Boston_Housing_CR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston=load_boston()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "bos= pd.DataFrame(boston.data)\n",
    "bos.columns=boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.head()"
    "bos.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.593761   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.596783   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%      3.647423   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  \n",
      "count  506.000000  \n",
      "mean    12.653063  \n",
      "std      7.141062  \n",
      "min      1.730000  \n",
      "25%      6.950000  \n",
      "50%     11.360000  \n",
      "75%     16.955000  \n",
      "max     37.970000  \n"
     ]
    }
   ],
   "source": [
    "print(bos.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "===========================\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :Median Value (attribute 14) is usually the target\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**References**\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - 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.\n",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "bos['PRICE']= boston.target  #making a new price column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=bos.drop('PRICE',axis=1)\n",
    "Y=bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  array([  0.,   3., 152., 195.,  84.,  17.,  18.,  17.,   7.,  13.])],\n",
       " array([   0. ,  288.5,  577. ,  865.5, 1154. , 1442.5, 1731. , 2019.5,\n",
       "        2308. , 2596.5, 2885. ]),\n",
       " <a list of 14 Lists of Patches objects>)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sSarZO7R+edvYoIJ+Pd4SWZJ6YQ1ARJzf9AWDCvquPn0kSWd1HnBX086DCvo54O0BHUuSavcBYHc3nQfhaeDVAR1LkmqWtObp/7rpCwZ2wVRE3EirMM/dl6Tl+QGwKTMbDaC9MlaSKufoWpIqZ9BLUuUMekmqnEEvSZUz6CWpcga9JFXOoJekyhn0klS5/wO1t7AhS53o5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227554156a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x227531197b8>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227519cc2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bos.PRICE.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.PRICE.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354, 13)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354,)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>13</th>\n",
