diff --git a/HousingPricePredicter.joblib b/HousingPricePredicter.joblib
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diff --git a/ML House Prediction.ipynb b/ML House Prediction.ipynb
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+++ b/ML House Prediction.ipynb	
@@ -0,0 +1,1709 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Housing Price Predictor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing= pd.read_csv(\"data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>CRIM</th>\n",
+       "      <th>ZN</th>\n",
+       "      <th>INDUS</th>\n",
+       "      <th>CHAS</th>\n",
+       "      <th>NOX</th>\n",
+       "      <th>RM</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>DIS</th>\n",
+       "      <th>RAD</th>\n",
+       "      <th>TAX</th>\n",
+       "      <th>PTRATIO</th>\n",
+       "      <th>B</th>\n",
+       "      <th>LSTAT</th>\n",
+       "      <th>MEDV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00632</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2.31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>6.575</td>\n",
+       "      <td>65.2</td>\n",
+       "      <td>4.0900</td>\n",
+       "      <td>1</td>\n",
+       "      <td>296</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>4.98</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.02731</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>6.421</td>\n",
+       "      <td>78.9</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>9.14</td>\n",
+       "      <td>21.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.02729</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.07</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>7.185</td>\n",
+       "      <td>61.1</td>\n",
+       "      <td>4.9671</td>\n",
+       "      <td>2</td>\n",
+       "      <td>242</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>392.83</td>\n",
+       "      <td>4.03</td>\n",
+       "      <td>34.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.03237</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>6.998</td>\n",
+       "      <td>45.8</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>394.63</td>\n",
+       "      <td>2.94</td>\n",
+       "      <td>33.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.06905</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.458</td>\n",
+       "      <td>7.147</td>\n",
+       "      <td>54.2</td>\n",
+       "      <td>6.0622</td>\n",
+       "      <td>3</td>\n",
+       "      <td>222</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>396.90</td>\n",
+       "      <td>5.33</td>\n",
+       "      <td>36.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     CRIM     ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
+       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
+       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
+       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
+       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
+       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
+       "\n",
+       "        B  LSTAT  MEDV  \n",
+       "0  396.90   4.98  24.0  \n",
+       "1  396.90   9.14  21.6  \n",
+       "2  392.83   4.03  34.7  \n",
+       "3  394.63   2.94  33.4  \n",
+       "4  396.90   5.33  36.2  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 506 entries, 0 to 505\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column   Non-Null Count  Dtype  \n",
+      "---  ------   --------------  -----  \n",
+      " 0   CRIM     506 non-null    float64\n",
+      " 1   ZN       506 non-null    float64\n",
+      " 2   INDUS    506 non-null    float64\n",
+      " 3   CHAS     506 non-null    int64  \n",
+      " 4   NOX      506 non-null    float64\n",
+      " 5   RM       506 non-null    float64\n",
+      " 6   AGE      506 non-null    float64\n",
+      " 7   DIS      506 non-null    float64\n",
+      " 8   RAD      506 non-null    int64  \n",
+      " 9   TAX      506 non-null    int64  \n",
+      " 10  PTRATIO  506 non-null    float64\n",
+      " 11  B        506 non-null    float64\n",
+      " 12  LSTAT    506 non-null    float64\n",
+      " 13  MEDV     506 non-null    float64\n",
+      "dtypes: float64(11), int64(3)\n",
+      "memory usage: 55.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "housing.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>CRIM</th>\n",
+       "      <th>ZN</th>\n",
+       "      <th>INDUS</th>\n",
+       "      <th>CHAS</th>\n",
+       "      <th>NOX</th>\n",
+       "      <th>RM</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>DIS</th>\n",
+       "      <th>RAD</th>\n",
+       "      <th>TAX</th>\n",
+       "      <th>PTRATIO</th>\n",
+       "      <th>B</th>\n",
+       "      <th>LSTAT</th>\n",
+       "      <th>MEDV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "      <td>506.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.613524</td>\n",
+       "      <td>11.363636</td>\n",
+       "      <td>11.136779</td>\n",
+       "      <td>0.069170</td>\n",
+       "      <td>0.554695</td>\n",
+       "      <td>6.284634</td>\n",
+       "      <td>68.574901</td>\n",
+       "      <td>3.795043</td>\n",
+       "      <td>9.549407</td>\n",
+       "      <td>408.237154</td>\n",
+       "      <td>18.455534</td>\n",
+       "      <td>356.674032</td>\n",
+       "      <td>12.653063</td>\n",
+       "      <td>22.532806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.601545</td>\n",
+       "      <td>23.322453</td>\n",
+       "      <td>6.860353</td>\n",
+       "      <td>0.253994</td>\n",
+       "      <td>0.115878</td>\n",
+       "      <td>0.702617</td>\n",
+       "      <td>28.148861</td>\n",
+       "      <td>2.105710</td>\n",
+       "      <td>8.707259</td>\n",
+       "      <td>168.537116</td>\n",
+       "      <td>2.164946</td>\n",
+       "      <td>91.294864</td>\n",
+       "      <td>7.141062</td>\n",
+       "      <td>9.197104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.006320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.460000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.385000</td>\n",
+       "      <td>3.561000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>1.730000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.082045</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.449000</td>\n",
+       "      <td>5.885500</td>\n",
+       "      <td>45.025000</td>\n",
+       "      <td>2.100175</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>279.000000</td>\n",
+       "      <td>17.400000</td>\n",
+       "      <td>375.377500</td>\n",
+       "      <td>6.950000</td>\n",
+       "      <td>17.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.256510</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.690000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.538000</td>\n",
+       "      <td>6.208500</td>\n",
+       "      <td>77.500000</td>\n",
+       "      <td>3.207450</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>330.000000</td>\n",
+       "      <td>19.050000</td>\n",
+       "      <td>391.440000</td>\n",
+       "      <td>11.360000</td>\n",
+       "      <td>21.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.677082</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.624000</td>\n",
+       "      <td>6.623500</td>\n",
+       "      <td>94.075000</td>\n",
+       "      <td>5.188425</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>666.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>396.225000</td>\n",
+       "      <td>16.955000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>88.976200</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>8.780000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>12.126500</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>37.970000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CRIM           ZN       INDUS        CHAS         NOX          RM  \\\n",
+       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
+       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
+       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
+       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
+       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
+       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
+       "75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
+       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
+       "\n",
+       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
+       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
+       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
+       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
+       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
+       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
+       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
+       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
+       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
+       "\n",
+       "            LSTAT        MEDV  \n",
+       "count  506.000000  506.000000  \n",
+       "mean    12.653063   22.532806  \n",
+       "std      7.141062    9.197104  \n",
+       "min      1.730000    5.000000  \n",
+       "25%      6.950000   17.025000  \n",
+       "50%     11.360000   21.200000  \n",
+       "75%     16.955000   25.000000  \n",
+       "max     37.970000   50.000000  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'CRIM '}>,\n",
+       "        <AxesSubplot:title={'center':'ZN'}>,\n",
+       "        <AxesSubplot:title={'center':'INDUS'}>,\n",
+       "        <AxesSubplot:title={'center':'CHAS'}>],\n",
+       "       [<AxesSubplot:title={'center':'NOX'}>,\n",
+       "        <AxesSubplot:title={'center':'RM'}>,\n",
+       "        <AxesSubplot:title={'center':'AGE'}>,\n",
+       "        <AxesSubplot:title={'center':'DIS'}>],\n",
+       "       [<AxesSubplot:title={'center':'RAD'}>,\n",
+       "        <AxesSubplot:title={'center':'TAX'}>,\n",
+       "        <AxesSubplot:title={'center':'PTRATIO'}>,\n",
+       "        <AxesSubplot:title={'center':'B'}>],\n",
+       "       [<AxesSubplot:title={'center':'LSTAT'}>,\n",
+       "        <AxesSubplot:title={'center':'MEDV'}>, <AxesSubplot:>,\n",
+       "        <AxesSubplot:>]], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.hist(bins=50,figsize=(20,15))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train-Test Splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "def split_train_test(data, test_ratio):\n",
+    "    np.random.seed(42)\n",
+    "    shuffled =np.random.permutation(len(data))\n",
+    "    test_set_size =int(len(data)*test_ratio)\n",
+    "    test_indices = shuffled[:test_set_size]\n",
+    "    train_indices = shuffled[test_set_size:]\n",
+    "    return data.iloc[train_indices],data.iloc[test_indices]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set, test_set =split_train_test(housing,0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows in train set: 405 \n",
+      "Rows in test set : 101\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Rows in train set: {len(train_set)} \\nRows in test set : {len(test_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows in train set: 404 \n",
+      "Rows in test set : 102\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "train_set, test_set =train_test_split(housing, test_size=0.2, random_state=42)\n",
+    "print(f\"Rows in train set: {len(train_set)} \\nRows in test set : {len(test_set)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import StratifiedShuffleSplit\n",
+    "split= StratifiedShuffleSplit(n_splits=1,test_size=0.2, random_state=42)\n",
+    "for train_index, test_index in split.split(housing, housing['CHAS']):\n",
+    "    strat_train_set=housing.loc[train_index]\n",
+    "    strat_test_set=housing.loc[test_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>CRIM</th>\n",
+       "      <th>ZN</th>\n",
+       "      <th>INDUS</th>\n",
+       "      <th>CHAS</th>\n",
+       "      <th>NOX</th>\n",
+       "      <th>RM</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>DIS</th>\n",
+       "      <th>RAD</th>\n",
+       "      <th>TAX</th>\n",
+       "      <th>PTRATIO</th>\n",
+       "      <th>B</th>\n",
+       "      <th>LSTAT</th>\n",
+       "      <th>MEDV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "      <td>102.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.655942</td>\n",
+       "      <td>13.450980</td>\n",
+       "      <td>10.312255</td>\n",
+       "      <td>0.068627</td>\n",
+       "      <td>0.541353</td>\n",
+       "      <td>6.303353</td>\n",
+       "      <td>66.733333</td>\n",
+       "      <td>3.988460</td>\n",
+       "      <td>8.813725</td>\n",
+       "      <td>391.980392</td>\n",
+       "      <td>18.385294</td>\n",
+       "      <td>369.670196</td>\n",
+       "      <td>12.104314</td>\n",
+       "      <td>22.625490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>10.400966</td>\n",
+       "      <td>27.503241</td>\n",
+       "      <td>6.761154</td>\n",
+       "      <td>0.254068</td>\n",
+       "      <td>0.111397</td>\n",
+       "      <td>0.662996</td>\n",
+       "      <td>27.772183</td>\n",
+       "      <td>2.131247</td>\n",
+       "      <td>8.614667</td>\n",
+       "      <td>167.837379</td>\n",
+       "      <td>2.310604</td>\n",
+       "      <td>68.075774</td>\n",
+       "      <td>6.759257</td>\n",
+       "      <td>8.452344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.009060</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.460000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.385000</td>\n",
+       "      <td>4.138000</td>\n",
+       "      <td>6.500000</td>\n",
+       "      <td>1.137000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>188.000000</td>\n",
+       "      <td>12.600000</td>\n",
+       "      <td>3.650000</td>\n",
+       "      <td>2.470000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.057828</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.950000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.448000</td>\n",
+       "      <td>5.912750</td>\n",
+       "      <td>45.850000</td>\n",
+       "      <td>2.223650</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>270.000000</td>\n",
+       "      <td>16.800000</td>\n",
+       "      <td>377.685000</td>\n",
+       "      <td>7.480000</td>\n",
+       "      <td>18.925000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.176150</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.760000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.515000</td>\n",
+       "      <td>6.176000</td>\n",
+       "      <td>71.100000</td>\n",
+       "      <td>3.422950</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>307.000000</td>\n",
+       "      <td>19.150000</td>\n",
+       "      <td>393.740000</td>\n",
+       "      <td>10.565000</td>\n",
+       "      <td>21.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.061955</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.612750</td>\n",
+       "      <td>6.539500</td>\n",
+       "      <td>93.500000</td>\n",
+       "      <td>5.609225</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>461.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>16.267500</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>88.976200</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>8.725000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>10.585700</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>37.970000</td>\n",
+       "      <td>50.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CRIM           ZN       INDUS        CHAS         NOX          RM  \\\n",
+       "count  102.000000  102.000000  102.000000  102.000000  102.000000  102.000000   \n",
+       "mean     3.655942   13.450980   10.312255    0.068627    0.541353    6.303353   \n",
+       "std     10.400966   27.503241    6.761154    0.254068    0.111397    0.662996   \n",
+       "min      0.009060    0.000000    0.460000    0.000000    0.385000    4.138000   \n",
+       "25%      0.057828    0.000000    4.950000    0.000000    0.448000    5.912750   \n",
+       "50%      0.176150    0.000000    7.760000    0.000000    0.515000    6.176000   \n",
+       "75%      2.061955    0.000000   18.100000    0.000000    0.612750    6.539500   \n",
+       "max     88.976200   90.000000   27.740000    1.000000    0.871000    8.725000   \n",
+       "\n",
+       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
+       "count  102.000000  102.000000  102.000000  102.000000  102.000000  102.000000   \n",
+       "mean    66.733333    3.988460    8.813725  391.980392   18.385294  369.670196   \n",
+       "std     27.772183    2.131247    8.614667  167.837379    2.310604   68.075774   \n",
+       "min      6.500000    1.137000    1.000000  188.000000   12.600000    3.650000   \n",
+       "25%     45.850000    2.223650    4.000000  270.000000   16.800000  377.685000   \n",
+       "50%     71.100000    3.422950    5.000000  307.000000   19.150000  393.740000   \n",
+       "75%     93.500000    5.609225    8.000000  461.000000   20.200000  396.900000   \n",
+       "max    100.000000   10.585700   24.000000  711.000000   22.000000  396.900000   \n",
+       "\n",
+       "            LSTAT        MEDV  \n",
+       "count  102.000000  102.000000  \n",
+       "mean    12.104314   22.625490  \n",
+       "std      6.759257    8.452344  \n",
+       "min      2.470000    5.000000  \n",
+       "25%      7.480000   18.925000  \n",
+       "50%     10.565000   21.500000  \n",
+       "75%     16.267500   25.000000  \n",
+       "max     37.970000   50.000000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_test_set.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    95\n",
+       "1     7\n",
+       "Name: CHAS, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_test_set['CHAS'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    376\n",
+       "1     28\n",
+       "Name: CHAS, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strat_train_set['CHAS'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing= strat_train_set.copy()   # use just after split data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Looking for Correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = housing.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MEDV       1.000000\n",
+       "RM         0.679894\n",
+       "B          0.361761\n",
+       "ZN         0.339741\n",
+       "DIS        0.240451\n",
+       "CHAS       0.205066\n",
+       "AGE       -0.364596\n",
+       "RAD       -0.374693\n",
+       "CRIM      -0.393715\n",
+       "NOX       -0.422873\n",
+       "TAX       -0.456657\n",
+       "INDUS     -0.473516\n",
+       "PTRATIO   -0.493534\n",
+       "LSTAT     -0.740494\n",
+       "Name: MEDV, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_matrix['MEDV'].sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:xlabel='MEDV', ylabel='MEDV'>,\n",
+       "        <AxesSubplot:xlabel='RM', ylabel='MEDV'>,\n",
+       "        <AxesSubplot:xlabel='ZN', ylabel='MEDV'>,\n",
+       "        <AxesSubplot:xlabel='LSTAT', ylabel='MEDV'>],\n",
+       "       [<AxesSubplot:xlabel='MEDV', ylabel='RM'>,\n",
+       "        <AxesSubplot:xlabel='RM', ylabel='RM'>,\n",
