Project 13 California Housing dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data processing\n",
    "import pandas as pd\n",
    "\n",
    "#Linear Algebra\n",
    "import numpy as np\n",
    "\n",
    "#Data Visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "\n",
    "# Algorithms\n",
    "import sklearn \n",
    "from sklearn import model_selection \n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression,LogisticRegression,RidgeCV\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split,cross_val_predict\n",
    "\n",
    "#datasets\n",
    "from sklearn import datasets\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing dataset from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cali_house=datasets.fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
       "          37.88      , -122.23      ],\n",
       "       [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
       "          37.86      , -122.22      ],\n",
       "       [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
       "          37.85      , -122.24      ],\n",
       "       ...,\n",
       "       [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
       "          39.43      , -121.22      ],\n",
       "       [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
       "          39.43      , -121.32      ],\n",
       "       [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
       "          39.37      , -121.24      ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calidata = cali_house.data\n",
    "calidata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column=cali_house.feature_names\n",
    "house=pd.DataFrame(calidata,columns=column)\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20640 rows and 8 columns present in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude  \n",
       "count  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704  \n",
       "std       10.386050      2.135952      2.003532  \n",
       "min        0.692308     32.540000   -124.350000  \n",
       "25%        2.429741     33.930000   -121.800000  \n",
       "50%        2.818116     34.260000   -118.490000  \n",
       "75%        3.282261     37.710000   -118.010000  \n",
       "max     1243.333333     41.950000   -114.310000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "house['target']=cali_house.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -122.23   4.526  \n",
       "1    -122.22   3.585  \n",
       "2    -122.24   3.521  \n",
       "3    -122.25   3.413  \n",
       "4    -122.25   3.422  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you may see that target column had been added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      "MedInc        20640 non-null float64\n",
      "HouseAge      20640 non-null float64\n",
      "AveRooms      20640 non-null float64\n",
      "AveBedrms     20640 non-null float64\n",
      "Population    20640 non-null float64\n",
      "AveOccup      20640 non-null float64\n",
      "Latitude      20640 non-null float64\n",
      "Longitude     20640 non-null float64\n",
      "target        20640 non-null float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you may see that every column data is in floating format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1670f969108>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check weather null value is present or not\n",
    "sns.heatmap(house.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you may observe that there is no values and missing values present in this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  target  \n",
       "0    -122.23   4.526  \n",
       "1    -122.22   3.585  \n",
       "2    -122.24   3.521  \n",
       "3    -122.25   3.413  \n",
       "4    -122.25   3.422  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1670fa8a248>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='AveRooms',y='AveBedrms',hue='target',data =house)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that averooms is in 15-20 and avebedrooms is in 2-5 in that case target variable is good ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001670FC8DF48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001670FD7AF08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001670FCB8E88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001670FDAC0C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000016710703BC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001671073ACC8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001670FC5DAC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000167107A4C08>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000167107B0808>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "house.hist(bins=50, figsize=(20,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1670fa860c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='AveRooms',y='HouseAge',hue='target',data =house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16710a24ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9,\n",
    "                      wspace=0.5, hspace=0.2)\n",
    "sns.barplot(x=\"HouseAge\", y=\"Population\", data=house)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the populations house age is 3 years and population is in 3000.In very less population and low age population is 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to check Cor-relation\n",
    "corr_hmap=house.corr()\n",
    "plt.figure(figsize=(8,7))\n",
    "sns.heatmap(corr_hmap,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Latitude is highly Negatively cor-relate with Longitude and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting and Removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc           AxesSubplot(0.125,0.536818;0.0824468x0.343182)\n",
       "HouseAge      AxesSubplot(0.223936,0.536818;0.0824468x0.343182)\n",
       "AveRooms      AxesSubplot(0.322872,0.536818;0.0824468x0.343182)\n",
       "AveBedrms     AxesSubplot(0.421809,0.536818;0.0824468x0.343182)\n",
       "Population    AxesSubplot(0.520745,0.536818;0.0824468x0.343182)\n",
       "AveOccup      AxesSubplot(0.619681,0.536818;0.0824468x0.343182)\n",
       "Latitude      AxesSubplot(0.718617,0.536818;0.0824468x0.343182)\n",
       "Longitude     AxesSubplot(0.817553,0.536818;0.0824468x0.343182)\n",
       "target              AxesSubplot(0.125,0.125;0.0824468x0.343182)\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "house.plot(kind='box',subplots=True,layout=(2,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we can observe that there is alot of outliers present in this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.34476576, 0.98214266, 0.62855945, ..., 1.05254828, 1.32783522,\n",
       "        2.12963148],\n",
       "       [2.33223796, 0.60701891, 0.32704136, ..., 1.04318455, 1.32284391,\n",
       "        1.31415614],\n",
       "       [1.7826994 , 1.85618152, 1.15562047, ..., 1.03850269, 1.33282653,\n",
       "        1.25869341],\n",
       "       ...,\n",
       "       [1.14259331, 0.92485123, 0.09031802, ..., 1.77823747, 0.8237132 ,\n",
       "        0.99274649],\n",
       "       [1.05458292, 0.84539315, 0.04021111, ..., 1.77823747, 0.87362627,\n",
       "        1.05860847],\n",
       "       [0.78012947, 1.00430931, 0.07044252, ..., 1.75014627, 0.83369581,\n",
       "        1.01787803]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Outliers\n",
    "from scipy.stats import zscore\n",
    "z=np.abs(zscore(house))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  131,   283,   409,   510,   511,   512,   514,   570,   576,\n",
       "          710,   780,   799,   864,   865,   867,   869,   871,   922,\n",
       "          923,   977,   985,   986,   995,  1010,  1021,  1024,  1024,\n",
       "         1039,  1060,  1086,  1102,  1102,  1233,  1233,  1234,  1234,\n",
       "         1235,  1235,  1238,  1238,  1239,  1239,  1240,  1240,  1541,\n",
       "         1560,  1561,  1563,  1564,  1566,  1566,  1574,  1582,  1583,\n",
       "         1586,  1591,  1593,  1602,  1617,  1621,  1636,  1637,  1642,\n",
       "         1644,  1645,  1646,  1700,  1867,  1867,  1872,  1872,  1879,\n",
       "         1889,  1889,  1910,  1910,  1911,  1911,  1912,  1912,  1913,\n",
       "         1913,  1914,  1914,  1925,  1926,  1926,  1930,  1978,  1978,\n",
       "         1979,  1979,  2025,  2119,  2213,  2294,  2311,  2392,  2392,\n",
       "         2395,  2395,  2396,  2396,  2397,  2397,  2398,  2398,  2511,\n",
       "         2511,  2776,  2826,  2963,  2969,  2975,  2978,  2999,  3004,\n",
       "         3086,  3086,  3167,  3177,  3258,  3258,  3292,  3334,  3350,\n",
       "         3350,  3364,  3413,  3486,  3533,  3542,  3554,  3560,  3603,\n",
       "         3607,  3621,  3648,  3829,  3858,  3871,  3942,  3953,  3981,\n",
       "         4014,  4018,  4035,  4042,  4044,  4045,  4046,  4047,  4049,\n",
       "         4050,  4056,  4070,  4074,  4111,  4116,  4220,  4229,  4264,\n",
       "         4266,  4267,  4319,  4345,  4352,  4492,  4579,  4604,  4605,\n",
       "         4606,  4616,  4622,  4626,  4634,  4636,  4670,  4677,  4678,\n",
       "         5242,  5243,  5244,  5246,  5247,  5248,  5250,  5252,  5253,\n",
       "         5254,  5256,  5257,  5258,  5259,  5260,  5264,  5269,  5271,\n",
       "         5272,  5275,  5277,  5281,  5291,  5292,  5304,  5318,  5352,\n",
       "         5360,  5361,  5368,  5493,  5494,  5538,  5540,  5724,  5728,\n",
       "         5738,  5815,  5921,  5924,  5963,  5964,  5968,  5983,  6055,\n",
