+Vy6fjx4/r69at1gq2yslK5ubnyeDxKT09Xd3f3lHVmz56t9vZ29ff3Kz8/X1VVVWpoaPgjMZaVlamqqkqnT5/W2rVr1dPTo5qampj3AgAAYLJZ5kcRDgAAAPyvZWdny+/3y+/3/9ehAAAA/C9wYg0AAAAAAACIA4k1AAAAAAAAIA68CgoAAAAAAADEgRNrAAAAAAAAQBxIrAEAAAAAAABxILEGAAAAAAAAxIHEGgAAAAAAABAHEmsAAAAAAABAHEisAQAAAAAAAHEgsQYAAAAAAADEgcQaAAAAAAAAEIe/AGsrTWyeduxcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location == location) & (df.BHK == 2)]\n",
    "    bhk3 = df[(df.location == location) & (df.BHK == 3)]\n",
    "    matplotlib.rcParams[\"figure.figsize\"] = (15,10)\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='red',label='3 BHK', s=100)\n",
    "    plt.xlabel('Total Sq feet area')\n",
    "    plt.ylabel('Price per sq feet')\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df6, 'Rajaji Nagar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5ee1258d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ddlpuu+22QcO1adOm5ec//3mS5Pnnn89PfvKTfOADH8jOO++cE044obffu9/97nz0ox/tc+3SpUtz0UUX9WmbMmVK5s2bl69+9as59dRT86pXvWoU3yEAAABA9QjWJrBJkyZlzz33TJIceOCBufvuu/PFL34xX/7ylzd7TV1dXe81SbLffvvlpptuyiWXXNInWGtubu7TL0mmT5/e73719fW59tprc9JJJ+WII47ILbfcMuSoOQAAAIDxwBprY6FGd8koiiJr164d8XX19fXp7Oys+HUnT56cpUuX5vWvf32OOOKIPPTQQxXfCwAAAKghjY3lGXhFMeE3KhiIEWtjYagv0lbYFeMjH/lIjjvuuMyaNSvPPfdcrrnmmtx666258cYbB72uKIqsWrUqSXmNtZtvvjk//vGP8/GPf3yL6pk0aVK+//3v5+STT86RRx6Zf/u3f8u+++67RfcEAAAAqCbB2gS1evXqvPe9783KlSvT3Nyc/fbbLzfeeGOOPvroQa9ra2vLbrvtlqQ80mz27Nn5+7//+3z4wx/e4pq22267/Mu//EtOPfXU3nBtv/322+L7AgAAAFRDqShGecX8caitrS3Nzc1pbW1NU1NTn3MvvPBCHn300eyxxx79drwckY6ODZsatLdvk8Mjh2PUPm8AAACACgyWE23KGmsAAAAAUAHBGgAAAABUwBprW8v6XTIAAAAAmBCMWAMAAACACgjWhskeD1uHzxkAAAAYLwRrQ9huu+2SJM8//3yVK9k2rP+c13/uAAAAALXKGmtDqK+vz0te8pKsWbMmSbLDDjukVCpVuaqJpyiKPP/881mzZk1e8pKXpL6+vtolAQAAAAxKsDYMLS0tSdIbrjF2XvKSl/R+3gAAAAC1TLA2DKVSKbvttlumT5+eF198sdrlTFjbbbedkWoAAADAuCFYG4H6+nrBDwAAAABJbF4AAAAAABURrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABWoarB2+eWXZ7/99ktTU1Oamppy8MEH51//9V97zxdFkQsvvDAzZ87MlClTcvjhh+cXv/hFn3usXbs255xzTnbZZZc0NjbmbW97W5588smt/VYAAAAA2MZUNVjbfffd85nPfCb33HNP7rnnnhx55JGZP39+b3j22c9+NosXL85ll12Wu+++Oy0tLTn66KPz3HPP9d5j4cKFWbZsWa655pqsWLEi7e3tOf7449Pd3V2ttwUAAADANqBUFEVR7SI2ttNOO+Vzn/tc/uIv/iIzZ87MwoUL8+EPfzhJeXTajBkzcskll+QDH/hAWltbs+uuu+ab3/xmTjnllCTJ008/nVmzZuWGG27IscceO6zXbGtrS3Nzc1pbW9PU1DRm7w0AAACA2jaSnKhm1ljr7u7ONddck46Ojhx88MF59NFHs2rVqhxzzDG9fSZPnpw3vvGNufPOO5Mk9957b1588cU+fWbOnJk5c+b09hnI2rVr09bW1ucAAAAAgJGoerD24IMPZurUqZk8eXLOOOOMLFu2LPvss09WrVqVJJkxY0af/jNmzOg9t2rVqkyaNCk77rjjZvsM5OKLL05zc3PvMWvWrFF+VwAAAABMdFUP1l75ylfm/vvvz1133ZUPfvCDOe200/Kf//mfvedLpVKf/kVR9Gvb1FB9LrjggrS2tvYeTzzxxJa9CQAAAAC2OVUP1iZNmpQ999wzBx54YC6++OLsv//++eIXv5iWlpYk6TfybM2aNb2j2FpaWrJu3bo8++yzm+0zkMmTJ/fuRLr+AAAAAICRqHqwtqmiKLJ27drsscceaWlpyc0339x7bt26dbnttttyyCGHJEkOOOCAbLfddn36rFy5Mg899FBvHwAAAAAYCw3VfPGPfOQjOe644zJr1qw899xzueaaa3LrrbfmxhtvTKlUysKFC3PRRRdlr732yl577ZWLLrooO+ywQ971rnclSZqbm/P+978/5513XnbeeefstNNOOf/887PvvvvmqKOOquZbAwAAAGCCq2qwtnr16rz3ve/NypUr09zcnP322y833nhjjj766CTJhz70oXR2dubMM8/Ms88+mze84Q256aabMm3atN57XHrppWloaMjJJ5+czs7OvOlNb8pVV12V+vr6ar0tAAAAALYBpaIoimoXUW1tbW1pbm5Oa2ur9dYAAAAAtmEjyYlqbo01AAAAABgPBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAMJF0dCSlUvno6Kh2NROaYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAKCNQAAAACoQEO1CwAAAACgQgNtTrBx2+Y2L2hsHJt6tjGCNQAAAIDxaurUwc/PmDFwe1GMfi3bIFNBAQAAAKACRqwBAAAAjFft7f3bOjo2jFRbvdq0zzEkWAMAAAAYr4YKzRobBWtjyFRQAAAAAKiAYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAI2LwAAAACYSBobk6KodhXbBCPWAAAAAKACgjUAAAAAqIBgDQAAAAAqIFgDAAAAgAoI1gAAAACgAoI1AAAAAKiAYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAKCNQAAAACogGANAACojo6OpFQqHx0d1a4GAEZMsAYAAAAAFRCsAQAAAEAFBGsAAAAAUIGqBmsXX3xxXve612XatGmZPn163v72t+eRRx7p0+f0009PqVTqcxx00EF9+qxduzbnnHNOdtlllzQ2NuZtb3tbnnzyya35VgAAAADYxlQ1WLvtttty1lln5a677srNN9+crq6uHHPMMenYZOHSN7/5zVm5cmXvccMNN/Q5v3DhwixbtizXXHNNVqxYkfb29hx//PHp7u7emm8HAADYnI6OgY/hngeAGtRQzRe/8cYb+zy/8sorM3369Nx777057LDDetsnT56clpaWAe/R2tqar3/96/nmN7+Zo446KknyrW99K7NmzcpPfvKTHHvssf2uWbt2bdauXdv7vK2tbTTeDgAAsDlTpw5+fsaMgduLYvRrAYBRUlNrrLW2tiZJdtpppz7tt956a6ZPn5699947f/mXf5k1a9b0nrv33nvz4osv5phjjultmzlzZubMmZM777xzwNe5+OKL09zc3HvMmjVrDN4NAAAAABNZzQRrRVFk0aJFmTdvXubMmdPbftxxx+Wf/umfcsstt+Tzn/987r777hx55JG9I85WrVqVSZMmZccdd+xzvxkzZmTVqlUDvtYFF1yQ1tbW3uOJJ54YuzcGAAAk7e39j9WrN5xfvXrgPgBQw6o6FXRjZ599dh544IGsWLGiT/spp5zS+3jOnDk58MADM3v27PzoRz/KggULNnu/oihSKpUGPDd58uRMnjx5dAoHAACG1tg49Pmh+gBAjamJEWvnnHNOfvCDH+SnP/1pdt9990H77rbbbpk9e3Z+9atfJUlaWlqybt26PPvss336rVmzJjM2t04DAAAAAGyhqgZrRVHk7LPPztKlS3PLLbdkjz32GPKaZ555Jk888UR22223JMkBBxyQ7bbbLjfffHNvn5UrV+ahhx7KIYccMma1AwAAALBtq+pU0LPOOivf/va3c91112XatGm9a6I1NzdnypQpaW9vz4UXXpiTTjopu+22W37729/mIx/5SHbZZZeceOKJvX3f//7357zzzsvOO++cnXbaKeeff3723Xff3l1CAQAAAGC0VTVYu/zyy5Mkhx9+eJ/2K6+8Mqeffnrq6+vz4IMP5uqrr84f//jH7LbbbjniiCPyne98J9OmTevtf+mll6ahoSEnn3xyOjs786Y3vSlXXXVV6uvrt+bbAQAAAGAbUiqKoqh2EdXW1taW5ubmtLa2pqmpqdrlAAAAAFAlI8mJamLzAgAAAAAYbwRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAQGU6OpJSqXx0dFS7mq1OsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUoKHaBQAAAAAwDgy0OcHGbZvbvKCxcWzqqQGCNQAAAACGNnXq4OdnzBi4vShGv5YaYSooAAAAAFTAiDUAAAAAhtbe3r+to2PDSLXVqyf0tM+BCNYAAAAAGNpQoVlj4zYXrJkKCgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFTA5gUAAAAAVKaxMSmKaldRNUasAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAE1tHR1IqlY+OjmpXAwBMIII1AAAAAKiAYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAIN1S4AAABGzUCbE2zctrnNCxobx6YeAGBCE6wBADBxTJ06+PkZMwZuL4rRrwUAmPBMBQUAAACAChixBgDAxNHe3r+to2PDSLXVq037BABGjWANAICJY6jQrLFRsAYAjBpTQQEAAACgAoI1AAAAAKiAYA0AAAAAKiBYAwAAAIAK2LwAAICJrbExKYpqVwEATEBGrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAAAABQAcEaAAAAAFRgxMHa1VdfnbVr1/ZrX7duXa6++upRKQoAAAAAal2pKIpiJBfU19dn5cqVmT59ep/2Z555JtOnT093d/eoFrg1tLW1pbm5Oa2trWlqaqp2OQAAAABUyUhyohGPWCuKIqVSqV/7k08+mebm5pHeDgAAAADGpYbhdnzNa16TUqmUUqmUN73pTWlo2HBpd3d3Hn300bz5zW8ekyIBAAAAoNYMO1h7+9vfniS5//77c+yxx2bq1Km95yZNmpSXv/zlOemkk0a9QAAAgC3W0ZGs/98w7e1JY2Nt3AuAcW3YwdonPvGJJMnLX/7ynHLKKdl+++23+MUvvvjiLF26NL/85S8zZcqUHHLIIbnkkkvyyle+srdPURT55Cc/ma985St59tln84Y3vCFf+tKX8upXv7q3z9q1a3P++efnn//5n9PZ2Zk3velNWbJkSXbfffctrhEAAAAABjLiNdZOO+20vPDCC/na176WCy64IH/4wx+SJD//+c/z1FNPjehet912W84666zcddddufnmm9PV1ZVjjjkmHR0dvX0++9nPZvHixbnsssty9913p6WlJUcffXSee+653j4LFy7MsmXLcs0112TFihVpb2/P8ccfPy43UgAAAABgfBjxrqAPPPBAjjrqqDQ3N+e3v/1tHnnkkbziFa/Ixz72sTz22GO5+uqrKy7md7/7XaZPn57bbrsthx12WIqiyMyZM7Nw4cJ8+MMfTlIenTZjxoxccskl+cAHPpDW1tbsuuuu+eY3v5lTTjklSfL0009n1qxZueGGG3LssccO+bp2BQUAgAnOVFAAhmlMdwU999xzc/rpp+dXv/pVn+mgxx13XG6//faRV7uR1tbWJMlOO+2UJHn00UezatWqHHPMMb19Jk+enDe+8Y258847kyT33ntvXnzxxT59Zs6cmTlz5vT22dTatWvT1tbW5wAAAACAkRj2Gmvr3XPPPfnKV77Sr/2lL31pVq1aVXEhRVFk0aJFmTdvXubMmZMkvfebMWNGn74zZszIY4891ttn0qRJ2XHHHfv12Vw9F198cT75yU9WXCsAAFDDNlpaZsC2gc4nA488G817ATDhjDhY23777Qcc4fXII49k1113rbiQs88+Ow888EBWrFjR71ypVOrzvCiKfm2bGqzPBRdckEWLFvU+b2try6xZsyqoGgAAqDnrp2luzib/4b7XQKvkjOa9AJhwRjwVdP78+fn7v//7vPjii0nKodfjjz+ev/3bv81JJ51UURHnnHNOfvCDH+SnP/1pn508W1pakqTfyLM1a9b0jmJraWnJunXr8uyzz262z6YmT56cpqamPgcAAAAAjMSIg7V/+Id/6N1koLOzM2984xuz5557Ztq0afn0pz89onsVRZGzzz47S5cuzS233JI99tijz/k99tgjLS0tufnmm3vb1q1bl9tuuy2HHHJIkuSAAw7Idttt16fPypUr89BDD/X2AQAAtiHt7f2P1as3nF+9euA+Y30vACacEU8FbWpqyooVK3LLLbfk5z//eXp6evLa1742Rx111Ihf/Kyzzsq3v/3tXHfddZk2bVrvyLTm5uZMmTIlpVIpCxcuzEUXXZS99tore+21Vy666KLssMMOede73tXb9/3vf3/OO++87Lzzztlpp51y/vnnZ999962oJgAAYJwban2zxsbhr4E2mvcCYMIZcbC23pFHHplDDjkkkydPHnK9s825/PLLkySHH354n/Yrr7wyp59+epLkQx/6UDo7O3PmmWfm2WefzRve8IbcdNNNmTZtWm//Sy+9NA0NDTn55JPT2dmZN73pTbnqqqtSX19fUV0AAFCTOjo2rPnV3i7QGYk1a/o+3mS2DABUolQUI1tVs6enJ5/+9KdzxRVXZPXq1fmv//qvvOIVr8jHPvaxvPzlL8/73//+sap1zLS1taW5uTmtra3WWwMAoHYJ1ir36KPJK15Rfvyb32xZsObfAWBCG0lONOI11j71qU/lqquuymc/+9lMmjSpt33ffffN1772tZFXCwAAAADj0IiDtauvvjpf+cpX8u53v7vPVMv99tsvv/zlL0e1OAAAAACoVSNeY+2pp57Knnvu2a+9p6cnL7744qgUBQAAMKo2nq65pVM3GxuTka2oA8AENeJg7dWvfnWWL1+e2bNn92n/7ne/m9e85jWjVhgAAGzTOjoGbxvofGK9r6TvRgXr/e53Az/e2PTpY1MPABPWiIO1T3ziE3nve9+bp556Kj09PVm6dGkeeeSRXH311fnhD384FjUCAMC2Z/3i+JszY8bA7UZSbf6zWW/OnIHbfXYAjNCI11g74YQT8p3vfCc33HBDSqVSPv7xj+fhhx/O9ddfn6OPPnosagQAAACAmjOsEWv/+I//mL/6q7/K9ttvn8cffzzHHHNMjj322LGuDQAAtl3t7f3bOjo2jMZavdq0z81Zvbp/2+9+t2Gk2kMPJbvuunVrAmBCKhXF0OOdGxoa8vTTT2f69Ompr6/PypUrM30CrT/Q1taW5ubmtLa2pqmpqdrlAADAwDo6NkwRbW8XrI3EmjV9Q8kJ9L9nABhdI8mJhjVibebMmfn+97+ft7zlLSmKIk8++WReeOGFAfu+7GUvG3nFAAAAADDODCtY+7u/+7ucc845Ofvss1MqlfK6172uX5+iKFIqldLd3T3qRQIAAABArRlWsPZXf/VXOfXUU/PYY49lv/32y09+8pPsvPPOY10bAAAAANSsYQVrSTJt2rTMmTMnV155ZebOnZvJkyePZV0AAAAAUNOGtXnBRGfzAgAAAACSkeVEdVupJgCAbUtHR1IqlY+OjmpXAwDAGBCsAQAAAEAFBGsAAAAAUAHBGgAAAABUYNi7gq63aNGiYfddvHjxSG8PAAAAAOPCiIO1++67Lz//+c/T1dWVV77ylUmS//qv/0p9fX1e+9rX9vYrlUqjVyUAQC0baHOCjds2t3lBY+PY1AMAwFYx4mDthBNOyLRp0/KNb3wjO+64Y5Lk2Wefzfve974ceuihOe+880a9SACAmjZ16uDnZ8wYuL0oRr8WAAC2mlJRjOz/R/fSl740N910U1796lf3aX/ooYdyzDHH5Omnnx7VAreGtra2NDc3p7W1NU1NTdUuBwAYbyodqS9YAwCoOSPJiUY8Yq2trS2rV6/uF6ytWbMmzz333EhvBwAw/rW392/r6NgwUm31atM+AQAmoBEHayeeeGLe97735fOf/3wOOuigJMldd92V/+//+/+yYMGCUS8QAKDmDRWaNTYK1gAAJqARB2tXXHFFzj///LznPe/Jiy++WL5JQ0Pe//7353Of+9yoFwgAAAAAtWjEa6yt19HRkf/+7/9OURTZc8890ziO/yusNdYAgFHX0bFhU4P2diPWAADGiZHkRHWVvkhjY2P222+/7LHHHrn55pvzy1/+stJbAQAAAMC4M+Jg7eSTT85ll12WJOns7MyBBx6Yk08+Ofvuu2++//3vj3qBAAAAAFCLRhys3X777Tn00EOTJMuWLUtRFPnjH/+Yf/zHf8ynPvWpUS8QAGBcamxMiqJ8mAYKADAhjThYa21tzU477ZQkufHGG3PSSSdlhx12yFvf+tb86le/GvUCAQAAAKAWjThYmzVrVn72s5+lo6MjN954Y4455pgkybPPPpvtt99+1AsEAAAAgFrUMNILFi5cmHe/+92ZOnVqZs+encMPPzxJeYrovvvuO9r1AQAAAEBNGnGwduaZZ+YNb3hDHn/88Rx99NGpqysPenvFK15hjTUAAAAAthmloiiKahdRbW1tbWlubk5ra2uampqqXQ4AAAAAVTKSnGjEa6wBAAAAAII1AAAAAKiIYA0AAAAAKjCiYK2rqyuf/OQn88QTT4xVPQAAwEh0dCSlUvno6Kh2NQCwTRlRsNbQ0JDPfe5z6e7uHqt6AAAAAGBcGPFU0KOOOiq33nrrGJQCAAAAAONHw0gvOO6443LBBRfkoYceygEHHJDGxsY+59/2treNWnEAAAAAUKtKRVEUI7mgrm7zg9xKpdK4nCba1taW5ubmtLa2pqmpqdrlAADA8HV0JFOnlh+3tyeb/IdvAGBkRpITjXjEWk9PT8WFAQAAW2CgzQk2btvc5gXCNgAYEyMO1jb2wgsvZPvttx+tWgAAgMGsH5m2OTNmDNw+skkqAMAwjXjzgu7u7vyf//N/8tKXvjRTp07Nb37zmyTJxz72sXz9618f9QIBAAAAoBaNOFj79Kc/nauuuiqf/exnM2nSpN72fffdN1/72tdGtTgAAGAj7e39j9WrN5xfvXrgPgDAmBhxsHb11VfnK1/5St797nenvr6+t32//fbLL3/5y1EtDgAA2Ehj48DHcM8DAKNqxMHaU089lT333LNfe09PT1588cVRKQoAAAAAat2Ig7VXv/rVWb58eb/27373u3nNa14zKkUBAAAAQK0b8a6gn/jEJ/Le9743Tz31VHp6erJ06dI88sgjufrqq/PDH/5wLGoEAAAAgJoz4hFrJ5xwQr7zne/khhtuSKlUysc//vE8/PDDuf7663P00UePRY0AAGyJjo6kVCofHR3VrgYAYMIoFUVRVLuIamtra0tzc3NaW1vT1NRU7XIAAEZXR0cydWr5cXu7xewBAAYxkpxoxFNB17vnnnvy8MMPp1Qq5VWvelUOOOCASm8FAAAAAOPOiIO1J598MqeeemruuOOOvOQlL0mS/PGPf8whhxySf/7nf86sWbNGu0YAAAAAqDkjXmPtL/7iL/Liiy/m4Ycfzh/+8If84Q9/yMMPP5yiKPL+979/LGoEAAAAgJoz4hFry5cvz5133plXvvKVvW2vfOUr83//7//N3LlzR7U4AABGaKDNCTZu29zmBdZdAwAYsREHay972cvy4osv9mvv6urKS1/60lEpCgCACq3fpGBzZswYuN1+VgAAIzbiqaCf/exnc8455+See+7J+g1F77nnnvzN3/xN/uEf/mHUCwQAAACAWlQqipH958kdd9wxzz//fLq6utLQUB7wtv5x4yZTCP7whz+MXqVjaCTbqAIA1LTNTQVdP1Jt9eqBp32aCgoAkGRkOdGIp4J+4QtfqLQuAADG2lABWWOjEA0AYJSMOFg77bTTxqIOAAAAABhXRrzGGgAAAAAgWAMAAACAigjWAAAAAKACI15jDQCAcaaxMRnZRvAAAAxDxSPWfv3rX+fHP/5xOjs7kySF/88aAAAAANuQEQdrzzzzTI466qjsvffeectb3pKVK1cmSf73//7fOe+880a9QAAAAACoRSMO1s4999w0NDTk8ccfzw477NDbfsopp+TGG28c1eIAAAAAoFaNeI21m266KT/+8Y+z++6792nfa6+98thjj41aYQAAAABQy0Y8Yq2jo6PPSLX1fv/732fy5MmjUhQAAAAA1LoRB2uHHXZYrr766t7npVIpPT09+dznPpcjjjhiVIsDAAAAgFo14qmgn/vc53L44Yfnnnvuybp16/KhD30ov/jFL/KHP/whd9xxx1jUCAAAAAA1Z8Qj1vbZZ5888MADef3rX5+jjz46HR0dWbBgQe6777782Z/92VjUCAAAAAA1p1QURVHtIqqtra0tzc3NaW1tTVNTU7XLAQAAAKBKRpITjXjE2pVXXpnvfve7/dq/+93v5hvf+MZIbwcAAAAA49KIg7XPfOYz2WWXXfq1T58+PRdddNGoFAUAMCF0dCSlUvno6Kh2NQAAjLIRB2uPPfZY9thjj37ts2fPzuOPPz4qRQEAAABArRtxsDZ9+vQ88MAD/dr/4z/+IzvvvPOoFAUAAAAAtW7Ewdo73/nO/PVf/3V++tOfpru7O93d3bnlllvyN3/zN3nnO985FjUCAAAAQM1pGOkFn/rUp/LYY4/lTW96Uxoaypf39PTkf/2v/2WNNQAAAAC2GaWiKIpKLvyv//qv/Md//EemTJmSfffdN7Nnzx7t2raakWyjCgAwoIE2J+joSGbMKD9evTppbOzfZ6A2AACqZiQ50YhHrK239957Z++99670cgCAiWXq1MHPrw/YNlXZf+MEAKAGDCtYW7RoUf7P//k/aWxszKJFiwbtu3jx4lEpDAAAAABq2bCCtfvuuy8vvvhikuTnP/95SqXSgP021w4AMOG1t/dvG85UUAAAxq2K11ibSKyxBgCMiY6ODVNE29sFawAA48BIcqK6kdy4q6srDQ0Neeihh7aoQAAAAAAY70YUrDU0NGT27Nnp7u4eq3oAAAAAYFwYUbCWJH/3d3+XCy64IH/4wx/Goh4AAAAAGBeGtXnBxv7xH/8xv/71rzNz5szMnj07jZusFfLzn/981IoDAAAAgFo14mBt/vz5dv8EABiOxsbEPlEAABOWXUFjV1AAAAAAysZkV9Dnn38+Z511Vl760pdm+vTpede73pXf//73W1wsAAAAAIxHww7WPvGJT+Sqq67KW9/61rzzne/MzTffnA9+8INjWRsAAAAA1Kxhr7G2dOnSfP3rX8873/nOJMl73vOezJ07N93d3amvrx+zAgEAAACgFg17xNoTTzyRQw89tPf561//+jQ0NOTpp58ek8IAAAAAoJYNO1jr7u7OpEmT+rQ1NDSkq6tr1IsCAAAAgFo37KmgRVHk9NNPz+TJk3vbXnjhhZxxxhlpbGzsbVu6dOnoVggAAAAANWjYwdppp53Wr+0973nPqBYDAAAAAOPFsIO1K6+8cizrAAAAAIBxZdhrrAEAAAAAGwjWAKAaOjqSUql8dHRUuxoAAKACgjUAAAAAqIBgDQAAAAAqIFgDAAAAgAoI1gAAAACgAg3VLgAAJryBNifYuG1zmxc0No5NPQAAwKgQrAHAWJs6dfDzM2YM3F4Uo18LAAAwakwFBQAAAIAKGLEGAGOtvb1/W0fHhpFqq1eb9gkAAOOQYA0AxtpQoVljo2ANAADGIVNBAQAAAKACgjUAAAAAqIBgDQAAAAAqIFgDALZNHR1JqVQ+OjqqXQ0AAOOQzQsAoBoaG5OiqHYVAADAFjBiDQAAAAAqUNVg7fbbb88JJ5yQmTNnplQq5dprr+1z/vTTT0+pVOpzHHTQQX36rF27Nuecc0522WWXNDY25m1ve1uefPLJrfguAAAAANgWVTVY6+joyP7775/LLrtss33e/OY3Z+XKlb3HDTfc0Of8woULs2zZslxzzTVZsWJF2tvbc/zxx6e7u3usywcAAABgG1bVNdaOO+64HHfccYP2mTx5clpaWgY819ramq9//ev55je/maOOOipJ8q1vfSuzZs3KT37ykxx77LGjXjMAMA4NtDnBxm2b27ygsXFs6gEAYEKo+TXWbr311kyfPj177713/vIv/zJr1qzpPXfvvffmxRdfzDHHHNPbNnPmzMyZMyd33nnnZu+5du3atLW19TkAgAls6tT+x4wZG87PmDFwHwAAGERNB2vHHXdc/umf/im33HJLPv/5z+fuu+/OkUcembVr1yZJVq1alUmTJmXHHXfsc92MGTOyatWqzd734osvTnNzc+8xa9asMX0fAAAAAEw8VZ0KOpRTTjml9/GcOXNy4IEHZvbs2fnRj36UBQsWbPa6oihSKpU2e/6CCy7IokWLep+3tbUJ1wBgImtv79/W0bFh1Nrq1aZ9AgAwYjUdrG1qt912y+zZs/OrX/0qSdLS0pJ169bl2Wef7TNqbc2aNTnkkEM2e5/Jkydn8uTJY14vAFAjhgrNGhsFawAAjFhNTwXd1DPPPJMnnngiu+22W5LkgAMOyHbbbZebb765t8/KlSvz0EMPDRqsAQAAAMCWquqItfb29vz617/uff7oo4/m/vvvz0477ZSddtopF154YU466aTstttu+e1vf5uPfOQj2WWXXXLiiScmSZqbm/P+978/5513XnbeeefstNNOOf/887Pvvvv27hIKAAAAAGOhqsHaPffckyOOOKL3+fp1z0477bRcfvnlefDBB3P11Vfnj3/8Y3bbbbccccQR+c53vpNp06b1XnPppZemoaEhJ598cjo7O/OmN70pV111Verr67f6+wEAAABg21EqiqKodhHV1tbWlubm5rS2tqapqana5QAAW0NHRzJ1avlxe7s11gAASDKynGhcbV4AADBqGhsT/30RAIAtMK42LwAAAACAWiFYAwAAAIAKCNYAAAAAoAKCNQAAAACogGANAAAAACogWAMAAACACgjWAAAAAKACgjUAAAAAqIBgDQAAAAAqIFgDAAAAgAoI1gCALdfRkZRK5aOjo9rVAADAViFYAwAAAIAKCNYAAAAAoAKCNQAAAACogGANAAAAACrQUO0CAIBxZqDNCTZu29zmBY2NY1MPAABUiWANABiZqVMHPz9jxsDtRTH6tQAAQBWZCgoAAAAAFTBiDQC2FR0dG0abtbdXPjWzvX3ge68fqbZ6tWmfAABsEwRrAMDIDBWaNTYK1gAA2CaYCgoAAAAAFRCsAQAAAEAFBGsAAAAAUAFrrAHAWBqtDQMqed3B2gY6n1gbDQAARkCwBgAT0fowb3PW7+C5qaKo7PUaGyu/FgAAxilTQQGAbdOaNUmpVD7WrKl2NQAAjENGrAHARNTe3r+to2PDSLXVq037BACALSRYA4CJaKjQrLFRsAYAAFtIsAYAo8WGAQAAsE0RrAHAaNnaGwYAAABVJVgDACa+gTYn+N3vBn68senTx6YeAAAmBMEaAIwWGwbUrs2NFlxvzpyB240mBABgEII1ABgttb5hQGOjoAgAAEaRYA0AmPhWr+7f9rvfbRip9tBDya67bt2aAAAY9wRrAMDEN9Raabvuaj01AABGrK7aBQAANaKjIymVykdHR7WrAQCAmidYAwAAAIAKmAoKAGPJhgEAADBhGbEGAAAAABUwYg0A2DZNn240IQAAW0SwBgDbooE2J9i4bXObFzQ2jk09AAAwDgnWAGBbNHXq4OdnzBi43QgvAADoZY01AAAAAKiAEWsAsC1qb+/f1tGxYaTa6tWDT/vs6Ngw6q293RRRAAC2SYI1ANgWDRWENTYKywAAYAimggIAAABABQRrAAAAAFABwRoAAAAAVMAaawDA4Do6Bm8b6HxijTYAACY8wRoAUNbYmBRF//b1u39uzvqdRDc10L0AAGACMRUUAAAAACpgxBoAjDcdHRtGkbW3j/2Uy/b2gWtYP1Jt9WrTPgEA2CYJ1gCAwQ0VmjU2CtYAANgmmQoKAJStWZOUSuVjzZpqVwMAADVPsAZAP52d5dl9nZ3VrgQAAKB2CdYA6LViRbJgQXn5rpaW8t8FC5I77qh2ZQAAALVHsAZAkuTyy5PDDkuuvz7p6Sm39fSUnx96aHLFFdWtb5vV0THwMdzzAADAmLF5AQBZsSI566ykKJKurr7n1j8/88xk332TuXO3fn3btPW7f27O+p05N1UUo1/Lxhobx/41AACgxgnWAMjixUl9ff9QbWP19cmllwrWRqyjY0M41t5eO7tnDrQ5we9+N/DjjU2fPjb1AADAOCRYA9jGdXYm1123Yfrn5nR1JcuWlftPmbJ1aiPlMG5THR0bRqqtXl1ZWLe5kW7rzZkzcLtRagAA0EuwBrCNa2sbOlRbr6en3F+wthUNFZo1NtbOKDgAANjGCNYAtnFNTUld3fDCtbq6cn9GYNONBmolBFu9un/b7363YaTaQw8lu+66dWsCAIBxRrAGsI2bMiWZP7+8++dga6w1NJT7Ga02QQy1Vtquu1pPDQAAhlBX7QIAqL5Fi5Lu7sH7dHcn5567deoZtzo6yseaNUmpVD423gRg/flND8ZGR8eGfwefMwAAY0CwBkDmzUuWLCnnDw2bjGVuaCi3L1liR9AhTZ1aPjbeGGDjTQBe8YoNfTY+AACAcUmwBkCS5IwzkuXLy9M96/70fx3q6srPly8vn6dGNDaWd+csitpZsw0AALZB1lgDoNfcueWjs7O8+2dTkzXVRmT9hgAdHeXRaUny7/+evP715cc2BAAAgAlFsAZAP1OmCNQqsvEU0PXWh2pJ32mhGyuKsalnpKZPr51aAABgHBCsAcCWsCh+bRjo32Hjts39O5lKCwDAFhCsAcCWqHTzgfb20a1jWzfUv8NAowkTI/QAANgigjUAqAYjpQAAYNyzKygAbIn29v7H+k0MkuSaazY8vvbaDX0YXUP9O6xePXAfAADYAkasAcCWGGrk2UtesuHxjjsaqTZWhvpcGxt99gAAjDrBGgCMlrvvLv/t7NzQ9l//teHxL3858Harr3vd2NYFAACMiVJRWLW3ra0tzc3NaW1tTVNTU7XLASaIzs6krS1paho4SxnPJvJ72yKlUmXX+T/FY6OjY8OmBu3tRqwBADAsI8mJrLEGMMpWrEgWLCj/7/mWlvLfBQuSO+6odmVbbiK/NwAAgJEyYi1GrAGj5/LLk7POSurrk66uDe0NDUl3d7JkSXLGGdWrb0tM5Pc2ajaeCvrGN5Yff/nLyQc+UH581VXJPvv0v85U0LFhxBoAABUYSU4kWItgDRgdK1Ykhx02+Ky+UilZvjyZO3fr1TUaJvJ7G3N33528/vXlx//+70I0AACocaaCAlTB4sXl0VyDqa9PLr1069QzmibyextzG29ksPFjAABg3BOsAYyCzs7kuuv6TpEcSFdXsmzZ+MpXJvJ7AwAA2BKCNYBR0NaW9PQMr29PT7n/eDGR3xsAAMCWEKwBjIKmpqRumL+odXXl/uPFRH5vAAAAW0KwBjAKpkxJ5s8v75A5mIaG5MQTy/3Hi4n83raKAw4Y+DEAADDuCdYARsmiRUl39+B9uruTc8/dOvWMpon83kZVR8fAx3DPAwAA44pgDWCUzJuXLFmSlEr9R3c1NJTblyxJ5s6tTn1bYiK/t1E1dWr/Y8aMDednzBi4DwAAMC4J1gBG0RlnJMuXl6dOrl+XrK6u/Hz58vL58WoivzcAAIBKlIqiKKpdRLW1tbWlubk5ra2tabLqNjBKOjvLO2Q2NU28dccm8nvbIgNN6+zo2DBqbfXqpLGxf5+B2gAAgKoYSU40xFLUAKNvWwllpkyZuO9vIr+3LTJUQNbYKEQDAIAJxFRQYKtZsSJZsKC8pFRLS/nvggXJHXdUuzIAAAAYOcEasFVcfnly2GHJ9dcnPT3ltp6e8vNDD02uuKK69QEAAMBICdaAMbdiRXLWWUlRJF1dfc91dZXbzzzTyDUAAADGF8EaMOYWL07q6wfvU1+fXHrp1qkHAAAARoNgDRhTnZ3Jddf1H6m2qa6uZNmycn/66+wsbyjp8xmHGhvLwzKLwsYFAAAwwQjWgDHV1rZhTbWh9PSU+7OBDR8AAABql2ANGFNNTUndMH9p6urK/Smr5oYPRsgBAAAMTbAGjKkpU5L585OGhsH7NTQkJ55Y7k/1NnwwQg4AAGD4BGvAmFu0KOnuHrxPd3dy7rlbp57xoBobPlRzhBwAAMB4JFgDxty8ecmSJUmp1H/kWkNDuX3JkmTu3OrUV2uqseFDtUbIAQAAjGeCNWCrOOOMZPny8rTQ9Wuu1dWVny9fXj5PWTU2fKjGCDkAAIDxrlQURVHtIqqtra0tzc3NaW1tTZOV02HMdXaWw6CmJmuqDaSzs7y22XDCtbq6pL19yz7Hrf16AAAAtWwkOZERa8BWN2VKMmOGcGZztvaGD9UYIQcAADARCNYAatDW3PChqWnD9Nyh1NWV+wMAACBYA6hJBxyQfOYz5cdjveHD1h4hBwAAMFEI1gBqyIoVyYIF5TXPPvzh8gix6dPLQVoydhs+bM0RcgAAABOFYA2gRlx+eXLYYcn1129Y86ynJ1mzpvz4kkvKGwd873ujM1JtY/PmlUfAlUpjP0IOAABgohCsAdSAFSuSs85KiiLp6up7rqur3P63f5v8/OdjV8MZZ5RHws2fv2HNtbEaIQcAADARDLGiDgBbw+LFSX19/1BtY/X1yaWXju2osblzy0dnZ3n3z6Yma6oBAABsjmANoMo6O5Prrtsw/XNzurqSZcvK/cc67JoyRaAGAAAwFFNBAaqsrW3oUG29np5yfwAAAKpPsAZQZU1NG9Y0G0pdXbk/AAAA1SdYA6iyKVPKGwRsuhvnphoakhNPNEUTAACgVgjWAGrAokVJd/fgfbq7k3PP3Tr1AAAAMDTBGsAY6+xMVq8u/92cefOSJUuSUqn/yLWGhnL7kiVjuyMoAAAAIyNYAxgjK1YkCxYkU6cmLS3lvwsWJHfcMXD/M85ILrssmT69b/v06cmXvlQ+DwAAQO2oarB2++2354QTTsjMmTNTKpVy7bXX9jlfFEUuvPDCzJw5M1OmTMnhhx+eX/ziF336rF27Nuecc0522WWXNDY25m1ve1uefPLJrfguAPq7/PLksMOS66/fsONnT0/5+aGHJldcMfA1Z5+drFnTt33NmuSsswa+Zqz8+tfJ0qXlvwAAAAysqsFaR0dH9t9//1x22WUDnv/sZz+bxYsX57LLLsvdd9+dlpaWHH300Xnuued6+yxcuDDLli3LNddckxUrVqS9vT3HH398uodarAhgjKxYUQ7CiiLp6up7rqur3H7mmX1HrlVyzVg45ZTyzqN77ZWcdFL5b11dcuqpY/u6AAAA41GpKIqi2kUkSalUyrJly/L2t789SXm02syZM7Nw4cJ8+MMfTlIenTZjxoxccskl+cAHPpDW1tbsuuuu+eY3v5lTTjklSfL0009n1qxZueGGG3LssccO67Xb2trS3Nyc1tbWNDU1jcn7A7YdCxaUR6ZtGpBtrKGhvBPo975X+TWjbfbs5PHHN3/+ZS9LHntsbF4bAACgVowkJ6rZNdYeffTRrFq1Ksccc0xv2+TJk/PGN74xd955Z5Lk3nvvzYsvvtinz8yZMzNnzpzePgNZu3Zt2tra+hwAo6GzM7nuusEDsqR8ftmycv+RXLN0afLjHydPPTV6NSflkWqDhWpJ+byRawAAABvUbLC2atWqJMmMGTP6tM+YMaP33KpVqzJp0qTsuOOOm+0zkIsvvjjNzc29x6xZs0a5emBb1da2YU21ofT0lPuP5JqiSN785mT33ZPtt0/OP7/yWjf23e8Or9+//MvovB4AAMBEULPB2nqlUqnP86Io+rVtaqg+F1xwQVpbW3uPJ554YlRqBWhqKq9JNhx1deX+I7lmY2vXJp//fPK614382o39+tflwG44enpsaAAAALBezQZrLS0tSdJv5NmaNWt6R7G1tLRk3bp1efbZZzfbZyCTJ09OU1NTnwNgNEyZUl4HraFh8H4NDcmJJ5b7D/eazbnnni0bufbAA2PbHwAAYKKq2WBtjz32SEtLS26++ebetnXr1uW2227LIYcckiQ54IADst122/Xps3Llyjz00EO9fQC2tkWLkqE2Ju7uTs49d2TXDOYf/7Hya/fbb2z7jzsdHUmpVD46OqpdDQAAUMOqGqy1t7fn/vvvz/3335+kvGHB/fffn8cffzylUikLFy7MRRddlGXLluWhhx7K6aefnh122CHvete7kiTNzc15//vfn/POOy//9m//lvvuuy/vec97su++++aoo46q4jsDtmXz5pU3AxjMO9+ZzJ3b95olS8pZTiUj1158sfINDfbcs/y6w1FXV+4PAABAlYO1e+65J695zWvymte8JkmyaNGivOY1r8nHP/7xJMmHPvShLFy4MGeeeWYOPPDAPPXUU7npppsybdq03ntceumlefvb356TTz45c+fOzQ477JDrr78+9fX1VXlPACtWJN/5zuB9rrkmueOOvm1nnJEsX16eFjrcoGtjDz008mvWe8c7htfv5JMrfw0AAICJplQUw12yeuJqa2tLc3NzWltbrbcG9PXoo8krXlF+/JvfJHvsMeQlCxYk11+fdHVtvk9DQzlA+973Bj4/f37ywx8Of7fQJLnvvuR//s/h99/U7NnJ449v/vzLXpY89ljl9x83OjqSqVPLj9vbk8bG6tazrfPvAQDAVjaSnKhm11gDGI86O5Prrhs8VEvK55ctK/cf6B4jDdWScvA1VG2rVw/8mkk5NHvnO/vvUFpXV27fJkI1AACAERCsAYyitrbhB2I9PeX+W3KPjb344sDtK1aUR9FNnZq0tJT/LljQfypqkvzzP5c3UfjVr5Lvf7/8t7u73D4hdXQMfAz3PAAAsE2rYIlsADanqak8wms4wVhdXbn/ltxjqHtdfnly1llJff2G+/X0lKeqXnttecOEM87of92ee24jmxSsn2K4OTNmDNxuFQUAACBGrAGMqilTyuujDbWzZ0NDcuKJ5f6V3mO9urqB77ViRTlUK4r+U1O7usrtZ5458Mg1AAAAhmbEGsB6jz7av+2JJwZ+vLFNNjRYtKg8Gmww3d3Juedu/vxw7rFeUQx8r8WLyyPVBlvvrb4+ufTSZO7c4b3WhNPe3r+to2PDSLXVqy2WvzUNNM1206m5A/FvBABAldgVNHYFBf6kVKrsugF+Rq+4ojwabNNgq6GhHKptbgrmQPcolQaeFlpXV37pge7V2Vme5TjcKant7QOPntsm2YWyekbx/w0CAECl7AoKUGVnnJEsX16e0rl+l826uvLz5cuHDtU2vseJJ/bPG9ZP/9zcvUZjEwUAAAAGZyoowHq/+U2fp50vJB2PPJFdTnxjueG225JZs4Z9u7lzy0dnZzm4amoaeFTYYOc3vcd225V3/9zcvdYbjU0UYKszNRcAgHFGsAaw3p/WSluxorw+2XXXJbv3JI/96fTdq2fldYftsfnrN2PKlIFDsI1fp6dnw4i2887rv+bZ5u4x2GvOn1/e/XOwNdYaGsr9TAOlJgwVmjU2CtYAAKgppoICbOTyy5PDDisHUpuO9nrHyeV1z8bqdXp6ys8PPXR0XmfRovJ6boMZahMFAAAANk+wBvAnK1YkZ51VXgd9oFFeRcqbCdxxx9i9TldXuX00XmfevPLGBqVSeWTaxhoayu1LlmzDO4JuTmNj+R+hKIyOAgAABiVYA/iTxYvLu3hubKf8vs/j+vrk0ktH/3U2NRqvk4zOJgrbjI6OctpYKpUfAwAADMEaawApbw6wfq2zwXR1JcuWlftXsi7Z1nqdjQ13EwUAAABGRrAGkHLgNFDY9WRm93vc01PuX0k4tbnXGciWvM5ARroBAtSE9VNzAQCgBpkKCpDyKK66Yf4i1tWV+1f6OmPZHwAAgK1HsAaQ8kiu+fP7L/K/qYaG5MQTjfwCAADAVFCAXh86fU1WLCvv/rnervldn8elruT/Oy3Jmo06TZ8+7NdoaxtZTaM5FZSNDLQ5wcZtm9u8wC6hAADARkpFYeGStra2NDc3p7W1NU3mXcG2q1Sq7LoR/Ix2diZTpw5vnbW6uqS9XbA2JrbCvzUAADA+jSQnMhUUYCsy5RQAAGDiMBUU4E/uum513ja//1TQ/8ycJMk+eSi/z665/gfJG95Q+essWpRce+3gfbq7k3PPrfw1GEJ7e/+2jo5kxozy49WrTfsEAACGJFgD+JPPXjU9zzYkXV0Dn/9dds0fG6bnc99IvndC5a8zb16yZEly5plJfX3f12toKIdqS5Ykc+dW/hoMYajQrLFRsAYAAAzJVFCAlNc+u+66zYdq63V1JcuWlftviTPOSJYvL08LrfvTL3FdXfn58uXl8wAAANQ2I9YAUt59czgbCiTlfqOxW+fcueWjs7N8v6Yma6oBAACMJ4I1gJRDrbq64e/WOZobCE+ZIlADAAAYj0wFBYjdOgfV0ZGUSuWjo6Pa1QAAANQMwRrAnyxaVN44YGO/z/SUUqSUIr/PdLt1TmSNjUlRlA8bFwAAAMMgWAP4k/W7dZZK/UeuNTSU2+3WCQAAwHqCNYCNjIvdOm+/fcPUzNtvr3Y1AAAA2yybFwBswm6dAAAADIdgDWAztsndOgfanGDjts1tXmBNMgAAYBskWANgg6lTBz8/Y8bA7UUx+rUAAADUOGusAQAAAEAFjFgDqGUDbU7wH/8x8OONHXZYZa/X3t6/raNjw0i11atN+wQAAPgTwRpAjersTKa88Y2Dd/rrvx64vdKpmUOFZo2NgjUAAIA/MRUUoMasWJEsWDD0cmcAAABUlxFrADXk8suTs85K6uuTnp7k4NyWJKlP0pPkvHOTk/b4jw0j1f7xH5P9969avQAAANsywRpAjVixohyqFUXS1VVuuyt910p7xxeS+/5v0hul7b9/5eupAQAAsEVMBQWoEYsXl0eqDaa+PvnOd7ZOPQAAAAzOiDWAGtDZmVx3XXn652C6upLblm+dmno1Nla+GQIAAMAEZsQaQA1oaxs6VFtvmN0AAAAYY4I1gBrQ1JTUDfMX2Q83AABAbfC/zwBqwJQpyfz5ScMQE/QbGpLdTjqsPDWzKGxcAAAAUEWCNYDB/PjHSalUPn784zF9qUWLku7uwft0dyfnnjumZQAAADBMgjWAGjFvXrJkSTnD23TkWkNDuX3JkmTu3OrUBwAAQF+CNYAacsYZyfLl5Wmh69dcq6srP1++vHweAACA2jDEaj4AbG1z55aPzs7ybqFNTeU12AAAAKgtgjWAGjVlikANAACglgnWANYbaHOCe+8d+PHGjj12bOoBAACgppWKoiiqXUS1tbW1pbm5Oa2trWlqaqp2OUC1lEqVXednFAAAYMIYSU5k8wIAJp6OjnJQWiqVHwMAAIwBU0EB1rvxxv5t996bfPSj5cef/nRywAFbtyYAAABqlmANYL2h1ko74ADrqQEAANDLVFAAAAAAqIBgDQAAAAAqYCoowEh1dCRTp5Yft7cnjY3VrWdbN9DmBBu3bW7zAv9uAADAFhKsATC+rQ85N2fGjIHbi2L0awEAALYpgjWAwRx7rAAGAACAAQnWABjf2tv7t3V0bBiptnq1aZ8AAMCYEKwBML4NFZo1NgrWAACAMSFYAxiMhfEBAADYDMEawGAsjA8AAMBm1FW7AAAAAAAYj4xYAxiMhfEBAADYDMEawGAsjD8+NTaajgsAAIw5U0EBAAAAoAKCNaAmdXaWZ1l2dla7EgAAABiYYA2oKStWJAsWlDfjbGkp/12wILnjjmpXBgAAAH0J1oCacfnlyWGHJddfn/T0lNt6esrPDz00ueKK6tYHAAAAGxOsATVhxYrkrLPK6813dfU919VVbj/zzBoZubZ+YfyisHEBAADANkywBtSExYuT+vrB+9TXJ5deunXqAQAAgKEI1oCq6+xMrruu/0i1TXV1JcuW2dAAAACA2iBYA6qurW3DmmpD6ekp9wcAAIBqE6wBVdfUlNQN89eorq7cHwAAAKpNsAaUdXQkpVL56OjYqi89ZUoyf37S0DB4v4aG5MQTy/0BAACg2gRrQE1YtCjp7h68T3d3cu65W6eeMVfFIBMAAIDRIVgDasK8ecmSJeWcadORaw0N5fYlS5K5c6tTHwAAAGxKsAbUjDPOSJYvL08LXb/mWl1d+fny5eXzAAAAUCuGWNEIYOuaO7d8dHaWd/9sarKmGgAAALVJsAbbooHW9Nq4bXNrfjU2jk09A5gyRaAGAABAbROswbZo6tTBz8+YMXB7UYx+LduCcRBkAgAAMHKCNYCxJsgEAACYkARrsC1qb+/f1tGxIeBZvdpoKQAAABiCYA22RUOFZo2NgrXRJMgEAACYkARrUGNqdTfMWq1rXBBkAgAATEh11S4AKFuxIlmwoLwcV0tL+e+CBckdd6gLAAAAapFgDWrA5Zcnhx2WXH990tNTbuvpKT8/9NDkiiuqV9ehhybXXtu3rmuvTebNq15dAAAAUAsEa1BlK1YkZ51V3gCyq6vvua6ucvuZZ279EWIrVpRfN+m/OeX65x/8oJFrAAAAbLsEa1Blixcn9fWD96mvTy69dIwLaWwsJ2ZFkTQ25qMfHd5lw+0HAAAAE02pKDYdi7LtaWtrS3Nzc1pbW9PU1FTtctiGdHaW1yxbP81yMHV15c0lt8bGAZ2dyQ47DL//M88kO+00dvWMhE0