       "      <td>0.62976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.949</td>\n",
       "      <td>61.8</td>\n",
       "      <td>4.7075</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.17171</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.453</td>\n",
       "      <td>5.966</td>\n",
       "      <td>93.4</td>\n",
       "      <td>6.8185</td>\n",
       "      <td>8.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>378.08</td>\n",
       "      <td>14.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>9.82349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.671</td>\n",
       "      <td>6.794</td>\n",
       "      <td>98.8</td>\n",
       "      <td>1.3580</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>21.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.02763</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>6.595</td>\n",
       "      <td>21.8</td>\n",
       "      <td>5.4011</td>\n",
       "      <td>3.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>395.63</td>\n",
       "      <td>4.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>4.55587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.718</td>\n",
       "      <td>3.561</td>\n",
       "      <td>87.9</td>\n",
       "      <td>1.6132</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>354.70</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "13   0.62976   0.0   8.14   0.0  0.538  5.949  61.8  4.7075   4.0  307.0   \n",
       "61   0.17171  25.0   5.13   0.0  0.453  5.966  93.4  6.8185   8.0  284.0   \n",
       "377  9.82349   0.0  18.10   0.0  0.671  6.794  98.8  1.3580  24.0  666.0   \n",
       "39   0.02763  75.0   2.95   0.0  0.428  6.595  21.8  5.4011   3.0  252.0   \n",
       "365  4.55587   0.0  18.10   0.0  0.718  3.561  87.9  1.6132  24.0  666.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "13      21.0  396.90   8.26  \n",
       "61      19.7  378.08  14.44  \n",
       "377     20.2  396.90  21.24  \n",
       "39      18.3  395.63   4.32  \n",
       "365     20.2  354.70   7.12  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalizing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "n=StandardScaler()\n",
    "X_train= n.fit_transform(X_train)\n",