       "         6057,  6058,  6061,  6063,  6065,  6066,  6070,  6073,  6143,\n",
       "         6204,  6226,  6287,  6289,  6297,  6298,  6303,  6328,  6329,\n",
       "         6338,  6340,  6389,  6391,  6399,  6401,  6502,  6516,  6518,\n",
       "         6550,  6551,  6576,  6590,  6591,  6592,  6712,  6720,  6724,\n",
       "         6725,  6726,  6727,  6728,  6731,  6732,  6735,  6736,  6844,\n",
       "         6915,  6919,  7130,  7357,  7361,  7617,  7864,  7907,  8311,\n",
       "         8467,  8468,  8520,  8716,  8717,  8728,  8768,  8790,  8792,\n",
       "         8793,  8794,  8795,  8796,  8797,  8799,  8800,  8804,  8805,\n",
       "         8806,  8809,  8814,  8816,  8817,  8846,  8847,  8848,  8849,\n",
       "         8850,  8851,  8852,  8853,  8854,  8874,  8875,  8876,  8878,\n",
       "         8882,  8985,  8986,  9004,  9010,  9011,  9013,  9018,  9019,\n",
       "         9027,  9036,  9040,  9064,  9081,  9084,  9114,  9118,  9122,\n",
       "         9123,  9128,  9131,  9133,  9135,  9136,  9145,  9149,  9154,\n",
       "         9159,  9166,  9167,  9170,  9172,  9172,  9183,  9185,  9193,\n",
       "         9193,  9282,  9286,  9289,  9316,  9331,  9354,  9356,  9370,\n",
       "         9381,  9382,  9418,  9431,  9431,  9451,  9451,  9579,  9671,\n",
       "         9671,  9672,  9676,  9676,  9678,  9678,  9681,  9681,  9682,\n",
       "         9682,  9683,  9683,  9692,  9693,  9697,  9716,  9744,  9748,\n",
       "         9794,  9811,  9880,  9881, 10067, 10067, 10076, 10076, 10077,\n",
       "        10077, 10078, 10079, 10079, 10080, 10080, 10081, 10081, 10082,\n",
       "        10082, 10083, 10083, 10084, 10155, 10166, 10169, 10309, 10317,\n",
       "        10318, 10324, 10332, 10376, 10389, 10401, 10402, 10405, 10417,\n",
       "        10458, 10464, 10472, 10483, 10484, 10517, 10519, 10527, 10539,\n",
       "        10562, 10644, 10661, 10664, 10667, 10670, 10671, 10673, 10682,\n",
       "        10724, 10725, 10726, 10727, 10742, 10743, 10744, 10745, 10749,\n",
       "        10757, 10759, 10760, 10761, 10763, 10839, 10842, 10910, 10921,\n",
       "        10934, 10948, 10957, 10997, 10999, 11000, 11007, 11008, 11010,\n",
       "        11020, 11037, 11038, 11041, 11042, 11066, 11067, 11083, 11349,\n",
       "        11469, 11490, 11518, 11519, 11520, 11521, 11704, 11705, 11705,\n",
       "        11706, 11706, 11707, 11707, 11708, 11708, 11709, 11709, 11710,\n",
       "        11710, 11713, 11713, 11714, 11714, 11715, 11715, 11716, 11716,\n",
       "        11717, 11717, 11719, 11720, 11720, 11721, 11721, 11722, 11722,\n",
       "        11725, 11725, 11729, 11729, 11775, 11828, 11831, 11831, 11832,\n",
       "        11832, 11833, 11833, 11834, 11834, 11848, 11848, 11849, 11849,\n",
       "        11862, 11862, 11865, 11866, 11866, 11867, 11867, 11870, 11870,\n",
       "        11912, 11960, 11970, 11976, 11982, 11989, 12002, 12006, 12022,\n",
       "        12039, 12040, 12041, 12067, 12072, 12077, 12097, 12104, 12106,\n",
       "        12109, 12114, 12115, 12119, 12122, 12126, 12127, 12128, 12129,\n",
       "        12132, 12133, 12135, 12136, 12136, 12137, 12144, 12152, 12191,\n",
       "        12194, 12201, 12204, 12214, 12215, 12218, 12227, 12258, 12303,\n",
       "        12303, 12305, 12306, 12306, 12307, 12344, 12344, 12349, 12349,\n",
       "        12354, 12361, 12365, 12371, 12372, 12376, 12376, 12390, 12394,\n",
       "        12394, 12396, 12411, 12422, 12430, 12430, 12430, 12441, 12447,\n",
       "        12447, 12623, 12658, 12663, 12671, 12703, 12813, 12814, 12817,\n",
       "        12868, 12872, 12948, 12954, 12957, 13034, 13034, 13047, 13051,\n",
       "        13053, 13080, 13098, 13099, 13132, 13139, 13176, 13181, 13182,\n",
       "        13221, 13223, 13226, 13316, 13370, 13374, 13377, 13378, 13379,\n",
       "        13382, 13383, 13387, 13399, 13403, 13422, 13423, 13424, 13459,\n",
       "        13517, 13519, 13715, 13742, 13788, 13816, 13834, 13848, 13878,\n",
       "        13889, 13890, 13900, 13911, 13912, 13919, 13923, 13923, 13924,\n",
       "        13939, 13939, 13940, 13940, 13941, 13941, 13942, 13942, 13943,\n",
       "        13943, 13944, 13944, 13945, 13945, 13946, 13946, 13947, 13947,\n",
       "        13948, 13948, 13950, 13950, 13955, 13955, 13956, 13956, 13958,\n",
       "        13958, 13959, 13961, 13961, 13963, 13963, 13964, 13964, 13965,\n",
       "        13965, 13966, 13966, 13967, 13967, 13970, 13970, 13971, 13971,\n",
       "        13972, 13972, 13973, 13973, 13975, 13976, 13976, 13978, 13978,\n",
       "        13979, 13979, 13980, 13980, 14209, 14459, 14467, 14469, 14471,\n",
       "        14482, 14486, 14491, 14505, 14515, 14543, 14553, 14558, 14686,\n",
       "        14718, 14756, 14805, 14913, 14931, 14959, 15049, 15059, 15061,\n",
       "        15105, 15108, 15114, 15117, 15152, 15183, 15203, 15215, 15220,\n",
       "        15230, 15231, 15232, 15233, 15235, 15241, 15286, 15336, 15337,\n",
       "        15360, 15413, 15459, 15491, 15500, 15523, 15524, 15526, 15527,\n",
       "        15533, 15544, 15574, 15579, 15691, 15693, 15694, 15697, 15698,\n",
       "        15996, 16011, 16019, 16020, 16137, 16171, 16175, 16304, 16420,\n",
       "        16559, 16606, 16669, 16669, 16818, 16820, 16828, 16839, 16908,\n",
       "        16909, 16910, 16911, 16912, 16913, 16914, 16915, 16918, 16938,\n",
       "        17024, 17073, 17106, 17107, 17108, 17109, 17110, 17111, 17112,\n",
       "        17113, 17118, 17157, 17158, 17160, 17161, 17162, 17166, 17167,\n",
       "        17170, 17171, 17173, 17185, 17310, 17310, 17413, 17460, 17493,\n",
       "        17494, 17551, 17690, 17752, 17754, 17761, 17770, 17777, 17798,\n",
       "        17845, 17854, 17855, 17858, 18043, 18052, 18053, 18055, 18059,\n",
       "        18061, 18065, 18066, 18067, 18069, 18076, 18078, 18079, 18080,\n",
       "        18081, 18087, 18209, 18269, 18277, 18280, 18281, 18282, 18283,\n",
       "        18287, 18298, 18325, 18327, 18341, 18346, 18347, 18348, 18349,\n",
       "        18351, 18352, 18353, 18354, 18355, 18356, 18360, 18361, 18362,\n",
       "        18363, 18366, 18462, 18501, 18504, 18505, 18619, 18681, 18681,\n",
       "        18745, 18858, 18860, 18875, 18911, 18926, 18980, 18985, 19006,\n",
       "        19006, 19006, 19362, 19362, 19421, 19452, 19536, 19536, 19574,\n",
       "        19658, 19736, 19736, 19789, 19800, 19802, 19803, 19806, 19807,\n",
       "        19975, 19975, 19976, 19976, 19977, 19977, 20093, 20093, 20094,\n",
       "        20094, 20112, 20113, 20113, 20121, 20163, 20249, 20278, 20305,\n",
       "        20308, 20316, 20318, 20333, 20336, 20376, 20380, 20389, 20424,\n",
       "        20426, 20427, 20436, 20440, 20451, 20529, 20530, 20544, 20563,\n",
       "        20629], dtype=int64),\n",
       " array([0, 4, 0, 0, 0, 0, 0, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 4, 0,\n",
       "        4, 4, 4, 2, 3, 4, 4, 4, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
       "        0, 4, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0,\n",
       "        4, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3, 2, 3, 4,\n",
       "        2, 3, 2, 3, 4, 4, 0, 4, 4, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3,\n",
       "        0, 4, 0, 4, 4, 4, 4, 2, 3, 4, 4, 2, 3, 2, 3, 2, 3, 5, 4, 0, 0, 0,\n",
       "        4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 4,\n",
       "        4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 4, 0, 0, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 4, 0, 0, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 5, 0, 0, 0, 0, 4, 4, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 2, 3,\n",
       "        4, 4, 4, 0, 4, 0, 0, 0, 0, 0, 0, 2, 3, 2, 3, 4, 2, 3, 3, 2, 3, 2,\n",
       "        3, 2, 3, 2, 3, 2, 3, 4, 4, 4, 4, 4, 4, 0, 0, 4, 4, 2, 3, 2, 3, 2,\n",
       "        3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 0, 0, 0, 4, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4,\n",
       "        4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0,\n",
       "        2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
       "        2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 4, 0, 2, 3, 2, 3, 2, 3, 2, 3, 2,\n",
       "        3, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 3,\n",
       "        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 3, 3, 2, 3, 3, 2, 3, 2, 3,\n",
       "        3, 3, 4, 2, 2, 2, 3, 3, 2, 3, 2, 4, 4, 0, 2, 3, 4, 2, 3, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "        0, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 2, 3, 3, 2, 3, 2, 3, 2, 3, 2,\n",
       "        3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3,\n",
       "        2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3, 2,\n",
       "        3, 2, 3, 2, 3, 2, 3, 4, 0, 0, 0, 4, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0,\n",
       "        4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0,\n",
       "        0, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 4, 4, 5, 4, 4, 0, 4, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 4, 0, 2, 4, 4, 0, 0, 4, 4, 4, 4, 4, 4, 4, 0, 4, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        4, 0, 0, 0, 0, 2, 3, 4, 3, 4, 4, 4, 4, 4, 4, 0, 4, 5, 2, 3, 4, 4,\n",
       "        2, 3, 4, 4, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
       "        3, 2, 3, 4, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 4, 0, 4, 0, 4, 4,\n",
       "        4, 4, 4, 4, 4], dtype=int64))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold=3\n",