WAAAA2BIjyYmMWIMqamsbXqiWlPu1tY1tPeutXj2y/rvuWv0NDWyyAAAAwNYmWIMqamoqj0Qbjrq6cv9atH1PR5YuK2XuvFK+9sWOrf76tbr5AwAAABObYA2qaMqUZP78pKFh8H4NDcmJJ269qY0zZlR+7cKFozNKrLOzPHKus3PwfrW6+QMAAAATn2ANqmzRoqS7e/A+3d3JuedunXqScoB36KGVXbulGy2MdEpnzWz+AAAAwDZHsAZVNm9esmRJUir1H7nW0FBuX7IkmTt369Z10UWVXdfVnSxbNvRIs4GMdEpnZ2dy3XX9R6r1q6mr8poAAABgcwRrUAPOOCNZvrw8LXT9mmt1deXny5eXz29t8+aVg65KVLLRQiVTOmt18wcAAAC2DUOs7ARsLXPnlo/OznIA1NS09dZU25wzzkj23bc8jfL73y+37ZD+mxM0btTWmI7UlZKm+qRP18bGQV9r/ZTOwUafrZ/SuX703vrNH4YTrtXy5g8AAACMT6WiKIpqF1FtbW1taW5uTmtra5r8L28YUGdn8ud/nvzohlJlNxjkp+YPf0h22WXQLr3q6pL29g2h44IF5amigwVyDQ3l0X/f+94IawYAAGCbM5KcyFRQYFimTEkuuGB077l+o4Jddx1eqJb0n9JZi5s/AAAAsG0QrAHDNm9e8rUvtGdq2tNc357GlI/pWd3b56pLVpeHlG16bGKgjQqGY9MpnbW6+QMAAAATn2ANGJH//TeN+fGKxhz99sa8UNeY59OYztKG9dNOP6uxvJ7apsdGBtuoYDANDcmJJ/Zfe64WN38AAABg4rN5ATBi/TZaqE+y6/CvH85GBQMZbEpnLW7+AAAAwMQmWKOmCEVG31h+plOm/Ome/TcKHbSe664b2fTPhoZyqDacKZ29NQEAAMAYMxWUmrB+EfupU5OWlvLfBQuSO+6odmXjV61+pm1tIwvVSiVTOgEAAKhNpaIY7l58E9dItlFl9F1+eXm9rU2nBm48SkmgMjKj9ZkONdptw1TQjkzZdWq5sb2935pqm14zderwwrW6uuR3v0t22mnovgAAADAaRpITGbFGVQ22iH1XV7n9zDOrP8pqPBmNz3So0W79zs9ozIITi9yxohg0VEvKAd38+f138NzU+o0KhGoAAADUKsEaVbV+EfvB1Ncnl166deqZCLb0M7388uSww5Lrr98wqqynp/z80EOTU08d/PwVVwxd46JF5ZFzgxlsowIAAACoBaaCxlTQahnplMD2dovSD2VLP9MVK8qh2Zb8KpRK5fXQhtpk4IoryiPnTAEGAACglpgKyrgwkkXse3rK/Rncln6mwxntNpThjjA844xyADd/fjnkS8p/bVQAAADAeGHEWoxYqxYj1kbflnymI7l2pPceylCbJAAAAMDWYsQa48JIF7EXuAxtSz7TkYx2G8pIRxhOmZLMmOHfGAAAgPFFsEZVWcR+9FX6mZ599ujVUFdXHn0GAAAAE5lgjaqaN6+8SH2p1H+UVUNDuX3JkqEXwmeDSj7TJUuS731vdF7fCEMAAAC2FYI1qm6wRex/8pNySNPZWd0ax5uRbgzwqU+N3msbYQgAAMC2QrBGTZg7tzxiqr09WbUquemm8jpdRx+dtLSUF9VfsCC5445qVzp+bPqZtreXn286+u8Pf0hWrhz+fRcsMMIQAAAAEsEaNWbKlGTp0nKgdv31GxbT7+kpPz/00OSKK6pb43gz1MYATz89svt98pMjGw0HAAAAE1VNB2sXXnhhSqVSn6OlpaX3fFEUufDCCzNz5sxMmTIlhx9+eH7xi19UsWK21IoVyVlnJUWRdHX1PdfVVW4/80wj10bTzJkj7z/c0XAAAAAwkdV0sJYkr371q7Ny5cre48EHH+w999nPfjaLFy/OZZddlrvvvjstLS05+uij89xzz1WxYrbE4sVJff3gferrk0sv3Tr1bKqzM1m9emKt+bbTTsluuw2v78yZ5f7rDTUaDgAAACaymg/WGhoa0tLS0nvsuuuuScqj1b7whS/kox/9aBYsWJA5c+bkG9/4Rp5//vl8+9vfrnLVVKKzM7nuuv4j1TbV1ZUsW7Z1w60VK8pri02dOjHXfPu7vxvdfgAAALAtqPlg7Ve/+lVmzpyZPfbYI+985zvzm9/8Jkny6KOPZtWqVTnmmGN6+06ePDlvfOMbc+eddw56z7Vr16atra3PQfW1tW1YU20oPT3l/lvD5Zcnhx02sdd8O/PM5NRTB+9z6qnJBz+4deoBAACA8aCmg7U3vOENufrqq/PjH/84X/3qV7Nq1aoccsgheeaZZ7Jq1aokyYwZM/pcM2PGjN5zm3PxxRenubm595g1a9aYvQeGr6lpw2L4Q6mrK/cfa9vSmm/f/nZ5R89N11ybObPcbiAoAAAA9FXTwdpxxx2Xk046Kfvuu2+OOuqo/OhHP0qSfOMb3+jtUyqV+lxTFEW/tk1dcMEFaW1t7T2eeOKJ0S+eEZsypbyzZEPD4P0aGpITT9w663rV+ppvo+2DH0yeeip55pnkwQfLf596ykg1AAAAGEhNB2ubamxszL777ptf/epXvbuDbjo6bc2aNf1GsW1q8uTJaWpq6nNQGxYtSrq7B+/T3Z2ce+7Y11LLa76NtZ12SubM6btRAQAAANDXuArW1q5dm4cffji77bZb9thjj7S0tOTmm2/uPb9u3brcdtttOeSQQ6pYJVti3rzytMNSqf/ItYaGcvuSJcncuWNfS62u+QYAAADUhpoO1s4///zcdtttefTRR/P//t//y5//+Z+nra0tp512WkqlUhYuXJiLLrooy5Yty0MPPZTTTz89O+ywQ971rndVu3S2wBlnJMuXl6eFrl9zra6u/Hz58vL5raEW13wDAAAAascQq1lV15NPPplTTz01v//977PrrrvmoIMOyl133ZXZs2cnST70oQ+ls7MzZ555Zp599tm84Q1vyE033ZRp06ZVuXK21Ny55aOzszwSrKlp66yptrH1a75df/3g00EbGsr9tnZ9AAAAQHWViqIoql1EtbW1taW5uTmtra3WW6OPFSuSww4r7/65OaVSeSTd1pieCgAAAIytkeRENT0VFKqtltZ8AwAAAGqLYA2GUCtrvgEAAAC1pabXWINaUQtrvgEAAAC1RbAGIzBlikANAAAAKDMVFAAAAAAqIFgDAAAAgAoI1iaozs5k9eryXwAAAABGn2BtglmxIlmwIJk6NWlpKf9dsCC5445qVwYAAAAwsQjWJpDLL08OOyy5/vqkp6fc1tNTfn7oockVV1S3PgAAAICJRLA2QaxYkZx1VlIUSVdX33NdXeX2M880cg0AAABgtAjWJojFi5P6+sH71Ncnl166deoBAAAAmOgEaxNAZ2dy3XX9R6ptqqsrWbbMhgYAAAAAo0GwNgG0tW1YU20oPT3l/gAAAABsGcHaBNDUlNQN81+yrq7cHwAAAIAtI1ibAKZMSebPTxoaBu/X0JCceGK5PwAAAABbRrA2QSxalHR3D96nuzs599ytUw8AAADARCdYmyDmzUuWLElKpf4j1xoayu1LliRz51anPgAAAICJRrA2gZxxRrJ8eXla6Po11+rqys+XLy+fBwAAAGB0DLEqF+PN3Lnlo7OzvPtnU5M11QAAAADGgmBtgpoyRaAGAAAAMJZMBQUAAACACgjWAAAAAKACgjUAAAAAqIBgDQAAAAAqIFgDAAAAgAoI1gAAAACgAoI1AAAAAKiAYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAKCNQAAAACogGANAAAAACogWAMAAACACgjWAAAAAKACgjUAAAAAqIBgDQAAAAAqIFgDAAAAgAoI1gAAAACgAoI1AAAAAKiAYA0AAAAAKiBYAwAAAIAKCNYAAAAAoAKCNQAAAACogGANAAAAACogWAMAAACACgjWAAAAAKACDdUuoBYURZEkaWtrq3IlAAAAAFTT+nxofV40GMFakueeey5JMmvWrCpXAgAAAEAteO6559Lc3Dxon1IxnPhtguvp6cnTTz+dadOmpVQqVbsc6KetrS2zZs3KE088kaampmqXA2PGd51tge852wrfdbYVvutsC7a173lRFHnuuecyc+bM1NUNvoqaEWtJ6urqsvvuu1e7DBhSU1PTNvEjBr7rbAt8z9lW+K6zrfBdZ1uwLX3Phxqptp7NCwAAAACgAoI1AAAAAKiAYA3GgcmTJ+cTn/hEJk+eXO1SYEz5rrMt8D1nW+G7zrbCd51tge/55tm8AAAAAAAqYMQaAAAAAFRAsAYAAAAAFRCsAQAAAEAFBGsAAAAAUAHBGmwlt99+e0444YTMnDkzpVIp1157bZ/zRVHkwgsvzMyZMzNlypQcfvjh+cUvftGnz9q1a3POOedkl112SWNjY972trflySef7NPn2WefzXvf+940Nzenubk5733ve/PHP/5xjN8dbDDUd/30009PqVTqcxx00EF9+viuU+suvvjivO51r8u0adMyffr0vP3tb88jjzzSp4/fdca74XzP/aYzEVx++eXZb7/90tTUlKamphx88MH513/9197zfs+ZCIb6nvs9r5xgDbaSjo6O7L///rnssssGPP/Zz342ixcvzmWXXZa77747LS0tOfroo/Pcc8/19lm4cGGWLVuWa665JitWrEh7e3uOP/74dHd39/Z517velfvvvz833nhjbrzxxtx///1573vfO+bvD9Yb6rueJG9+85uzcuXK3uOGG27oc953nVp322235ayzzspdd92Vm2++OV1dXTnmmGPS0dHR28fvOuPdcL7nid90xr/dd989n/nMZ3LPPffknnvuyZFHHpn58+f3hmd+z5kIhvqeJ37PK1YAW12SYtmyZb3Pe3p6ipaWluIzn/lMb9sLL7xQNDc3F1dccUVRFEXxxz/+sdhuu+2Ka665prfPU089VdTV1RU33nhjURRF8Z//+Z9FkuKuu+7q7fOzn/2sSFL88pe/HON3Bf1t+l0viqI47bTTivnz52/2Gt91xqM1a9YUSYrbbrutKAq/60xMm37Pi8JvOhPXjjvuWHzta1/ze86Etv57XhR+z7eEEWtQAx599NGsWrUqxxxzTG/b5MmT88Y3vjF33nlnkuTee+/Niy++2KfPzJkzM2fOnN4+P/vZz9Lc3Jw3vOENvX0OOuigNDc39/aBWnDrrbdm+vTp2XvvvfOXf/mXWbNmTe8533XGo9bW1iTJTjvtlMTvOhPTpt/z9fymM5F0d3fnmmuuSUdHRw4++GC/50xIm37P1/N7XpmGahcAJKtWrUqSzJgxo0/7jBkz8thjj/X2mTRpUnbcccd+fdZfv2rVqkyfPr3f/adPn97bB6rtuOOOyzve8Y7Mnj07jz76aD72sY/lyCOPzL333pvJkyf7rjPuFEWRRYsWZd68eZkzZ04Sv+tMPAN9zxO/6UwcDz74YA4++OC88MILmTp1apYtW5Z99tmnNwzwe85EsLnveeL3fEsI1qCGlEqlPs+LoujXtqlN+wzUfzj3ga3llFNO6X08Z86cHHjggZk9e3Z+9KMfZcGCBZu9znedWnX22WfngQceyIoVK/qd87vORLG577nfdCaKV77ylbn//vvzxz/+Md///vdz2mmn5bbbbus97/eciWBz3/N99tnH7/kWMBUUakBLS0uS9Evx16xZ0/tfx1paWrJu3bo8++yzg/ZZvXp1v/v/7ne/6/df2aBW7Lbbbpk9e3Z+9atfJfFdZ3w555xz8oMf/CA//elPs/vuu/e2+11nItnc93wgftMZryZNmpQ999wzBx54YC6++OLsv//++eIXv+j3nAllc9/zgfg9Hz7BGtSAPfbYIy0tLbn55pt729atW5fbbrsthxxySJLkgAMOyHbbbdenz8qVK/PQQw/19jn44IPT2tqaf//3f+/t8//+3/9La2trbx+oNc8880yeeOKJ7Lbbbkl81xkfiqLI2WefnaVLl+aWW27JHnvs0ee833UmgqG+5wPxm85EURRF1q5d6/ecCW3993wgfs9HYKtulQDbsOeee6647777ivvuu69IUixevLi47777iscee6woiqL4zGc+UzQ3NxdLly4tHnzwweLUU08tdtttt6Ktra33HmeccUax++67Fz/5yU+Kn//858WRRx5Z7L///kVXV1dvnze/+c3FfvvtV/zsZz8rfvaznxX77rtvcfzxx2/198u2a7Dv+nPPPVecd955xZ133lk8+uijxU9/+tPi4IMPLl760pf6rjOufPCDHyyam5uLW2+9tVi5cmXv8fzzz/f28bvOeDfU99xvOhPFBRdcUNx+++3Fo48+WjzwwAPFRz7ykaKurq646aabiqLwe87EMNj33O/5lhGswVby05/+tEjS7zjttNOKoiiKnp6e4hOf+ETR0tJSTJ48uTjssMOKBx98sM89Ojs7i7PPPrvYaaediilTphTHH3988fjjj/fp88wzzxTvfve7i2nTphXTpk0r3v3udxfPPvvsVnqXMPh3/fnnny+OOeaYYtdddy2222674mUve1lx2mmn9fse+65T6wb6jicprrzyyt4+ftcZ74b6nvtNZ6L4i7/4i2L27NnFpEmTil133bV405ve1BuqFYXfcyaGwb7nfs+3TKkoimLrjY8DAAAAgInBGmsAAAAAUAHBGgAAAABUQLAGAAAAABUQrAEAAABABQRrAAAAAFABwRoAAAAAVECwBgAAAAAVEKwBAAAAQAUEawAANeblL395vvCFL1TltS+88MLMmDEjpVIp1157bVVqAAAYLwRrAACbUSqVBj1OP/30Ia8fi3Cqo6MjH/7wh/OKV7wi22+/fXbdddccfvjh+eEPf7hF93344YfzyU9+Ml/+8pezcuXKHHfccaNS7+mnn563v/3to3IvAIBa0lDtAgAAatXKlSt7H3/nO9/Jxz/+8TzyyCO9bVOmTKlGWTnjjDPy7//+77nsssuyzz775Jlnnsmdd96ZZ555Zovu+9///d9Jkvnz56dUKo1GqWPuxRdfzHbbbVftMgCAbZQRawAAm9HS0tJ7NDc3p1Qq9Wn79re/nT/7sz/LpEmT8spXvjLf/OY3e699+ctfniQ58cQTUyqVep//93//d+bPn58ZM2Zk6tSped3rXpef/OQnI6rr+uuvz0c+8pG85S1vyctf/vIccMABOeecc3Laaaf19lmzZk1OOOGETJkyJXvssUf+6Z/+adApphdeeGFOOOGEJEldXV2fYO3KK6/Mq171qmy//fb5H//jf2TJkiV9rn3qqadyyimnZMcdd8zOO++c+fPn57e//W3vfb/xjW/kuuuu6x3pd+uttw5Yw4033ph58+blJS95SXbeeeccf/zxvWFfkvz2t79NqVTKv/zLv+Twww/P9ttvn29961vDqvHDH/5w9t577+ywww55xStekY997GN58cUXh/V5AwBsjmANAKACy5Yty9/8zd/kvPPOy0MPPZQPfOADed/73pef/vSnSZK77747STnwWblyZe/z9vb2vOUtb8lPfvKT3HfffTn22GNzwgkn5PHHHx/2a7e0tOSGG27Ic889t9k+p59+en7729/mlltuyfe+970sWbIka9as2Wz/888/P1deeWWS8ki99aP1vvrVr+ajH/1oPv3pT+fhhx/ORRddlI997GP5xje+kSR5/vnnc8QRR2Tq1Km5/fbbs2LFikydOjVvfvObs27dupx//vk5+eST8+Y3v7n3vocccsiANXR0dGTRokW5++6782//9m+pq6vLiSeemJ6enj79PvzhD+ev//qv8/DDD+fYY48dssYkmTZtWq666qr853/+Z774xS/mq1/9ai699NLhfeAAAJtTAAAwpCuvvLJobm7ufX7IIYcUf/mXf9mnzzve8Y7iLW95S+/z5P9v735Com7iOI5/KrfYslUT7Z+bWyzYbga1bkXZphG0dRDLIiqhorJTxRpsSbFQ6ikTi05qoR2KLWL3EB0kPFSreNBT1LZQRnYroi6RhjnP4aEl//TY7hM88PB+wR5+M/Ob+c6cli8z85OJRqPT9u12u83169eTz4WFhaalpeWX7R8/fmwKCgqMxWIxXq/XBAIBE4vFkvWJRMJIMn19fcmyeDxuJP1jv9Fo1Ez8e2i3282dO3fGlTU0NJiNGzcaY4y5efOmKSoqMmNjY8n6kZERY7VaTVdXlzHGmMOHD5vKyspfjvsr79+/N5LMs2fPjDHGvHnzxkgyV69eTSnGqVy+fNmUlJSkHBMAAMDP2LEGAACQhng8rtLS0nFlpaWlisfj//jely9fdPbsWbndbmVnZyszM1MvX75Macfali1bNDg4qO7ubu3Zs0fPnz+Xz+dTQ0NDMraMjAx5vd7kOytXrlR2dvbvT1DShw8f9O7dOx07dkyZmZnJX2NjY/KI5sDAgF69eqX58+cn6xcsWKDh4eFxxzh/x+vXr3Xw4EGtWLFCNptNy5cvl6RJa/PzvH4nRkm6f/++Nm/erEWLFikzM1OhUCilNQcAAJgKHy8AAABI08QL/o0x0176HwwG1dXVpStXrsjpdMpqtWrv3r369u1bSmNbLBb5fD75fD7V1dWpsbFR9fX1OnfunIwxU8aXqh9HMNvb27Vhw4ZxdbNmzUq2KSkp0e3btye9n5eXl9J4FRUVstvtam9v15IlSzQ2Nqbi4uJJazNv3ryUYuzr69P+/ft16dIl+f1+ZWVlKRwOq7m5OaX4AAAAJiKxBgAAkAaXy6VYLKZDhw4ly3p7e+VyuZLPFotF379/H/fe06dPdeTIEe3evVvS33eu/bjo/99wu90aHR3V8PCwXC6XRkdH1d/fr/Xr10uSEomEPn/+nFKfCxcu1NKlSzU4OKjq6uop23g8Ht29e1f5+fmy2WxTtpk9e/akdZjo48ePisfjam1tlc/nkyTFYrE/EmNPT48KCwt14cKFZNnbt2+n7RsAAGA6JNYAAADSEAwGtW/fPnk8Hm3btk0PHjxQJBIZ94VPh8Oh7u5ulZaWas6cOcrJyZHT6VQkElFFRYVmzJihUCg06XL+6ZSXl+vAgQPyer3Kzc3VixcvdP78eW3dulU2m002m007duxQTU2N2tralJGRoUAgIKvVmvI8L168qNOnT8tms2nnzp0aGRlRf3+/Pn36pDNnzqi6ulpNTU2qrKxUfX29CgoKNDQ0pEgkomAwqIKCAjkcDnV1dSmRSCg3N1dZWVmyWCzjxvnxRdG2tjYtXrxYQ0NDqqur+yMxOp1ODQ0NKRwOa926dXr48KGi0WjKawEAADARd6wBAACkYdeuXbp27Zqampq0atUqtba2qqOjQ+Xl5ck2zc3NevTokex2u9auXStJamlpUU5OjjZt2qSKigr5/X55PJ6Uxvb7/bp165a2b98ul8ulU6dOye/36969e8k2HR0dstvtKisrU1VVlU6cOKH8/PyU53n8+HHduHFDnZ2dWr16tcrKytTZ2Zm8/2zu3Ll68uSJli1bpqqqKrlcLh09elRfv35N7mCrqalRUVGRvF6v8vLy1NPTM2mcmTNnKhwOa2BgQMXFxaqtrVVTU9MfibGyslK1tbU6efKk1qxZo97eXoVCoZTXAgAAYKIZ5sclHAAAAPhfczgcCgQCCgQC/3UoAAAA/wvsWAMAAAAAAADSQGINAAAAAAAASANHQQEAAAAAAIA0sGMNAAAAAAAASAOJNQAAAAAAACANJNYAAAAAAACANJBYAwAAAAAAANJAYg0AAAAAAABIA4k1AAAAAAAAIA0k1gAAAAAAAIA0kFgDAAAAAAAA0vAXVk9dm59SFHoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_scatter_chart(df,location):\n",
    "    bhk2 = df[(df.location == location) & (df.BHK == 2)]\n",
    "    bhk3 = df[(df.location == location) & (df.BHK == 3)]\n",
    "    matplotlib.rcParams[\"figure.figsize\"] = (15,10)\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='red',label='3 BHK', s=100)\n",
    "    plt.xlabel('Total Sq feet area')\n",
    "    plt.ylabel('Price per sq feet')\n",
    "    plt.title(location)\n",
    "    plt.legend()\n",
    "    \n",
    "plot_scatter_chart(df6, 'Hebbal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aa6b7f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_bhk_outliers(df):\n",
    "    exclude_indices = np.array([])\n",
    "    for location, location_df in df.groupby('location'):\n",
    "        bhk_stats = {}\n",
    "        for bhk, bhk_df in location_df.groupby('BHK'):\n",
    "            bhk_stats[bhk] = {\n",
    "                'mean': np.mean(bhk_df.price_per_sqft),\n",
    "                'std': np.std(bhk_df.price_per_sqft),\n",
    "                'count':bhk_df.shape[0]\n",
    "            }\n",
    "            \n",
    "        for bhk, bhk_df in location_df.groupby('BHK'):\n",
    "            stats = bhk_stats.get(bhk-1)\n",
    "            if stats and stats['count'] > 5:\n",
    "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index)\n",
    "    return df.drop(exclude_indices, axis='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f8da7907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10246</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10247</th>\n",
       "      <td>other</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10250</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8130.081301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10251</th>\n",
       "      <td>other</td>\n",
       "      <td>1 Bedroom</td>\n",
       "      <td>812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3201.970443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10254</th>\n",
       "      <td>other</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7305 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  location       size  total_sqft  bath  price  BHK  \\\n",
       "0      1st Block Jayanagar      4 BHK      2850.0   4.0  428.0    4   \n",
       "1      1st Block Jayanagar      3 BHK      1630.0   3.0  194.0    3   \n",
       "2      1st Block Jayanagar      3 BHK      1875.0   2.0  235.0    3   \n",
       "3      1st Block Jayanagar      3 BHK      1200.0   2.0  130.0    3   \n",
       "4      1st Block Jayanagar      2 BHK      1235.0   2.0  148.0    2   \n",
       "...                    ...        ...         ...   ...    ...  ...   \n",
       "10246                other      2 BHK      1200.0   2.0   70.0    2   \n",
       "10247                other      1 BHK      1800.0   1.0  200.0    1   \n",
       "10250                other      2 BHK      1353.0   2.0  110.0    2   \n",
       "10251                other  1 Bedroom       812.0   1.0   26.0    1   \n",
       "10254                other      4 BHK      3600.0   5.0  400.0    4   \n",
       "\n",
       "       price_per_sqft  \n",
       "0        15017.543860  \n",
       "1        11901.840491  \n",
       "2        12533.333333  \n",
       "3        10833.333333  \n",
       "4        11983.805668  \n",
       "...               ...  \n",
       "10246     5833.333333  \n",
       "10247    11111.111111  \n",
       "10250     8130.081301  \n",
       "10251     3201.970443  \n",
       "10254    11111.111111  \n",
       "\n",
       "[7305 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = remove_bhk_outliers(df6)\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a4283f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_chart(df7, 'Hebbal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9e029db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df7.price_per_sqft, rwidth=.8)\n",
    "plt.xlabel('Price per SQFT')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e865ebfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 10., 12., 16., 15.,\n",
       "       13.])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.bath.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2ca5cf12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8497</th>\n",
       "      <td>other</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8586</th>\n",
       "      <td>other</td>\n",
       "      <td>16 BHK</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8816</th>\n",
       "      <td>other</td>\n",
       "      <td>14 BHK</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9322</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>other</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location    size  total_sqft  bath  price  BHK  price_per_sqft\n",
       "5286  Neeladri Nagar  10 BHK      4000.0  12.0  160.0    1     4000.000000\n",
       "8497           other  10 BHK     12000.0  12.0  525.0    1     4375.000000\n",
       "8586           other  16 BHK     10000.0  16.0  550.0    1     5500.000000\n",
       "8816           other  14 BHK      1250.0  15.0  125.0    1    10000.000000\n",
       "9322           other  11 BHK      6000.0  12.0  150.0    1     2500.000000\n",
       "9653           other  13 BHK      5425.0  13.0  275.0    1     5069.124424"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d90691a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df7.bath, rwidth=.8)\n",
    "plt.xlabel('Price per SQFT')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b4a5d35f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1626</th>\n",
       "      <td>Chikkabanavar</td>\n",
       "      <td>4 Bedroom</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>5138</th>\n",