+       "        <AxesSubplot:xlabel='ZN', ylabel='RM'>,\n",
+       "        <AxesSubplot:xlabel='LSTAT', ylabel='RM'>],\n",
+       "       [<AxesSubplot:xlabel='MEDV', ylabel='ZN'>,\n",
+       "        <AxesSubplot:xlabel='RM', ylabel='ZN'>,\n",
+       "        <AxesSubplot:xlabel='ZN', ylabel='ZN'>,\n",
+       "        <AxesSubplot:xlabel='LSTAT', ylabel='ZN'>],\n",
+       "       [<AxesSubplot:xlabel='MEDV', ylabel='LSTAT'>,\n",
+       "        <AxesSubplot:xlabel='RM', ylabel='LSTAT'>,\n",
+       "        <AxesSubplot:xlabel='ZN', ylabel='LSTAT'>,\n",
+       "        <AxesSubplot:xlabel='LSTAT', ylabel='LSTAT'>]], dtype=object)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pandas.plotting import scatter_matrix\n",
+    "attributes=[\"MEDV\",\"RM\",\"ZN\",\"LSTAT\"]\n",
+    "scatter_matrix(housing[attributes],figsize =(12,8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='RM', ylabel='MEDV'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+1vk3Pmr8FaWByDeaEzMZXhieZGgyySfvfbao5z6e8DV2LIFjV/kqm4Fe40BnLFc2WsjzXmhNwXJq9bXOv/FRVU9FaTAe3TPMjdt3sv/IDADre9qIhKyCKpr5Nf77j8ww0Bmluy3CZDLDwfEkx/fFiUdCpBx33vbjiQxX3Pb4rDUFhY6xnIodrfapLwupelbN8xeRmIj8TER+KSI7ReSm7Ou9IvKwiLyQfVxVrTEoykrkvM0DfOG9WzimK8amgU46Y+GCnvt4IsON23fieH4bxv6OCMOTKRJph0jI4k/PPYG04zExkynoeZcbl19Oe0Zt7di4VLPUMwW8wxgzJSJh4DEReQi4AnjEGPM5EfkM8Bng01Uch6KsOPo7fOnklOPmPPe5MfMHnzvA/iMzCAICa7tirOtu4/qLTyHtGG59eA8iQspx+fg7Ns8rs4yFbNKOBzhFj6E0Lwt6/iKyZqk7Nj5T2afh7D8DXAbclX39LuDypR5DUerFUoXXyuHRPcNcdefPyLgee8cSjEym5nnu44kMt//4FQDEt/0cGJ9BxG+7eOvDe0hmXIYmkoxOp7nu357j3589kNv27p++xoe+8tMFj6E0N6U8/1+KyHPAN4BvG2PGF7NzEbGBp4GNwD8YY34qImuMMQcBjDEHRaTgqg8R2QZsAzjuuOMWc1hFqSrVXLwUhHIM0NsepT0aIu143HnV2bNaJg5PJrEtPx9wcDyZWw2w7dwTSWZj93NX/X72oV8D8Nffe579RxIArOtu47jeeMFjKM1NqZj/euDzwNuA50XkfhF5n4i0lbNzY4xrjNkCHAucLSKnlzswY8ztxpitxpit/f395W6mKFWlWouXgpnEvz69l/1HZjh4JMmLI1M4rt9cPVDbDAhCM7YlHLuqjTWdUdb3xLn4jHWzVv1aOVE2X4Xzsw/9GpN93RLh4ESSaMjGEmHP0IQuwmohFjT+WeP9PWPMHwODwD/jh2leEZF/KfcgxpgjwI+APwCGRGQtQPZxcStPFKWOVGPxUlBu+SdffYrPPbQHzzOzQjmeMQUlmS/fso69Ywn2jiV4fSLJH561LifLkL/q1wCr2/1Qjm0J7dG8CX9WpvnA+Ay3PLh7SYvBlJVJ2dU+xpg0sAvYDUwApy70eRHpF5Ge7N9twDuBXwPbgSuzH7sS+M6iR60odaJSi5cCT3/faCI3kwjbFiJgWb52fn4oJ5BbDrZ55rXDfPuZ/RzXG2dDXzvH9ca5f8eBnOcerPo9pivG2q4YbZEQ1114CrYlOJ7H2u4YnjF4xuPwdIaBziidMf8Gcf39v2LfaKIyF0xpWEpW+4jIccD7gA8A7cA9wGXGmN0lNl0L3JWN+1vAvcaYB0TkCeBeEfkwsBd4z3JOQFFqSb6qZSm9/GLk5wzSjkfG9ejPhmpEBGMMg71tOK7//G0b+7njJy/zlcdewfEMY4k03bEQ40mHdd1tdLX5P+O50s8XnbmOczb2z6qzb4/6nbosEdb3xLlsy1q+s+MAnbFwbm2AZ8yyROGUlcGCxl9E/gM/7v8tYJsxpuyVVsaYZ4E3FXh9FDh/keNUlIZhOU1K5jZrAYfXJ5K0R0PEIyFWt4cZmUrjeoaQLVy+ZR3v/qf/YGgildtHyIKJpIsxhgPjM7RHQ9mmK7NnIIUWWM0dO8ADzx4kkXZyhj8QhdOG681NKc//OuDHZiUsA1aUGrLUJiVzcwbxSIi+9mi2D65DWyTEF993KpvWdBIL2XzoKz9jdCqFcDQM5HgQsX1DPzKVYmImQzRszZqBLFSRNHfsN1xyKtff/6uc4V/bFSMeCWnD9SZnQeNvjHlURK4UkauBN2Rf3g18yRjz1aqPTlGajELNWtqjNndedTZJx53lpb8wNInreYBg5kg7GwxtEZv1PXG+8N4zOam/c9YagMW0gjxv8wB3f+QtfPDLTxIJWbrgq0UotcjrvwCfAP4CWIcfAvoUcE32vYammgtxFGUpFBM8G+yLz5NBGOiMISI43vyJd082zv+pd23OJWoDgtlFyLJIZlwEIe14vDQyNW8/AYN9cW653K/EViG21mBBYTcReRJ4vzHm1TmvbwDuMca8paqjy7IUYTftIqQ0MuUKnt3909f4n9/Zief5vr8l0Nse4XNXnEHa8bj14edznw3+j48nMvzBF3/MoakUxoDjGWxLGFzVxk2Xnrbg70CF2JqL5Qi7dc01/ADZ17qWP7TqoF2ElFqwnJlluYJnF5+xjsFVbRy7qo0TV7dzwup2utvCWQmH54v+Hw+cumDWIJiyfgcqxNY6lDL+M0t8r65oFyGl2iykg19JuuNhbrr0NCKhoz/VGy45Nbfit9D/8eFJv3pocFWcsCXEQhaWZWGJ6O9AyVGq2ucUEXm2wOsCnFiF8VSEQkk1TV4plWKxCdXlct7mAe5c3cGeoQk2r+lisC+e896D/+OJtEMq43LwSJITVrcDWcE3S3DzZB48Yzh4JMlkMjMrSay0HiWNf01GUWEqsRBHaWzqGZsuNLNcblnkQudTLH8V/B8fnkgyMpVCRPjwV3/O6o4oHzx7kPt3HGBVW5ixRJpV8QjTKYeU4/Hhr/4cgL72CP/7D8/UXFiLUsr4txljfg0gIlFjTG6liYi8BXitmoNbDstZiKM0NvVO5ld6ZrnQ+Sw0y9gyuIobLzmNT3/7l9iWL9QGcGgqxbefOcDX/qtfPhoL2YxMJfnEN3/J6HQ697nR6TQ3bt/Jdz6mC7lakVIx/7vz/n5iznv/WOGxVBxNXjUfjZDMr2R/2lLnUyx/9eBzB7jitsf5y/ufYyRb1RModYoIrueRdFw2relksC9OZyw8S83TEkEQHNdoDqBFKeX5S5G/Cz1XlKpTjZDLUqjUzLLU+RSaZXjGcPuPXyESEjpjYQ6Oz+B4BsvyEHxtINuyZs1E/EbuFl5eabfBl5DQXFhrUsrzN0X+LvRcUapOpVQ1K0ElZpalzqfQLGPbuSdiW/6Nwtfzj2MJeB54xrC6I8pNl86eifhVQ6eyuiOaS/z2tUe46dLTdGbcopRa5DWMr+Ip+Mqe9wRvAe81xiy5zeNiWMoiL6V5CWLkxpBL5tcjaTk3SbvUJHQ555O/b4Arbnt8Vh4g7RhuvuxUOqJhTurvKHr88UQmu9LXaLVPC7DQIq9Sxv/Kom8Cxpi7Fnq/UqjxV+ZSj2qf/GPu2HeYmx/YhesZXM/wrtPW8L2dQ9iWYFtS8oa02BvH3Pfn3jCuveBkNq3pXNb10NW9zceSjX+joMZfqQXlllu6HiQzDpbAoekMjuvhGbAFQrbF6o4IsXBhsbbxRIYHnzuQbb7u3ziuu/AULjpzbdGxBDeagHwZh+HJJC8MTXHrw3vmvb8Y6l1BpVSH5Xj+2xfasTHm0mWOrSzU+CvVJN8g29ks2Nxyy/wwy8RMhv1HZvxFVAJpJ5BQILcSt7stlIvJBwa+PWpz4/ad7D8yk5Vf8JOzIsKX3r+Fi85cB8y90RhSjkc8YucWagHc99FzcjOGuSEg1zO598s9/+XuQ2lMFjL+pap9fgfYB3wD+Cla4aM0GY/uGc4ZZID1PW3zGpn88jeHmU45dLdF/CYrluS0cyyxCGofAjfK9TzGpjP0dwpDExk843H1Pb+gtz1MWySEIGQ8c3QLY/jUt57lmO4Y/R2xWXX9E8kMw5MpbBGy5fmsikdy1UCVqH5qlAoqpbaUMv7HABfgt3D8IPAg8A1jzM5qD0xRqk1QY+/74L5xPTie5KT+DhzXZXgyyVefeIUvPvIijmd4fSKFBdi27/K7ngHjAX7ZnIdfbWOM7/mPTPoducAvqxyZTHPymuiscsuAqbTLx/7lGUK2heP5FTtpxyPjeLiewbLAtiwcz2N0OkUsa6grseBM5VBakwVLPY0xrjHmu8aYK4G3AC8CPxKRj9dkdIpSRQKPtz0Sys1pDeQkQTKOx5d++CL5Vc0e4HmGjojlN1oXP8GLQH9HhO5YiN6OCBNJJ6eoGWxtgMlkhohdeDwhyyISsjg0lWLv6BR7hiY5MO6P0fWM36oRoTceYWQqyQtDkwBLWnCWr0hayUVrysqhnAbuUeBifO9/A/Al4L7qDktpRWpdbRJ4to7nsbYrxoFxP/Qj4lfrHBifAeMnePPbKLoGxpN+Xf7a7hgR28IAt1x+Gjd8ZxeRkGAjvD6Zys4qwLb8/QxNpHCL5NmiYZu2iE3Mltz+AzxAXINYhpTj8sl7n83lJ6694GT+5t1nArJgmWdAseSuyqG0FqUauN8FnA48BNxkjPlVTUaltBz1qDbJFwC0LWF9Txvbzj2Ri89YR3c8zL7RBEFkvlCyS4DRqTQbBzpIZlzSjgcYjBE6YmGsqZRfBZQ10n7MvrDhF2Dv4QS98TCJjJd7LX8Lye5nMuXS1+H3/x2dSnLNN3ewvqet7BLThRRJ1ei3DqU8/w8B08DJwNUiuZ+AAMYY07ANXSqJ1j9Xl8VIJFfyuxhPZFjX08adV53NyFSSuZ7zYF+cq956PF9+7NWCJjuUDff4YSLh169P8JvDCTwD+c59d1uESMhieCKFZYHnHg0HCf5NIWQJnusxPJmis81mPOHOu+kc09VGNGSxdyyBlc05HJr2NYDCtoUIJaWlNbmrBJRq4F5K/qHp0frn6lOuQarkd5G/r0TaxRhDezSU2++WwVU8+NwBfrB7hP6OCGPTabrbQkwkHVbFw8TCIV6fSOJ5hpmMH6L54iMvYgwELXcF6O+MYItw8Rlr+PJjr+HOjuZgAFuEqC1MZm8K4wmXqA0p96jXbwkMT6XojYdB/MRyxvVyYm1h28K2pKQh1+SuEtDyxn0hGkFBshUoR68n+C6AnCTxUr+LfaMJrr/fj2BGQzaHplKMTqdzdfmfue85Lvm7n2RLQBO0RUKs62mjLRLic1ecQXs0zEzaxfMMXbEQY9MphKxipuWPLRLyjfGRRIZDU2m+/Fhx9XPHM0ymvdxzC9/wD3REsC1hVVuYkGXheR6HptJse9sJAMxkb1q98TC2JWUZck3uKgElE76tjE6Ra0M5zXeGJ5Mk0i5j0+nca/n17gvh69lMAsLr4zPc8uBuhidTiPj7kGxwJeN6hG2/2qavPZLbft9YgrBtYTA8/dphUo7H4ZkMliXEwhaTKeFwwgGOhmk8z8P1IGTnp4rnY4ufQM7HZF9/75sH2f7Lg7iex3TaRbJtGNevinPtBZv57EO76WuPcmg6jWegIxYqy5BrclcBNf4LolPk2lHKIMWyHjr4JZFz692L8eieYT797WcZnU6D8b1sK8/gDk8G+/RDJ9MpB8/A6FSKzFFnHJOVYvjGz39DyPIVNC1LODSVyb0/0BlleDKF7/wLlkV2LUDx8c01/CLk9HoGOqMcOOLLNUt2jCJw249exragLWLTE4/QEQuRdjzuvOpsBvvipS41gCZ3FQ37LIROkWvLQhLJScelrz2SbVTiSyL0xiO5RuaFGE9kuHH7zlndqwzzDa6VjaEn0o5f3ZO1vnk908m4JhfLB8FAdpUu9LT5PlTYtljfE+eWy09n+8fOoTtm4xm/TLNcgkTxlb+zgbueeC23b/BvXMd0xQg0gYIZaTwSImxbJB13Vv3+3GtR6HWldVHPvwQ6RW6MaqeBzhjt0RDt0dAsjZuFZmHDk8ls+WXQzJyClrgzFqKnLcL1F59Cb3uE//YvzzCWyGTDQf4GIYGshE9u8Zb/t0d7NMZNl54+S1VzPJHBsor7Vv4NZ/5rqzuifOpdm3njYA8PPneQ8aQfTgpCQUGnLjDzZqQvDE3xZ19/Ore/ICGuRQsrl2r+9tT4l0ErT5EbxXDk5wV8z39+XmAuLwxNMjyZxPF8zz1UxBaPzzh0xSJsXtPFyFSKWNhmXbfvtU+lMoxNZxCrQHAe6GkL848fPItwyJpl+H/+6ihtYZuwJbkZQj5zjX/Y8g38599zJueePMC+0YQfqoLcPtxsBOnmS08FmJUjufaCk7n14T3zymXvXN1Rdhmt0lhU+7enxl8pymLq72vBYmZh44kMtz78PAOdUYYmU7geOB7Ew1ZuEVU+61dFuerOnwFwZCbDRCJD4Lj3xMN0RkMcODIzy/77Ej/CR776NJGQkHE8zj15NU+8PIbrGQ6OJ2mP2ozPOLOOZc1ZMRYSsLItFjui/jklHZfVHVHGptMYgYhAVyzM377vjZx1fC/ArGtRrDhhz9BEwde1aKGxqcVvT42/UpRGrHYqdxYWjL2vI0ZPPEoy4zKTcbnhklP5f+9+Zp4T/9SrRzi+L07GNRzJxsUtyLU9TDlHbxghyzf6GdcwOp32F2rhB4i+8fPfYMlRz358xqE9bJF0DT1tYUK25KSf9x+e8Ruv21au/eJJ/R2AH86KR2zikbZZYa6T+jsXvBZzQ0Gb13QVfF2LFhqbWvz2NOGrFKWR+uUulvyx25YQsoV4xMYYQ3yOslp3WyhXonlwfCZnzEWEQ1NpxqbTWS8fumNWzvAHGGanEjzjzzKCH1fSNaztinHt75/M9z9xHt/7xHncceWb+bsPvInj+tpZ0xllfU+cz11xRu6HHYS5gJwyaBDmKpS8LVacMNgX16KFFUgtfnvayUtZkGr3y61mQmt+q8PN/NWDuzg0mZoVh7ezlv+Y7hgjk2nSrpf1zoWUYwgJHL+6nbHpFIcTTsFk7UKEbWFddxshW+Y1SFlq+8aAud9Hsf01QtJeWRyV+O1pG0dlWVTLcNQimZw/9pdGpnjf7U9gjMGZE/b3lTf98IqIZIs5fQ8+ZPlVNhnXr7fvjNnzVDcXQoDB3jZClsU/fei32LSms+Q2xc5FO261Fsv97S1k/DXso5Rkofr7pVJKOqNSdemzx+43Wplr+ME3/IEjZDzDqniU/s4YFn74R0Ry4Z3FGH7wV/keHE/imYWn7aXOuVAc2JijryvNRzV+ewFVS/iKyCDwVfxuYB5wuzHmiyLSC3wTvzfAq8B7jTGHqzUOpTEZnkz6Haqyi7byE1rFGpaXy1xvKXje3+EnUSeSzrxtHM8vBXWzsfqx6RR/fsEmvv7kXsYSGZxCd4wsxcJAgY6/P48wbDv3hKI/4nJmQbriXKkk1az2cYBrjTHPiEgn8LSIPAxcBTxijPmciHwG+Azw6SqOQ2lAXhiazPXNtUTobffFyzKOt6wSt7lG9PIt6/j2MwdwPY/JlFPQ8INv8AP77gEY+Mrjr9IVi9Abh5HJVFGZBgF64+FZ3buyu8ASi+54iPZIiIvPWFdw+0BoLhKyiEdCRc+5HA0kRSmXqhl/Y8xB4GD270kR2Q2sBy4D3p792F3Aj1Dj31IENfj9HREOTWdwPMPB8RT9HRH+610/xxgY6PK92cWUuM2tjU6kHf72kRf92KYwq0JnLnPfEWBiJsMfn3MCX/zBC4XWd+VY3RlldCo9y/AHeMZv5v7x39tYcPyP7hnm+vt/xesTSSwR1nbH6IyFi56zrjhXKkVN6vxFZAPwJuCnwJrsjQFjzEERKTifF5FtwDaA4447rhbDVGpEfg1+ZyzCy4emMPiLq7zsSlbLgtUdsUWFNl4amSLteHTGwrieIR00Pw/KeQog+AJt7XnhoCBcYwxMJNL0ZfX8YfYNxBI4dlUb3W0RZtLurFlFKJtDcLLTiJsf2MX4TIaPn39y7jPBzSoS8stHPeMvDLOzAm7FzrmVV5wrlaPqCV8R6QC+DXzCGDNR7nbGmNuNMVuNMVv7+/urN0Cl5uTHroPFSx6zxdOGJ1JMJjNl16U/umeYT967g9cnkjw/NMHzQ5McyIaVMq7ByTPa+bcCA3RGbfo7o7mST1v8EJBr4Cv/8RpDEykc189P5EtE2CJYIiTSDlNzwklO9iaWf7wv/fBF9o0mcp8JboLxSIi1XbGcaF3a8TSco1Sdqnr+IhLGN/z/YowJmr4PicjarNe/Fhiu5hiUxiM/du24JqdkGSjfB8byz9+5ibdvXlN2uCcSsjimK8a+wzOAIWwLboF4Ta4nblY++cjMUeNt5Qm4wdEFVgZIO96szloG2H9khtXtEcQSwvhGP/+IgRRzyLbIuB57hiZyssv5N8Gu7OrftONx90feUrY0s6Islap5/uJLD94B7DbGfCHvre3Aldm/rwS+U60xKI3LeZsHuO+j5/DlK7fyiXduyr0ehFwsSxjsbV+UlEM0ZBML20RsIWwJve2Ro9685S+26mqzc9o6+UtcHON7+gst3mqP+NtGQxZh28IS6O+IcvX5J/uSDpYQDVtEsi0VrexxQ7bffyBfbgHmr8oFuOXy09XwKzWhmp7/OfgN4J8TkR3Z1/4H8DngXhH5MLAXeE8Vx6A0MEHseqAzxteefI3RqVRWrphZOjeFyC/nzPegg9JR18DoZArX+DeUtV1tIH7rQ88srk4/YDodLLX3iNiWL7NsCb+7cTW97ZHc+E1Wp+fdZ63n9p+8Qsb1Df/V79g4z7AHCdyg09hC56wolaSa1T6PMTu8ms/51TqusvLojof56z88kxu378L1PGzL4qZLi8e8C9XEX3vBydywfSeHp9O5WHvgxAfhGQ8/vr9UghmDayDteoQs4boLT+HlQ1O5kJXjGj9hC5x9Qh/vf/PxPLN3jFXxKG8c7Cm43+Wua1CUpaDyDk3IStVxKWfchSQOJmYyREI2B7MtD4O8QdAAxe+q6FfQCELG9YqV7Oe2m0tn1GImY3LlnBbwlxefwrt/a5ArbnscgL1jCYzxE8ODvb6H7+vsP5/bT75hD3oLf/LeZ4mEVLJBqTwq79BCPLpnmCtue5w/+/rTXHHb4zy6pz759KXIM5SzlH2uxEHIshidTudi6nMNt5sVxfIrdSwQ6OuIUAgr2zf3A28+lpAlvhqoJXzoLYMkHb+BTCxsEbYEyxI2renkpZGp7LaCIIRtG7JVQK5n+OxDv85JWABcf/+v2DeayH1P19yzg/1HErmOY80k2aCtIxsb1fNvIhql+Uo1BdtiIZu045FxM4iAky3N6YyGGZlM5Tz3/JtAdzzMRNLF8TwsETqjYQ5Pp5nbW72nLUxbxOa/vX0T524aYO/YNG/b1E84ZPGD3SOM5W1jgL/8t+ewLSHleL5MtJA7hmf8PrvBdzExk+HgRBLjGd7//z2B6xm62sJ0xsK8PpFk/5EZNg2EcjexlS7Z0Cgd4JTiqOffRDSC8Fcpwbbl8OieYa6682eMTafYO5bgtdEE+8dnsslXv7l5YMzDtrCmM8Karigd0TCr2vyb36p4hETG8bX95+x/LJHhyEyG/+fvf8LV3/wFn3/4eT781ad4YWiSeMTm2FVtDK5q8xeGCXTGwkRCFsYYXI9ZxwC47sJTsC1/HcDBiaQfErL8WcHodJqQ5VcFretuAyhrXcNK8Kar+X9AqRzq+TcRjSD8Va0ORIFB8TxIpGeLrE2nXY4k0nREw/R3RHA8Q2csRNi2uOGSU9mSlUOIhWy+v+sgn33o17lt7Wxr3qA711Qyg+NBxJZsM5cUf/295/nUuzZz68N7crX+A9nQUTRkE48YPv+eM+mMhYmFbJKOm8tbtEdtrr//VxjPN/xru2O5m/JkMkNnzL8mx3S18cX3b+Gk/o5lib81Ao3YAU6Zj3r+TUSxbk61/MFVqwNRYFCKp2rhP//2cbRHQ3S3hTHGT7aet3kgV046MpXkjsdeQ8RfdCUc7ckelJjmP2b/JO26bFrTwX0fPYer3roBMIxMpXlxZIrRqSQifnvFTWs6GeyLz8pbnLd5gLs/8haO6Y5x7Crfww/kLPYfmWHP0CQHxmfIuC6TycyCHv9K8aZXcge4VkKNf5MRLJ76pw/9Fvd99Jyae4bVugEFhsPKtVmZjYVw53+8SiRk0RnzY/e3Pvw844lMLrn68bt/wdCE3zfXGAjbR//7G2NY2x3LJQFc1yOV8ci4huGJFC8MTQJw98/2sqYrhpWt5x+ZSnPtBScveH6DfXFuufx0XI+ckum67hiWgGA4cXUH7dFQLhlciEYI6ZVLIzghSmk07NOE1FP4azyRYV1PG3dedfas8MdyyZeE6GkLc2TmqMdrCRyaTtPbHqYnfjQcM51yeGlkkpsf2MVM2uHQVMb39LPlzSK+9MJ7fms9T7x8GEt8hc5EymE8T6vHM4a/enA3//BB33PvbY/S3RYh43pkXK+szlznbR7gC+8Ncc09O+iMhcm4Hpb41UdTqQyHptJ4xvDBLz/JLZefXlLLP5F2yLgesdDS1y1UE1UfbXzU+CtlUU4NfqGY9FJbFhYi36D8cPcQf/395xEMtmXR2x7m0FSaaMhGRLDEX30LfsnloWm/OihiW6RdD2MMA50xrr/4FC46c92s8/vlb47wx//8s+w+/JW8h6ZSTGUlGAID7Hj+MYqFM+Zes5P6O4mErFkVQRgYmUxj8NtHRkJWSS3/4YkkY4k0qzuiXHXnzxo29q/qo42NGn+lJOUkGmtVZppvUL7x832EbV9nx7aEiZlMVtTN54o3reOk/g5cz2CMf5PwMERsi9WdEf7+A2dx1vGrcvuFIIxytConkGsA6IjaZTdTKXbNbrjkVD797WcZnfY9fQFc/JvI2q4Y8UhoQS3/O1d38MEvP8lxvfEFG78oSinU+CsLUq5Rr3SFR6mZRuBtJzP+itiM6zGd9nILsDxj+D/PHuTP37mZ6y58A9d8c0fO4+7vjBAL2zkdnfFEhgefO8DtP34Z2xJczxdxm067fn4AQ197hJP6/URuKS2eha7ZlsFVxMIh1nXbtEdDJNIOvzmcYF13G52xcMnkaNJxcx2/KnGdldZFjb+yIOUa9UqWmZYz03j8xUOMTadnNWARfAVN8FfcBhLKF525DhA++9Bu7OzK3cBjf3TPMDdu38X+I36idV13G9GwRVskRGfMrxoK2cJNl56WO99SWjwLXTPwlT7jEX9fnbEwqztiZByPw9PpeceaSyOU8yrNgRp/ZUHKTTRWqr9s4DUDubj43JnGvz97gKvv2YHjmZwENAiOZ8i4LmHbniehfNGZazln4+p5jd1vfmBXTo8H4OBEko39HbSFbT7/njfSGQvNmn2UM75yDHT+e2CwbTsbXiredayS11lR1PgrC7KYRGMlKjyGJ5Mk0m6ubSL4K2aDmcZ4IpNbpBWYSdfLavXHjt6cAK566/EkHZfxRCaXKygUqmqP5v0MDEynHUKWFFxwVWp8+dfsxu07SaR8w//Rt5/ExEyGpONy7QX+grHplOPH/UWIha2ycyX51zlYVBacY71YqWKCrYwaf6Uki0k0LrfCIxayOTSVAnzRNsfzGJ1O5WYaw5N+j1tLBAeT0/LxjKG3PcJf/P7J/OOPXsLxPO564jUefO514hG7YOgo8MQdz2NtdyxXgy8c9abnGrVS45uNkMxkODLj8Dff28ON23fS1x6hPRri2gtOZtOaTiaTDv/9W79cdK6kOx5uGCnolbLyWJmNLvJSipKvI1Mo0ViNRUZJx6WvPZLrZysi9MYjJLOrRQc6Y9iWsLoj7DdJz27X2x7hU+96A3c98Rrt0RATSf/zgYd+8wO72DeamKWLk78YyRJhfU+cmy49je987Hc5b/PALIXUy/7hMe7+6WuMTCUXHF9w3fyEL0xkPf/DiQzGGMam/WPf+vDzDHTGcgnjxa6GbZQVv40yDmXxqOevFGSuN3ftBScDkEg7uVh3NRKNA50x2qMh2qOho7XwHD1OfhhqfY9NyvG4bMta/ui3N+QMsJXVdg5ZFm42nj+ZdPjgl58kku3AHninxUJV+UYtlfE4MD7Djdt3sjYrwja4qq3g+ODoDTEYR4DrgYNHIu3SFrZ5aWSSzlg4p/m/mBh+o+jnNMo4lMWjxl+ZR6FSxVsffp4/OO0Ybv/Jy7nPXf2OjRX/gecbd9+znm8MA4MdlGf+YPcwP9g9nLtBecbMkld2XMPodKpoyKpQqOqlkUnSjkc8EuLgRNKXcyBbVZT1+o34PX+3nXvCrG2DG0FwY8i4R5vACzAymaS3PcIn732WQGHi2gs2s2lNR9kx/FJJ5VrF4LX6aOWinbyUebwwNMmffPWpeQuoPGOIhKxZHm+lO04FRmuuOuZc9o0mcp58YNBdz+SSqVNJh7FEmr72KGHbL/vszzNI0ymHf/rQbxVcgZxf/unrAJlcCelJ/R0k0g5//s5NHJpK8bUn9wLgeobrLnxDtqz06MzpyHSa0UQGO3vN/H4yQk88TG97ZFb3rmDsAXNj53MNenAMk21YE3y+1jH4YuNQ6s9CnbzU+Cvz+PdnD3DNN3cAfuhidUcES/z6+ECCGBY2oEuhXKP16J5hrr//V7w+kUTEXxnb1RbOjWegMzbrBhIL2Vx1589mzWSKtUrMv6m4nmH/4RkyniFkwbGr4mRcj+HJFMd0xXh9Ikln1GYq7eVWAX/xfW/iojPXAkfbNF5zzy+xLD+Zncz4TWXCtjXrWgY317aIXXCMxa7N3BtCoTaXtWgLqdU+jYm2cVTKZjyR4daHn6e/w09qesYwPJnimvM3ZbtWVUemdzyR4cbtu3BcQzRkF00cBiGpYAZijOHgRJJE2smNJ2gHGcgrD/bFZ6lMph2PD//uCfP2e/dPX+N9tz/B6xNJfpOVidi0ppPVHREGOttwPcPQRJK+9jCxsI3nGcYSjj8zsPyf0mcf2j0roXzW8b3ccvlphCx/9hGyhb+86NR519IzR7t+weyEun9tduJ4869NIFcdfO6lkSnSjpcbT63UP8tpwak0FhrzV2YRGIm+jhg98SgZ1yPteLxxsGfZi4sW8g4ffO4A+48kcout1nb7sslzE4fB+OKREGu7YxwcT+J6hrTjccvlpwN+2GruMWbnCV7hjsf8fzdccioA//M7OzlwZIYgMu9hODie5NhVbXS3hfnoeSfx+e/vQRDGEg6Oa3K9ABzXAF5udjR3zIWSyu1RO3ctPQNXvXUDX3tyb8HYuX9tZhD8bvRru2K54+SXeybSLq7ncTiR4fWJJOt72oiELI3BKwVR46/MYm4Cz/F8eYOBzhib1nQueRHXQiGd8USG23/88qzP7z8yw/qe+DyjlT++zlgY2xLSjsfdH3kLLx+a4orbHi94jIA7HnuFSOhoSOTG7buYyTiMTqVwPd/0W/gx/uCmct2Fb+DWh5+nPRricMIPzxyazmABHv42jms4pjtSVOVzblJ5y+Aq/ubdb+QXew/ztSdf4+6f7SWZcUg5ghM+mugGuP3HrwDkmsscGJ9hfU8bsZCdS8yHLIu9Y75ExZrOGEOTydw1vOlSXQGszEfDPsosSjXiWMr0vlQteLBwa113G3ly+2w794R5xyk0vo++/SSAWRVKjme4cfvOWWGjQmWJaddldCqFnQ2TCL5B720Ps7Y7xt0feUsupxHMNgJsW+jviBC2JWeAy5kNBesHPnnvDj733V+TdlzikRBdbWGiIYvPv+eNuUY8/rWB9T1+iWmQodt27om50tZoyM72BxAEoS1is2mgkzWdMb7w3jM1+aoURD1/ZR6VbsRRqhY88JSjYYuN/R1Mpx0EuPiMdQuOLz+Ec9uPXiLjekRDNq9OJCCrxvngcwf44G8fDxQpS8zbr235+kAAI5MpPvHOTQz2xXM3kGC2cWyPsO9wgvU9vhJnV1s4N/voagsXDDsFBLkNv4LIP/qh6Qw98ah/03L9/sNzdYIiIYuT+juyITeZdW1SjkvYtnIVWGHbX3kcDVuc1F+5fgpKc6Gev1KQSibwSvV0zffmkxmXkLWwsmVAEMKJR0JEQhaHplIcGJ/J1uL7n7n9x68UXNEbzBr+8qJTWd0RxTMGN2v4LYHB3jj37ziQS6rmb2dZcM35G7EtYTrb4OWWy0/PhZ3+7OtPc8Vtj/PonuF5Yw5yG69PJNk35ieVjfElqQsl0eddG1tyYZy5763uiNLXHsnJXKvgm7IQ6vkrVWeuEqVnDNvOPXHWZxY725g7m4hHQvS0RTgyk8H4eVHW97RhCbMSsMWSr//jvucYmvRLRwNt/fzZSaHt/svvnJB7DswrsZyrfZSf2/CyK49d15eISDt+JVAhg73QtZn7XnBttORSKYUa/xqw0mqgqzHehaptgpj0YkThCoVwOmIh4tEQgq/U6XgermfmJWDnHue8zQPcs+135i0aK+SFFxOxe+a1w6QdL1e7X0jmYHgyScrx1wT4wqN+x7AP/+4G3nXa2oIqosWOvdB7K+H/mFJ/NOxTZfLFwYqFAhqJao83P1STn/jNF5Erh0IhnJsuPY2/uuw0QrYsOvQx2BfPlYoWSnQvxKN7hvnkvTt4fSLJC8OTTCYzBW8egSKoiBANWVjirwz+9+de579/65fs2He4rHNXlEqgnn8VqVVf20pR7fEWS/w++NwB7njsldznivUInjsbKRYOWWqyeimJ7vxFZ+u62zgwPlO0xDJQLB1LZPCMrwtkCcTCNiI09P8NpflQ419FVpriYbXHWyhU4xm4/ccvEwkVb2ay0BqBQuGQ5fQUWOy2+dcsGvL78k7MZPjCe8/krON7Z302X7E04xoOHElgWUf1kxr5/4bSfGjYp4qUqnJpNKo93kKhmm3nnlBU1gCqoxe/2BDTQgx0xkg7HoemUqQdb8ESy+D8wff4RYTV7eGc1ENwrSs5PkUphnr+VWRulUuj91utxXgLVafc8dgrRSWBKz0bqbTi5VefeIUDWYmJg+NJetpCfPH9byqrBeMLQ5PzdPwbpTuX0vyo8a8ylV4wtRiWUrVT6/GWuuFUUi9+32iC6+//1ayKnuXE2feNJvjSD1/EEgiHLVzXMJVyOXF1R8lzDtZRnLOxf1HloopSKdT414DlxKCXylI93GqXpRYbV7EbTqVmI4EM9MHxGUSEY7pi9MQjy5pF7BmaAMgpaIayfQP2DE0w2Bcvax/5/zdeGJoEVk6OSFnZVM34i8hXgEuAYWPM6dnXeoFvAhuAV4H3GmO0vq3CLLVqp9pNQEqNq5xQyVJuSsFxM66Hk62v/83hGTKuR3s0tOScxuY1XYDfMSxo5i5y9PXFol2xlFpSzYTvncAfzHntM8AjxphNwCPZ50qFKRQnL6XpvpjE6lITkksZV8By5CaGJ/2Y/FgiQ9gSBF8g7dBUmmsvOHnJXvVgX5yr37ERgIy/aour37GxbK9/LqVE9RSlklTN8zfG/FhENsx5+TLg7dm/7wJ+BHy6WmNoVZbiQZabWF3O7KBenu1AZ8zvuZttx2hZBs8Y1nbHlt2F7OPnn8zlW45lz9AEm9d0lTT8hTpv5T+vZ45IaS1qHfNfY4w5CGCMOSgiRa2GiGwDtgEcd9xxNRpec7CUOHk5hnm5i8DqVf3UHQ9z3YVv4Jpv7sg1dR/ojBK2rYrceAb74mV5+3NvnJdvWcf9Ow7kngc30nrkiJTWo2ETvsaY24Hbwe/hW+fhrDgW60GWY5grUXZZL8/Wb6wufPah3diWr79fy5DK3BtnIu3wpR++yHG98YpUHinKYqm18R8SkbVZr38t0NhCN3WkElU3i/UgSxnmSoVt6uXZXnTmWs7ZuLro+VWz0mnujdMSAUOubaVW9ii1ptbGfztwJfC57ON3anz8FUG1q24WopR65EpatFaIYue32Gu+2BvF3BunZwwIuQYsWtmj1BoxpjoRFRH5Bn5ydzUwBNwI3A/cCxwH7AXeY4wZK7WvrVu3mqeeeqoq42w0xhOZeQt9XM9w30fPaRgjWykPeTH7WcoxS20TvB8L2XzoKz/DGDNLCrrYNV/qzTnYzhi/2UwQ8w+eV+omv9IkxJXqISJPG2O2FnqvmtU+Hyjy1vnVOmYzsBLE4OZ6z0sxNosxoEsxtqW2yX9/PJHm8IxDyPJDMGu7Y1giBa/5cpLepRrCVOL7reesUVlZqLBbg7HSxOCWov+/2DUFixV2K7XNXAN+ZCbjl4Jm26PvPzKDZwpf8+WsVYD56xWWs35hseetKPmo8W8wVtJCn6Uam8UY0KUY21Lb5L+fcT0ssbAtMMb/B7Dt3BMKXvNGvjkv98aktBYNW+rZyjTqQp+54Z2lhqgWUzW0lAqjUtvkvx+2LQx+P90TV3eQdFwEuPiMdQX33chJ71jIJu14gFO0FaWiBKjxb1AabaFPoVjylsFVwOJLPxdjQJdibEttk/++4/rdtUR8UbZQGfX/lbo5VzIxG3w/Gdfj9Ykkfe1R2qN2w9yYlMajatU+laSVqn0WQ62qOhaqQAr055dSsdIo1T75vQNqNdOqZGJ27veTSDukHY+7P/KWJesMKc1BXap9lOpSy6qOhcI7y/GCFzO7WcpMqNQ2c9+v12rf5a7unfv9xCMhjHFIZvMSilIITfiuQGpd1VEqyVnJipVWoNKJ2UZOQiuNixr/FUitqzpWUgVSOdS7R26ljXWzfT9KbdCwzwqkHtLIjVqBtFgaYRFUNSqGmuX7UWqHJnxXKHOlAnQlZ2kaTTpDZRiUaqMJ3yZEPb3FU+66hFoZ5UYr51VaCzX+Kxg1HoujnHBZI4SFFKUWaMJXaRlKJUZVG0dpJdTzV5qGcsI1C4XLVoKiqqJUCjX+SlOwmHBNsXBZvRrMK0o90LCPsuKpVLhG6+WVVkI9f2XFU8lwjVZRKa2CGn9lxVPpcI1WUSmtgIZ9lBWPhmsUZfGo5680BRquUZTFocZfaRo0XKMo5aNhH0VRlBZEjb+iKEoLosZfURSlBVHjryiK0oKo8VcURWlBVkQzFxEZAV6r9zhKsBo4VO9B1AA9z+ajVc61Fc/zeGNMf6EPrQjjvxIQkaeKdcxpJvQ8m49WOVc9z9lo2EdRFKUFUeOvKIrSgqjxrxy313sANULPs/lolXPV88xDY/6KoigtiHr+iqIoLYgaf0VRlBZEjX8FEBFbRH4hIg/UeyzVREReFZHnRGSHiDxV7/FUCxHpEZFvicivRWS3iPxOvcdUaURkc/Z7DP5NiMgn6j2uaiAify4iO0XkVyLyDRFp2qbMInJN9jx3lvo+VdK5MlwD7Aa66j2QGvB7xphmXyjzReC7xph3i0gEiNd7QJXGGLMH2AK+8wLsB/6tnmOqBiKyHrgaONUYMyMi9wLvB+6s68CqgIicDvwJcDaQBr4rIg8aY14o9Hn1/JeJiBwLXAx8ud5jUZaPiHQB5wJ3ABhj0saYI3UdVPU5H3jJGNPoq+iXSghoE5EQ/o38QJ3HUy1OAZ40xiSMMQ7wKPCfin1Yjf/y+VvgU4BX53HUAgN8X0SeFpFt9R5MlTgRGAH+ORvK+7KItNd7UFXm/cA36j2IamCM2Q98HtgLHATGjTHfr++oqsavgHNFpE9E4sBFwGCxD6vxXwYicgkwbIx5ut5jqRHnGGPOAi4EPiYi59Z7QFUgBJwF3GaMeRMwDXymvkOqHtmw1qXAv9Z7LNVARFYBlwEnAOuAdhH5o/qOqjoYY3YD/xt4GPgu8EvAKfZ5Nf7L4xzgUhF5FbgHeIeIfL2+Q6oexpgD2cdh/Pjw2fUdUVX4DfAbY8xPs8+/hX8zaFYuBJ4xxgzVeyBV4p3AK8aYEWNMBrgPeGudx1Q1jDF3GGPOMsacC4wBBeP9oMZ/WRhjrjPGHGuM2YA/df6hMaYpvQoRaReRzuBv4Pfxp5lNhTHmdWCfiGzOvnQ+sKuOQ6o2H6BJQz5Z9gJvEZG4iAj+97m7zmOqGiIykH08DriCBb5brfZRymUN8G/+74cQcLcx5rv1HVLV+DjwL9mQyMvAH9d5PFUhGxe+APjTeo+lWhhjfioi3wKewQ+B/ILmlnn4toj0ARngY8aYw8U+qPIOiqIoLYiGfRRFUVoQNf6KoigtiBp/RVGUFkSNv6IoSguixl9RFKUFUeOvKGUgIm5W/fJXIvJ/RKQn+/oGETEi8ld5n10tIhkR+fu6DVhRSqDGX1HKY8YYs8UYczr+ysmP5b33MnBJ3vP3ADtrOThFWSxq/BVl8TwBrM97PgPsFpGt2efvA+6t+agUZRGo8VeURZDVvj8f2D7nrXuA92clvl2aVzZYaRLU+CtKebSJyA5gFOjFV07M57v4UgkfAL5Z26EpyuJR468o5TFjjNkCHA9EmB3zxxiTBp4GrgW+XfPRKcoiUeOvKIvAGDOO3xbwL0QkPOftW4FPG2NGaz8yRVkcavwVZZEYY36B3yjj/XNe32mMuas+o1KUxaGqnoqiKC2Iev6KoigtiBp/RVGUFkSNv6IoSguixl9RFKUFUeOvKIrSgqjxVxRFaUHU+CuKorQg/z9r+zDXtYJ/JwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "housing.plot(kind=\"scatter\",x=\"RM\",y=\"MEDV\",alpha=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing = strat_train_set.drop(\"MEDV\", axis=1)\n",
+    "housing_labels = strat_train_set[\"MEDV\"].copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing  Attributes"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "To take care of missing attributes, you have 3 options\n",