    "X_test= n.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr=LinearRegression()\n",
    "lr.fit(X_train,Y_train)\n",
    "Y_prd=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2275b63d9b0>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K3r26lufOnRFon6m3LOVialxssf/rZs7UoFd0XfHll2H4cE1L14RlyzSPP7DnLpo8fb+OqRw7Vis/7k/37jp0shwZGbquc9GKQfPm6ebGhJsFdBNRHTqUnGjknM4srQlvvKFFHmNi4NOcU0hzi3AFBTrO/ZdfdIWIKnjlFR1vX1Tm/d574c03Q9hwYypgKRcTUYMGaZrFOe25N2yoIxRrwsMP69D2PXuge346Li9Pv12ysvTCZxU1aaJfEqCp9ubNQ9RgYwKwgG7C4ocfYMoUWLky8LaTJ8Py5fDQQ1rvZdq0mhna3bWr/9rm157jKIxP1G+Vxo2rtVj0hRfqYJzUVDjmGLj77hA12JgALOViQm7xYl172Tnt8H78ceBh440awW23QWamXnNctUpn+4fTk0/qpNOVK+GHy95nYP4T2oDLL/eXHKiC2Fh49dUQNtSYIFlANyH35pv+yUOgPfVAAX3Jt/m0ZAeZtGDvXsdHH1WvDZsmz2b3qzNIPrk/HW8+t9xtmjQpPh8oGbixeic1JsIsoJuQO+wwnaWfna33RxwRYIdVqxh88XGszMxkCd05JfFzTjopqeQ2S5dqd79794BjvtdN+ow2o4bRjmyy5r7I8j9y6PbgiOq9KWPqAMuhm328Xl00eMgQeOmlqh/noovgiis0hxwXF8RgkXHjiNm5nQRy6eUW8+URV/D4o4X+10eP1gpf/frpsJQANr46j0R0FmcK2ex6a3bV34wxdYgFdLPPf/8Ld90FH36oAbmqaQ/ntORKdjbs2qXxeOVKyM/XGfJlFBuYHisFHL7oZWKvuUKfyMuDJ57Qg2Vn65jAXbv234CBA8lBe/hZJLOjzylVeyPG1DEW0M0+X39dskLtkiVVP9aGDf7x5bGxep0xMVHXrCgz8uXhh9nQ4FD/Ciler/+qYmysv8pi0eMAXf4Btw/kg1Hv8Wara5l+xhQGv3Jh1d9IHbd7t9Yya9UKrrwywOIips6zgG72+fvfNXbGx+tt8OCqH+vWW/VYDRroKMDFizWYbN2qgaWETp14SMaWeEo8voHcHo8WeunYUevuvvtuwBEozsFZz/yFc7c8yvnvnFViZmp9M368VibIyNA02htvRLpFJpzq8f/qprRhw3QSTHo6nHgiHHlk1Y81ahT8+c+webMW3ypKfYuUHWPu9cJrWWfwL8bSiN0IjpyrxrFvUaMBA+Dnn/VA69b5r7aagH79VX9tgaa8fv89su0x4WX10E3Y5edrHasvv9Qx5p98AmmlKjtffTUkPDuRGwvuY3fDA+m6+n1c61b+DZYs8Q9uT03VLn+rVpj9W7AATjhBZ64mJurH2KZNpFtlKivYeujBLEF3oHNurnNulXNuhXNutO/5O5xzvzrnlvhuQ0LRcBN94uJ0daJNm/Snf+lgDvDYqGU86LmF1rKFrlnf4UZcVHKD+32Fs3bv1kIpr7xSM42v4445RouPvf++rsdhwTy6BZNyKQBuEJHFzrkGwCLnXNHiYY+IyMPha56pM3buhEcf1YpUo0eX6T07By1aVLy727SRmPgYyEULZK1bV3KDFi00sZ+XpxdGmzYNw5uITq1b681Ev4ABXUQ2A5t9f+9xzq0C2oa7YaaOOeEELcji9cLrr2u3sKhCVTD69/dXsSoshJtuKvn6XXfp8Rcvhr/9TQumGGNKqFQO3TnXEZgHdAPGACOB3UA62ovfUc4+o4BRAO3bt+/1yy+/VLfNprYpKNDec9H/S4mJWgO31O/75cu1/vhxx0G7duUcJzMT5s7V0Sw9elR8vqVL4auv9KqrFRo39UCwOfSgA7pzLhX4ArhHRN5xzrUCtgEC3AW0EZGL93cMuygaxXr2hBUrtIfetq2mTIr10D/6CM48U0chejy6mk/nzlU4z9df60rTRWt4zp4Nxx5bYpPcXL3w2qSJxnxj6rqQXRT1HSwOmAa8KiLvAIjI7yJSKCJeYBJwdHUabOq4zz6Dm2+GMWNg/vwy6ZYnn9TRhpmZugTdu++W2n/DBjj3XPjrX7UrX44lS2DF7W/qgfbu1ftSA6sLCjSIn3eexv3x4yv5PvLyYMIEGDeubB4fdEHobt20kPv69ZU8uDFhJiL7vQEOeAmYUOr5NsX+vh54I9CxevXqJSaKbdkicuihIs6JHHusSFbWvpduvlkkKUkERFJSRKZPL7XvQQeJeDy6b5MmIjk5JV7+4AOR5GSRUfEvSCbJeqDkZJHJk0tst2SJSGqqvgwiDRtW8j0MHy6SmKhtadJEZPt2/2ubNvnfhMcjctRRlTy4MVUDpEuA+CoiQY1y6QdcCCxzzhVNBr8VGO6c64GmXNYDl4Xwe8bURXfeqWPjRHR20nPPwbXXAnDHHToQ5ptv4IILtCNOerqOZ+zZU3vDRem/PXt0SmmxRPukSdohf5YRNHEZXNlhJu0vGwL/+EeJJrRurddUQVM7lV6nYs4c/QkBmj5ascKf0vntN/8vD69Xl6kzphYJZpTLfLSXXlr5K+Sa+qtoCTfQ4Jyfv++lxER4+uli2y5apDNAc3P1gqpz/oBeWAgpKQC88IKuaOT16jFychyPJ99E3wk30b6cpepatdIlQceNg2bN4PnnK/keBgyAWbP8C4Ieeqj/teRkHS5Z1E7fl5UxtYVN/TehM368BsOdO6FDB1b2vYTxw3Ri5wOjf6PNtzPgoIO0rsDs2Ro0Cws1H168xnlcHOTlMXeuziDNztYZpt266WbDh+vIxYoMHaq3KnnlFS07+dtvcNVV+q0AOs118GDtoXu9+k1x3nlVPIkx4WEB3QD+peIKCjRuVamgVceOurjytm1kp7bk6NYe38pFwlev7GVd4hjNg9x3H/TujcQn4Pb66rKceKI2ALTwS6tWrHjb3+Hfu1eXqfvkk9C83wolJmplsdKefdZfitLjgTVrwtwQYyrPAroBtNf7gS+J1q+f1kQPsDBQ+WJjoXVrflhSfBk6x0/SWaMysGDiAobuuIZduXsY2/Vd7rtyo6YvtmzRNE2HDoAutHHrrdoO5+DSS6v9Nqvu0EP1Z8LevRr0Dzoogo0xpnxWnMuQl6exqqg3HBenVRKLsg1VsXYtHHywP93s8OJFK0R1jVvP2j1aGiApSQtIVbRM3c8/63XKI47QRYsiJj8frr8ePv9cy1LeeWcVv/GMqbxgx6FbD90QF6fBe+tWfZyc5KVBqqP8a+HB6dJFsyjz5mlQv/aUdbBrIHTqRO5L/vq5HuclN7fi6RCdOmkp3oiLi4PHH490K4zZL1vgwuAcfPop9OuVwzFxi5iTfSzxvbtrZcNqHPPDDzXn/b//wUPvddVp/c2a8YRcRRLZJJDDKQ3m06tXCN+MMfWY9dANoCmN+d0uh+9e1tzLj4k6ZnD06CofMyamzKx8iI9nqPd9ttCKTBrQZttW3NqV0LVr9d6AMcZ66KaY2FgdwQHaxQ7F2m3r1mn+JS4OGTGSURvGs4WWNCSTA9iMS0zQ+ubGmGqzgG78/v1vnVrp8cBRR5WZhVklV16pNU8KCvC+PZU/3v6Mf/Fvskkiy6VqtcTevat/HmOMpVxMMQccoMNT8vIqXIg5Lw++/Van2HfpEsQxs7KKLTXvSCabyVzKZ5xAz3bbmPpFz9D8EjDGWA/dlOJchcE8N1eXNBs8WHPuL70UxPEefhgaNICkJDzdDiX+zL8SFwcZKZ25+qWjLZgbE0IW0E3Q5s/XlHhmps6vueOOstt884129Bs0gMceA/r00Wn0K1bgvvmGya8msm0b7Nih3x033QTvvFPT78SY6GTdIxO0li1LVjIsb53Ks8/WSUkAN96osz27dEnVgi4+DRvqUMZTTtEvhuRkHSE5cmT434Mx0cx66KZiv/4K33+/L4ofcYSWYWndWq+Zvvpq2V0yM/1/5+ZqB33jxrLbzZ2r+XjQEinvvx+G9htTz1hAr+dyc2HaNJg5E7wZ2+C117T7/PrrOja8Xz8YOFCrdqElVzZv1lLmnTppUJ46FWbM0Lj/0EMl0+Lbt+tzpfXr50/VJyfDSSeF/70aE+0s5VKHiehSbps2wVlnae66svsPGqRrLjeVP/i+sBuNY32jUhIS9hXTYskSDfLHHVdm/xNP1PVBQVMob72l9VcefliDfWysBuzSBgzQL4Jp0/SwF11UhQ+gmjZu1NIsRx5pa02bKBFoSSPgQGAusApYAYz2Pd8UmAP86LtvEuhYtgRdaI0bp8u5JSSING8u8scfldt/40Zdba0dG2QiV/mXdgM9qHP6d1KSyLJl+/ZbvFjkqadE5s4ViY/37+KcSEGByJ49ugKdxyPSq1fJVdxqi3XrdHm61FRdyW7mzEi3yJiKEcIl6AqAG0RksXOuAbDIOTcHGAl8KiL3O+fGAeOAm0P6bWMqtnQprz1/MFlZiYB2qL/9Fk4+OfhDNGsGnWN+4SuOJI48ksnxv9i8uV