       "      <td>Munnekollal</td>\n",
       "      <td>10 Bedroom</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2777.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5247</th>\n",
       "      <td>Nagasandra</td>\n",
       "      <td>4 Bedroom</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>5286</th>\n",
       "      <td>Neeladri Nagar</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6720</th>\n",
       "      <td>Thanisandra</td>\n",
       "      <td>3 BHK</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>8035</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8265</th>\n",
       "      <td>other</td>\n",
       "      <td>10 Bedroom</td>\n",
       "      <td>750.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8422</th>\n",
       "      <td>other</td>\n",
       "      <td>6 BHK</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8497</th>\n",
       "      <td>other</td>\n",
       "      <td>10 BHK</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8586</th>\n",
       "      <td>other</td>\n",
       "      <td>16 BHK</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8641</th>\n",
       "      <td>other</td>\n",
       "      <td>11 Bedroom</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8816</th>\n",
       "      <td>other</td>\n",
       "      <td>14 BHK</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9322</th>\n",
       "      <td>other</td>\n",
       "      <td>11 BHK</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9623</th>\n",
       "      <td>other</td>\n",
       "      <td>10 Bedroom</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13636.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>other</td>\n",
       "      <td>13 BHK</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5069.124424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location        size  total_sqft  bath   price  BHK  \\\n",
       "1626   Chikkabanavar   4 Bedroom      2460.0   7.0    80.0    4   \n",
       "5138     Munnekollal  10 Bedroom      7200.0  10.0   200.0    1   \n",
       "5247      Nagasandra   4 Bedroom      7000.0   8.0   450.0    4   \n",
       "5286  Neeladri Nagar      10 BHK      4000.0  12.0   160.0    1   \n",
       "6720     Thanisandra       3 BHK      1806.0   6.0   116.0    3   \n",
       "8035           other      11 BHK      5000.0   9.0   360.0    1   \n",
       "8265           other  10 Bedroom       750.0  10.0    90.0    1   \n",
       "8422           other       6 BHK     11338.0   9.0  1000.0    6   \n",
       "8497           other      10 BHK     12000.0  12.0   525.0    1   \n",
       "8586           other      16 BHK     10000.0  16.0   550.0    1   \n",
       "8641           other  11 Bedroom      1200.0   6.0   150.0    1   \n",
       "8816           other      14 BHK      1250.0  15.0   125.0    1   \n",
       "9322           other      11 BHK      6000.0  12.0   150.0    1   \n",
       "9623           other  10 Bedroom      3300.0   9.0   450.0    1   \n",
       "9653           other      13 BHK      5425.0  13.0   275.0    1   \n",
       "\n",
       "      price_per_sqft  \n",
       "1626     3252.032520  \n",
       "5138     2777.777778  \n",
       "5247     6428.571429  \n",
       "5286     4000.000000  \n",
       "6720     6423.034330  \n",
       "8035     7200.000000  \n",
       "8265    12000.000000  \n",
       "8422     8819.897689  \n",
       "8497     4375.000000  \n",
       "8586     5500.000000  \n",
       "8641    12500.000000  \n",
       "8816    10000.000000  \n",
       "9322     2500.000000  \n",
       "9623    13636.363636  \n",
       "9653     5069.124424  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[df7.bath>df7.BHK+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f542d7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = df7[df7.bath<df7.BHK+2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6d8ed57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>location</th>\n",
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       "      <th>total_sqft</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15017.543860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11901.840491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12533.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st Block Jayanagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11983.805668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>10246</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10247</th>\n",
       "      <td>other</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10250</th>\n",
       "      <td>other</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8130.081301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10251</th>\n",
       "      <td>other</td>\n",
       "      <td>1 Bedroom</td>\n",
       "      <td>812.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3201.970443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10254</th>\n",
       "      <td>other</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11111.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7218 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  location       size  total_sqft  bath  price  BHK  \\\n",
       "0      1st Block Jayanagar      4 BHK      2850.0   4.0  428.0    4   \n",
       "1      1st Block Jayanagar      3 BHK      1630.0   3.0  194.0    3   \n",
       "2      1st Block Jayanagar      3 BHK      1875.0   2.0  235.0    3   \n",
       "3      1st Block Jayanagar      3 BHK      1200.0   2.0  130.0    3   \n",
       "4      1st Block Jayanagar      2 BHK      1235.0   2.0  148.0    2   \n",
       "...                    ...        ...         ...   ...    ...  ...   \n",
       "10246                other      2 BHK      1200.0   2.0   70.0    2   \n",
       "10247                other      1 BHK      1800.0   1.0  200.0    1   \n",
       "10250                other      2 BHK      1353.0   2.0  110.0    2   \n",
       "10251                other  1 Bedroom       812.0   1.0   26.0    1   \n",
       "10254                other      4 BHK      3600.0   5.0  400.0    4   \n",
       "\n",
       "       price_per_sqft  \n",
       "0        15017.543860  \n",
       "1        11901.840491  \n",
       "2        12533.333333  \n",
       "3        10833.333333  \n",
       "4        11983.805668  \n",
       "...               ...  \n",
       "10246     5833.333333  \n",
       "10247    11111.111111  \n",
       "10250     8130.081301  \n",
       "10251     3201.970443  \n",
       "10254    11111.111111  \n",
       "\n",
       "[7218 rows x 7 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "413f2f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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      ],
      "text/plain": [
       "              location  total_sqft  bath  price  BHK\n",
       "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
       "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
       "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
       "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
       "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df8.drop(['size', 'price_per_sqft'], axis=1)\n",
    "df9.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f4e2a128",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df9.location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bed9c6e9",
   "metadata": {},
   "outputs": [
    {
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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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10250</th>\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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10251</th>\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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10254</th>\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",