    "np.where((z>3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0694430501296885"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[131][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.330954631498988"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[20544][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_new=house[(z<3).all(axis=1)]#removing outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 9)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19794, 9)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "846 rows had been removed as an outleirs ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# check distribution of skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1670d43dd48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(house_new['Latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "collist=house_new.columns\n",
    "ncol=9\n",
    "nrows=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3bGji8Re7ePZUotfho8+f59Hnzxd5hcYURywWA9hA5pNBx6OqeoOq3gR8CfhKYVe5NC+cSfYDWjl3EKhnaJKIZQlWhGg8jsctqUygiUi0yCtavI6ODtrb29m8eTM+n4+9e/eyf//+Kdvs37+fj340MdTxzjvv5KmnnkJV2b9/P3v37sXv97Np0yba29sBaoBbgJOqelpVwyQCPR9M7u73gAdUNQ6gqkWbwz5XEAgSJWEne0cJTtgBYCkLx+JU+dyIJLL0jDHGmIXY/2o34Wicndc0p27btrqeaFx59tSAncvPY9kGgf70B0cZHI9w51vb+OBNa2mu8fHNn50p9rKMKbqOjg6A0CwngwCo6nDatzUkpoeUjY6zV/B5XKkeIpk01/iIxZWLQ5MFXJnJl2hM8bhcV3sChcv3xLO7u5v1669OwGhra6O7u3vWbTweDw0NDQwMDGT8WcAHrAMupO2iK3kbwLXA3SJySEQOiMiWTOsSkY8ntznU15f7adKqSnAiQv0cQSCnx9crFywbqJSFo4lyMJ/bZZlAxhhj5jU9qPPdQ11cv6aetY1VqdvWN1Xh87h44cyVQi+v7CzLINDXf3Kax1/s4j/80uZUT5AP3bKep45e5oKVf5hlLnkyGU67Kf1kMEVE7hWRUyQygTL2CMn3SeFi/ejoZVrr/LP2AwJoSk4fspKwyhCJ67RMoPItB8s0qVlEstpmlinPCmSaqe5s7Acmk9OE/hr45izr+pqq7lTVnS0tLXP8BoszEooSjsZpnCMItC55MPh6dzDnj29yJxyN43O78HtclglkjDH5N2ebB4eI3CkiKiI7Z9umFBy7NMzh7iC/+da2Kbd73C5uWt/IC2ctCDSfZRcEevHcIH/6g6O8/02r+YNf2Za6/cNvuwYR4X89e7ZoazOmFMxxkjh9u4dV9Vrgc8AfzbKvvJ4ULoZqIrtnTUNgzu2aqy0IVEmisThel+D1OCPiy7ccrK2tjQsXribtdHV1sXbt2lm3iUajBINBmpubM/4sECER7F2fvgugx9kM+Lvk198Hbszl75MtJytvrkygKp+bxmovTx6xxpClLJJsDO3zuC0IZIwxeZRlmwdEpI7ERd3nC7rARfi7F7vwuoVYfOY5y66NTbzeM2ztHOaxrIJAZ/rH+N5LXbyzfSV/sfemKdNF1jRUcceO1XznUJf90ZhlLa08JHUTV08GM9kH/Fo+15RLPcFJJiIx1jRUzbldfZUXn9tlQaAKEY0rHrcLtwguKe9MoF27dnHixAnOnDlDOBxm37597NmzZ8o2e/bs4ZFHHgHg8ccf57bbbkNE2LNnD/v27SMUCnHmzBlOnDgBMAa8AGwRkU0i4gP2Ak8kd/f3wG3Jr98FHM//bzlT70gyCBSYPQgEsLo+wMWglXGWsnDsaiaQlYMZY0z+ZNPmIekLJLL7S/oDNBSN8f2Xu7ntulZqkgMG0t2wrpFYXLlkxwFzWjZBoHA0zvde6qKpxsd//8hb8XvcM7b57bdtIDgR4bClkZtlbNeuXQCBWU4GAZjWE+QDwInCrXBpjl1MtDOaLxPIJUJbU5WViFaAeFyJxRWPSxARvG5XWY+I93g8PPTQQ9x+++1cf/313HXXXezYsYP777+fJ55IvFTvueceBgYGaG9v5ytf+UpqjPyOHTu466672L59O3fccQcPP/wwAKoaBT4BPAkcBb6jqkeSD/kg8Bsichj4IvDvCvoLJ/WPhgCozXDQl25NQ4D+kRCTZRzoq3RdgxNcGQ8ny8HseTLGmHzJps2DiNwMrFfVf5xrX6XQ5uFbz52nfzTMusbMfT3ftK4egJ7gRCGXVXbmPpKqIP/wag8DY2H+zduvmfUA8u2bV7C5pYbnTw/wlg1NBV6hMaXB4/EAnCdxMugGvqmqR0TkAeCQqj4BfEJE3keijGQQ+Gix1rtQZ/rHAGip9c+77YYV1ZYJVAHCyexOT7IfkM/tKvsAwe7du9m9e/eU2x544IHU14FAgO9+97sZf/a+++7jvvvum3G7qv4Q+GGG24dIBHuLqm8kEQSqC8x96LK6oQoFjl8e4ca2xgKszCxULK74PS5cIpYJZIwxeTRfmwcRcQFfBX4ni319DfgawM6dO/M+FGYsFOVT+17m+OVRmqu9xOJxvvzkMdpba2lvrc34M//yRh9VXjc9QxYEmkvFB4GcTuKPdZxndX2AbavqZtyX/vVv33oNX/jHTvvDMctdMNkENkVV70/7+lOFX1JunBsYJ+B1UeWbmQ043WQkxsne0QKsyuRTKJIMAiVLgL2e8s4EWq76R8N4XILfM3cSs5Pld/TisAWBSlQsrrhdLnxuSQVpjTHG5F4WbR7qgDcB/5wcMrEaeEJE9qjqoUKtczpV5XsvdXGqb4ybNzTy7OkBfnZqgG2r6viNaQ2h04kI6xqr6LHpvnNaFuVgvSOTHO4O8tZrmmZMUJnuzre04XULHTZazpiKdHZgjBU1/nnfCyDRHHoiEiM4HinAyky+OOUmHnfiOfe5XUxYEKjs9I2EqA145n3tNtf48LqFoxdHCrQys1DRZHnm0ESE8wOWbWmMMfkyX5sHVQ2q6kpV3aiqG4HngKIGgAC6hyY4emmEz/7KVr79H97Ofbu3c/DTv8iBT/3ivGXhaxsDXBqetEzTOSyLINDhriAicENbA5DI+knPAkrXUO3lxnWNvHJhiLFQ+U6PMcZkdv7KOM01vvk3hNR2VhJW3pzpQ15X4iPP65aybgy9XPWPhuY98INEP6/V9QGOJvt/mdKTyAQSPC4hmmG6izHGmNyY1uYh1fNPRB4QkT1z/nARvXhuEI9L+K1bNwCJ6Z/Xra7H5Zr/Iu7axipiceVEr10Mms2yCAK9cXmEm9c3zjtRBBIBorde00Q4FrcRs8ZUmEgsTtfgBCtqswsCNVkQqCJMzwTyeiwTqBz1jYSoyyIIBLC6IcBrXcHZeiGYIovG4nhcgsflIhq3K7XGGJNnQVXdqqrXquqfQaLNQ7LP5xSq+u5iZwGpKp09w1y3pj6r8/fp1jYmJgAf6baLQbOp+CDQeChK9+AE79ramvXPXLOimqZqL99/uTuPKzPGFNrl4UlicaWpOstMoGoLAlWCyVRPoKuNoS0TqPz0jybKwbKxqj7ARCRGb7KZtCktqUwgtxCNWaDOGGPMVaf6xhgJRdkyS/Pn+TTX+PB7XLzeYxO/Z1PxQaBT/WMo8M4tK7P+GRHhpvVN/OxkP5eCk7OWjhlTquYqeVzOLgUTTeIaqrK7quD3uqnxuS0IVOaccrBUJpD1BCo7sbhyZSxMrT+71+6q+kRz6DcuWSp4KYqp4nIJbpcQs3IwY4wxaZ49PQDA5pU1i/p5lwirG6wsfC4VHwQ6PzCGxyXcsK5hQT9384ZG4gr7X7FsIGMqxcUFBoEgcTXhggWBylrGxtCWCVRWBsZCxJUFZQJBYky8KT3xOLgl0RMoZiV7xhhj0rx49gr1AQ/NNb5FX9he01BFZ88wcbvQkFHlB4GujNPWVIVvnpGy062s9XPT+kYrCTOmgiw0EwgSfYEsE6i8zWgMbSPiy07/SBgg655AtX4PtX4PxywTqCTFNFEOZplAxhhjpnu9Z5h1jVVZTfKdzdqGAGPhmB3Dz6Kig0CTkRg9Q5NsaF5cKtmvv2Udxy6NcDE4keOVGWOK4WJwkhqfG/8CgsLNNT66hyaIxqx5abkKRaaWg/lsOljZ6RtN9PbJZjqYY3V9wDKBSlQ8rrhEcIkFgYwxxlw1Ho5yqm801dw53UKygtY0JH6+00rCMqroINCpvlFiqqxtDCwqjexXb1yL1y28fH4oD6szxhTapeEJVjcEFnRlobnaRyyuqVIyU35S5WBpmUDhaNxOPstIf7LBc12W5WAAq+r9HL88YqngJSYWVxRwu0iUg9nzY4wxJunoxWFUyRgEWojWej8el3CkAppD56PXa0UHgZyGkE5vgIVqrvHx7m2tvHphyA5SjKkAF4OTqSsD2Wq2MfFlb3pjaJ878dFn2UDlYzGZQKvqA0xG4vbaLTHOSHiXWDlYEdWLyBsiclJEPp9pAxG5S0Q6ReSIiDxa6AUaY5anoxcT5+9rGhZ3/u7wul20t9bS2WOZQJlUdhDo8ghul7Cy1r/ofex581pGQlFeOj+Yw5UZY4rhdN/YgnvBOEGgcwN2IlmuUkEg19XpYJBIOTbloX8khNctC+rvl5oQZiVhJcUZCW89gYojFosBbADeD2wHPiQi29O3EZEtwB8C71DVHcCnC71OY8zydLJ3lBqfe0H9O2ezfU09RywIlFFlB4EujdBS68ftWnxTqXdva8Etwj91Xs7hyowxhRaNxRmZjNBQlX0mAUB9lRe3Szj4+qU8rczkWyiZ8eMEf5xMoMmw9XkqF32jIWr9ngWVcrbWJy4AHbfm0CXFCQI5mUAKFggqoI6ODoCQqp5W1TCwD/jgtM3+PfCwqg4CqGpvYVdpjFmOHn3+PCd6R2hvrV1SU2jHm9Y10DsSSg2GMVdVdBDo+KURVi8xlawu4GVzSw3ff7k757V4xpjC6R8NE9dEUGchXCI0VXu5Mh7O08pMvs3IBEpmk4xHLBOoXPSPhqgLLOy16/e42dBczTHLBCopkWQ5WCITKPFajFjj/YLp7u4GSP9A6wLWTdtsK7BVRH4mIs+JyB2Z9iUiHxeRQyJyqK+vLz8LNsYsKyd7R2lvrcvJvvqS/QStomemig0CBSci9AQnWVW3+FIwx7UttfSNhBiZjORgZcaYYnCm/C0mvbS5xsfgmAWBHCLiFpGXReQfi72WbDhBICcr1JfsDTRhY+LLRt9IaEH9gBxbV9VZJlCJSZWDiZB8KRK2IFDBqGbMupp+owfYArwb+BDwdRFpzLCvr6nqTlXd2dLSkuulGmOWmclIjMvDIdpba3OyvzWNATwu4aVzFgSarmKDQCeSV/5WLTETCGDTysSI+TP9Y0velzGmOJxU0MUEgZqqfVyxIFC6TwFHi72IbIWiMTwuSaUWe60xdNnpHw1Tu4DJYI5tq2s50z+WmhBnis/J+nG5rgZmI1ELAhVKW1sbgC/9JqBn2mZdwH5VjajqGeANEkEhY4zJG+dYe9PK6pzsz+Nysa6pihfOXsnJ/ipJxQaBjiWv/K1e5GSwdGsbq/B5XJwdsCCQMeXq0nAyCLTAkhJIZAJNRGIExy0bUETagA8AXy/2WrIVisRTk8GAVHNhywQqD5FYnCtj4UVlAm1bXU80rpzus8/vUuH0/0n0BEq8FqPWE6hgdu3aBRAQkU0i4gP2Ak9M2+zvgfcAiMhKEuVhpwu5TmPM8uMEgdqachMEAmhvqeW17mAqoz8f49bLUVZBIBG5Y65RkiLy2eQYyddE5CkRuSb3S12YH7x2EZ/HlZPO4m6XsK6xiq7BiRyszBhTDBeDk3hcQpXPveCfdSaEXRi0CWHAXwD/GZj10n2p9YkIReN4XVc/7iwTqLw4B4V1i8kEWpXoK3Dc+gKVjOiUnkCJ4GzYMoEKxuPxAJwHniSR0fkdVT0iIg+IyJ7kZk8CAyLSCTwN/CdVHSjKgo0xy8Zgsv/m+ubcBYG2rKpDFZ45Ufzj0VIybxBIRNzAw8wxShJ4GdipqjcCjwNfyvVCF2pgLMTKWl9OOosDrGus4lJw0g5UjClTPUMTNFR5F/We4ASBzl9Z3kEgEflVoFdVX5xru1LrExGKxqZmAqVGxFsQqBw4jR0Xkwn0/JkB3CK8YX2BSkZk2nSwxG12bFVgQVXdqqrXquqfAajq/ar6RPJrVdXPqup2Vb1BVfcVd7kmR+rnuqjvEJE7RURFZGchF2fMlbEwDVXenCRxONqaqlhZ6+PA4UuWAZQmm0ygW4CTc42SVNWnVdU5O3qORH1xUQ2MhllRs/Sm0I62piqicbWricaUqZ6hCRqrF/eh0lRtQaCkdwB7ROQsic+C20TkW8Vd0vxC0Tie9EygZDnYpGUClYW+0UQQqG4RQSCPy8XKOp8FgUpIqjG0Kz0IZOVgxuRTLBYD2MDcF/URkTrgk8DzBV2gMSQygdY3V+V0ny4R/vXN6/jR0cuMhmwqrCObINA64ELa95lGSaa7BziQ6Y5ClQhEYnEGx8OsqPHNv/Ec0msG1/ErTg0AACAASURBVDUm/iBf6woueX2l5uDBg2zbto329nYefPDBGfeLiF9Evp28cvC8iGycdv8GERkVkf+jQEsuGVZXWj56hiZprFrce0LA66ba5+bcwPIOAqnqH6pqm6puJNFH4seq+uEiL2teM3oCWSZQWUllAi2inxfAqvpAqk+gmaHgmQHOiHiXCG6xTCBjCqGjowMgNNdF/aQvkKjomCzg8owB4MpYhA05LAVz3L1rAzFVfnLcSsIc2QSBMtVOZLxkIyIfBnYCX850f6FKBLoHJ4grrKjNXSZQc42PgNfF4e6hnO2zFMRiMe69914OHDhAZ2cnjz32GJ2dndM3uwcYVNV24KvAn0+7/6vMEvgzphREYnEuj0zSsMhMIEi8B1ywTKCy5EwHc3hsRHxZ6R9dfDkYJC7idA9N2IS/aYqVGZApE8hGxBuTX93d3QDpb4IzLuqLyM3AelX9x7n2VWp9/4rhRO8If/Gj43z1n44XeynZmjPgLyK/KyKHReQVEflpps+CfIvHlcHxMCOT0ZxfYG9vreVf37yOZ08PMJA8pljusgkCdQHr077PNEoSEXkfcB+wR1WL+q/rTPFaaiZQOhGhrbG64jKBOjo6aG9vZ/Pmzfh8Pvbu3cv+/funb/ZB4JHk148D75VkYxUR+TUSEyOOFGzRxizQpeAkqtC4hBrjxiovPUPWHN6hqv+sqr9a7HVkIxSN43Ff/bhzieBxCS+dGyziqky2+kZC1PjcqaluC+Vk8v63p07kclllr1iZAU5j6PQR8VErBzMmr1QzvsZSN4qIi8RF3T/IYl8l1fevGA53BekdCfFoR+lXA2QZ8H802f/rJhLv918p7CqhdyRELK6pPpy59rk7rsPtEv7+le7ZXg/LSjZHVC8AW+YaJZmMHP8ViQBQb+6XuTBn+5NBoNrc/hGta6rijUsjFdVHoru7m/Xrr8b42tranKsF6VIlgaoaBYLAChGpAT4H/Mlcj2FXDEyxOcGbxurFvyc0VHnpCU7YB0cZCkXjeN1Tk1q9bleqLMWUtv7RMC11i8/sXZsMAnVbEHeKYmUGpDKBrDG0MQXT1tYGkH4QNP2ifh3wJuCfk33/3gY8Yc2hM7sYTMTE+0ZCXB4u7cq5bAL+qjqc9m0Ns1T95JPTd7NpCcfqc1lVH+D2Has51TfGq12VVdmzGPMGgZIn/Z9g7lGSXwZqge8m08iemGV3BXF2YByfx7Xo1PHZrGtMNIc+enF4/o3LRKYT2gzTk2YrCfwT4KuqOjrPYyz7KwamuHqCiZO/pUwbaKj2MRmJE5yI5GpZpkBCkdiUxtAAXrcQiVpArxz0jUyycgnl3QGvm5W1froHLQiUrliZAekj4j1WDmZMQezatQsgMNtFfVUNqupKVd2Y7Pv3HImL+4eKsuASFo3FuTw8yfqmxAWG17tLu0okm4A/gIjcKyKnSGQCfTLTvvJ5Yd9puZCvTCCAWzY1s6YhwFNHe4nFl/cxYFa51ar6w3lGSb5PVVep6k3J//bMvcf8Ojcwxoqa3I2Hd7Q1VV5z6La2Ni5cuNr3u6uri7Vr107fLFUSKCIeoAG4AtwKfCl5xeDTwH8RkU8UYNl5Y42eK1PPUOIqzZKCQMmfdfZlykc4OrUxNFgmUDk51TfGxBIzcNc1BiwTaJpiZQakj4h3OZlAUXstGpNPHo8H4DxzX9Q3WTg7MEY0rty8oQkRONJT2skB8wX807Z7WFWvJVHl8Uez7CtvF/YvDI4jLK11w3xcIrx7WysDY2GePlb04qWiWlyBfYk7OzCe035AjoYqLytr/RWVQrZr1y5OnDjBmTNnCIfD7Nu3jz17ZnwWPAF8NPn1nSQmAqmq/mLaFYO/AP6rqj5UuNUbk53uoQmaa3yL7ikCVz+ULgbtRLLcTB8RD8kgkPUhKQujk9ElZ/aua6omOBGhd8SCuI5iZQY45WAuGxFvTKEF57qon05V321ZQJk5ZUtrG6torfOX/NCQLAL+0+0Dfi2fa8qke3CCuoBnSg/HfNi+pp4an5vvvdyV18cpdRUXBIrG4ly4Mp7TyWAOEeHGtoaKygTyeDw89NBD3H777Vx//fXcdddd7Nixg/vvvx8SGT8A3yDRA+gk8Flg1jGypmLMN0XgsyLSKSKvichTInJNMRaZrZ6hCdY2Bpa0j1QmUNBOIstNKBrL0BNIyr4PycGDB9m2bRvt7e08+OCDM+4PhULcfffdtLe3c+utt3L27NnUfV/84hdpb29n27ZtAPXO7SJyxzyv/b8UkTlLgHMpFI0xEYlRF1hiEKixPNL2C6lYmQGpcjARPMmM7ahl5ZlZOBnalqVtSkXvcGL+UV3Aw5qGqlR/oFI1X8AfQES2pH37AaDgkxQuj4Soz2MWkMPtEm5c38iPOnsZmVy+LR4qLgh0MThJNK4056mp1I1tDZzqG2U0FM3L/oth9+7dHD9+nFOnTnHfffcB8MADD0CiATSqOqmqv6mq7ap6i6qenr4PVf1jVf2/CrpwkxdZThF4GdipqjeSmBj3pYIucoGOXhxmqecYtQEPLoEfdV7OzaJMwYQi8Skj4