+    " 1. get Rid of the missing data points\n",
+    "    a=housing.dropna(subset=[\"RM\"])\n",
+    "    a.shape\n",
+    " 2. Get rid of the whole attribute\n",
+    "     housing.drop(\"RM\", axis=1)\n",
+    " 3. Set the value to some value(0,mean or medium)\n",
+    "    #median=housing[\"RM\"].median()\n",
+    "    #housing[\"RM\"].fillna(median)\n",
+    "    #housing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(404, 13)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median=housing[\"RM\"].median()\n",
+    "housing[\"RM\"].fillna(median)\n",
+    "housing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SimpleImputer(strategy='median')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.impute import SimpleImputer\n",
+    "imputer = SimpleImputer(strategy = \"median\")\n",
+    "imputer.fit(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.86735e-01, 0.00000e+00, 9.90000e+00, 0.00000e+00, 5.38000e-01,\n",
+       "       6.21000e+00, 7.82000e+01, 3.12220e+00, 5.00000e+00, 3.37000e+02,\n",
+       "       1.90000e+01, 3.90955e+02, 1.15700e+01])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imputer.statistics_.shape\n",
+    "imputer.statistics_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>CRIM</th>\n",
+       "      <th>ZN</th>\n",
+       "      <th>INDUS</th>\n",
+       "      <th>CHAS</th>\n",
+       "      <th>NOX</th>\n",
+       "      <th>RM</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>DIS</th>\n",
+       "      <th>RAD</th>\n",
+       "      <th>TAX</th>\n",
+       "      <th>PTRATIO</th>\n",
+       "      <th>B</th>\n",
+       "      <th>LSTAT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "      <td>404.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>3.602814</td>\n",
+       "      <td>10.836634</td>\n",
+       "      <td>11.344950</td>\n",
+       "      <td>0.069307</td>\n",
+       "      <td>0.558064</td>\n",
+       "      <td>6.279908</td>\n",
+       "      <td>69.039851</td>\n",
+       "      <td>3.746210</td>\n",
+       "      <td>9.735149</td>\n",
+       "      <td>412.341584</td>\n",
+       "      <td>18.473267</td>\n",
+       "      <td>353.392822</td>\n",
+       "      <td>12.791609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.099383</td>\n",
+       "      <td>22.150636</td>\n",
+       "      <td>6.877817</td>\n",
+       "      <td>0.254290</td>\n",
+       "      <td>0.116875</td>\n",
+       "      <td>0.712983</td>\n",
+       "      <td>28.258248</td>\n",
+       "      <td>2.099057</td>\n",
+       "      <td>8.731259</td>\n",
+       "      <td>168.672623</td>\n",
+       "      <td>2.129243</td>\n",
+       "      <td>96.069235</td>\n",
+       "      <td>7.235740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.006320</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.740000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.389000</td>\n",
+       "      <td>3.561000</td>\n",
+       "      <td>2.900000</td>\n",
+       "      <td>1.129600</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>1.730000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.086963</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.190000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.453000</td>\n",
+       "      <td>5.878750</td>\n",
+       "      <td>44.850000</td>\n",
+       "      <td>2.035975</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>284.000000</td>\n",
+       "      <td>17.400000</td>\n",
+       "      <td>374.617500</td>\n",
+       "      <td>6.847500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.286735</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.900000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.538000</td>\n",
+       "      <td>6.210000</td>\n",
+       "      <td>78.200000</td>\n",
+       "      <td>3.122200</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>337.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>390.955000</td>\n",
+       "      <td>11.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.731923</td>\n",
+       "      <td>12.500000</td>\n",
+       "      <td>18.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.631000</td>\n",
+       "      <td>6.630250</td>\n",
+       "      <td>94.100000</td>\n",
+       "      <td>5.100400</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>666.000000</td>\n",
+       "      <td>20.200000</td>\n",
+       "      <td>395.630000</td>\n",
+       "      <td>17.102500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>73.534100</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>27.740000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.871000</td>\n",
+       "      <td>8.780000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>12.126500</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>711.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>396.900000</td>\n",
+       "      <td>36.980000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            CRIM           ZN       INDUS        CHAS         NOX          RM  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
+       "mean     3.602814   10.836634   11.344950    0.069307    0.558064    6.279908   \n",
+       "std      8.099383   22.150636    6.877817    0.254290    0.116875    0.712983   \n",
+       "min      0.006320    0.000000    0.740000    0.000000    0.389000    3.561000   \n",
+       "25%      0.086963    0.000000    5.190000    0.000000    0.453000    5.878750   \n",
+       "50%      0.286735    0.000000    9.900000    0.000000    0.538000    6.210000   \n",
+       "75%      3.731923   12.500000   18.100000    0.000000    0.631000    6.630250   \n",
+       "max     73.534100  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
+       "\n",
+       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
+       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
+       "mean    69.039851    3.746210    9.735149  412.341584   18.473267  353.392822   \n",
+       "std     28.258248    2.099057    8.731259  168.672623    2.129243   96.069235   \n",
+       "min      2.900000    1.129600    1.000000  187.000000   13.000000    0.320000   \n",
+       "25%     44.850000    2.035975    4.000000  284.000000   17.400000  374.617500   \n",
+       "50%     78.200000    3.122200    5.000000  337.000000   19.000000  390.955000   \n",
+       "75%     94.100000    5.100400   24.000000  666.000000   20.200000  395.630000   \n",
+       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
+       "\n",
+       "            LSTAT  \n",
+       "count  404.000000  \n",
+       "mean    12.791609  \n",
+       "std      7.235740  \n",
+       "min      1.730000  \n",
+       "25%      6.847500  \n",
+       "50%     11.570000  \n",
+       "75%     17.102500  \n",
+       "max     36.980000  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X= imputer.transform(housing)\n",
+    "housing_tr =pd.DataFrame(X,columns = housing.columns)\n",
+    "housing_tr.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scikit-learn Design "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Basically, there are 3 types of objects:\n",
+    "1. Estimators - it estimates some parameter based on a dataset. Eg. imputer. It has a fit method and transform method.Fit method -Fits the dataset and calculates internal parameters\n",
+    "\n",
+    "2. Transformers - transform method takes input and returns output based on the learning from fit(). It also has a convenience function called fit_transform() which fits and then transforms.\n",
+    "\n",
+    "3. Predictors - LinearRegression model is an example of predictor. fit() and predict() are two common functions. It also gives score() function which will evaluate the predictions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Primarily, two types of features scaling methods:\n",
+    "1. Min-max scaling (Normalization)\n",
+    "   0 < (value-min)/(max-min) >1\n",
+    "   Sklearn provides a class called MinMaxScaler for this\n",
+    "   \n",
+    "2. Standardization\n",
+    "   (value-mean)/std\n",
+    "   Sklearn provides a class called StandardScaler for this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "my_pipeline= Pipeline([\n",
+    "    ('imputer',SimpleImputer(strategy=\"median\")),\n",
+    "    ('std_scaler',StandardScaler())\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "housing_num_tr =my_pipeline.fit_transform(housing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(404, 13)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_num_tr.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting a desired model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "#model = LinearRegression()\n",
+    "#model = DecisionTreeRegressor()\n",
+    "model = RandomForestRegressor()\n",
+    "model.fit(housing_num_tr, housing_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_data = housing.iloc[:5]\n",
+    "some_labels = housing_labels.iloc[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prepared_data = my_pipeline.transform(some_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([22.508, 25.587, 16.363, 23.376, 23.391])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(prepared_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[21.9, 24.5, 16.7, 23.1, 23.0]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(some_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "housing_predictions=model.predict(housing_num_tr)\n",
+    "lin_mse= mean_squared_error(housing_labels, housing_predictions)\n",