       "      <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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7218 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
       "0                        1                   0                          0   \n",
       "1                        1                   0                          0   \n",
       "2                        1                   0                          0   \n",
       "3                        1                   0                          0   \n",
       "4                        1                   0                          0   \n",
       "...                    ...                 ...                        ...   \n",
       "10246                    0                   0                          0   \n",
       "10247                    0                   0                          0   \n",
       "10250                    0                   0                          0   \n",
       "10251                    0                   0                          0   \n",
       "10254                    0                   0                          0   \n",
       "\n",
       "       2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
       "0                         0                     0                   0   \n",
       "1                         0                     0                   0   \n",
       "2                         0                     0                   0   \n",
       "3                         0                     0                   0   \n",
       "4                         0                     0                   0   \n",
       "...                     ...                   ...                 ...   \n",
       "10246                     0                     0                   0   \n",
       "10247                     0                     0                   0   \n",
       "10250                     0                     0                   0   \n",
       "10251                     0                     0                   0   \n",
       "10254                     0                     0                   0   \n",
       "\n",
       "       6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
       "0                       0                   0                   0   \n",
       "1                       0                   0                   0   \n",
       "2                       0                   0                   0   \n",
       "3                       0                   0                   0   \n",
       "4                       0                   0                   0   \n",
       "...                   ...                 ...                 ...   \n",
       "10246                   0                   0                   0   \n",
       "10247                   0                   0                   0   \n",
       "10250                   0                   0                   0   \n",
       "10251                   0                   0                   0   \n",
       "10254                   0                   0                   0   \n",
       "\n",
       "       9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
       "0                       0  ...                     0                   0   \n",
       "1                       0  ...                     0                   0   \n",
       "2                       0  ...                     0                   0   \n",
       "3                       0  ...                     0                   0   \n",
       "4                       0  ...                     0                   0   \n",
       "...                   ...  ...                   ...                 ...   \n",
       "10246                   0  ...                     0                   0   \n",
       "10247                   0  ...                     0                   0   \n",
       "10250                   0  ...                     0                   0   \n",
       "10251                   0  ...                     0                   0   \n",
       "10254                   0  ...                     0                   0   \n",
       "\n",
       "       Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
       "0                0           0               0          0                   0   \n",
       "1                0           0               0          0                   0   \n",
       "2                0           0               0          0                   0   \n",
       "3                0           0               0          0                   0   \n",
       "4                0           0               0          0                   0   \n",
       "...            ...         ...             ...        ...                 ...   \n",
       "10246            0           0               0          0                   0   \n",
       "10247            0           0               0          0                   0   \n",
       "10250            0           0               0          0                   0   \n",
       "10251            0           0               0          0                   0   \n",
       "10254            0           0               0          0                   0   \n",
       "\n",
       "       Yelenahalli  Yeshwanthpur  other  \n",
       "0                0             0      0  \n",
       "1                0             0      0  \n",
       "2                0             0      0  \n",
       "3                0             0      0  \n",
       "4                0             0      0  \n",
       "...            ...           ...    ...  \n",
       "10246            0             0      1  \n",
       "10247            0             0      1  \n",
       "10250            0             0      1  \n",
       "10251            0             0      1  \n",
       "10254            0             0      1  \n",
       "\n",
       "[7218 rows x 242 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "14507a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df10 = pd.concat([df9,dummies.drop('other', axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "920200a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df11 = df10.drop('location', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "df182229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>price</th>\n",
       "      <th>BHK</th>\n",
       "      <th>1st Block Jayanagar</th>\n",
       "      <th>1st Phase JP Nagar</th>\n",
       "      <th>2nd Phase Judicial Layout</th>\n",
       "      <th>2nd Stage Nagarbhavi</th>\n",
       "      <th>5th Block Hbr Layout</th>\n",
       "      <th>5th Phase JP Nagar</th>\n",
       "      <th>...</th>\n",
       "      <th>Vijayanagar</th>\n",
       "      <th>Vishveshwarya Layout</th>\n",
       "      <th>Vishwapriya Layout</th>\n",
       "      <th>Vittasandra</th>\n",
       "      <th>Whitefield</th>\n",
       "      <th>Yelachenahalli</th>\n",
       "      <th>Yelahanka</th>\n",
       "      <th>Yelahanka New Town</th>\n",
       "      <th>Yelenahalli</th>\n",
       "      <th>Yeshwanthpur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>194.0</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>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1875.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.0</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</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>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1235.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>148.0</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",