7I3LJFa9h26wZoj3bo0KIQXmJRImJi9JaaqutBiFShIGHRwhIZGXD33XD88ZU8QHCmT9eV5ory+E8+WY1FMYypJQLm0EVks4gs9v29B+2ptwVOA6b4NpsCnB6uRppS7r0X+val1x+zSXAakQoK4E9/qtxhkpJg1pWzSHR5pJBTsrZiSgqsXq2rD910kz43axZrrnmMA7NXsXevBkRPsf+DCgv9o2CgitVlTztNr5B+/TWceqoG9jA45BAtoAj6OVRUvteYuqRSF0Wdcx2Bo4AFQCsR2Qwa9IGWoW6cqcCDD0J2NlMK/86omMkM7b+bWbP2rQsRlPvu03UaxjxzCHEJxf43cE6D+fnn6+OtWzXAPvAAD57xNdct/z820IHGbCcpSdfwTEjQoHjccRrQN2woGdhL+PJLXSH6qqvKr+b4448lxkZmrvl1Xyo/lIYM0R8APXtqhYM77wz9OYypccHkZTSFQyqwCBjme7yz1Os7KthvFJAOpLdv3z7MmaZ64uCD/YnrpCSR336r1O5r12ruvOgQ45o8JdK7t8gFF4jcfbfI1KkiXq/IL7+INGmiyWaPRxLYu2+fWJcv48eL7Ngh8sgjIo8/LrJihUjLltqkgw4qJ6e/caMm/UGT73/9a9nGXXedSEqKfJfYRwYnfy4xMV6Jjxd56aWqf1zG1HUEmUMPNpjHAbOBMcWeWwO08f3dBlgT6Dh2UTREVq4UOeookY4dRd56q9K7L1umFwKLgnOLFhVs+J//lLjq2Yyt+/ZJjC8sE7AvvlgvhBbF64ceKnW8Tz7RLwffQbwHHFD2nF6vLJjwtSTF5wt4/edL1O8YY+qjYAN6wJSLc84Bk4FVIvLfYi/NAEb4/h4BTK/67wRTKYceCosX6/jAs8+u9O6HH66p6sREvU2YAG++CbffrkMY92nf3p9oTkzk3e53ckDidpqk5vHk0x7OO09f7tUL/vhD0y5FOXWPp5ySMGlpFMYnkkMimaTwXNbwfask7eMc0zP6sjcvluIrJnlsxoQxgQWK+MCxgABLgSW+2xCgGfApOmzxU6BpoGNZD732KMqobN8u8uCD2mN3TjMia9YU2+if/xTp3Flk+HCRrKx9+z/2mD9tExcncu21IhkZIkccob30448Xyc4ue947r9gsoz0T5UzelrhYr4wfX3abqVNL/oKIiRF5443wfA7G1AWEatiiiMyn4sUlT6j2N4qJCOe0Aw46uSc72//a/Pm6wDPO6ZXDu+8us392tr8qbkEB7NkDLVpoD39/wxVzGrfmyZhryPdCvMf/A6C4YcP0Wuw77+ioxRtugPj46r1fY+oD+yFbW4jAwoU6mFx/GQW/3/jx0LGjRsI9eyp96hNO8M/mFIG0gGuLw8UXQ9u2ul+zZjpUvcj+hiuOHasZI+c09VPeCnfOweWXw8cfwy23WDA3Jlg29b+W2HLedWyasZgjPcuJH34mPPdccDtOn65J8KwsnQR0yy37X51eRIuxNGqkwxPRhYqaNtU6XCNH6lT4QJo39bJmaT4bfk+gbVvNxQej6Dx5eRaojQk166HXAh9P30uXt+5lUM4semR/RdaLb5fMgezPxo3+cdu5ufDTTxVvW1io0yG7dNEB5J99Bujszhtu0AUrBg0K4pxz50KjRsQ1TqHLY9cFHcyLs2BuTOhZQK8F/nlXAtmQ59fEAAAT2klEQVSksIeGbORAZsWeVuGqQWWceabOtW/YUPMfY8dWvO0XX+jEnpwc7dFfc03VGjxypNbJLSyESZNg+fKqHccYE1KWcqkFWrbyEOMRCr0OcR6a3z9Wu83BOOAAWLMGFizQuf+dOpW/3fbtmp8vPoUziK6116s57RJ58X1rhFbw2BgTEdZDrwWeeQZ6HOVo3BiuujGF40cHkcQurmlTrV1bUTDfulWvRN59tyavPR5NuUyevN/DPvyw/lBo2BA++qjYC5Mm6aDzuDi44AIrhGJMLeGkMiMqqiktLU3S09Nr7HyV5vXqQpiLFsGFF8Jf/lKlw/z6q9b6PvBAOOOMKhapCqUXX4Srr9Y0C+hKz998s99dNm/W74fcXH3cpIl28vfJzdXUTaNGYWmyMcbPObdIRAKOP7OUS3H33AP3368XJKdNg3nzdBpkJfzxhy6WkJmpizuMHq2HDUZhYfCZlkrp2NH/d3y8lhoMID+/5BdR8TK5FBTocYLN8xtjaoSlXIr75BP/6JKiceGVtHChBr/cXO0Ql7fuZmmrV+uY7vh4OO+8MKSkBw7UsYldu+ool4kTA+7Svj2MGuWP248/jn4mV1yhufdmzTQnb4ypNSygFzdsmH+GjXPQv3+lD9GggT+zER8PvXsH3ufKKzXF