+    "lin_rmse=np.sqrt(lin_mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.3529252128712854"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1631531338870584"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_rmse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "scores = cross_val_score(model, housing_num_tr, housing_labels,scoring=\"neg_mean_squared_error\",cv=10)\n",
+    "rmse_scores = np.sqrt(-scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.79289168, 2.69441597, 4.40018895, 2.56972379, 3.33073436,\n",
+       "       2.62687167, 4.77007351, 3.27403209, 3.38378214, 3.16691711])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rmse_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_scores(scores):\n",
+    "    print(\"scores: \",scores)\n",
+    "    print(\"Mean: \",scores.mean())\n",
+    "    print(\"Standard deviation: \",scores.std()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores:  [2.79289168 2.69441597 4.40018895 2.56972379 3.33073436 2.62687167\n",
+      " 4.77007351 3.27403209 3.38378214 3.16691711]\n",
+      "Mean:  3.3009631251857217\n",
+      "Standard deviation:  0.7076841067486248\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_scores(rmse_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving Model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['HousingPricePredicter.joblib']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from joblib import dump, load\n",
+    "dump(model, 'HousingPricePredicter.joblib')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing the model on test data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = strat_test_set.drop(\"MEDV\" , axis=1)\n",
+    "Y_test = strat_test_set[\"MEDV\"].copy()\n",
+    "X_test_prepared = my_pipeline.transform(X_test)\n",
+    "final_predictions = model.predict(X_test_prepared)\n",
+    "final_mse = mean_squared_error(Y_test, final_predictions)\n",
+    "final_rmse = np.sqrt(final_mse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.948844070638726"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_rmse"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Model Usage.ipynb b/Model Usage.ipynb
new file mode 100644
index 00000000000..9a630b2068a
--- /dev/null
+++ b/Model Usage.ipynb	
@@ -0,0 +1,67 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from joblib import dump, load\n",
+    "import numpy as np\n",
+    "model = load('HousingPricePredicter.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = np.array([[-0.43942006,  3.12628155, -1.12165014, -0.27288841, -1.42262747,\n",
+    "       -0.24141041, -1.31238772,  2.61111401, -1.0016859 , -0.5778192 ,\n",
+    "       -0.97491834,  0.41164221, -0.86091034]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([22.508])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.predict(features)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data.csv b/data.csv
new file mode 100644
index 00000000000..73a9897589c
--- /dev/null
+++ b/data.csv
@@ -0,0 +1,507 @@
+CRIM ,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
+0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
+0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
+0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
+0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
+0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
+0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
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+5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
+5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
+5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
+2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
+2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
+3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
+5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
+4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
+0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
+0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
+0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
+0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
+0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
+0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
+0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
+0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
+0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
+0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
+0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
+0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
+0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
+0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
+0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
+0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
+0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
+0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
diff --git a/different model output b/different model output
new file mode 100644
index 00000000000..c45cf1dff2e
--- /dev/null
+++ b/different model output	
@@ -0,0 +1,13 @@
+Model Output
+
+Decision tree :
+    Mean:  4.207225187158817
+    Standard deviation:  0.8745567547159991
+
+linear Regression :
+    Mean:  5.030437102767307
+    Standard deviation:  1.0607661158294834
+
+Random forest regressor:
+    Mean:  3.3009631251857217
+    Standard deviation:  0.7076841067486248
\ No newline at end of file
diff --git a/housing.data b/housing.data
new file mode 100644
index 00000000000..f83ac56475f
--- /dev/null
+++ b/housing.data
@@ -0,0 +1,506 @@
+ 0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00
+ 0.02731   0.00   7.070  0  0.4690  6.4210  78.90  4.9671   2  242.0  17.80 396.90   9.14  21.60
+ 0.02729   0.00   7.070  0  0.4690  7.1850  61.10  4.9671   2  242.0  17.80 392.83   4.03  34.70
+ 0.03237   0.00   2.180  0  0.4580  6.9980  45.80  6.0622   3  222.0  18.70 394.63   2.94  33.40
+ 0.06905   0.00   2.180  0  0.4580  7.1470  54.20  6.0622   3  222.0  18.70 396.90   5.33  36.20
+ 0.02985   0.00   2.180  0  0.4580  6.4300  58.70  6.0622   3  222.0  18.70 394.12   5.21  28.70
+ 0.08829  12.50   7.870  0  0.5240  6.0120  66.60  5.5605   5  311.0  15.20 395.60  12.43  22.90
+ 0.14455  12.50   7.870  0  0.5240  6.1720  96.10  5.9505   5  311.0  15.20 396.90  19.15  27.10
+ 0.21124  12.50   7.870  0  0.5240  5.6310 100.00  6.0821   5  311.0  15.20 386.63  29.93  16.50
+ 0.17004  12.50   7.870  0  0.5240  6.0040  85.90  6.5921   5  311.0  15.20 386.71  17.10  18.90
+ 0.22489  12.50   7.870  0  0.5240  6.3770  94.30  6.3467   5  311.0  15.20 392.52  20.45  15.00
+ 0.11747  12.50   7.870  0  0.5240  6.0090  82.90  6.2267   5  311.0  15.20 396.90  13.27  18.90
+ 0.09378  12.50   7.870  0  0.5240  5.8890  39.00  5.4509   5  311.0  15.20 390.50  15.71  21.70
+ 0.62976   0.00   8.140  0  0.5380  5.9490  61.80  4.7075   4  307.0  21.00 396.90   8.26  20.40
+ 0.63796   0.00   8.140  0  0.5380  6.0960  84.50  4.4619   4  307.0  21.00 380.02  10.26  18.20
+ 0.62739   0.00   8.140  0  0.5380  5.8340  56.50  4.4986   4  307.0  21.00 395.62   8.47  19.90
+ 1.05393   0.00   8.140  0  0.5380  5.9350  29.30  4.4986   4  307.0  21.00 386.85   6.58  23.10
+ 0.78420   0.00   8.140  0  0.5380  5.9900  81.70  4.2579   4  307.0  21.00 386.75  14.67  17.50
+ 0.80271   0.00   8.140  0  0.5380  5.4560  36.60  3.7965   4  307.0  21.00 288.99  11.69  20.20
+ 0.72580   0.00   8.140  0  0.5380  5.7270  69.50  3.7965   4  307.0  21.00 390.95  11.28  18.20
+ 1.25179   0.00   8.140  0  0.5380  5.5700  98.10  3.7979   4  307.0  21.00 376.57  21.02  13.60
+ 0.85204   0.00   8.140  0  0.5380  5.9650  89.20  4.0123   4  307.0  21.00 392.53  13.83  19.60
+ 1.23247   0.00   8.140  0  0.5380  6.1420  91.70  3.9769   4  307.0  21.00 396.90  18.72  15.20
+ 0.98843   0.00   8.140  0  0.5380  5.8130 100.00  4.0952   4  307.0  21.00 394.54  19.88  14.50
+ 0.75026   0.00   8.140  0  0.5380  5.9240  94.10  4.3996   4  307.0  21.00 394.33  16.30  15.60
+ 0.84054   0.00   8.140  0  0.5380  5.5990  85.70  4.4546   4  307.0  21.00 303.42  16.51  13.90
+ 0.67191   0.00   8.140  0  0.5380  5.8130  90.30  4.6820   4  307.0  21.00 376.88  14.81  16.60
+ 0.95577   0.00   8.140  0  0.5380  6.0470  88.80  4.4534   4  307.0  21.00 306.38  17.28  14.80
+ 0.77299   0.00   8.140  0  0.5380  6.4950  94.40  4.4547   4  307.0  21.00 387.94  12.80  18.40
+ 1.00245   0.00   8.140  0  0.5380  6.6740  87.30  4.2390   4  307.0  21.00 380.23  11.98  21.00
+ 1.13081   0.00   8.140  0  0.5380  5.7130  94.10  4.2330   4  307.0  21.00 360.17  22.60  12.70
+ 1.35472   0.00   8.140  0  0.5380  6.0720 100.00  4.1750   4  307.0  21.00 376.73  13.04  14.50
+ 1.38799   0.00   8.140  0  0.5380  5.9500  82.00  3.9900   4  307.0  21.00 232.60  27.71  13.20
+ 1.15172   0.00   8.140  0  0.5380  5.7010  95.00  3.7872   4  307.0  21.00 358.77  18.35  13.10
+ 1.61282   0.00   8.140  0  0.5380  6.0960  96.90  3.7598   4  307.0  21.00 248.31  20.34  13.50
+ 0.06417   0.00   5.960  0  0.4990  5.9330  68.20  3.3603   5  279.0  19.20 396.90   9.68  18.90
+ 0.09744   0.00   5.960  0  0.4990  5.8410  61.40  3.3779   5  279.0  19.20 377.56  11.41  20.00
+ 0.08014   0.00   5.960  0  0.4990  5.8500  41.50  3.9342   5  279.0  19.20 396.90   8.77  21.00
+ 0.17505   0.00   5.960  0  0.4990  5.9660  30.20  3.8473   5  279.0  19.20 393.43  10.13  24.70
+ 0.02763  75.00   2.950  0  0.4280  6.5950  21.80  5.4011   3  252.0  18.30 395.63   4.32  30.80
+ 0.03359  75.00   2.950  0  0.4280  7.0240  15.80  5.4011   3  252.0  18.30 395.62   1.98  34.90
+ 0.12744   0.00   6.910  0  0.4480  6.7700   2.90  5.7209   3  233.0  17.90 385.41   4.84  26.60
+ 0.14150   0.00   6.910  0  0.4480  6.1690   6.60  5.7209   3  233.0  17.90 383.37   5.81  25.30
+ 0.15936   0.00   6.910  0  0.4480  6.2110   6.50  5.7209   3  233.0  17.90 394.46   7.44  24.70