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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>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_sqft  bath  price  BHK  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
       "0      2850.0   4.0  428.0    4                    1                   0   \n",
       "1      1630.0   3.0  194.0    3                    1                   0   \n",
       "2      1875.0   2.0  235.0    3                    1                   0   \n",
       "3      1200.0   2.0  130.0    3                    1                   0   \n",
       "4      1235.0   2.0  148.0    2                    1                   0   \n",
       "\n",
       "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
       "0                          0                     0                     0   \n",
       "1                          0                     0                     0   \n",
       "2                          0                     0                     0   \n",
       "3                          0                     0                     0   \n",
       "4                          0                     0                     0   \n",
       "\n",
       "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
       "0                   0  ...            0                     0   \n",
       "1                   0  ...            0                     0   \n",
       "2                   0  ...            0                     0   \n",
       "3                   0  ...            0                     0   \n",
       "4                   0  ...            0                     0   \n",
       "\n",
       "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
       "0                   0            0           0               0          0   \n",
       "1                   0            0           0               0          0   \n",
       "2                   0            0           0               0          0   \n",
       "3                   0            0           0               0          0   \n",
       "4                   0            0           0               0          0   \n",
       "\n",
       "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
       "0                   0            0             0  \n",
       "1                   0            0             0  \n",
       "2                   0            0             0  \n",
       "3                   0            0             0  \n",
       "4                   0            0             0  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dd7308e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7218, 245)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f8bb4032",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting into Training(X) & Testing data(y)\n",
    "\n",
    "X = df11.drop('price', axis = 1)\n",
    "y = df11['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "eeebfe9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "34c76fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d943c083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8516376110931843"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr_clf = LinearRegression()\n",
    "lr_clf.fit(X_train,y_train)\n",
    "lr_clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6e56fdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85405497, 0.87370235, 0.87099251, 0.88532432, 0.81187077])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n",
    "cross_val_score(LinearRegression(), X, y, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e17510d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>linear_regression</td>\n",
       "      <td>0.859189</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lasso</td>\n",
       "      <td>0.721385</td>\n",
       "      <td>{'alpha': 1, 'selection': 'cyclic'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.736822</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'splitter': 'best'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  best_score  \\\n",
       "0  linear_regression    0.859189   \n",
       "1              lasso    0.721385   \n",
       "2      decision_tree    0.736822   \n",
       "\n",
       "                                         best_params  \n",
       "0                                                 {}  \n",
       "1                {'alpha': 1, 'selection': 'cyclic'}  \n",
       "2  {'criterion': 'friedman_mse', 'splitter': 'best'}  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "def find_best_model_GSCV(X,y):\n",
    "    algos = {\n",
    "        'linear_regression':{\n",
    "            'model':LinearRegression(),\n",
    "            'params':{\n",
    "               # 'normalize':[True, False]\n",
    "            }\n",
    "        },\n",
    "        'lasso':{\n",
    "            'model':Lasso(),\n",
    "            'params':{\n",
    "                'alpha':[1,2],\n",
    "                'selection':['random','cyclic']\n",
    "            }\n",
    "        },\n",
    "        'decision_tree':{\n",
    "            'model':DecisionTreeRegressor(),\n",
    "            'params':{\n",
    "                'criterion':['squared_error', 'friedman_mse'],\n",
    "                'splitter':['best','random']\n",
    "            }\n",
    "        }\n",
    "        \n",
    "    }\n",
    "    \n",
    "    scores = []\n",
    "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)\n",
    "    for algo_name, config in algos.items():\n",
    "        gs = GridSearchCV(config['model'], config['params'], cv = cv, return_train_score=False)\n",
    "        gs.fit(X,y)\n",
    "        scores.append({\n",
    "            'model':algo_name,\n",
    "            'best_score':gs.best_score_,\n",
    "            'best_params':gs.best_params_\n",
    "        })\n",
    "    \n",
    "    return pd.DataFrame(scores, columns=['model', 'best_score', 'best_params'])\n",
    "\n",
    "find_best_model_GSCV(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0bfe3632",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "186.54718561868202"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_price(X, location, sqft, bath, bhk):\n",
    "    loc_index = np.where(X.columns == location)[0][0]\n",
    "    \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",
    "        \n",
    "    return lr_clf.predict([x])[0]\n",
    "X = X_train\n",
    "predict_price(X,'Indira Nagar', 1000,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4d09741",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8ec4f3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_sqft', 'bath', 'BHK', '1st Block Jayanagar',\n",
       "       '1st Phase JP Nagar', '2nd Phase Judicial Layout',\n",
       "       '2nd Stage Nagarbhavi', '5th Block Hbr Layout', '5th Phase JP Nagar',\n",
       "       '6th Phase JP Nagar',\n",
       "       ...\n",
       "       'Vijayanagar', 'Vishveshwarya Layout', 'Vishwapriya Layout',\n",
       "       'Vittasandra', 'Whitefield', 'Yelachenahalli', 'Yelahanka',\n",
       "       'Yelahanka New Town', 'Yelenahalli', 'Yeshwanthpur'],\n",
       "      dtype='object', length=244)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be7e0be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('bnglre_home_price_model_pickle', 'wb') as f:\n",
    "    pickle.dump(lr_clf, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a3b4607",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "columns = {'data_columns':[col.lower() for col in X_train.columns]}\n",
    "\n",
    "with open('columns.json','w') as f:\n",
    "    f.write(json.dumps(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee76694",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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