4fXCrFnw4YeVOGFhoY5eWbhw/xOSxoyBH37Q6ZdBpkkefVSXt8vIgBEj0AuvL7+s59yxAy65pBINNcaEm6Vcirv2Wh01smyZVq86/PBKH+If/yjZw37yycD75OaWjMVFq+gEJKKLQMyfr39fdhn85z+Vam8gLVoUe1A6DxN0Q40xNcEuioaQiObNiwJ6UhKsWhV44Ylvv9XFlnNz9XrlJ4+vJi4/G446ipxcx7p1eozU1FI7btig+fAc39JxsbGlkt3BW79eizf27g2pSb6lh0rP/vF6tbrjzJk6wuX998O2RJwxxi/Yi6KWcgkh5zRrk5Kitz/9SUe6BNK7N/z+uwbUz0+8i7hjekL//mScejFduwp9+uhxVq0qtWPjxiV7zC2rtmjURx/pj5HTT4dDO+1le2p7fQO3375vm19/hd+2ePRi8a+/wrZtFQfzTZt0mbxJk/QCqjGmZgRTkjFUt6grn1tQUGbVhYICXXNiypQS1WZLys8XeeEFkQkTRLZtK/laQsK+urEPxN4q8XGFAlra9sILyznWRx/pCkY9eogsWVJhU71ebdO114p8+WXJ1/r185eqTSFTXuQi/6oS69fL+PHarIQEkTvuCPCZ7NqlK2bExmoN3IsuCrCDMSYQQrliUahuURXQx4zRQt1Nmoj873/lbzN7tkivXiKDB2vx8SLnnKPBLiFBpEMHkb17/a+1arUvuj4Ve7UkJXn3rQA0enQl2uf1ikybJvLkkyIZGfLww/4a48nJIosX+zcdPlxrmhcF9Nn8ZV9Az1r+k8TG+gN+bKxITs5+zvv11yVWJZKmTSvRaGNMeSygF1m3TuTVV0V++CF0x1y8uOQKDAcfXHabLVv823g8umRckeL7pqaKLF3qf23BApEuXUTatJHc196W007TNTr79tXFKIKVd/X1khuXIjkxSZLXoo0M6p9fYjm3xx7zb7ttm8iJJ4occIDIv85aId74BI3wY8ZITk6JHw2SmKg/MCq0datIgwa6cUKCyJAhwTfaGFOuYAN6wFEuzrnngVOBDBHp5nvuDuBSoGgBsVtF5IOw5ISqY8kSOPZYnepeWKjVBY85pvrHzcsLPNrjt9/866Z5vSWrIB5xhC4hl5+v2xS/anr00bB2LQDxwHvDq9bErEmv0jhfx0/u3BpP35M38k1yJ7Kz9ZR//rN/22bNYM6cokeHQc5OfU8NG5KAVmH8v//T/Z5/Xq+9Vqh5cy0A9p//QJs2cNttVXsDxpjKCxTxgf5AT2B5sefuAMYG841R/FbjPfQbb/R3LUFk1KjQHNfrFTn7bO2BJiWJfPhh2W3y8kS6dRNJTZWCpBR5u/ONMmyYyPr1ol3iUaNEzjpL5PvvQ9OmUj6L+4vkEC+L6SFN+EM8Hq/07ClyxRUiX3wRllMaY8KEEC5BN8851zFs3yjhdMghOlEoO1vvDzssNMd1Dt56S4empKbuWyiihLg4WLCAvPc+4OzLmjLjp+PxrNcfDevWNdOKXEES0Q59XFxwi08ATD37Lda/eSMPFl7PDpqCV+cV3XNPleZLGWPqgOoMW7zaObfUOfe8c65JyFoUSv/4B1x/vUbBK6/UAlVVsXChzrDcvbvk861alR/MiyQnk9H/LD4uGAQ4vF4d713Zqf0XXQQDBkDfvroQRTAendKYvCcmsadtyS8xq3RrTPQKamKRr4c+U/w59FbANkCAu4A2InJxBfuOAkYBtG/fvtcvv/wSkobXmIkTdVm3mBhNNi9bVs4Mn4p5vTrOfPVq7difcIKuGhesbdu0zktRmj4mRucR7TePXcyiRTppKTMThgzR76WwFAAzxoRNsBOLqhTQg32ttDo5U7RzZ53xA1oY/K234OSTK3WI7GzdLSFBJ1ruLxj/9JP+mMjMhIce0sqNzZvD3r36eqNGWkalMiV5Cwq0DQ0bVqrZxphaIqzlc51zbURks+/hGUD0rkF20EG6bmdBgY5KCTSPvxzJybpqWzBOPlmDutcLJ52kRbumTdMyLXFxMGVK5eurx8ZaMDemPghm2OLrwECguXNuE3A7MNA51wNNuawHLgtjGyPrlVd0zN7PP8M//6nz+cOoeI69sFCvu55yipZtMcaY/QlmlEt5I6H3v3ZZNGnZEmbMqLHT/f3vutoR7H+JUGOMKc2Kc5Xjv//VKrp9+2q2hZkzddWfQw9lx5x00tI0H37GGZUobrh9ux7nxx/3u9nkyfDGGzqB58sv/XOTjDEmECufW8rixXDcceybUTmgXwGfpTfcd1Vyd2JLmhX8TkGB5sYnTgxinYctW3ToZG6uDleZOBEuvTT8b8YYExWsfC46kea66+CBB/wlwwPZssU/rM/rhV9