+ 0.12269   0.00   6.910  0  0.4480  6.0690  40.00  5.7209   3  233.0  17.90 389.39   9.55  21.20
+ 0.17142   0.00   6.910  0  0.4480  5.6820  33.80  5.1004   3  233.0  17.90 396.90  10.21  19.30
+ 0.18836   0.00   6.910  0  0.4480  5.7860  33.30  5.1004   3  233.0  17.90 396.90  14.15  20.00
+ 0.22927   0.00   6.910  0  0.4480  6.0300  85.50  5.6894   3  233.0  17.90 392.74  18.80  16.60
+ 0.25387   0.00   6.910  0  0.4480  5.3990  95.30  5.8700   3  233.0  17.90 396.90  30.81  14.40
+ 0.21977   0.00   6.910  0  0.4480  5.6020  62.00  6.0877   3  233.0  17.90 396.90  16.20  19.40
+ 0.08873  21.00   5.640  0  0.4390  5.9630  45.70  6.8147   4  243.0  16.80 395.56  13.45  19.70
+ 0.04337  21.00   5.640  0  0.4390  6.1150  63.00  6.8147   4  243.0  16.80 393.97   9.43  20.50
+ 0.05360  21.00   5.640  0  0.4390  6.5110  21.10  6.8147   4  243.0  16.80 396.90   5.28  25.00
+ 0.04981  21.00   5.640  0  0.4390  5.9980  21.40  6.8147   4  243.0  16.80 396.90   8.43  23.40
+ 0.01360  75.00   4.000  0  0.4100  5.8880  47.60  7.3197   3  469.0  21.10 396.90  14.80  18.90
+ 0.01311  90.00   1.220  0  0.4030  7.2490  21.90  8.6966   5  226.0  17.90 395.93   4.81  35.40
+ 0.02055  85.00   0.740  0  0.4100  6.3830  35.70  9.1876   2  313.0  17.30 396.90   5.77  24.70
+ 0.01432 100.00   1.320  0  0.4110  6.8160  40.50  8.3248   5  256.0  15.10 392.90   3.95  31.60
+ 0.15445  25.00   5.130  0  0.4530  6.1450  29.20  7.8148   8  284.0  19.70 390.68   6.86  23.30
+ 0.10328  25.00   5.130  0  0.4530  5.9270  47.20  6.9320   8  284.0  19.70 396.90   9.22  19.60
+ 0.14932  25.00   5.130  0  0.4530  5.7410  66.20  7.2254   8  284.0  19.70 395.11  13.15  18.70
+ 0.17171  25.00   5.130  0  0.4530  5.9660  93.40  6.8185   8  284.0  19.70 378.08  14.44  16.00
+ 0.11027  25.00   5.130  0  0.4530  6.4560  67.80  7.2255   8  284.0  19.70 396.90   6.73  22.20
+ 0.12650  25.00   5.130  0  0.4530  6.7620  43.40  7.9809   8  284.0  19.70 395.58   9.50  25.00
+ 0.01951  17.50   1.380  0  0.4161  7.1040  59.50  9.2229   3  216.0  18.60 393.24   8.05  33.00
+ 0.03584  80.00   3.370  0  0.3980  6.2900  17.80  6.6115   4  337.0  16.10 396.90   4.67  23.50
+ 0.04379  80.00   3.370  0  0.3980  5.7870  31.10  6.6115   4  337.0  16.10 396.90  10.24  19.40
+ 0.05789  12.50   6.070  0  0.4090  5.8780  21.40  6.4980   4  345.0  18.90 396.21   8.10  22.00
+ 0.13554  12.50   6.070  0  0.4090  5.5940  36.80  6.4980   4  345.0  18.90 396.90  13.09  17.40
+ 0.12816  12.50   6.070  0  0.4090  5.8850  33.00  6.4980   4  345.0  18.90 396.90   8.79  20.90
+ 0.08826   0.00  10.810  0  0.4130  6.4170   6.60  5.2873   4  305.0  19.20 383.73   6.72  24.20
+ 0.15876   0.00  10.810  0  0.4130  5.9610  17.50  5.2873   4  305.0  19.20 376.94   9.88  21.70
+ 0.09164   0.00  10.810  0  0.4130  6.0650   7.80  5.2873   4  305.0  19.20 390.91   5.52  22.80
+ 0.19539   0.00  10.810  0  0.4130  6.2450   6.20  5.2873   4  305.0  19.20 377.17   7.54  23.40
+ 0.07896   0.00  12.830  0  0.4370  6.2730   6.00  4.2515   5  398.0  18.70 394.92   6.78  24.10
+ 0.09512   0.00  12.830  0  0.4370  6.2860  45.00  4.5026   5  398.0  18.70 383.23   8.94  21.40
+ 0.10153   0.00  12.830  0  0.4370  6.2790  74.50  4.0522   5  398.0  18.70 373.66  11.97  20.00
+ 0.08707   0.00  12.830  0  0.4370  6.1400  45.80  4.0905   5  398.0  18.70 386.96  10.27  20.80
+ 0.05646   0.00  12.830  0  0.4370  6.2320  53.70  5.0141   5  398.0  18.70 386.40  12.34  21.20
+ 0.08387   0.00  12.830  0  0.4370  5.8740  36.60  4.5026   5  398.0  18.70 396.06   9.10  20.30
+ 0.04113  25.00   4.860  0  0.4260  6.7270  33.50  5.4007   4  281.0  19.00 396.90   5.29  28.00
+ 0.04462  25.00   4.860  0  0.4260  6.6190  70.40  5.4007   4  281.0  19.00 395.63   7.22  23.90
+ 0.03659  25.00   4.860  0  0.4260  6.3020  32.20  5.4007   4  281.0  19.00 396.90   6.72  24.80
+ 0.03551  25.00   4.860  0  0.4260  6.1670  46.70  5.4007   4  281.0  19.00 390.64   7.51  22.90
+ 0.05059   0.00   4.490  0  0.4490  6.3890  48.00  4.7794   3  247.0  18.50 396.90   9.62  23.90
+ 0.05735   0.00   4.490  0  0.4490  6.6300  56.10  4.4377   3  247.0  18.50 392.30   6.53  26.60
+ 0.05188   0.00   4.490  0  0.4490  6.0150  45.10  4.4272   3  247.0  18.50 395.99  12.86  22.50
+ 0.07151   0.00   4.490  0  0.4490  6.1210  56.80  3.7476   3  247.0  18.50 395.15   8.44  22.20
+ 0.05660   0.00   3.410  0  0.4890  7.0070  86.30  3.4217   2  270.0  17.80 396.90   5.50  23.60
+ 0.05302   0.00   3.410  0  0.4890  7.0790  63.10  3.4145   2  270.0  17.80 396.06   5.70  28.70
+ 0.04684   0.00   3.410  0  0.4890  6.4170  66.10  3.0923   2  270.0  17.80 392.18   8.81  22.60
+ 0.03932   0.00   3.410  0  0.4890  6.4050  73.90  3.0921   2  270.0  17.80 393.55   8.20  22.00
+ 0.04203  28.00  15.040  0  0.4640  6.4420  53.60  3.6659   4  270.0  18.20 395.01   8.16  22.90
+ 0.02875  28.00  15.040  0  0.4640  6.2110  28.90  3.6659   4  270.0  18.20 396.33   6.21  25.00
+ 0.04294  28.00  15.040  0  0.4640  6.2490  77.30  3.6150   4  270.0  18.20 396.90  10.59  20.60
+ 0.12204   0.00   2.890  0  0.4450  6.6250  57.80  3.4952   2  276.0  18.00 357.98   6.65  28.40
+ 0.11504   0.00   2.890  0  0.4450  6.1630  69.60  3.4952   2  276.0  18.00 391.83  11.34  21.40
+ 0.12083   0.00   2.890  0  0.4450  8.0690  76.00  3.4952   2  276.0  18.00 396.90   4.21  38.70
+ 0.08187   0.00   2.890  0  0.4450  7.8200  36.90  3.4952   2  276.0  18.00 393.53   3.57  43.80
+ 0.06860   0.00   2.890  0  0.4450  7.4160  62.50  3.4952   2  276.0  18.00 396.90   6.19  33.20
+ 0.14866   0.00   8.560  0  0.5200  6.7270  79.90  2.7778   5  384.0  20.90 394.76   9.42  27.50
+ 0.11432   0.00   8.560  0  0.5200  6.7810  71.30  2.8561   5  384.0  20.90 395.58   7.67  26.50
+ 0.22876   0.00   8.560  0  0.5200  6.4050  85.40  2.7147   5  384.0  20.90  70.80  10.63  18.60
+ 0.21161   0.00   8.560  0  0.5200  6.1370  87.40  2.7147   5  384.0  20.90 394.47  13.44  19.30
+ 0.13960   0.00   8.560  0  0.5200  6.1670  90.00  2.4210   5  384.0  20.90 392.69  12.33  20.10
+ 0.13262   0.00   8.560  0  0.5200  5.8510  96.70  2.1069   5  384.0  20.90 394.05  16.47  19.50
+ 0.17120   0.00   8.560  0  0.5200  5.8360  91.90  2.2110   5  384.0  20.90 395.67  18.66  19.50
+ 0.13117   0.00   8.560  0  0.5200  6.1270  85.20  2.1224   5  384.0  20.90 387.69  14.09  20.40
+ 0.12802   0.00   8.560  0  0.5200  6.4740  97.10  2.4329   5  384.0  20.90 395.24  12.27  19.80
+ 0.26363   0.00   8.560  0  0.5200  6.2290  91.20  2.5451   5  384.0  20.90 391.23  15.55  19.40
+ 0.10793   0.00   8.560  0  0.5200  6.1950  54.40  2.7778   5  384.0  20.90 393.49  13.00  21.70
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+ 5.87205   0.00  18.100  0  0.6930  6.4050  96.00  1.6768  24  666.0  20.20 396.90  19.37  12.50
+ 7.67202   0.00  18.100  0  0.6930  5.7470  98.90  1.6334  24  666.0  20.20 393.10  19.92   8.50
+38.35180   0.00  18.100  0  0.6930  5.4530 100.00  1.4896  24  666.0  20.20 396.90  30.59   5.00
+ 9.91655   0.00  18.100  0  0.6930  5.8520  77.80  1.5004  24  666.0  20.20 338.16  29.97   6.30
+25.04610   0.00  18.100  0  0.6930  5.9870 100.00  1.5888  24  666.0  20.20 396.90  26.77   5.60
+14.23620   0.00  18.100  0  0.6930  6.3430 100.00  1.5741  24  666.0  20.20 396.90  20.32   7.20
+ 9.59571   0.00  18.100  0  0.6930  6.4040 100.00  1.6390  24  666.0  20.20 376.11  20.31  12.10
+24.80170   0.00  18.100  0  0.6930  5.3490  96.00  1.7028  24  666.0  20.20 396.90  19.77   8.30
+41.52920   0.00  18.100  0  0.6930  5.5310  85.40  1.6074  24  666.0  20.20 329.46  27.38   8.50
+67.92080   0.00  18.100  0  0.6930  5.6830 100.00  1.4254  24  666.0  20.20 384.97  22.98   5.00
+20.71620   0.00  18.100  0  0.6590  4.1380 100.00  1.1781  24  666.0  20.20 370.22  23.34  11.90
+11.95110   0.00  18.100  0  0.6590  5.6080 100.00  1.2852  24  666.0  20.20 332.09  12.13  27.90
+ 7.40389   0.00  18.100  0  0.5970  5.6170  97.90  1.4547  24  666.0  20.20 314.64  26.40  17.20
+14.43830   0.00  18.100  0  0.5970  6.8520 100.00  1.4655  24  666.0  20.20 179.36  19.78  27.50
+51.13580   0.00  18.100  0  0.5970  5.7570 100.00  1.4130  24  666.0  20.20   2.60  10.11  15.00
+14.05070   0.00  18.100  0  0.5970  6.6570 100.00  1.5275  24  666.0  20.20  35.05  21.22  17.20
+18.81100   0.00  18.100  0  0.5970  4.6280 100.00  1.5539  24  666.0  20.20  28.79  34.37  17.90
+28.65580   0.00  18.100  0  0.5970  5.1550 100.00  1.5894  24  666.0  20.20 210.97  20.08  16.30
+45.74610   0.00  18.100  0  0.6930  4.5190 100.00  1.6582  24  666.0  20.20  88.27  36.98   7.00
+18.08460   0.00  18.100  0  0.6790  6.4340 100.00  1.8347  24  666.0  20.20  27.25  29.05   7.20
+10.83420   0.00  18.100  0  0.6790  6.7820  90.80  1.8195  24  666.0  20.20  21.57  25.79   7.50
+25.94060   0.00  18.100  0  0.6790  5.3040  89.10  1.6475  24  666.0  20.20 127.36  26.64  10.40
+73.53410   0.00  18.100  0  0.6790  5.9570 100.00  1.8026  24  666.0  20.20  16.45  20.62   8.80
+11.81230   0.00  18.100  0  0.7180  6.8240  76.50  1.7940  24  666.0  20.20  48.45  22.74   8.40
+11.08740   0.00  18.100  0  0.7180  6.4110 100.00  1.8589  24  666.0  20.20 318.75  15.02  16.70
+ 7.02259   0.00  18.100  0  0.7180  6.0060  95.30  1.8746  24  666.0  20.20 319.98  15.70  14.20
+12.04820   0.00  18.100  0  0.6140  5.6480  87.60  1.9512  24  666.0  20.20 291.55  14.10  20.80
+ 7.05042   0.00  18.100  0  0.6140  6.1030  85.10  2.0218  24  666.0  20.20   2.52  23.29  13.40
+ 8.79212   0.00  18.100  0  0.5840  5.5650  70.60  2.0635  24  666.0  20.20   3.65  17.16  11.70
+15.86030   0.00  18.100  0  0.6790  5.8960  95.40  1.9096  24  666.0  20.20   7.68  24.39   8.30
+12.24720   0.00  18.100  0  0.5840  5.8370  59.70  1.9976  24  666.0  20.20  24.65  15.69  10.20
+37.66190   0.00  18.100  0  0.6790  6.2020  78.70  1.8629  24  666.0  20.20  18.82  14.52  10.90
+ 7.36711   0.00  18.100  0  0.6790  6.1930  78.10  1.9356  24  666.0  20.20  96.73  21.52  11.00