sHJBCrfE89YLMa9997LgQMH6Ozs5LHHHqOzs3PKNt/4xjdoamri5MmTfOYzn+Fzn/scAJ2dnezbt48jR45w8OBBgA0i4hYRN/Aws7z2k2VAjQX6FQEYGE20Maj1L+3AcG1jAKGyyrlzpOCZARlHxFs5mDGmTPSOJINAfg/RWJxjl0ZKOkiZZcD/EyJyREReIXHB/6OZ95Y/vcOT1AfyHwQC2LGmnnAszoMHjhXk8UpRxQWBnJr/xpr8/BG9ua0RVbuaaCpXllMEnlZVJ//1ORKppSVraDxCQ/XS3hNcItRXea0xdBmaPiIeyj8I1NHRQXt7O5s3b8bn87F37172798/ZZv9+/fz0Y8mjuPuvPNOnnrqKVSV/fv3s3fvXvx+P5s2bQIIAbck/zuZ6bWfDBB9GfjPBfslgf7Rq1dcl8LvcdNS5+ewBYGKLjUiXrByMJO1SCxOf/Lk25hi6h2ZpMrrxuN20VDlJTgRLofJsXMG/FX1U6q6I9nb9z2qeqTQC7w8PLnkz/psXbOiBr/HxfHLIwV5vFJUeUGg5PSPfI2Xu7EtUSH1WgX1BTImXbZTBNLcAxzIdEc+pwhkKzgRIRSN56TRXIMFgcpOPK6EY5kaQ0tZn3h2d3ezfv361PdtbW3OazfjNh6Ph4aGBgYGBmb8LInX+7rkfxfSbk9/7X8CeEJVL862pny83vuSJ325mPa5rrGK17qD5XCwXtEiaT2BXDYi3mTp0efP85UfHWc8XDmZ+KY8XR4OUV+V+ExqqPYRiSkT4aUNL1juQtEYg+MR6gqUCeR2Ce2ttbxxaWTZHhNUXBCoa3Dpo6DnsqLWz4bmal44O5iX/RtTbNlOEQAQkQ8DO0lkCGTaV96mCGSrx8kOzEFg2IJA5ccZPe3N2Bi6fE88M71Op0/EnG2bOV7jmUZqqoisBX4T+Mt51pTz17uTCVSbg6uD65qq6BsJcWm4tPs3VLpoLK0nkAWBTBbGQlHeSF6xP3qxtCcxmcrXOxJKBSuc880hOzZcEqfPUn2BMoEAtq2qY3jy6nvLclNRQaBHnz/PMyf6aK3z481jZ/G3b17B86cHiMWXZ+TQVLZspwiIyPuA+0hMiynZHO1UECiHmUBxe+2XjVAkcXKZcUR8GWcCtbW1ceHC1aSdrq4u1q5dO+s20WiUYDBIc3PzjJ8l8XrvIZH5k54i5Lz2bwbagZPJUeHVyUEBedef6gm09APDtqZqAF69YJm8xRSNz+wJVM6vRZN/Z/rHUl+X+jhuU/n6hiepS34mOceWwxYEWpJvPXcOoCCNoR1bV9UB8PSx4lQqFFtFBYEABsfD+JcwBno+jz5/HgWGJ6N02geRmUU5T7LIcorAzcBfkQgA9RZ+ldlL9QlbYk8gSASBYnFlYCw8/8amJISiiRTtmSPiy3s62K5duzhx4gRnzpwhHA6zb98+9uyZOtBpz549PPLIIwA8/vjj3HbbbYgIe/bsYd++fYRCIc6cOQMQADqAF4At01/7qvoDVV2dNip8XFXbC/F79o2ECHhdObmws6YhgNctvGxBoKJygkAuSQ8Cle9r0eTfleRn7tZVtakSUWOKQVXpGw2letc4/x+ZtDLFpRhO/vsVqicQJAJOq+sDPHN8eQaBCvcvXSBD45HUKNh82dxSA8Czp/u5oa1hnq2NuapnaII//N5hugcn+O23bSj2cjKaNkXADXzTmSIAHEo2kfsyUAt8N1mCcl5V8zZSeCm6hyZwu4SaHGQSOFd8LgYnaKnzL3l/Jv9C0dkzgaKxOKo6o4yqHHg8Hh566CFuv/12YrEYH/vYx9ixYwf3338/O3fuZM+ePdxzzz185CMfob29nebmZvbt2wfAjh07uOuuu9i+fbvzej+nqjEAEfkE0177xfodAfpGQ0ueDObwul2sqg/wynkLAhWTUw7mciUCQYIFgczc+kdD1Pg9rK6v4lTvGLG4pgKIxhTSaChKJKapY0qnVHkkZJlAS+GMai/UdDDHltZanjszwFgompPzhHJSUb9tXJXgeIQ3rc1vYKY+4KWl1s/PTw3w8V+6Nq+PZSrL5793OBVxPvj6Jf7tOzYVeUWzCqrqzvQbVPX+tK/fV/glLU7P0CQNVV5cOTjRb6hKVMldDE5yY0nPQzOOq5lA04JAHhdxTZSh+DzleTKxe/dudu/ePeW2Bx54IPV1IBDgu9/9bsafve+++7jvvvsAEJFUWquq/hD44VyPq6q1i170AvWNhHJSCuZY31TNq11DRGMzJ8aZwnBKv9zJ92S3S1K9u4zJZGAszIoaH001XmKqXB6eZG2eL/gak8nQeCJYUe1zA4ks42qf2zKBlmh4IopbJPXvmmuzVWdsWVXHT0728/yZAW67blVeHrtUVdQR0MhklJhqTso+5rO5pYaOM1cIR+3AxWTn9e4gzxzv47/svo63bmji5QtDTEZsmkC+dQ+O5+w9wRkzfzFZYmZK32SyJ9D0ciJvMig0GbXXYCnrT0u7z4X1zVWMh2Mcvzyas32ahYnG47jkaiNzt0uIRK0nkJndwGiIlbU+mpMDHi5cGS/yisxy5QSBqrxXP5dq/R4LAi3RyGSEuipPwTOzr1lRTcDr4pnj/QV93FJQUUGgofFEzXC+xsOn27qqjvFwjOdOD+T9sUxl2PfCefweF3fv2sBNGxoJR+P8+FhJt9OpCD1DkzRW5eY9ocbnxuMSLgZtulC5cMrBppcOeJO94yZtrGtJy0cmEMAr1heoaKIxnZKZ6XaVd38uk1/RWJzhyShNNT6aapJBoEG7EGOKYzB5rpmesVIf8KbKmcziDE9GCl4KBokLhLduWsEzJ5ZfX6CKCgINJqOzhcgEam+tpdrn5sDrl/L+WKb8xePKgcOX+OXtq2io8rJpZQ1VXjf/8sbye9MppEgszuXhyZy9J4gI9VVeeiwIVDZCyWy7mZlAySBQxE4+S9VkJMbIZDSnmUDNNT6aqr28cmEwZ/s0CxOd1s/F4xLLqjazGg0lMizq/V4aq70I0DVomUCmOENYnFHwVWlBoLqAZQIt1XCOP+sX4l1bWzjdN8bZtCmEy0FFBYEKmQnkdbt4z3Wt/FPnJRsVb+b1atcQA2Nhfnl7ot7UJcKG5moOnbtS5JVVtkvBSZTcjId3NFR5rRysjEykgkBTM4GcRtETVpJZsnqHE1OA6nJ4dVBEePP6RssEKqJoLD4lE8jjdllZppnVWCjxt1Hj9+BxuajyuW1C2DLmBH6KNYF3KEMmUF3Aw0goiqqdDy7WSJEygYDUudmTR5ZXYkeFBYEiVPvc+PI4Ij5dnd9D/2iYQ2ftRN7M7cfHenFJItrs2LiimlN9YwyM2sFMvlwdD5+7wHBjldfKwcrIbD2BfG4nE8hOPkvV5ZHE6yzXVwdvXt/Eid5RS98vkkhccaVlAvncLiasLLPQ6kXkDRE5KSKfn20jEblTRFREds62Tb45mUC1fnfy/x767bjJFMnVxtBXP5fqAl5icSU4YZ8pizEejjIZiVNfpEyg9c3V3LCugYMWBCpfg+PhgmQBObatrsPjEv7htR5g9s7jpvwt9arDU0d72XlN85RgxIYVNUAiS8jkR48TBMpxJtCl4UnLACwTE7OUgzmToSwTqHQ5mUC5vjo4OB5GFQ53BXO6X5OdaCxOemKexy1MWjlYwcRiMYANwPuB7cCHRGT79O1EpA74JPB8QRc4TSoIlHwfqPV7GBgNF3NJZhkbHA9T6/dMKWl1LlT0WobaojiZfbVFygR69PnzrGkI8PL5IS4Gl0+mf0UFgYbGIwXpB+Twe9zcsK6B77/UbVcUzawuBifovDjMbde3Trl9TUMAgKMXR4qxrGWhO9k8siGH7wv1VYkrPpaOXh5mKwfzWTlYyevNUybQ+qZqBDh0zvoCFUM0NrUnkNftsgbtBdTR0QEQUtXTqhoG9gEfzLDpF4AvAUVNfR1LBoFqnEyggGUCmeIJZjjXrHWCQMP2d7kY/cmgbi6HQCzU9rX1ADy5jHr9VkwQSFUZmihsJhDA269dwVg4xvde6i7o45ry4UwAu+26qUGggNfNusYqjl4cLsayloWe4CQ1PveMLJClcLKKepbR1YJyNmtj6GTZsJWhlK7LwyG8bpnSeyEXqnxuVtUHeMFKuYsiEp86HczrFusJVEDd3d0A6ak0XcC69G1E5GZgvar+41z7EpGPi8ghETnU15efQRejoShet6RKeGuTrRiMKYbB8fCMIFC9P/G9c+HCLIzTFqOYQaDWugBbV9XyP39+btlU9lRMEGhgLEwkpgXNBAJoa6rmzesbeeTZs9YQzGT046O9rG+uYktr7Yz7rl9Tb0GgPOodnqQ+h6VgcDWr6OKQfdiXAyfIMz0IFPAkAgtOqYEpPb0jk7TWBRCR+TdeoI0ra3jx3KCNJi+CaCw+pSeQ13oCFdQsx6qpG0XEBXwV+IMs9vU1Vd2pqjtbWlrm23xRRkNRav2e1PtArd/DaChq/dxMUQxNRGYkHFg52NIMjCWCuk62X7F84Ia1nBsYY3iZ9HaqmCCQU/ZR6EwggI++/RpO941xsm+04I9tliYUjfFfvn+YDz78s7xcFZ4Ix/jpyX7ee92qjCcy29fUcaZ/zA5m8uTyyGTOS0kakkGl5VQ3XM4mozE8LplSfgLgT2YCjdpY15LVOxyipc6fl31vWlnDeDjGkR4LwhdaOBrHO70czDKBCqatrQ0g/WC5DehJ+74OeBPwzyJyFngb8ESxmkOPhaLUpGUIONkCVpJtimFoPJI6DnT4vW58bteMv8liTjErJ04mUE0RM4EAPnDjahR4vWd59AusmCBQ16AzBajwTaU+cOMaVtT4ePbUQMEf2yzNpx57hUefP8+R7iAf+58v5Dwr4Oen+glF47x3Wj8gx3Vr6okrHL9sfYHyoXc4lPOmslVeN1632ISwMjERjlPlnXl1yedNfPyNWSZQyUpkAuUnCLRxRTUAHWfsc7vQwrF4qjE7OJlAlpFVKLt27QIIiMgmEfEBe4EnnPtVNaiqK1V1o6puBJ4D9qjqoWKs18kEcjj9V6wvkCmGoVmGENUFPJYJtEj9o2H8HldOWzcsRseZQVbXB5bN0IiKCQJ1D40DxckE8nvcfOiWDbxxaYTzA+MFf3yzOJFYnH853se2VXUc/PQvMh6O8c9v9Ob0MZ461kuNz80tm5oz3n/9mkQjsmPWHDrnYnGlfzSU80wgEaGhymeZQGXi9e4gmYofPC4XHpcwGrYgUKnqHQmxqj6Ql33XBbxsXllDxxnrC1RooUgcj2tqT6CQZcMWjMfjATgPPAkcBb6jqkdE5AER2VPUxWUwIwiU/PrKmPUFMoUVT46Bb8qQcFAX8NA7XNIXB+tF5A0ROSkin59+p4h8VkQ6ReQ1EXlKRK4p1MIGxsJF7QeU7oa2Bs5dGV8Wx/iVEwQanCDgdRHIcMW3EH77bRsQgb/tOFeUxzcL97OT/UxEYtyyqZn21jr+1Y1reOn8IOEcjaqNxZX/feQS79rWgt+T+e9yQ3M1VV43ndYXKOcGRkPENXGyl2uNVV56rCdQWYjE4jMmgzl8HpeVg5WoyUiMofFIXjPuVtT6+OnJfqLWF6igQtEYHve0nkAWBCq0oKpuVdVrVfXPAFT1flV9YvqGqvruYmUBxeM6oxys2mdBIFMcw5MR4goNGRIOagPeki1RjMViABuA9wPbgQ+JyPZpm70M7FTVG4HHSUwGLIiB0VDRS8EcN6xtAOAHr10s8kryr2KCQF2DE0XJAnKsaaiivbWWA4cvWYPoMvHkkUv4PS7akw2bP3TLBiYjcQ535yYN8FTfKP2jYT5407pZt3G7hG2r6+ZtDm11xQt3OTmqsz7HmUCQ6Au0HK4SVIJEECjzR53f47JysBLlHEzn4/XruLallslInNdy9J5vshOKxvG40svBhGhcrUm3mSE4kTjpTs8SqElOCxwctyDQchYvwrnW0HiiYfCsmUAlGgTq6OgACKnqaVUNA/uAD6Zvo6pPq6pTzvIciV5hBTEwWjqZQCvr/KxpCPC/nq38KWEVEwTqHppIjW4ulh1rGjh/ZZyjVtpTFn5+aoDNLbWpE8RbNjXTUuvPWYPojjNXaKr28p5tmfsBOa5fU8+xSyMWPMwxZ1RnPjKBGqq99I6E7KSlDERiOkcQyM1oyDIQSlE+X7+O9pZaBPjJ8f68PYaZKRSNz8gEAmxAgplhYGzm6Gifx4XP40pNFDLLz3g4yl/86AT7Xjhf0GCQE3h85cLQjPvqk1PrxkuwxLy7uxsg/QXTBcx+hRruAQ5kukNEPi4ih0TkUF9fX07WNzBWOplAkDgvu3BlvOKnVlZEEEhV6RqcoLGmOJlATpbG9WvrEUlkmJjS5DxXl4KTnBsYZ9PKmtR9IsKuTc3JQN7cmTlHLw7zb/9HB//tqRP86OjlGQev/aMhjl4c5rdvvQaf5+rLLFNGz/Y1dQQn8lv2sBw5mUC57gkEiUwgVbhc2vXfhrnLwfxeywQqVb15fP06qv0e1jZW8dOTuTmQNdkJRWLTMoGcIJAF1c1U/aPO6Oir7wMiQnO1j0ELAi1b/7vzMv2jIV7rCuYsez8bQ8nR4U5JYjrngkUploTNcpE5440i8mFgJ/DlWfb1NVXdqao7W1palry2WFy5Mhamtsjj4dNtaa1FSVR0VLKKCAINT0QZDUVpKnImUK3fw65rmi0IVAaeT06E2bQiEQRygjNv2dCIxyU81jF7CuAbl0a466+e5bWuINU+Nz8+1sud//3nXLiSyKJUVX7w2kW8Hhf/5hfm76t2XbI59NGLw1b2lUN5zQRKjYm3IFCpm68cLNcTAU1uOGn19Xn+XN/SWstL54cYmYzk9XHMVZYJZLLlTACbXirSVOOznkDLlKpy/NIIO9bWU+f30NlTuJ6aQ8lMoOoM/WedCxalWBLW1tYGkJ4p0Qb0TN9ORN4H3EdiGmBBfpGh8TBxLf54+HRtTdX4PS5O9FoQqOR1JSeDNRaxJ5BjZZ2fYzYlrOS9eiFIwOtidcPUyTPVPg9vWtfA91/qzpjSGY7G+fS3X8HvcfPE77+Tf/eLm/nI267h3MA4v/qXP+XrPznNH3z3Vd64PMIvX7+K1rr5J9tct7oOgGOXrIwwly4Ph1hR48PtypwFshROEKhnyPoClbr5ysEsE6g09QQncItQ7cvv1cH21lpiceXZUzYqvlDC0TjeadPBwIJAZqaBZCZQ7bSMwBUWBFq2royFGZqIcG1LLdtW13H88kjOBrrMx+kJlOlzybng6GSxlpJdu3YBBERkk4j4gL3AlCbwInIz8FckAkC5HZU8B6ess1R6AkGiX+u1LbWc6K3sVh2VEQQaTJyIFbMxtOO6VYkT+n85Yenlpez17iDb19RnDBDs2tjMSCjKP746szP8X/74BEcvDvPFX7+BdY1VQKJ29B8+8U6ubanhT39wlP2v9PCurS38wrUrslpLXcDL+uYqmxCWY73Dk7Tmaby0ZQKVjznLwSwTqGT1DE3SUO3FJbkP4qbb0FyNz+3imz87k9fHMVclMoFmloPZhDAz3cBoCGHmSXdTjY/B8cJl71mWduk4OzAGwKaVNWxZVUcoGp+3hUOuDI5HECCQIQhUm8oEKr3jQo/HA3AeeBI4CnxHVY+IyAMisie52ZeBWuC7IvKKiMyYFJgP337hAlBamUCQuEA0NB7hbAUndZTWv/gidSeDQI0ZurUX2opaH03VXp453sdH3jZ/KZApvLgqR3qC3PnWzI3vN66oZtuqOv7qmVP8xlvbUoGirsFx/uqZ0/z6W9bxy9tXTf2ZlTX83e/9Al2DEwS8bv6p8/KC1nT96nqOXhzmHdeuXNwvZWboHQmxqt6fl30HvG4aqrypEkBTuqwcrDx1D44XZNiDx+1i08oaTlyu7LTvUhGPK+FYHI8rUzmY9QQyU/WPhan2uWcEg5urvQyMll7Ghcm/y8MhPC6hpc6PL/necbg7yJvXN+b9sYPjYQLemX+PkAhUet2S6kdZgoKqujP9BlW9P+3r9xV+SaSysUspEwgSpeIAPz3RN6V/bCWpmEygap8772nj2RARtqyq4+cn+wuWnmgWpn80xFg4xpvWNWS8X0T41Pu2cKpvjEeTvYEisTjffbGL1jo//+e/2jHrz61vrqalbmbgYb6rSNevqeds/5j9zeTQ5eFJWjM8F7myaWVN6orUciEi60XkaRE5KiJHRORTxV7TfOYsB/O6GQ/HiMcrN923XHUPTRSsxLu9tZaBsbAFdQsgnJyo6MlQDmaZQGa6gdHMU4Oaa/wMT0ZtQucy1DsySUudH5cIjdVeqrxuDncVpjn04Hhk1nNNlwhtTdX2ObJAzoW4UssEaq7x0Vjl5WcnK7dUvCKCZE5eUgAAIABJREFUQN1D46xrrELynDaera2ttYyFY7x0frDYSzEZOJljN7RlDgIB3LFjNb+4ZSV/9oNO/sfPzvCt587RNxLiz3/jxlQpUC5dv6aOuE2byplYXOkfDbEqT+VgkAgCnelbXkEgIAr8gapeD7wNuFdEthd5TXOaLxMIYLQER7ouZ+FonN6RUMGye9udK34nbVR8voWS2T6ZysGsJ5CZbmA0nDFDoLkm8d4wVMCSMFMaekdCqQt8IsK6pqqCTQgbmohQNUfCwTUrqjnTv+yOC5dkLBTNWPJZbCKJvkDPnh4gVqEXCrMKAonIHSLyhoicFJHPZ7j/l0TkJRGJisiduV/m3LqHJmhrqir0w85qc0stHpfwL8etL1Ap6hmaIOB10d5SO+s2LpfwlbtuYuuqOv7kHzo50z/Gv755Hb+0denjEDO5oS2Rxto1aFcQcmFgNERcyXsmUE9wclmduKjqRVV9Kfn1CIna8nXFXdXsorE40bji9WS+QFCVnPAxPGEnEqXkYnACVWgqUBCotc5PfcDDT09YECjfQtHE+2Wm6WAT4eXzXmqyMzAWzpgh0FSTyBK05tALVj/P+dxnRaRTRF4TkadEpKT6WoyFogyNR2hJG7qyrrGK45dHCnIsNjQenjNYsXFFIkO8kpsJ59poKJax5LMUXNtaQ3AiUtAJdIU0bxBIRNzAw8D7ge3AhzJc+T0P/A7waK4XmI1TvWOMl9DBQ8Drpq2pmv0vdxd7KVk5ePAg27Zto729nQcffHDG/SLiF5FvJz80nheRjcnbf1lEXhSRw8n/31bgpS9K99AE29fUT7kSmUlLnZ/v/d4v8INPvpPP3XEduzY2521NaxsCrKr3c97SSHPCadi8uiF/weHu5GSw5VYS5ki+D9wMPF/clcxuPHlQ6PdkPmgLJINAQQsClRTntVWocjARob21jp+e7K/YK35zKOhJYSjqlINd/fz1JTPySuk4zpSG/tHQLJlAFgRaqFgsBrCBuc/nXgZ2quqNwOPAlwq6yHmcSzbpTW+7sK6ximhcCzJhd2g8QrVv9rKlTStrGA/H6CvBMfGlajQUnTH9r1RsTiYL/PxUZV4gyiYT6BbgpKqeVtUwsA/4YPoGqnpWVV8DCl6cOxqKMhGJlcR4+HRbV9XSE5ws+TeCWCzGvffey4EDB+js7OSxxx6js7Nz+mb3AIOq2g58Ffjz5O39wL9S1RuAjwJ/U7CFL1JclZ7gJDfM0g9oOo/bxY61DYuuVc12ooSI8JYNTRYEypGLwcRJ5KsXhvL2GC21iYOQk73Lr6GsiNQCfwd8WlVnXCIRkY+LyCEROdTXV7yMSKfhoFP2NZ2T1j08YeVgpSQ17KEAjaEdW1prCU5EClZWUAqKcVKYCgKlZQI5zV3HrSzTpAlFY4xMRqnxzwziWxBo4To6OgBC85zPPa2qzoHoc0DmCSpF4mTLp2eJOpN6C/HePTgenrMcbGOygbCVhGVvLBQtuabQjvqAl9Y6Pz87VZl9gbIJAq0DLqR938Ui0//zcWLQnRoPX/zJYOm2JEfF/6TER8V3dHTQ3t7O5s2b8fl87N27l/3790/f7IPAI8mvHwfeKyKiqi+rak/y9iNAQETyV3+TA/2jIcLR+KxNobOVj3Ghb72micHxCEPjdlCzVE4mUH0eTyJb6/y4BI5dzP/Vp1IiIl4SAaC/VdXvZdpGVb+mqjtVdWdLS35KKLPhBIF8swWBLBOoJHUPTSACDQX8XL82bRLIclGMk0KnHMyb1hjaMoFMJoNjiffljI2hkxd+r9jxUta6u7sB0v/B5jufuwc4kOmOYl3o6Uqd81298N9Y7aWp2svhrvxd9INEefnIZHTOcrDrVyfO/ZbTxYSlGi3hIBDAtS21vHDmSkUO7skmCJSpSG9R+dL5ODHoHkocm5RaJtCahgA1fk/J9wXq7u5m/fr1qe/b2tqcD4p0qUCgqkaBILBi2ja/AbysqiWd+tQzNH9T6EJKDyY5/YZOLMPMkly7GJzE4xJq8thozuN2sbLWz7FLlVkrnIkkuu9/Aziqql8p9nrmMxpyysHmDgINT1oQqJR0D05Q5/dMKRnKt1q/h7UNAZ45Xplp35kU46TQyQRypz23bpfg87gYs0wgk6Y/OQI+0wmi0xNo0DKBsjZLn5qMN4rIh4GdwJdn2VdRLvR0D03gdcuUQIyI8Ob1jbySx8xvuHqxqNo7+3Fla32ADc3VvHD2Sl7XUklGJ0s/CDQRifFyBQ57yuYIqwtYn/Z9G9Azy7YFV6qZQC4RtrbW8pMT/SU9fjjTh0KGKWtzBgJFZAeJErH/kOkxSqU0BBJ/L163zNkUuli2tNbSUOXljQLUNWdhvj4RRW0GP5+LwUnqq7x5nxi4uiHA0eWVCfQO4CPAbSLySvK/3cVe1GzGU+Vgc/cE+udjvQVbU67M18stFApx9913097ezq233srZs2dT933xi1+kvb2dbdu28eSTT6Zun20IhIj8bfL210Xkm8lssLwp5Hj4dO2tdbx0fjA1srbSFeOk8Op0sKnvzTU+N+MhywQyVw0kAzyZThC9bhd1AU/By8H+f/bOOz6u6kzYzztdvXdZsmVLrriAC6aaXgMpQEwJJCGbZAm7CUk2JB9JNiHZNFI2WUKABBKSAKaDQwdTTDHuvcm2ZDVLsnqXRjNzvj/mjpBljTQjTdd9fp6f79y55dU95Z7znrd4Fu4CbQkeCgoLCwGGd6yjzudE5ELgLuCqSFvYrW3rJS3ectLYbsm0NA4d76YriAs6bVomurFiAgEsLU5ja1WbHhzaB3rtDuxOV0QrgWZkJmAQ+CAGs4f6ogTaDJSKyAwRsQCrgbXBFct3atv63Cv+EViBynKSaO2xs70mcrWHhYWF1NR87O1XW1tLfn7+yMOGFIEiYgJSgFbteyHwHHCzUurIaPeIFNcQgJq2PvJS4sYNCh0ORIS5eUmUN3aF1T3FxzgRYQ0GPx717X2khCCeSH5KHHXtfbR0R9Q4KWgopd5XSolSaqFSarH2eTnccnmjexx3MKvZgAB9g9Fl5utLLLeHHnqItLQ0Dh8+zB133MGdd94JwL59+1izZg179+7l1Vdf5bbbbgPGTQLxKDAHOAWIA74UzL+vLkTtdySlOYk4XIoNMer/P5JwTApHcwcDt+Zp7zHdhULnYzzvVW/j+/QEix4TyA+WLVsG7rANXudzIrIEeAB3W4+41RH3AsHJ74YlRakoBbtqg9eHdPS569pYMYEAzirNpLnbzgeHp8Z7ZDK0dLufaSTO4T3EWYycVpzG6/sawy1KwBl3Jqy5/9wOvIY7HfCTSqm9InK3iFwFICLLRKQWuBZ4QET2BlPo4VS39pIab4nI1HKzc5MwGoSXdjWEWxSvLFu2jEOHDlFZWYndbmfNmjVcddVVIw9bizvwM8A1wFtKKSUiqcBLwPeUUh+EUOwJMeBwUtfeR3FGfMSu5JxalIbDpXhhR/gyy/kYJyJsweB9ob6jPySTyOKMeAA2H41cRe9UxuNe4s0dzCCC1WygLwSpZQOJL7HcXnjhBW65xd1tX3PNNaxbtw6lFC+88AKrV6/GarUyY8YMZs2aBZDAGEkglFIvKw1gE0EMFup0Kerb+8Ni3VucHo/ZKDz0fkXI7x0OwjEp/Dgw9Ilt0mI0DP2mowMfTxC9WQmkJ1ho02MC+YzJZAL3Ap7X+RxuS79E4CnN0jdiFv3BvfCfNoqV6KJpqQBBdQlr9lFhccXCPDITrfxh3SFcujXQmAy5fEZodjAPly3I40BDFxVNsRWuwydzCG0AWKaUmqmU+h9t3w+VUmu17c1KqUKlVIJSKkMpNT+YQg+nqqWXjITIigfkwWY2UpqdyEu7j+FwRubgxmQyce+993LJJZcwd+5crrvuOubPn88Pf/hDcFv8gDsGSIaIHAa+CXjcBG4HZgE/GOYakh3yP8JH9tR14nQpitLjwy2KVwpS45iWFse9bx0eCmobaiYQJ8Ir4XAFdLoUjZ2hUQIVpMVhNRnYVKn7f0ci48UEAndcoP4oUwL5Estt+DEmk4mUlBRaWlpGPRe3Nci4SSA0N7DPAa+OJlcg2vux9j7sTheZiaHPMWAyGijJTORQY2wN9LwRjkmhfShF/IkLdxaTISYDb+pMnOaeAcxG8dp/p8dbhhRFwaarf5BHPjzK89vrGIzQ8byPdIwzn7tQKZUzzNL3pFXhcNE94KC9d3BUV+GUODMzsxKCGrdlrBhVw7GajPzXJWVsOtrKuv2xZz0SSJrHUfRGCpedkotB4KmttV6PiVTjgrGIPJ8YP1BKUd3aO5QqMhI5rTiNxs6BiA4Qffnll1NeXs6RI0e46667ALj77rvBHQAapVS/UupapdQspdRypVSFtv+nmuJv8bBPxJmPethW5X45TEYJ5K2RB6rxiwhXnJJHU/cAtz26LSymzv7EifDhWiF3BWzpHsDhUiFRApkMBpYUpbLpqG72G4mMFxMI3EqgvijLSuRLLDdvx4zRvn1JAnEfsF4p9Z4XuSbd3o9oK20ZYVACgdslrKXHTlXLlEnxG9JJoVdLIF0JpDOClm47GQlWr7H90kJoCfT89joONnax6WhrRI/nY5nxYsAuKUpje3V70GLxNHf5rrC4buk0rltayNsHm6bSu8RvWnxUrIWbvJQ4Lpybw5pN1WFboA8GUa0Eau2x0z3giGgl0JzcZLKTrPzlvcpwizLl2VrVRnqChSRb8JQDgVAGFWUk8LNPncL7h5tZ+fN1/Mfj24cmRqHA1zgRkcoxLT18qGKKLJ+Rwb5jnUENSKgzMXoGHAhgNnp3F7ZZjFHnDuZLLLfhxzgcDjo6OkhPTx/1XGCQcZJAiMh/A1m4rUGDRkWTe8CclRQeJdCc3GQAXtsbuW7c0YwnJtBISyCr0YA9ui0sdAJMa4+djETv4/uMBAstPfagB+A9fLyb/Q1dXDAnm7l5yXxU0aIrLMNAbdvY2aCXFKXS0mPnaEtvUO7f3D1AarwZo2H88CMiwo+vWoDNbOCjCn2R0BvN48T9iiS+umombb2D/Ob18qF9f99wlL++Xxl1FkAeoloJVNXqbuiR6g4G7tSnXz13JhsqWvTVgxAyUhmjlGJrdZvPVkDhNuu7fnkRr379bD67bBrry5t46P1K/rXrWEiyDfgSJyKSaehwrxaFSgnU3e/ApdxKRp3IonvAicVkGDNLXILFRG+Upab2JZbbVVddxSOPPALA008/zfnnn4+IcNVVV7FmzRoGBgaorKzk0KFDAD2MkQRCRL4EXAJcr5QK6uynormbZJuJhHGCbwaL9AQLBalxvLRbVwIFg34v2cHMuiVQqBkvA+g3RWSfiOwSkXUiUhxqAVu6B8a0CExLsGB3uOgNsiXnCzvqEGBFSQYrSzLotTs5dHxKZQWNCOrax7YEOnuW2/r07SBl+2zuHvDLTTnOYmTxtFT2HuuMKeuRQNLcbcdqMmCOwGQ9w3lsYzUH6rv43OnFPPxBJTf9ZSOffWADd/9rHz9+cR8Prq+gPQrjk0X2Ux+Hak3bG8mWQAA3rCiiJDOBrz++nYff1y2CwkFNax9NXQN+u4KFUxlUmpPE3VcvYNNdF7ByZgYbjrRw3zujJmAblV217RPyj/YlTkQ4g8GPx7H20FoCFaXHYxDYqMcFijh67Y4x4wGBOyBhV390DdDGiuW2dq1bX3vrrbfS0tLCrFmz+O1vfzuURn7+/Plcd911zJs3j0svvZQ//vGPgPckENot7wdygA1aXJgfButvO3y8m5KsxDEVd8FmQX4yO2vah1aedQKHJ/6WZcSg36orgUKGjxlAtwNLlVILgaeBX4VUSNwTxMwxxvfpmkVIMN3mH9tYzVNbainOSCDRamJGZgI2s4H99Z1Bu6fO6NS29WExGbxajRRlxDMrO5G3gqoE8m++OSc3GYdLsS2IsYqimebugYh3BRvOj66az9cvKKWhs59+h4vTSzK4YE42DZ19/Pm9iqiLLxk9T34Uqlp6EXGvBkQyNrORX1+3iM/c9yEv767ni2fNCLdIU453D7mtsGZmJYZZEv+xmoxceUoe3f0OfvdGOedrJsljsbmyled21LFmcw0v/sdZLChIGfP4UehQSi0dvkMp9cNh25sJYoagydDQ2Y/VZCA+RJYEFpOBgtQ4PTh0BNI94PCaHt5DstXEgMNFr91BvCV6XomXX345l19++Qn7tFhuANhsNp566qlRz73rrruG4r8NRyn1MvDyKPtD8mBcLsXeuk6uXpI//sFBZEFBCq/ta+TVPQ186eySsMoSa/QPOjEIJ7lUWIwGBnR3sJAwPAMogIh4MgHu8xyjlHp72CkfATeFUkalFC09A2O6g3kWgFt77EwLUsKP9l47DZ39XLYgF3DX2zm5yRxo6MLlUhh8cA3SCQx1bX0UpMaNmQ36onk5/Hl9xbhWZBOhudvOvPyxx94jKUqPR3CPyc8uDU1czGiiuXsg4jODDcdoEO64qIw7LioDGDISmJWdyJ/fq+CVPdE1x49qS6Cq1h6SbeaINyMDd+rvc8qy2FLVxg9f2BNucaYc7xw4TlF6vN9a/FAxnsWRiHD14nziLUZ+/drBMa/ldCneOnicrEQrFpOBxzdFp6/qRKlp7aUgNS6klgQzMhPZVdsedQGGY52eAceYQaGBoRhhxzsHQiGSzhhUNPfQNeBgYWFqWOXISLSyoCCZp7fWhsQFdyqxvbodk/FkF02Lycigw6U/7xAwgQygtwKvjPZDsDKAdg846B90jesOBtAaRDeMimZ3jLJZ2R8vIJblJNJrd7L3mG4NFEpq23opTIsb9TfPGPqTiwtwuBQv7qoP+P2buwbI8lOxZDMbyUu1sfmobgk0Gi3d9qiyBPJGcUYCp5dksOVoG4ejyFU08rUnY1DdEtmZwUZywZxscpNtPLetLip9B6OV7gEHHxxp5rzZWWF1MZgs8RYTXzl3JusOHB/TtPS9Q0109A1y4bwcLpybzWt7G3G6ps7A+mhLL9MzE0J6zxmZCQw6FfeMo6DTCS3dA+O7gyVpq1DHu3QlULjZVdsOwOJp4VUCAXzu9GIONHSxQQ/qGVDsThfmUawnLCYDio9jBukED38ygIrITcBS4B4v1wpKBtB6LcFDXorN6zGe8X9bEN3BKpt7iDMbyUn+WA6PRfn6Q3qcz1BS29ZHYdrYFl+zc5OYm5fMs9vrAnrvPruTrgHHuAkLRlvQLUiNZ199ZyQouMeLA3aOiGwTEYeIXBMKgaLNHczDaOW8anY2JqPw5/XRE/YlqpVAVRGeHt6Dp7KYjAauOa2QHruD/3vrcLjFmjK8vreB/kEXn1gUXheDiTCyo/n8GdPJSLDwWy06/Wgd0Su7G7CaDMzNTeLCuTk0dw9woGFqrFgppahu6fE79tNkKc5wm/xWNuupQCOJ9t5B4sZxC/RYAjXpSqCws7OmnXiLMSLcdq9eXEBavJmfvrg/3KLEFIMOF+ZRFLMet81uPYBq0PE1A6iIXAjcBVyllAppB+kJAlyQOrrlB5zoDhYsKpt7mJGZcIILUpLNTF6Kjfd0JVDI6LU7aOmxe7UEGs6nluSzs6adP7x5KGAxPeva3fHhxqqP3shJttLRN0hTd/jGGD7GAasGPg88FgqZHE4Xbb2DUZEZzMNYXhuJVhNLitJ4bkcdLWEsa3+IWiVQr91BU9dAVCiBhpOfGseiwlQe3VjF8a7+cIsT9fgSuPmpLbUUpMZxalFaiKQKHglWE/92TgnvH25md23HSb87XYp1Bxopy0nCZDSwRPubRzs2FmnuttNjdzI9I7RKIJvZSH5qnK4EijDaegfHjfPzsSWQ3h+Hm521HWQn2Xhic834BwcZm9nIDSuK2F/fSVWL3q4DxaDTNaoLv2c1WFfGBh9fMoCKyBLgAdwKoOBE2sX7GK5eS/CQN8akO9lmwmwUmruDowQ61t5Ha4+dGaNYFpdmJ7K1qk3P+hQijmlKwapx0r8/trEalwsE2F7THrD717a57++LEmok2UluK7LDjd0Bk8dfhscBU0rZAU8csCGUUkeVUruAkJhjepS30WgJ5I0zSjKwO1xBSSj0/I467n5xL89srQ3YNaNWCXT4uLsx+eufGQmcNycbu8PFg+9WhFuUmGffsU42VLRw0+nFMRPA74YVRSRZTdy//uRMYTtq2tzB67TA0dMz4km2mdg5RZRAnslaZXPos/rMyEygtq036rIDxCpKKTr67OMGCI+zGDGI7g4WbuwOF/vqO5k2gUF2MHhsYzVJVjMGg/Dgev1dHSgGneqkzGDwsVVHdauekS3Y+JIBFLf7VyLwlJYRcO3oVwsO9R19GARyvLjfPLaxmsc31ZCdZON4Z3AU+Bsr3a6gJVknK4FmZScx6FRDx+gEl5q2sdPDDyc5zszMrER21LQFzAXLowQqmIgSKNldh8sbwxcrZgJxwLwSqDhgjVocxuQoCgw9HtnJNs4py+IfH1UFNNtlXXsfmytb6R908cD6IwGr11GrBDrY4G5Mucne/YUjlcxEK59cUsA/N1bpq15B5vfrykmwGLl++bRwixIwkm1mbjy9mFd2159kcvj6vkZMBqEsJwlwB5ReWJg6FGsj1vEEcRwro0iwmJ6RgMOl2FM3NRRukU6v3cmgUxFnHlsJZBAhyWbWA0OHmYMNXdgdrgkNsoNFcpyZU4tSeWprrW4pFiDclkAnL8h40n3X6EqgUNGhlCpTSs1USv0PuDOAKqXWatsXKqVylFKLtc9VY18usNS195GTbMM0TuKX7GQrDUFSAm040nJSPCAPxRnx2MwG1pc3B+XeOidSO6QE8m1st7golbbewYApleva+zAZZMiqxx+SrCaSbSYOHQ+fJZA/ccB8uFZA4oB52m1y3PiKvWhiRkY8x7sGeHVvQ8Cu+axm/XPe7GzKG7vZHaB5RtQqgcobu7CYDKRHaLan8fiP80vd1kCjWHPoBIbDx7t5bW8jXz5nJqk+vjiihS+eOR2TwcB7hz8egCileGNvI6eXZJwQB2VefjKHGrtxTIH0u4ePd2MyiM8DhUBSpLmg6VkgIoM2Lfj+eJZA4HYJ0yf54WWnpqgeL/BnqDmnNAuH08XD7x8NtygxgTd3sDiLkTizUbcE0gHc7mBjBYX2kJtsozEISiClFB8cbqEkK2HUlORmo4EVMzL04NAhoratF6NBfE4nPj8vGbNRAuYSVtfWR16qDeMEPApE3Auzh8LoDuZrHLBQMqQEssWWEqg0J4npGfH87YPABYjeWNlKboqNFSXpQODmGVGrBDrY2E1pduKonXM0MCMzgU8uLuAfH1XRHCUBpKKJ/kEnz2yrpSQrgS+fUxJucfxmvFhH2ck2Pn1qAduq2ujoGwRgW3UbFc09fGJR3gnHluUkYXe6qJoCg+tDjV1kJlon9KKeLIlWE5mJVrZWtYb83jon097rbhc+KYGsJt0qM8zsqm0nPcHik7l/KMlItHLZKXk8+lEVnf2D4RYn6hl0qlGVQABpCWZdCaQDuAMyT88YP8tnTrJtyK0k0Peva+87ITX8SM4uzaSiqUe3XgsBtW19pMaZfZ7zWc1G5uUls7u2gwHH5F30q1t7KUz1fYFi5Bi+NCeR8uNdYcsQ5kscsFDT2NHvl2IvWjCIMD8/hW3V7QHxwnA4XWyrbqM4I55km5n8FBvbx8gQ7Q9RqwQqb+hitubyEo08trGa6RkJDAy6uGPNjnCLE1MopXh+Rx1d/YP89rrF42YHiiaGv1i+dt4sAF7eXY9SioffP0qCxciVC0/MglaW4x7ElDeEzx85VJQ3dg/5X4eD4ox4tlS14XKFPRXolMejBIobJzA0uLO96Eqg8LKzpoOFhSlIBC7s/Pu5M+kacPBnPTbQpLE7XUOZwEaSHm/RJ9Q6dPYP0tDZT6kPY/zcFBvdA46AZ5X7QLOynjVGpsKL5+UC8K9dYTWomBLUtfWR5mcioMXT0ugbdPLOwclZazldioMNXczOnficszQ7ifbewaAFMR8PX+KAicgyEakFrgUeEJG9wZSpobOfrERr1BpzjMVpxWnYzAb+sG7ymcAPNHTRa3dSrCnFFxelsiNAFm5RqQTq6HW/IHrt0R2ANTPJyqJpqXxU2aK7IgSQ+945wq7aDi6cm8PiaanhFidoTEuPZ9XsbHbXdXDdAxt4aXc9Xzq75KR0i56VrPIwmqKGgp4BB3XtfRPy2Q4UxenxtPcOUtEc2886Gmjvcw+2fFECJ9lMtPTYGZwCLpORSK/dQXljV8QOBhcUpPDJxfk88G4F+451hlucqMZbTCCAlDhz0OK76EQPnsQvpWNY4XjwxAUNtEvYe4eaKUyLGzMDcVFGPKcWpfL89rqwWXhMFTyWQP4wKzuRBKuJ57bVTereVS099A06mZefPOFrlGqLsYfCGBya8eOAbVZKFSqlEpRSGUqp+cEUprGznxwfXD6jEZvZyNmlWby5v3HSVjubj7q9C4rT3ZZoiwpTqW3ro61n8grFqFQClR93N6KcMK74B4rz52TjdCl+90Z5uEWJCZ7cXMM9rx1kUWEK55ZNPGBZtLBqdhYrZ2aw91gnVy7M47bzZp50TLzFRFF6/FC7iVX217snZ+EMFu8xX9fjAoUff9zBPObIumtueNhT14liYul3Q8FjG6uZl59CWoKZLz2yeSgxhY7/eIsJBG6LvF67M+BWHTrRhWei7Jk4j4UnaHNDR+CUQA6niw1HWji7NHNcy8TVy4oob+zm7hf3Bez+OifSP+ikuXvAb0sgo0FYVJjCWweO09E7cVfe/fXu+ujJujsRPMlawpkhLNJo6OgnNwbm8d44oySD9AQLv3r14KSUxFuq2shPsQ3Ftp2j1cODAahLUakE8gzARovYH21kJlpZWZLBmk01Q5NYHf9RSvGPDUe589ldnFOWxadPLYxIt4JAYxDhEwvz2Xf3pdwhHsNAAAAgAElEQVR7w6lYTaNPeMtyEmPeHWxXrTtafjgnkhmJFjITrXx4RE8bG27eLXebgI+XHQw+DkyoZwgLDztrIjMo9HASrSauPW0aXQMOPn3fB7you4BMiEGH95hAyXFuZWwwAv3qRA97j3USZzb61B9MS3e/7wMZS2rT0Va6BhycXTr+QuLVS/JJtplYX64HiA4WtW3usp1IvLgl09KwO128tLt+wvffWduOySBjxocaj+wkKylxZsrDmCEsklBKUdfeR35qZC78BAKr2cgdF5WxoaKFf44R43UslFJsOdrK0unpQ/vmaG6JgViMilolkNVkICVG0sqdPycHm9nIT1/ap5uUToABh5M7ntjBD17Yy3mzs3nwc6d5HWTGCuMFjh5JaU4Slc092B2x6+6yu66DnGRrWNNNighF6XGsL2+aEtnYIpmufgc2s8GnviBJswSqD+Bqso5vPLaxmrU7j5EabybRGtkBIvNT47ht1SzSEizc/th2bn9sW0BMsqcKDqcLp1Je3cGSNGWsrgSa2rx3qJkVJek+JXjIS4nDYjRwtKUnYPd/cVc98RYjq2aPrwSymoycMTOTI009AQkCq3MyFU3uss1M9N9qJD/VxsysBJ7fPnGXsDf3N7JyZgY2HxaUvCEizM5JivnFWF9p7bHTa3cyLYIXfgLBTSuKOLcsi5++uI8DDf4betS29dHYOcDS6WlD+7KTrKTGmyd0vZFE5Ux5Z207BalxMWPpEWcxcsHcbD443MK6/cfDLU5U0dpj5763j7B25zG+dVEZf7556QkdtUdZ4q/SJNaYnZOEw6UCOlCKNHbUtHNKQfhjQM3OTaajb5CtVbpLWDjp6h8cmlSOR1aSFSEwKys6/lPX3kdhlKwIpsSZ+co5M7l4Xg6v7G7got+t58MjzeEWKyro1xYhvFoC6RZ5U57qll4qm3t8duc3GoTC9DiqmgNjCdQ/6OTl3fVcMDeHeB+SCgAsn5FOnNnIb17XwzoEgyOTUAKJCCVZiWw62jqhoPPljV1UNPVw0bwcv88dSWlOIuWN4csQFknUtPUB7timsYyIcM+1C7EYDVz7pw1UNvs3B/PMI5YWf2wJ5FEoHpiKlkD9g07213dGtNn4RFgxI4OZWQn87OX9MW2tEUgONHRy/7tH6B5w8M8vreA/LigNS2rwaMDjWx+rk9yaVvfA0eol60woKctOJN5i5PkdkwtGqDM5uvodJPuYetRqMpKRaGHvsY4gS6Uzkp4BB6099qh6pxsNwqrZ2dx23kzS4s18/uHNvLRr4u4GU4U+LZmHdyWQu72+uqchZDLpRAad/YMca+/jN28cxGQQLpzr+6R7ekYCVQFyB3txVz3tvYNcv3yaz+fYzG6roXfLm/jwsK4QDjRHmrrJSbZO2BJncaF7cfCFMcZkoy0UK6X4xpodWIyGob5rMszOTaKz30GjruQeUsh53Dljlcc2VvPmvuN88awZOJVi9YMb2F3r+zhz89FWkqymkzLTzc1Lpryha9KZiMM/Y/KT/fWdDDpVzFUco0G464q5VDT38M+PqsItTsTT3mvn1r9twSDwlXNKOGNmZrhFimhmZiVikLBnJgganvgvZT6klA02VrORyxbk8a+d9XqA0zDS2T84ZFngC/mpcezVMz+FnLp294pgpAaFHou8lDhWLysiL9XG7Y9v4/FNU9fa1Bf6B90TKYsXJZDVbMRiMtDZP/EgrjrRhVKKl3fXs+jHr3PGL97ihR3H+Oq5M/2yECjOiKeqpWfSFhb//KiKX716gLKcRFaWZPh17uklGeSn2PjFqwcmPTHTOZEjTd2UZE48Hk9agoXpGfE8s60O5xhl09U/yPPb63h8UzVPbq7htke3sa++k/PmZPtsVTwWpdl6cGgPnhhese4O5iEn2caXzi5hwOHimvs/5PFN1T71VxsqWji1OO0kA4fZuUn02J1D46eJEnVKII9pVCxWnPr2fmZlJ/K7N8qpimG3nUDw05f209jZz02nF5MdAwHCg43NbKQ4IyFm08S/vLue9AQLmYn+ZY8IFrecUUz3gIO/fVAZblGmJEopuvodfg3c8lPiqGsPTNpNHd+pbetFIGoDRMZZjHzxzBmUZSfxvWd388C7R8ItUsTSpymBTF5iAgGkxpn1NjiF2FrVxvuHm/nUkgJ+8skF/O0Ly/jWxWV+XWNmViK9die1bZObEG2vbuN41wBfv6DM73ATZqOBb148m121HZMKQqxzIkopDh/vZmZ2wqSuc3pJBpXNPV6tgbZXt/Hr1w/yjSd28L1nd/OdZ3axvryJC+dmc05pYBaZyzSLfF0J5H7vpydYSIjwOICBJDfZxm2rZnFacRrfe3Y3n3toE3vqvFsFVTR1U9HUw/lzsk/6zWMZNNmEUlGnBPqoooUZmQlhDf4aLESETy4uwGAQbnl4E9Utgct2EEvsPdbB01tr+bdzSk5yIZjqsX/GokzzR441Dh/v5sMjLSwtTouYOGF76jqZk5vEA+9WcFwPchpy2nsHcbrUUMBnX8hLdSuT9+lZGkNKTWsfWUkTN/WPBMxGAzeeXsQpBSn8/JUDfOvJnfToVoAn0dnntvAZK2NfdrKN4126u8RUwOF08ca+Rooz4vn1NYv43OnFrJqd7fd7fGFhCuBODjFRWnvsvLKngaL0eC5bkDuha3xqSQFzcpP49esH9bAOAaK6tZeufgfz81MmdZ0FBSksKEjmF68cOCld/Iu7jvHU1lqmpcXztfNmceelc/j2xbO589I5nD8nJ2DjyoxEK5mJlpgNy+APh493MzNrcoq9aCTRauLyU/L4xKJ8dtd1cOX/vc+tf9vM1qpW4MQ57Gt7GwG4YO4oSqCcwGQIiyolkNOl2FTZSkZCZKz2B4P0BAsPf34ZLT12LvjtO3zn6Z2sL28aMqPWgT+sO4TNbCAzwf8gcVOZspwkjrb0xFxduuOJHZiNckIKxUjg8lPy6B108uV/bA23KFOOxi634s2fxYL8FLclylgrMzqBRSlFTVtvTASHNBkMfHbZNM6bnc2z22u54g/vsbGiJdxiRRRt2uRrrIC72UlWWnvsMfee0jmZtw82uVOxz8rCMIl4jrNzkzAbhV1+xNoYjlKK7z27iwGHa2gh1hdGLjo+sbmGFTPSqWrp1V1DA4RHsXdKweSUQAYRfvHphbT22PnGE9uHlHQbK1r45pM7Kc6I55YzplOQGkdKnJn0BAumIGQZLstJisnFWH9QSnGwoSsiwjeEA4MIK0sy+PoFpVwwN5tt1W185k8buO7+jwNH2x0uHvnwKCtmpI8aLzHBaqIoPZ4Dk6xLUWWHtbuug85+ByVZE/cNjQZOK07j1W+cw38+vp2XdtXz5JZaLCYDy6ankZlo5ZefWRjVq6aTYd+xTl7b28gFc7KJs5yYBUxnbGbnJuFSblPUhYXhz6IVCLYcbWV3XQfnz8mOuPTSmYlWzinN5O2DTWysaGGFnzEGdCaOx4oy1Q8lUILVRH6KTY8LFEKqW3tjKk2sQYSL5uUwMzuBZ7fV8dkHP+LmlcV859I5Edc/hYP2Xreb1/B390iykqwo4GhLD3Nyk0MkmU44eGJz9VDQ0+FjuBtWFPl1HavJyOzcJHbUTCwj5zPb6nhtbyOXzs8lN2Vy4QXKcpKYkZnAH9Yd4jOnFertfpLsqevEbBRKcxInrOTzsKAghbuvXsD/e2431z2wgWXT0/jnR9UUpsVxw7IirwHrA4GnfpsMwr76TvoHnVN2Hne8a4DOfseUVQJ5sJmNXDAnh7NnZbGlqpX15U1sOtrKvLxkXt3bQENnP/dcu9Dr+bNzk6aWJdBb+xsxiDv7Tizz2MZq3j3YxGdOLeS/LpnDLSuns6w4jfLGbl7YcYyzf/U2z2ytnZJpBv+w7hBJNpMeCHoEnhWpsdzhFmmKn52TfJFGCi6X4icv7SfZZuKcUt/SyYaac8uySY0384MX9jDo1M3DQ8X++i4EdzA+f1g0LZUPjzTTa9ddeULB9up2IPYyhJRkJvKf55dy5swM/rGhirN++RabKlvDLVbYaR+yBBrDHSzJbeF7KEbj1+m4Od7Zz9sHm1hSdHLQUw/+uPefOTOTLUfb6OjzL6h4RVM3P3xhD8tnpHNWAGK/iAiXzs+lpcfOg+srJn29qc626jbm5iVjNU1eYeKpS59dOo2Kpm7+/F4lK2dm8ORXVhIfImVdcUYCg07Fzpr2kNwvEvFYQpXlJOkL+IDFZOCMmZl886LZnD8nm8rmHj6qaOF7l83h7DHmNnNzk6hsnpx3R1Qpgd7cf5xTi9JC1lgjAYvJwOzcJK5YmM83Lijl1rNmUJgWx7ee2sn1f/6Iw8enzkBpf30nr+5t4ItnzhhzJVFndArT4shIsLCjOjZePmt3HmNnTTsXz8vFEgGp4UfDYjJw5Sl5lDd286ie9S9k7KvvICPR4ne9KEqPp7nbzt8+PBocwXROYEdNOxajgeyk2AvubzEZuGJhPl8+pwSjCDf+5SOe2OxbRpBYpb3PjkHAOka7zEy0IjClxjZTkae31eJ0KZZOTwvI9S6en4vDpXjrQKPP5ww4nNz+2HYsJgO/X70YQ4Biv0xLj+eKU/L4y3sVHO/SYwJOlJ4BB9ur2wK+6LtoWirfuXQOB396KQ9/fhmZiaELLVGsuT5vqZqY1Vos4LHompM7tS2BRmIxGbhwbg53XTGXfT++hK+cO3PM42fnJuN0qUm9KyNz5jQKhxq72FffyWWn5IVblLAhIszMSuQzpxbyycUF7Kzp4JL/Xc+9bx2aElYGf1h3iCSriS+eOSPcokQlIsLiaalsn6DJdCTR0TfI/7y8n4LUOBYXRbZr29y8ZGZlJfKLVw/Qqme9CQn767vITfHfuqQ4I4HS7ET++sFRBhx6TJJgs72mnYK0OK+WALFAcUYCt62axeklGdz5zG5uemgjf/2gkg8PN9PSPbUCILf1DhJnMY0ZaNVsNJCWYNGVQDGMUoonN9ewfEb6qBPwiST4WDItlaL0eB5cXzmUBnw86+ifvbSfffWdfGJhPm8faDrp/pOxUpidm0T/oJNfvHJgSit+J8OmylYGnYqzZgXe8t8gEhDrIn+Jt5qYn5/Muv2+KytjjS1HW5mVnUhaDMf3nQwGEZ/iUXkC4m+vnvicLmqUQM9ur8Mg8IlFU1cJ5MEgwvIZ6XzjwlLm5iXz69fL+cT/vc+u2tiw8BiNHTXtvLKngS+cNYOU+NjLDBcqVpSkU9HUw7H2yaVSDTe/fu0gLd0D7iCOEZIRzBsiwuUL87A7XPzylQPhFifmaekeoLq1l4IJxnY4a1YmTV0D/OkdPdV3MOmzO9l3rINpabHlCjYacRYjF8/L5fIFueyp6+TH/9rHDX/ZyGk/fZPLf/8eD64/wqbKVr9dWaKN9l478T7EwchOsupKoBhmY2UrR1t6+ezSaQG7psEgfOviMvbXd/L953fTPI6C9fbHtvHIhirOnJnB3LzAx57KTLRyblkWz26r4wHdLWxCvLS7nkSriSNN3RHhNhSo7MOXn5LHtup26juiexw+EZwuxZaqNpZFWCKXSGWs+laYFkd+io2PKibuah4VflW9dgePb6pmdm4yb+47Hm5xIoYkm5kblhex71gnb+xv4JN//IDPLivimxeVkZUUO5mzlFL89MV9ZCZa+fI5JeEWJ6pZNTubn718gHfLm7h+uX/BFyOF9eVN/HNjFZ8/YzoFUTKBzE22cdasLJ7YUsM5ZVlcsVBXZgeLD4+4MzJNNIHArOxEPn1qAf/75iHm5SVz8fyJpQvWGZsNFc0MOhUzYzzGnwejQTirNIszZ2XSPeCgobOf+vZ+dtd18LOXP1YOF6TGcdXifG5YXhQTWdOG0947OGY8IA/ZSVY2VLTgcLqCkqFHJ7w8ubmGJC1V8nPb6yZ1reGTpOuXT2NffScPvFvB45tqyE22UZKVwCkFKSilEBG3FdKWGl7aVc/cvOSgehdcMDeHBKuJX7xygIFBF/9x/qxJZUGbSnQPOHh5dz1XLcoPWsDmcCmWrjglj3teO8ijH1Xz7Utmh0WGcLGjpp2ufgenl+hKoLEYXje91VMRYUVJBu8dasLlUhPqW6JCCfSPDVW09w5y3WmBWzWIJeblJ1OSlcCb+xt5YnM1z26r5esXlnLLyukkxED8pBd31bOlqo2ff/oUPdPCJCnNTqQgNY5X9jREpRKoprWX/1yznZwkG8XpCeEWxy8unJvN0ZYe7nhyBwrFlQvzwy1STPLeoSZsZsOEFYQiwqLCVDZWtHLbo9v48dXzuXFFcYCl1HnnYBNxZiMzMqKrHU8WESHJZibJZqY0O4lzyrLo7BvkWEcfTV0DVDb38MC7R3hwfQVXnJLH1YvzWTkzY8y06tGC2x1sfCVQfmocg07F7Y9t5/7PnRYCyXRCRUNHP//adYzrlxcFPLajiPC9y+by6SWFvLm/kee317GpspUPj7Tw0u56Vs3Ooryxm02VrZRmJ7J62bSgWhIbRPj1tYuwGA387s1yNlQ08/0r5rFgkunOpwIPrq+g1+7k+uVFMZexc3pmApefksvfPjzK9SuKKEiNjsXMQLB2Rx1Wk4Hz52SHW5SY4NyyLJ7bXsfW6olZV0X8qKKmtZf/e+swF8zJZnrm1Bos+oPNbOTKhfmsmJHBK3vq+dWrB7n/nSNcv6KIG5cXU5QRnSuKtW29/OCFPRSkxuF0qYgwCY1mRIRrlxby+3WHqGrpoTiKJmBVLT187qFNuFyKG1cURWwwaG+YjAZuPr2Yl/bUc/tj23l+ex3fvmS2ngY5gBxo6OT57cdYUJA8qcG92Wjgi2fO4MktNdz13B6e317H5afkcf3yoimb1jWQ2B0uXt7dwNmlmbqlB5AcZyY5zsycXDi7NIuOvkGauwd4cnMNa3cewyBQmp3EwsIU5ucn41TQ2jNAa48dq8lIcpyZnGQrMzISWFKUFrGJEzp67T7F6lpQkMK82g7e3N9IR98gKXG6C3iscO/bh3Ap+LezA2/VPXx8mBZv4QtnzqB/0Mm++k6augZ4blsdOSk2vn/FXKwmY0hikT27rY7TitNQCt4pP86V//c+K0syuHJRHounpZJsMzPgcNHWa6e1x85rexpIjbfwjYtKSbbFbr33lNUNK05ejNxwpIX73znClQvzWDQtNeaUQADfuWQO75W/zxf+uon7bzrtJMvlsZ5PtNLRO8jzO45x4dwckmK4boeSi+blEGc28uy22uApgUTkUuD3gBH4i1LqFyN+twJ/B04DWoDPKqWO+i3NCJq6Bvi3v2/B4XKxpCgwGQRinawkKzevnE51ay8fHG7mwXcreODdCk4tSuVTSwq4YmE+6SOCcb366qt8/etfx+l08qUvfYnvfve7J/w+VvmKyPeAWwEn8J9KqdcC9bfUtPZyy8ObcLoUn10a3BWbqcT1y4u4750j/PzlA/zpplO9BelMFpGDhLjNj4bLpXh9XyPff34PvXYHt6ycTkYIszkEkniriU8tKSAnycZbB47z5v7jLJ+ezsqZGSwuSmVhQUrE/23jvQ/CRW1bL996cidJNhOXLpi8iX+cxciNK4p4t7yJffXuOC5rNtWQZDPR73By5sxMVpSkk58aR2l2EkaD0NZjx2SUkA1wxuu7BwYGuPnmm9m6dSsZGRk88cQTTJ8+HYCf//znPPTQQxiNRoAhTaS38hWRGcAaIB3YBnxOKTWhSOdrdx6juXuAG08vpq5t6sVFGI+UODMpcWb+69LZVDb1UNXaS11bH28dOM5TW2sBMBkEm9mIw+ViYNCFJ/SszWxgZUkm/7t6sa/Kk5D09e29dhq7BpjjQ/wVgwjnzc5mX30nP3h+Dz//9CkxYdUcYYT8Hf/+oWYe3VjNLSunh8zV0WY2cmpR2tBkOhwLiSLCshnpLChIYWNlC1uq2tjwXMuY5/x9w1FWzszgsgV5XDw/J5AZrMI6tusfdPKvncd4cH0FDZ193PPaAYrS4ynLSaIwLZ7K5m5e3FVPUUY8i6elxuTCr+dvum7ZNB7dWMVFv1vPosIU5uYlE2c2YjAIlc095CbbaOuxBzKActjKXinFL17dT2f/IF87b1YgLqkDJFhNXL04n2e21vGVc2b6bSwz7ltVRIzAH4GLgFpgs4isVUrtG3bYrUCbUmqWiKwGfgl81i9JcFeS/kEX1a29vHeoifvfraB7YJAblhefpLjQGZui9HiKlhfR3mtnZ20HO2va+cELe/nxv/ZxTlkWn1xSwNLiNDLiTXzta1/jjTfeoLCwkGXLlnHVVVcxb9684ZcbtXxFZB6wGpgP5ANvikiZUsrntDodvYM4lSLZZsLhUnQPODh8vJv15U088uFRjAbhr59fRnmjHiQyUOQk27jjwjJ++eoB7nxmF9+6eDY5yR8H0XU6nQBFwDyC3ObBreQZcLjo7B+ko0/79A7S2mtny9FW3i1vorFzgLKcRG5aUUR2cnSnkzYZDJxTlsXS6Wlsqmxlz7EO/rCudWgSNy09jsxEK/EWIw6noqvfQa/dwaBTYTMbSNGsBpJt7oliks1Eos1Eks1Mss1EaryFjAQLiVYTFpPh44/RgNVkGDMzz3j4+D6YFA6ni75BJwYRugccCJASb8YogsI9iOwecNDd76Czf5AjTT08ubmGLVVtCHDjiqKAuY2ajAYumJvDBXNz2F/fydqdx+ixmzAbDfzlvcqhgJ/JNhMuBT12B2aDgTNmZVCYFsfMrERcyi1zsjaxT4kzk2AxUt7YTVf/IKeXZNDQ2U+f3cmCghSOd/XT3G0nPd5CRqIFo0Ho6neQZDNhd7ho7x0kNd5Mv32QL3/133nkqbUsmjOTc888nYsuu5y5c+fhSUbz4AMPkpicws69B3jumSe58847eeKJJ9i3bx9r1qxh7969HDt2jJKSkiKtbMF7+f4S+J1Sao2I3I+7D/iTP8/T6VJsrGjhJy/uY2FhCmfPymTN5pqAlFUsYjIYKM1JojTHnUpXKfc70mQwYDUbhhZGXMrdTzR09LP5aCtvHzzOufe8zb+dXcIl83MoSk8Y1XIylH39uv3HcboUs3N8Swucn2pjTm4Sa3ceY/PRVj61pIBPLikgK9FK94ADq8mA1Wx0/28yoBT0O5xeLTy6BxwYRbCZJ9cHulwKESZ1jXATynK3O1w0dvbz2t4GfvN6OaXZiWGJgxIJyoQ4i5FVs7M5tyyL1h47JVmJ9NodmI0Gdta0E281EWc20tpj50hTN3vqOnjvUDPff3438/NTKMlKICvRSlqChdR4M+nxFlLjLaQlfLw9loV0qMd2SikGnYr2PjvlDd28ub+RF3bU0dY7SFaSlcXTUinJSuRocw9vHThOS4+dtHgzq5dP478unsNLu+snctuoYWZWIndcWEbPgIP3Djfz0u567A4XLm0uDPDPjVUsyE/hjFkZFKcnkBJn1vo+AzazkXiLcWhMmGgxeY0LE4qydzhdQ2MVo0EYdCo6+gY51NjFo5uqeWlXPV85t4R5+ckR0R5jhW9eVMaLu+q59ZHN3HPtIk71w2jGl5HycuCwUqoCQETWAFcDwyvO1cCPtO2ngXtFRJQfeRFX3fM2Va29DD/jtOI0fnL1AnbUxG7Wq2CTGm/h3LIszi3Lor6jD7vTxdodx3jrgDvA9kDdfjpUKpf/9SC/Xx3P6tWreeGFF0YqgUYtX23/GqXUAFApIodx15cNvsr3x3cO8+AomRNE4OJ5OXz/inlMS4/XlUAB5ivnlNDZP8gD7x6hsXOAR764fOi3TZs2AQwEs83vqm1n9YMfYXe4cLi8n5JkM1GckcCqsmwWFKTEVCrpeIuJVbOzWTU7m/5BJ/Ud/dS09lLX3kdXv4OWbjsGbdKSHGfGZBDsDhedfQ4aOwfoG3TSZ3cy4HAyxiM8CbNRMBoEgwh7f3yJv5MZX94HY3LRb9+lblh2upE1ZtA5dp0YjSSricsW5DI3N5nMIAXFn5uXfEIWmQGtzFp77JQf78JmMhJnMeJwuth3rJONFa30DQYvzfxA3X7aJY0vPFMNVNORtoQL//M3pKy8buiYxif+SupZN7Duv19DuRKp/derzPn+K5ze9R6rV6/GarUyY8YMgAHcZQujlK+I7AfOB27QjnkEd/v3WQl0z2sHuO+dIyjlDn78h9VL9CCpfuKJJzQSg8iQgnF2bhLH2vvYWdvOPa8d5J7XDgJgMRm4eF4O995w6tB5oejr99R1cN0DG+i1O0mymnyO1SUi3LxyOjtr2nl1bwP3vXOE+8bI2GcQhvpBd//mvoZBQJChtmgQ9/MSbb/2D1+7wQGHe3JmNRnc5w/J69v5k2Eyt9jx3xcPBdgNRbkDXPy7d08Yu51dmslvrls05WM7iggZidahbICDTueQohcgPcHCrOxELp6XQ0NnP3vqOqlp6+Xd8iZ6BtwLQt7w1G9PHf/VNQu5enEBELpy//2bh7jvncPYna4T3u9Wk4EL5+Zw4+lFVDb1DI09FuSncOVC9yKBZ4wX6wogD574cNeOiHk7MOikobOfBKuJ9w8189B7leOOi0TAbDBgMMD5c7K578aP46mFouwrm3u46Hfrh2QZWfZ3XFhGRqJFVwAFmOxkGw/dspSv/nMr1/zpQz747vnk+eB2DSDjla2IXANcqpT6kvb9c8AKpdTtw47Zox1Tq30/oh3TPOJaXwa+rH2dDRz0cttMoNnLb5FKNMoMbs2wAFXa93QgEagGipVSWd7KF3dn8ZFS6p/a/oeAV5RSTw+/gR/lPhqR/FwjVbbJypUG5Cul4iAobT5Wn1uwCKVcxUqpLG8/+vI+0PZPps0Hk0gt45H4Imcabjeu0fpuD/OBcsCTe3wBcAC35WY30DrsuM9p2yeVLx/39bO0/dNw9/ULhgs0wXKPtDKJJHmCLUuw+/pQ/A2RQLT9jaEo93ASLeURajnDUe56WQSOycgY7uKZemUAABkPSURBVDYf6883lPgrp9dxvS/q+NEWH0Zqjnw5BqXUg8CD495QZItSaqkPskUM0SgzDDXyt0cM+pcrpf5j+GGjnKrG2H/iDh/L3Yt8EftcI1W2ycolItcCl4zYHbA2H6vPLVhEmFxBb/PBJMKepVd8kdPTTsfqu0VkL3DFiEHdBcDdwIYRCnwFjOZLENS+PtLKJJLkCbYswe7rtXtEzPMMFtH2N4ai3MNJtJRHqOUMR7nrZRE4JiNjuNt8rD/fUBJIOX1Jy1ELDLdTKwSOeTtGRExACh+vMOpENnYmXr6+1A2d6ENv8zre0Nt85DCZdurtXG/7m4FU7Rre7qUTfeh9/dREL/epiV7uUxe97HVOwhcl0GagVERmiIgFdyDgtSOOWQvcom1fA7zlj/+oTljpYeLluxZYLSJWcWeOKQU2hUhuneCht3kdb/hSN3RCw2Taqbe+e9Rraue8rV0D7ZovBPFv0wkNel8/NdHLfWqil/vURS97nZMY1x1MKeUQkduB13CnlXtYKbVXRO4Gtiil1gIPAf8Qd2DgVtyVazJElGmpj0SjzOCWu5YJlK923JO4A4s5gK8pPzKD+SFfpBKpsk1KrhC0+Zh8bkEkYuTyVjfCLJY/RMyzHIdx5ZxMOx2r7x6jfO8E1ojIT4Ht2rVD8reGmEiSJ6iyhGh8F0nPM1hE1d8YpnF9KImW8gipnPp8bkyiQc4JyxgBbT6mn2+ICZic4waG1tHR0dHR0dHR0dHR0dHR0dGJfnxxB9PR0dHR0dHR0dHR0dHR0dHRiXJ0JZCOjo6Ojo6Ojo6Ojo6Ojo7OFCCilEAicqmIHBSRwyLy3XDL4ysiclREdovIDhHZEm55vCEiD4vIcRHZM2xfuoi8ISKHtP/TwinjcCKlPojINBF5W0T2i8heEfm6tv9HIlKnlfsOEbk8TPKdVP8isVwjpTxHI1LacLS10WhhtOcaiXjra2KVUPQJY/Tfo7YrcfMHTaZdInLqsGvdoh1/SERu8XZPH2Qyish2EXlR+z5DRDZq131CC9yJuAN3P6HJslFEpg+7xve0/QdFZGTq34ggkvv8ieBvXdIJLiJiE5FNIrJTK48fa/v/JiKVw8Zmi8MtK/je7qMVEblWKweXiCwdtv8iEdmqjbG2isj5w357R+sjPGWVHYEynqbtP6y9G0ZLpR4KGTO0/qdbRO4dcU5In6MXucfs78d6n4VQxnHHWCKySkQ6hj3LH4ZaTk2OMeclY41VfEYpFREf3IGqjgAlgAXYCcwLt1w+yn4UyAy3HD7IeQ5wKrBn2L5fAd/Vtr8L/DLcckZafQDygFO17SSgHJgH/Aj4dgQ8q5PqX6SVaySVp6/PMExyRE0bjabPaM81Ej/e+ppwyxWkvzUkfcIY/feo7Qq4HHgFEOB0YKO2Px2o0P5P07bTJijTN4HHgBe1708Cq7Xt+4F/17ZvA+7XtlcDT2jb87TnZQVmaM/RGO4yDUf5hvhv8qsu6Z+gl4cAidq2Gdiotdm/AdeEW75R5PWp3UfrB5gLzAbeAZYO278EyNe2FwB1w3474dgIlXETsFKrb68Al4VJxgTgLOCrwL0jzgnpcxxF5nH7e2/vsxDLOe4YC1jlaaPh/DDOvAQvYxV/PpFkCbQcOKyUqlBK2YE1wNVhlimmUEqtxx3xfThXA49o248AnwypUN6JmPqglKpXSm3TtruA/UBBOGTxg0gr14gpz0gmytpo1ODluUYcUdrXTJSQ9AljPFNv7epq4O/KzUdAqojkAZcAbyilWpVSbcAbwKX+yiMihcAVwF+07wKcDzztRRaPjE8DF2jHXw2sUUoNKKUqgcO4n2ckEXN9/gTqkk4Q0dpot/bVrH0iMtuNn+0+KlFK7VdKHRxl/3al1DHt617AJiLW0Eo3JItfMmp9f7JSaoNyz7z/TpDLaQwZe5RS7wP9wbz/BPGlv/f2PgsZMTbG8jZW8ZlIUgIVADXDvtcSPQWjgNc1E8Ivh1sYP8lRStWDu3EAITch9EJE1gfNfHEJ7hUngNs1M7yHw2gCPlr9i7RyjcjyHEYkt+FIK0udEDBKXxNrhLxPGPFMvbUrb3IFSt7/Bb4DuLTvGUC7UsoxynWH7qn93qEdH+n9KUSHjBPGx7qkE2Q0F6sdwHHcSlpPf/k/2tjsd+FSOIzAn3Yfy3wG2K6UGhi276+ay8sPQq0U8MJwGQtwl42HSC6ncD5HX/p7b++zsDDOGGuluN1MXxGR+SEV7GPGm5dM+h1rmoRwgWa0ChuRGv1ROFMpdUzzwXxDRA5oK886Eyfi6oOIJALPAN9QSnWKyJ+An2hy/QT4DfDFMIh2Uv0LgwzjEXHlOQK9DetEDCP7mnDLEyRC2ieM0n/7K9ek5RWRK4HjSqmtIrJqnPsFVZYQEA0yTgg/6pJOkFFKOYHFIpIKPCciC4DvAQ243VIeBO4E7g6XjBNo9xGLiLwJ5I7y011KqRfGOXc+8Evg4mG7b1RK1YlIEu429Tnc1jaRImNQymkyMnoh4M/RT3x5ThFT58cZY20DipVS3eKO9fo8UBpqGRl/XjLp5xlJSqBaYNqw74XAMS/HRhQeE0Kl1HEReQ63WVy0TCAbRSRPKVWvmZEdD7dAGhFVH0TEjLvDeFQp9SyAUqpx2O9/Bl4Mh2xe6l+klWtEledIIrwNR1pZ6gSR0fqaGCVkfYKXZ+qtXXmTqxZ3rIDh+9/xU5Qzgau0gaUNSMZtIZAqIiZtdXT4c/DIUisiJiAFt1tjRPenGtEgo9/4WZd0QoRSql1E3gEuVUr9Wts9ICJ/Bb4dPskA/9t9xKKUunAi52nucM8BNyuljgy7Xp32f5eIPIZ77DUp5UWAZazFXTYeAlJOE5VxjOsF/Dn6iS/9vbf3WUgZb4w1XCmklHpZRO4TkUylVHMo5fRhXjLpd2wkuYNtBkrFHS3fgjto1NowyzQuIpKgaV4RkQTc2uOIzkAzgrWAJ8vJLcBENNDBIGLqg2ZW+RCwXyn122H7h/tefoowlPsY9S/SyjViynMkUdCGI60sdYKEt74mRglJnzDGM/XWrtYCN4ub04EOzcXnNeBiEUnTXH8v1vb5jFLqe0qpQqXUdNx/71tKqRuBt4FrvMjikfEa7Xil7V8t7pgVM3CvUm7yR5YQELF9/kSZQF3SCSIikqVZACEiccCFwAHP2Ewrr08S5vf5BNp9TKGV0UvA95RSHwzbbxKRTG3bDFxJmMrKm4xa398lIqdr9elmIqycIuQ5+tLfe3ufhQxfxlgikqsdh4gsx60raQmdlD7PS7yNVXxHhTn69fAP7kjX5bgjjN8Vbnl8lLkEdxT0nbiDiUWs3MDjQD0wiFuDeCtuf8x1wCHt//Rwyxlp9QF3NH4F7AJ2aJ/LgX8Au7X9a4G8MMg2av2LxHKNlPL09RmGSZaoaqPR8hntuYZbJi9yjtrXhFuuIP69Qe8Txui/R21XuE2s/6jJtJsTs7N8EXcQ5sPAFyYp1yo+zhJUgluJcxh4CrBq+23a98Pa7yXDzr9Lk/EgQc5WE8nlG+K/x6+6pH+CXh4Lge1aeewBfqjtf0tru3uAf6JlEIuEjy/tPlo/uBdDa4EBoBF4Tdv/faBnWJvZgTtuVgKwVSu/vcDvCXKWQ39l1H5bqtWlI8C9gIRDRu23o7itZ7q1Y+aF4zl6kfuk/h63G+ZV2rbX91kIZfTWh38V+Kp2zO3ac9wJfAScEQY5vc3thsvpdazi60e0C+no6Ojo6Ojo6Ojo6Ojo6OjoxDCR5A6mo6Ojo6Ojo6Ojo6Ojo6OjoxMkdCWQjo6Ojo6Ojo6Ojo6Ojo6OzhRAVwLp6Ojo6Ojo6Ojo6Ojo6OjoTAF0JZCOjo6Ojo6Ojo6Ojo6Ojo7OFEBXAuno6Ojo6Ojo6Ojo6Ojo6OhMAXQlkIaIKBH5x7DvJhFpEpEX/bzOOyKyVNs+KiKZgZZVxz9EpHvE98+LyL0hvH+WiAyKyFdCdU8dEJFPae16zgTPny4ifSKyQ0T2icjfRcQcaDl1AkOAy3uniHwoIrP9vMbfROSaidxfZ3KIiFMruz0i8pSIxAf4+uO+N0RklYicMez7V0Xk5kDKoTM6k23/2jXmi8hbIlIuIodE5AciIoGUUye4jBzvjXOs1/aqtff8CdxfH/eHAH/KeYLX/4uIzNO2/98Ezp8uInsCL5nOeIhIqojcFoL7nNB/RCO6EuhjeoAFIhKnfb8IqAujPDqxw7XAR8D14RZkinE98D6wehLXOKKUWgycAhQC1wVCMJ2gELDyVkotAh4B/B78jYaIGANxHZ0x6dPKbgFgB74aBhlWAUODQqXU/Uqpv4dBjqnIpNq/NvZbC/xCKVUGLMJdlkGfTOiEjVV4b6+fB/xWAunEBkqpLyml9mlfAzIO0AkZqfjRb4ubiehDVjGs/4hGdCXQibwCXKFtXw887vlBRBJE5GER2Swi20Xkam1/nIisEZFdIvIEEDfyoppGeL+I/FlE9orI6x5lk4jMEpE3tZXnbSIyM/h/po4HESkWkXVa+a0TkSJt/wkr+p5VBxHJE5H1w1acz9b2XywiG7QyfEpEEofd5nrgW0ChiBQMu+at2orjO1rduFfbnyUiz2h1bbOInBmCRxFTaM//TOBWtEmBiDwhIpcPO+ZvIvIZETGKyD3as941msWWUsoJbAIKtHNtIvJXEdmt9QfnjbP/8yLyvIj8S0QqReR2EfmmdsxHIpKuHfef4rY62iUia4L8mGKGQJe3RjLQpp076jna4OFercxeArKH3e+oiPxQRN4HrtXa+e+0/mO/iCwTkWfFbXHwU+2cBBF5SXsf7BGRzwbhcU0F3gNmAWjtbI/2+Ya2b7qIHBCRR7TyfFo0yyEZtpIvIktF5J2RFxeRT4jIRq39vikiOSIyHbfi6Q7t/XC2iPxIRL6tnbNYa+u7ROQ5EUnT9r8jIr8UkU3a++Ds4D+e2CJA7f8G4AOl1OsASqle4Hbgu557DOvbd4nIZ7T9l4r7vb9TRNZp+4bKXfu+R6tzXuudTvDwt72Ke+y3FHhU+y3OW78gIhniHtNvF5EHABl235u0dr1DRB4QfTEgqMjY4/k/iNu6t0IrX0TEICL3iXte9qKIvDzst3e0cv4FEKeV4aMywsJHqy8/0rZP0/qBDcDXhh3j65hDJzD8ApipldnvtLqwTeu7PXN3z7z8PmAbME38mJON1n+E6W+dHEop/aMUQDewEHgasAE7cGv5XtR+/xlwk7adCpQDCcA3gYe1/QsBB7BU+34UyASma/sXa/ufHHatjcCntG0bEB/uZxFrH8CplafnUw3cq/32L+AWbfuLwPPa9t+Aa4bXD+3/bwF3adtGIEkr4/VAgrb/TuCH2vY04NCwOvRNbTtfqx/pgBn3pMUj02PAWdp2EbA/3M8w2j7ATcBD2vaHwKnAp4BHtH0WoAa30vbLwP9v79yDrSqrAP5beevySuCCEpoCOjGV2SgwJQPhi6nxMWrBTN5IgRmniT8UpzGmJmoQnByjNMpXYloCow4Dl65UA1jANU0eAt6LBmWBRtJLkcDICFZ/rLU5H4ezz30duFzP+s3sud9ee3/7nLu/9a3vudaZ6fJaYCMwzOvtVpf3AFYDH0/04FFPf9h1qkcZ+RTgFdeX04C9wJf9vnuAWz39OlDr6X5d/R67y1HB8j7gNuKPwG7gbL8vL8/ngFVuC84A3srshtfvGcl3XAPc5enpXtaD/Xm7gAHABGB+kqdvV7/b7nJQsNE1wM+AacBIoAVrq/sALwEXelkrMMbzPALclpTbQE+PAtZ4egoFG90fEE/fBHzP07Oy5xSfA83AxZ6eDXw/0Yss/5XA0139LrvbUaH6fzcwvcSz92ATwndlZZbowGn+3GEuq8vRg62uc7l6F0fFdGF/CVlH6usavC/v53l24QcU+ntXefkOBD6C9S/f69fuB27s6vfzbjlyyrlcf34xtvHho8ArLp8I/MLlH/C6nrXfR8o//SySfqGf3wbM8nRq4+dS6D+WtDld/Q7frQdH991rgFM9PRDrh4vfcxi4yK+1e0xWbD+64xE7gRJUtRlTjHrMMKR8GviaiGzBjEMPTBnGAQuT/M05j9+hqls8/QIwVETeD5ypqg2e/z9qq09BZcncBC5Qc+35VnJtNFbBARYAY1t51gZgqs/8n6+q+4CLsIblWdePycAQv/96bNIP4AkKLmGfANaq6puqehBroDLGA/f6sxqBU11XgrZTj71vKLz3XwKXiUgtcAXQpKoHsLp9o7/vddhg/EOe91yXvwG85nUcTE8WAKjqNuBVYHgZOcBqVd2nqv/AJoGecnkLZnfA7MciEfkiNnEctI1KlXfmDnYucCvwkMvz8owDHlfVQ6r6OvDrou/1ZNF5o/9tAV5S1d2q+g7wJ2zCuAUYL7Yz5FOqurcT76Ta6OnlsxGbfP0xVh8bVPVtVd0PLAWyFbs/q+qznl5I67Y/5YPAChFpAb4KnFfuZhHpi03qrnXRTzHdyVjqf1+gYAuCtlOJ+i/YAL4UirXL9x0RqO7B2v4mVd3hsjfb8F07o3dBx2hXfW0n6Rjg5/juUeBybBJ6g+va5cA5Ffzc4FjK9eeXqephNRevQS4bCyx2+V+xhb4OUcLGL0gul+tzBMcXAb4tIs3A09hu/qz8X1XV5z1dlWOymq7+AichjcB3sV1AAxK5ABNUdXt6s1jMwLyOQ8o7SfoQtiIVAQdPPrKy/B/uLilWyO8DUNUmERmHrfgsEJG5WKO/SlVLxfypBwaJyCQ/P0NEsg5nHu8BRnuHNWgnIjIAuAyL8aXYLg0FZmATuJ8BPk/B3VOAm1V1RdFzhuKTAiIyGFgjIteoaiP55VeuXFMbcDg5P0zBFl+FdSqvAb4pIuepakwGlaHC5Z3SCDzaSp4rKW//3y46T8u8WB9qVPX3IjIS2xFyp4isVNXZZZ4fFDjgk/xHcNudR3G5HWP7scWeUvwQuFtVG0XkEmxFsDNkunCI6Je1iwrW/2xRL5Wdg+0C2Oe6VKwzeRNHqQ7B0XqUp3fB8aMS9bWcXShVhoLtRPt6Bz4rqAxpuaTtrRT9bQ95dbvcJHJJmxOcECZhOzZHqupBEdlJoczS/lm7x2Tluxfdg9gJdCyPALNVtaVIvgK4OetUisiFLm/ClAwR+RjmEtYmVPVfwC4Ruc7z10r4h59onqMQSHISFlgSbFvgSE9fi20PRESGAH9X1fnYSvMILOjzGBHJYlD0EpHhYr8s1FtVz1TVoao6FLjTP289cLGI9BeRGswNJGMlFosAf95RA5ugVSYCj6nqEH/vZwE7sFWfJ4Cp2G6ArEFeAUwT/+UvL7ve6QNVdTcWGyLr0KX1fji2K3B7GXmriAWmO0tVV2MDmH6YC0tQnoqXtzMWcwsrl6cJuN59/gcDl3bmHxH7NZp/q+pCbDFiRGeeF9AEXOc2uTfmIvSMXztbREZ7OgsqDEfb/tQup/Sl8MMRkxP5Pszl8yh8R9eeJG7ADcDa4vuCDlGp+r8IGCsi413eE3P1+Y7nK26X+wO/xdrxYS6r88s78borIiMwd7OMPL0Ljh/tqq8513ZS2i6kbf4VmOsZwK+AiSJyul+r8/5jcPzI68/n8RtgglhsoEHY4n8pDkrhl2H/BpwuFguqFrgaQFXfAvaKSLb7aFKSv619jqAypHW3LzZmOygWozOvDnZkTFbOfnQLYhKoCFXdparzSlyag00ENIsFBZvj8geAPr7VbAamSO3hBuAWz/8c5pcanDhuwdy7mrGymO7y+ZhBWA98ksKM8SXAFhHZjBmJee7eMwV43J/zPBYPph5oKPq8JUC9qv4FixG0Dtui+DLmIpR9p1FiAeRepmt+5aY7k/fev4AZ83FY3I3/+rWHsfe/yev2jyi9Gr8M6OUDufuBU3x7+ZPAFHfryZO3hVOAhZ53M3CPdyyC8lSyvLNggi9i9fOmVvI0AH/A3LgeoPMD+/OB9b7t+BvAHZ18XlWjqpuweBDrMVv7sKpu9su/Aya7za7Dyg/gdmCeiDyD7cwpxSxgsd/zz0T+FPBZKR0ocjIw1z/vAiwuUNB5KlL/fZX3WmCmiGzH6vQG4F7PdwfQXyzI84vApd72fwlY6rLM/XMJUOf1eBoWQzIjT++CytBLRHYlx1foWH39CfCgX+tJvl24HRgnIpswt5/XANztaCaw0st6FRYDLqgMpco5rz+fxxIsHl9mB9ZR6IenPISN/Ra5q9Bsv3c5sC25bypwn1hg6HTXSFv7mEEFUNU3sPAcW7G2dpSIbMQm5rbl5OnImKyc/egWZIHSgiA4wYhIH1Xd77PODViA8eLObBAEQVBBxFz/lqv9nHwQnBBC74Lg5CLphw/AFgvGeHygoMqoxjFZzEQGQdcxy7ee98BWLJd18fcJgiAIgiAIgmpguYj0w+J+zokJoKqm6sZksRMoCIIgCIIgCIIgCIKgCoiYQEEQBEEQBEEQBEEQBFVATAIFQRAEQRAEQRAEQRBUATEJFARBEARBEARBEARBUAXEJFAQBEEQBEEQBEEQBEEVEJNAQRAEQRAEQRAEQRAEVcD/AXKaBO5nefjWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "for i in range(0,len(collist)):\n",