/lRKrTyTnbqewQB8XFsKePUEcdPp0Hbaye7c25PLLdSiLMcaEUNQG9B07tCzKxIlwxx1w4YXB7Xf88Vp7vEEDXaDizrtjdehJSgokJ7Pj4hto1NiRkqJpmYsuCuKg7duXzJ14vfDcc9pIY4wJkahdgm7lSn8Rl5wcmDs3uP2SkjTtsmCBTujp0sXBue/obNGkJFp0784m31qbnTqVXdSnXIMHw80364IRRb39mBhdbDlCvvpK0/onnqjv2RhT90VtQD/sMO0UO6cXMAcNCn7fhIRS9U48HujTZ9/DlBQtE1OR3Fz45hstNnjwwWgjbrtNr7JedJGOaX/mmYhF0n/9Sy/8ejz6pZSermPcjTF1W9SmXJo00U71mDFw9926WH1NyMmBXr3gr3+FHj209CygOfSXX9ZhkHfeqUNkfLKz4ZprNN0zbVr42/j445CVpfn/n36CpUvDf05jTPjZKJcQ+/hjOOss/8XSzp1h3Tpg1CiN7rm5Ojzmww/3/Qy45BJ47TX9MkhO1nRIjx7ha2OPHlqSxuvVHwlr1+r1AGNM7WSjXCKkdWsd/QKa0mjb1vfCihUazIsUG4++eLF/FI7Ho/n/cHrvPRg4ELp10xozFsyNiQ4W0EPsyCN1mGTbtro40iuv6POv9nyYEzyfMS7uP+QlNNDShz7/93/aM09K0rorxx8f3jZ27Aiffqq99FNPDe+5jDE1x1IuNWD+fB35mJ0NSfEFXHNpLg88XrKO+mefaad96FBo1y5CDTXG1EphrbZoKmfZMv9oxb15sSxcUfZjHzSociNxjDGmNAvoNeCkk/zDzj0eXUjJGGNCzQJ6DejSBb77DmbPhsMP1wuSxhgTahbQa8hBB+nNGGPCxUa5GGNMlLCAbowxUSJgQHfOPe+cy3DOLS/2XFPn3Bzn3I+++ybhbaYxxphAgumhvwgMLvXcOOBTEekKfOp7bIwxJoICBnQRmQdsL/X0acAU399TgNND3K5qy8rSOujXXuurpWKMMVGuqqNcWonIZgAR2eyca1nRhs65UcAogPbt21fxdJV3xhkwb56u+fnaa7B+PaSm1tjpTWUUFmo93/R0GDGiRFkEY0zwwn5RVESeFZE0EUlr0aJFuE+3z1dfaS0sr1eX8fzpp4rap3WzNmyosaaZ0u6+W39OvfUWnH221j02xlRaVQP67865NgC++4zQNamUwkKYMgXuvx82bgx6t4EDdWZmTIwWverSpew2IjBsmC5Vd8ghWie8UjIz4YYb4NxzLQhVx+efa6Eb0P8o9bDejzGhUNWAPgMY4ft7BDA9NM0px9VX64LKt90GRx0V9DqcU6fCXXfBTTfBt9/qKkOlrVmj9cuzs7V87a23VrJt558PTzyhPctBg3SFaVN555yj5SY9Hr3ZVFpjqiRgDt059zowEGjunNsE3A7cD7zlnLsE2ACcHbYWvveev/eWnw9LlgRVXzYpCcaO3f82DRtqSqZIo0aVbNvChf4a5zEx+g3RunUlD2K44gotyr5smS71dNhhkW6RMXVSwIAuIsMreOmEELelfH37wgcfaOAsLIQ//Slkhz7gAJg4UddvbtAA3n67kgcYNkxXIcrL09Wiu3cPWdvqndNO05sxpspqfz30rCzNnWzYoAuEpgUsCVxzvF549VVNtZx/frHliYwxJnSCrYde+wO6McbUc7amqDHG1DMW0I0xJkpYQDfGmChhAd0YY6KEBXRjjIkSFtCNMSZK1N2AvncvnHUWtGqlFfry8yPdImOMiai6G9AfeghmzYKMDJ3iOWlSpFtULSLw3Xd6q8GpAcaYKFJ3A/qvv/rrqOTmwubNkW1PNY0aBcceq7crroh0a4wxdVHdnSm6fDn8+c/gnBbGSk+Hzp1Dc+walpUFjRtDQYE+jo2FXbu0AKExxgQ7U7SqKxZFXrduurbcqlX6d9OmIT382rVa2LFPH2jXLqSHLiMhQW9FAb3osTHGVEbdDegALVroLcS++gpOOkl7yiJaJTeERR7LiI2FGTPgkkv08fPP648OY4ypjLqbQw+jZ57REuy7d2s65M03w3/OQYPg55/1FkS5d2OMKcMCejkOOUQXyABdxq685euMMaa2qVbKxTm3HtgDFAIFwSTt64Ibb4RNm+CLL+CMM+CCCyLdImOMCSwUOfTjRWRbCI5Ta8THw1NPRboVxhhTOZZyMcaYKFHdgC7Ax865Rc65UaFokDHGmKqpbsqln4j85pxrCcxxzq0WkXnFN/AF+lEA7du3r+bpjDHGVKRaPXQR+c13nwG8CxxdzjbPikiaiKS1CMOYcWOMMarKAd05l+Kca1D0N3ASsDxUDTPGGFM51Um5tALedc4VHec