+ 9.33889   0.00  18.100  0  0.6790  6.3800  95.60  1.9682  24  666.0  20.20  60.72  24.08   9.50
+ 8.49213   0.00  18.100  0  0.5840  6.3480  86.10  2.0527  24  666.0  20.20  83.45  17.64  14.50
+10.06230   0.00  18.100  0  0.5840  6.8330  94.30  2.0882  24  666.0  20.20  81.33  19.69  14.10
+ 6.44405   0.00  18.100  0  0.5840  6.4250  74.80  2.2004  24  666.0  20.20  97.95  12.03  16.10
+ 5.58107   0.00  18.100  0  0.7130  6.4360  87.90  2.3158  24  666.0  20.20 100.19  16.22  14.30
+13.91340   0.00  18.100  0  0.7130  6.2080  95.00  2.2222  24  666.0  20.20 100.63  15.17  11.70
+11.16040   0.00  18.100  0  0.7400  6.6290  94.60  2.1247  24  666.0  20.20 109.85  23.27  13.40
+14.42080   0.00  18.100  0  0.7400  6.4610  93.30  2.0026  24  666.0  20.20  27.49  18.05   9.60
+15.17720   0.00  18.100  0  0.7400  6.1520 100.00  1.9142  24  666.0  20.20   9.32  26.45   8.70
+13.67810   0.00  18.100  0  0.7400  5.9350  87.90  1.8206  24  666.0  20.20  68.95  34.02   8.40
+ 9.39063   0.00  18.100  0  0.7400  5.6270  93.90  1.8172  24  666.0  20.20 396.90  22.88  12.80
+22.05110   0.00  18.100  0  0.7400  5.8180  92.40  1.8662  24  666.0  20.20 391.45  22.11  10.50
+ 9.72418   0.00  18.100  0  0.7400  6.4060  97.20  2.0651  24  666.0  20.20 385.96  19.52  17.10
+ 5.66637   0.00  18.100  0  0.7400  6.2190 100.00  2.0048  24  666.0  20.20 395.69  16.59  18.40
+ 9.96654   0.00  18.100  0  0.7400  6.4850 100.00  1.9784  24  666.0  20.20 386.73  18.85  15.40
+12.80230   0.00  18.100  0  0.7400  5.8540  96.60  1.8956  24  666.0  20.20 240.52  23.79  10.80
+10.67180   0.00  18.100  0  0.7400  6.4590  94.80  1.9879  24  666.0  20.20  43.06  23.98  11.80
+ 6.28807   0.00  18.100  0  0.7400  6.3410  96.40  2.0720  24  666.0  20.20 318.01  17.79  14.90
+ 9.92485   0.00  18.100  0  0.7400  6.2510  96.60  2.1980  24  666.0  20.20 388.52  16.44  12.60
+ 9.32909   0.00  18.100  0  0.7130  6.1850  98.70  2.2616  24  666.0  20.20 396.90  18.13  14.10
+ 7.52601   0.00  18.100  0  0.7130  6.4170  98.30  2.1850  24  666.0  20.20 304.21  19.31  13.00
+ 6.71772   0.00  18.100  0  0.7130  6.7490  92.60  2.3236  24  666.0  20.20   0.32  17.44  13.40
+ 5.44114   0.00  18.100  0  0.7130  6.6550  98.20  2.3552  24  666.0  20.20 355.29  17.73  15.20
+ 5.09017   0.00  18.100  0  0.7130  6.2970  91.80  2.3682  24  666.0  20.20 385.09  17.27  16.10
+ 8.24809   0.00  18.100  0  0.7130  7.3930  99.30  2.4527  24  666.0  20.20 375.87  16.74  17.80
+ 9.51363   0.00  18.100  0  0.7130  6.7280  94.10  2.4961  24  666.0  20.20   6.68  18.71  14.90
+ 4.75237   0.00  18.100  0  0.7130  6.5250  86.50  2.4358  24  666.0  20.20  50.92  18.13  14.10
+ 4.66883   0.00  18.100  0  0.7130  5.9760  87.90  2.5806  24  666.0  20.20  10.48  19.01  12.70
+ 8.20058   0.00  18.100  0  0.7130  5.9360  80.30  2.7792  24  666.0  20.20   3.50  16.94  13.50
+ 7.75223   0.00  18.100  0  0.7130  6.3010  83.70  2.7831  24  666.0  20.20 272.21  16.23  14.90
+ 6.80117   0.00  18.100  0  0.7130  6.0810  84.40  2.7175  24  666.0  20.20 396.90  14.70  20.00
+ 4.81213   0.00  18.100  0  0.7130  6.7010  90.00  2.5975  24  666.0  20.20 255.23  16.42  16.40
+ 3.69311   0.00  18.100  0  0.7130  6.3760  88.40  2.5671  24  666.0  20.20 391.43  14.65  17.70
+ 6.65492   0.00  18.100  0  0.7130  6.3170  83.00  2.7344  24  666.0  20.20 396.90  13.99  19.50
+ 5.82115   0.00  18.100  0  0.7130  6.5130  89.90  2.8016  24  666.0  20.20 393.82  10.29  20.20
+ 7.83932   0.00  18.100  0  0.6550  6.2090  65.40  2.9634  24  666.0  20.20 396.90  13.22  21.40
+ 3.16360   0.00  18.100  0  0.6550  5.7590  48.20  3.0665  24  666.0  20.20 334.40  14.13  19.90
+ 3.77498   0.00  18.100  0  0.6550  5.9520  84.70  2.8715  24  666.0  20.20  22.01  17.15  19.00
+ 4.42228   0.00  18.100  0  0.5840  6.0030  94.50  2.5403  24  666.0  20.20 331.29  21.32  19.10
+15.57570   0.00  18.100  0  0.5800  5.9260  71.00  2.9084  24  666.0  20.20 368.74  18.13  19.10
+13.07510   0.00  18.100  0  0.5800  5.7130  56.70  2.8237  24  666.0  20.20 396.90  14.76  20.10
+ 4.34879   0.00  18.100  0  0.5800  6.1670  84.00  3.0334  24  666.0  20.20 396.90  16.29  19.90
+ 4.03841   0.00  18.100  0  0.5320  6.2290  90.70  3.0993  24  666.0  20.20 395.33  12.87  19.60
+ 3.56868   0.00  18.100  0  0.5800  6.4370  75.00  2.8965  24  666.0  20.20 393.37  14.36  23.20
+ 4.64689   0.00  18.100  0  0.6140  6.9800  67.60  2.5329  24  666.0  20.20 374.68  11.66  29.80
+ 8.05579   0.00  18.100  0  0.5840  5.4270  95.40  2.4298  24  666.0  20.20 352.58  18.14  13.80
+ 6.39312   0.00  18.100  0  0.5840  6.1620  97.40  2.2060  24  666.0  20.20 302.76  24.10  13.30
+ 4.87141   0.00  18.100  0  0.6140  6.4840  93.60  2.3053  24  666.0  20.20 396.21  18.68  16.70
+15.02340   0.00  18.100  0  0.6140  5.3040  97.30  2.1007  24  666.0  20.20 349.48  24.91  12.00
+10.23300   0.00  18.100  0  0.6140  6.1850  96.70  2.1705  24  666.0  20.20 379.70  18.03  14.60
+14.33370   0.00  18.100  0  0.6140  6.2290  88.00  1.9512  24  666.0  20.20 383.32  13.11  21.40
+ 5.82401   0.00  18.100  0  0.5320  6.2420  64.70  3.4242  24  666.0  20.20 396.90  10.74  23.00
+ 5.70818   0.00  18.100  0  0.5320  6.7500  74.90  3.3317  24  666.0  20.20 393.07   7.74  23.70
+ 5.73116   0.00  18.100  0  0.5320  7.0610  77.00  3.4106  24  666.0  20.20 395.28   7.01  25.00
+ 2.81838   0.00  18.100  0  0.5320  5.7620  40.30  4.0983  24  666.0  20.20 392.92  10.42  21.80
+ 2.37857   0.00  18.100  0  0.5830  5.8710  41.90  3.7240  24  666.0  20.20 370.73  13.34  20.60
+ 3.67367   0.00  18.100  0  0.5830  6.3120  51.90  3.9917  24  666.0  20.20 388.62  10.58  21.20
+ 5.69175   0.00  18.100  0  0.5830  6.1140  79.80  3.5459  24  666.0  20.20 392.68  14.98  19.10
+ 4.83567   0.00  18.100  0  0.5830  5.9050  53.20  3.1523  24  666.0  20.20 388.22  11.45  20.60
+ 0.15086   0.00  27.740  0  0.6090  5.4540  92.70  1.8209   4  711.0  20.10 395.09  18.06  15.20
+ 0.18337   0.00  27.740  0  0.6090  5.4140  98.30  1.7554   4  711.0  20.10 344.05  23.97   7.00
+ 0.20746   0.00  27.740  0  0.6090  5.0930  98.00  1.8226   4  711.0  20.10 318.43  29.68   8.10
+ 0.10574   0.00  27.740  0  0.6090  5.9830  98.80  1.8681   4  711.0  20.10 390.11  18.07  13.60
+ 0.11132   0.00  27.740  0  0.6090  5.9830  83.50  2.1099   4  711.0  20.10 396.90  13.35  20.10
+ 0.17331   0.00   9.690  0  0.5850  5.7070  54.00  2.3817   6  391.0  19.20 396.90  12.01  21.80
+ 0.27957   0.00   9.690  0  0.5850  5.9260  42.60  2.3817   6  391.0  19.20 396.90  13.59  24.50
+ 0.17899   0.00   9.690  0  0.5850  5.6700  28.80  2.7986   6  391.0  19.20 393.29  17.60  23.10
+ 0.28960   0.00   9.690  0  0.5850  5.3900  72.90  2.7986   6  391.0  19.20 396.90  21.14  19.70
+ 0.26838   0.00   9.690  0  0.5850  5.7940  70.60  2.8927   6  391.0  19.20 396.90  14.10  18.30
+ 0.23912   0.00   9.690  0  0.5850  6.0190  65.30  2.4091   6  391.0  19.20 396.90  12.92  21.20
+ 0.17783   0.00   9.690  0  0.5850  5.5690  73.50  2.3999   6  391.0  19.20 395.77  15.10  17.50
+ 0.22438   0.00   9.690  0  0.5850  6.0270  79.70  2.4982   6  391.0  19.20 396.90  14.33  16.80
+ 0.06263   0.00  11.930  0  0.5730  6.5930  69.10  2.4786   1  273.0  21.00 391.99   9.67  22.40
+ 0.04527   0.00  11.930  0  0.5730  6.1200  76.70  2.2875   1  273.0  21.00 396.90   9.08  20.60
+ 0.06076   0.00  11.930  0  0.5730  6.9760  91.00  2.1675   1  273.0  21.00 396.90   5.64  23.90
+ 0.10959   0.00  11.930  0  0.5730  6.7940  89.30  2.3889   1  273.0  21.00 393.45   6.48  22.00
+ 0.04741   0.00  11.930  0  0.5730  6.0300  80.80  2.5050   1  273.0  21.00 396.90   7.88  11.90
diff --git a/housing.names b/housing.names
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+1. Title: Boston Housing Data
+
+2. Sources:
+   (a) Origin:  This dataset was taken from the StatLib library which is
+                maintained at Carnegie Mellon University.
+   (b) Creator:  Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 
+                 demand for clean air', J. Environ. Economics & Management,
+                 vol.5, 81-102, 1978.
+   (c) Date: July 7, 1993
+
+3. Past Usage:
+   -   Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 
+       1980.   N.B. Various transformations are used in the table on
+       pages 244-261.
+    -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
+       In Proceedings on the Tenth International Conference of Machine 
+       Learning, 236-243, University of Massachusetts, Amherst. Morgan
+       Kaufmann.
+
+4. Relevant Information:
+
+   Concerns housing values in suburbs of Boston.
+
+5. Number of Instances: 506
+
+6. Number of Attributes: 13 continuous attributes (including "class"
+                         attribute "MEDV"), 1 binary-valued attribute.
+
+7. Attribute Information:
+
+    1. CRIM      per capita crime rate by town
+    2. ZN        proportion of residential land zoned for lots over 
+                 25,000 sq.ft.
+    3. INDUS     proportion of non-retail business acres per town
+    4. CHAS      Charles River dummy variable (= 1 if tract bounds 
+                 river; 0 otherwise)
+    5. NOX       nitric oxides concentration (parts per 10 million)
+    6. RM        average number of rooms per dwelling
+    7. AGE       proportion of owner-occupied units built prior to 1940
+    8. DIS       weighted distances to five Boston employment centres
+    9. RAD       index of accessibility to radial highways
+    10. TAX      full-value property-tax rate per $10,000
+    11. PTRATIO  pupil-teacher ratio by town
+    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
+                 by town
+    13. LSTAT    % lower status of the population
+    14. MEDV     Median value of owner-occupied homes in $1000's
+
+8. Missing Attribute Values:  None.
+
+
+