    "    plt.subplot(nrows,ncol,i+1)\n",
    "    sns.distplot(house_new[collist[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0.786048\n",
       "HouseAge      0.046496\n",
       "AveRooms      0.555865\n",
       "AveBedrms     3.913734\n",
       "Population    1.346151\n",
       "AveOccup      5.789707\n",
       "Latitude      0.460162\n",
       "Longitude    -0.292814\n",
       "target        0.970197\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_new.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset has there is skewness and it's better to drop this skewness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=house_new.iloc[:,:-1]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "list1=x.columns\n",
    "for i in list1:\n",
    "    x[i]=le.fit_transform(x[i].astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12120</td>\n",
       "      <td>40</td>\n",
       "      <td>17109</td>\n",
       "      <td>4561</td>\n",
       "      <td>291</td>\n",
       "      <td>6033</td>\n",
       "      <td>514</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>12115</td>\n",
       "      <td>20</td>\n",
       "      <td>15045</td>\n",
       "      <td>1716</td>\n",
       "      <td>2340</td>\n",
       "      <td>2129</td>\n",
       "      <td>512</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11660</td>\n",
       "      <td>51</td>\n",
       "      <td>18327</td>\n",
       "      <td>8162</td>\n",
       "      <td>463</td>\n",
       "      <td>8853</td>\n",
       "      <td>511</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10038</td>\n",
       "      <td>51</td>\n",
       "      <td>13097</td>\n",
       "      <td>8143</td>\n",
       "      <td>525</td>\n",
       "      <td>5939</td>\n",
       "      <td>511</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6530</td>\n",
       "      <td>51</td>\n",
       "      <td>15209</td>\n",
       "      <td>8658</td>\n",
       "      <td>532</td>\n",
       "      <td>2554</td>\n",
       "      <td>511</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0   12120        40     17109       4561         291      6033       514   \n",
       "1   12115        20     15045       1716        2340      2129       512   \n",
       "2   11660        51     18327       8162         463      8853       511   \n",
       "3   10038        51     13097       8143         525      5939       511   \n",
       "4    6530        51     15209       8658         532      2554       511   \n",
       "\n",
       "   Longitude  \n",
       "0        169  \n",
       "1        170  \n",
       "2        168  \n",
       "3        167  \n",
       "4        167  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.526\n",
       "1    3.585\n",
       "2    3.521\n",
       "3    3.413\n",
       "4    3.422\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=house_new.iloc[:,-1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19794,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in x.columns:\n",
    "    if x.skew().loc[col]>0.55:\n",
    "        x[col]=np.log1p(x[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0.158251\n",
       "HouseAge      0.046496\n",
       "AveRooms      0.001447\n",
       "AveBedrms     0.105681\n",
       "Population   -1.645419\n",
       "AveOccup      0.007379\n",
       "Latitude      0.452384\n",
       "Longitude    -0.300426\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's perfect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.24,random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15043, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15043,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4751, 8)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4751,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying Standard Scaling to get optimized result\n",
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# training a machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression(normalize=True)\n",
    "rfr=RandomForestRegressor()\n",
    "rg=RidgeCV(alphas=(0.01, 0.1, 1.0, 10.0), normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*********************** LinearRegression ************\n",
      "\n",
      "\n",
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)\n",
      "\n",
      "\n",
      "score 0.6248170703336808\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.5130547884839264\n",
      "MSE 0.4734356620318809\n",
      "RMSE 0.6880666116241079\n",
      "R-square 0.6140408297792881\n",
      "*********************** RandomForestRegressor ************\n",
      "\n",
      "\n",
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "                      max_features='auto', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=2,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                      n_jobs=None, oob_score=False, random_state=None,\n",
      "                      verbose=0, warm_start=False)\n",
      "\n",
      "\n",
      "score 0.9575066013825629\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.36650835014121813\n",
      "MSE 0.30435577777804845\n",
      "RMSE 0.5516844911523692\n",
      "R-square 0.7518799007684709\n",
      "*********************** RidgeCV ************\n",
      "\n",
      "\n",
      "RidgeCV(alphas=array([ 0.01,  0.1 ,  1.  , 10.  ]), cv=None, fit_intercept=True,\n",
      "        gcv_mode=None, normalize=True, scoring=None, store_cv_values=False)\n",
      "\n",
      "\n",
      "score 0.6235675008033223\n",
      "\n",
      "\n",
      "Performance Measures:\n",
      "MAE 0.5130613117886164\n",
      "MSE 0.47605587411903466\n",
      "RMSE 0.689968023983021\n",
      "R-square 0.6119047530870088\n"
     ]
    }
   ],
   "source": [
    "#prepare models\n",
    "models=[]\n",
    "models.append(('LinearRegression',lr))\n",
    "models.append(('RandomForestRegressor',rfr)) \n",
    "models.append(('RidgeCV',rg)) \n",
    "#evaluate each model\n",
    "Model=[]\n",
    "Score=[]\n",
    "MAE=[]\n",
    "MSE=[]\n",
    "RMSE=[]\n",
    "RSquare=[]\n",
    "for name,model in models:\n",
    "    print('***********************',name,'************')\n",
    "    Model.append(name)\n",
    "    print('\\n')\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pred=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    sr=model.score(x_train,y_train)\n",
    "    print('score',sr)\n",
    "    Score.append(sr*100)\n",
    "    print('\\n')\n",
    "    print('Performance Measures:')\n",
    "    mae=metrics.mean_absolute_error(y_test, pred)\n",
    "    print('MAE',mae)\n",
    "    MAE.append(mae)\n",
    "    mse=metrics.mean_squared_error(y_test, pred)\n",
    "    print('MSE',mse)\n",
    "    MSE.append(mse)\n",
    "    rmse=np.sqrt(metrics.mean_squared_error(y_test, pred))\n",
    "    print('RMSE',rmse)\n",
    "    RMSE.append(rmse)\n",
    "    rsquare=metrics.r2_score(y_test,pred)\n",
    "    print('R-square',rsquare)\n",
    "    RSquare.append(rsquare)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>models</th>\n",
       "      <th>score</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>RSquare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>62.481707</td>\n",
       "      <td>0.513055</td>\n",
       "      <td>0.473436</td>\n",
       "      <td>0.688067</td>\n",
       "      <td>0.614041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>95.750660</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>0.304356</td>\n",
       "      <td>0.551684</td>\n",
       "      <td>0.751880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>RidgeCV</td>\n",
       "      <td>62.356750</td>\n",
       "      <td>0.513061</td>\n",
       "      <td>0.476056</td>\n",
       "      <td>0.689968</td>\n",
       "      <td>0.611905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  models      score       MAE       MSE      RMSE   RSquare\n",
       "0       LinearRegression  62.481707  0.513055  0.473436  0.688067  0.614041\n",
       "1  RandomForestRegressor  95.750660  0.366508  0.304356  0.551684  0.751880\n",
       "2                RidgeCV  62.356750  0.513061  0.476056  0.689968  0.611905"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'models':Model,'score':Score,'MAE':MAE,'MSE':MSE,'RMSE':RMSE,'RSquare':RSquare},columns=['models','score','MAE','MSE','RMSE','RSquare'])\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted target [2.0445 1.6748 1.5593 ... 2.3423 3.3331 2.7734]\n",
      "actual target 6197     1.767\n",
      "7249     1.768\n",
      "6259     1.565\n",
      "12343    1.125\n",
      "17510    2.042\n",
      "         ...  \n",
      "20362    2.476\n",
      "12782    0.705\n",
      "3775     2.750\n",
      "17228    3.831\n",
      "20461    2.399\n",
      "Name: target, Length: 4751, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pred=rfr.predict(x_test)\n",
    "print(\"predicted target\",pred)\n",
    "print(\"actual target\",y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(pred)\n",
    "x.to_csv('linear_house.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.0445, 1.6748, 1.5593, ..., 2.3423, 3.3331, 2.7734])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#save the model as a pickle in a file\n",
    "joblib.dump(rfr,'house.obj')\n",
    "#load the model from a file\n",
    "rfr_from_joblib=joblib.load('house.obj')\n",
    "#use the loaded model to make prediction\n",
    "rfr_from_joblib.predict(x_test)"
   ]
  }
 ],
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