1EfkoJK0yxhhTaVUO6CLyE9A9hG0xxhhTDdE5bLGwEFauhG1RNTzeGGP2K/oCem4u9O0LRx8NHTrA7NmRbpExxtSI6Avon3yiJXWzsrTC1o03RrpFxhhTI6IvoDds6F/DzTlo0iSy7THGmBoSfQH92GPhyishJQUOPhgmT450i4wxpkZEX0B3Dh58EDIzYfVqOOigSLfIGGNqRPQFdGOMqacsoBtjTJSwgG6MMVHCAroxxkQJC+jGGBMlLKAbY0yUsIBujDFRwknRrMqaOJlzW4FfauyEVdMcsKpeZdnnUpZ9JuWzz6Ws6n4mHUQk4ApBNRrQ6wLnXLqIpEW6HbWNfS5l2WdSPvtcyqqpz8RSLsYYEyUsoBtjTJSwgF7Ws5FuQC1ln0tZ9pmUzz6XsmrkM7EcujHGRAnroRtjTJSo1wHdOfe8cy7DObe82HNNnXNznHM/+u7r1QoZzrkDnXNznXOrnHMrnHOjfc/X988l0Tm30Dn3ve9zudP3fCfn3ALf5/Kmcy4+0m2tac65GOfcd865mb7H9pk4t945t8w5t8Q5l+57Luz/hup1QAdeBAaXem4c8KmIdAU+9T2uTwqAG0TkUKAPcJVz7jDsc8kFBolId6AHMNg51wd4AHjE97nsAC6JYBsjZTSwqthj+0zU8SLSo9hwxbD/G6rXAV1E5gHbSz19GjDF9/cU4PQabVSEichmEVns+3sP+g+1Lfa5iIhk+h7G+W4CDAKm+p6vd5+Lc64dMBR4zvfYUc8/k/0I+7+heh3QK9BKRDaDBjegZYTbEzHOuY7AUcAC7HMpSi0sATKAOcA6YKeIFPg22YR++dUnE4CbAK/vcTPsMwH9sv/YObfIOTfK91zY/w3FhvqAJjo451KBacB1IrJbO171m4gUAj2cc42Bd4FDy9usZlsVOc65U4EMEVnknBtY9HQ5m9abz6SYfiLym3OuJTDHObe6Jk5qPfSyfnfOtQHw3WdEuD01zjkXhwbzV0XkHd/T9f5zKSIiO4HP0WsMjZ1zRR2jdsBvkWpXBPQD/uacWw+8gaZaJlC/PxMAROQ3330G+uV/NDXwb8gCelkzgBG+v0cA0yPYlhrny4FOBlaJyH+LvVTfP5cWvp45zrkk4ET0+sJc4CzfZvXqcxGRW0SknYh0BM4DPhORC6jHnwmAcy7FOdeg6G/gJGA5NfBvqF5PLHLOvQ4MRCuh/Q7cDrwHvAW0BzYAZ4tI6QunUcs5dyzwJbAMf170VjSPXp8/lyPRC1kxaEfoLRH5t3OuM9o7bQp8B/xdRHIj19LI8KVcxorIqfX9M/G9/3d9D2OB10TkHudcM8L8b6heB3RjjIkmlnIxxpgoYQHdGGOihAV0Y4yJEhbQjTEmSlhAN8aYKGEB3RhjooQFdGOMiRIW0I0xJkr8Px0Q/8TDvjAvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2275b7fbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test,Y_prd,c=['red','blue'],s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7815633043716496"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
    "score=cross_val_score(lr,X_test,Y_test,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "aukaat=cross_val_predict(lr,X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.36453052  0.83092648  0.81948549  0.81012345  0.55552056  0.89157476\n",
      "  0.93616076  0.84689807  0.80020948  0.77318125]\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(score)\n",
    "s=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(score)):\n",
    "    \n",
    "    s+=score[i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8113930536407057\n"
     ]
    }
   ],
   "source": [
    "print(s/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}