boston_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics,model_selection,linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=pd.read_csv('boston_house_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS         int64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD          int64\n",
       "TAX          int64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "MEDV       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.apply(lambda x:sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(boston.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axes=plt.subplots(1,1,figsize=(8,8))\n",
    "sns.distplot(boston.MEDV,bins=50,fit=stats.norm,hist_kws={'color':'steelblue','label':'直方图'},\n",
    "             kde_kws={'color':'green','linestyle':':','label':'核密度曲线'},\n",
    "             fit_kws={'color':'yellow','linestyle':'-','label':'正态分布曲线'},ax=axes)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston.MEDV=np.log1p(boston.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test=model_selection.train_test_split(boston,test_size=0.25,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=sm.formula.ols('MEDV~CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+PTRATIO+B+LSTAT',data=train).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1=model1.predict(test.drop('MEDV',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred=model1.predict(train.drop('MEDV',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.381508590370178"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(np.expm1(train.MEDV),np.expm1(train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.37376466668332"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(np.expm1(test.MEDV),np.expm1(pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({'real':np.expm1(test.MEDV),'predict':np.expm1(pred1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>real</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>33.0</td>\n",
       "      <td>20.873263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>27.5</td>\n",
       "      <td>22.296060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>5.6</td>\n",
       "      <td>11.547405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>21.2</td>\n",
       "      <td>20.953713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>14.9</td>\n",
       "      <td>12.877872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>22.3</td>\n",
       "      <td>26.976483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>18.8</td>\n",
       "      <td>19.883253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>14.6</td>\n",
       "      <td>17.412548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>19.4</td>\n",
       "      <td>25.683863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>32.0</td>\n",
       "      <td>33.032632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>13.8</td>\n",
       "      <td>16.969114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>21.7</td>\n",
       "      <td>21.875249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>22.6</td>\n",
       "      <td>22.750782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>18.4</td>\n",
       "      <td>15.939197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>20.5</td>\n",
       "      <td>19.504920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>22.2</td>\n",
       "      <td>21.942568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>10.8</td>\n",
       "      <td>11.857166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>22.5</td>\n",
       "      <td>17.821404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>13.8</td>\n",
       "      <td>18.459672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.110466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>32.9</td>\n",
       "      <td>27.768911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>18.6</td>\n",
       "      <td>17.529857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>16.8</td>\n",
       "      <td>19.100385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.1</td>\n",
       "      <td>18.146384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>22.9</td>\n",
       "      <td>28.486638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>19.6</td>\n",
       "      <td>21.483810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>22.7</td>\n",
       "      <td>20.514364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>28.1</td>\n",
       "      <td>24.213563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>26.6</td>\n",
       "      <td>19.790243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>18.8</td>\n",
       "      <td>19.888380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>22.9</td>\n",
       "      <td>19.441287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>30.5</td>\n",
       "      <td>29.378326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>23.3</td>\n",
       "      <td>27.549802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>19.4</td>\n",
       "      <td>18.380967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>23.0</td>\n",
       "      <td>29.076782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>21.7</td>\n",
       "      <td>21.431189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>15.0</td>\n",
       "      <td>19.943651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>13.4</td>\n",
       "      <td>13.786000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>50.0</td>\n",
       "      <td>39.776746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>12.1</td>\n",
       "      <td>15.835539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>13.1</td>\n",
       "      <td>13.517697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>29.607066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>16.2</td>\n",
       "      <td>21.027592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>8.7</td>\n",
       "      <td>9.584807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>29.1</td>\n",
       "      <td>31.630533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>19.1</td>\n",
       "      <td>18.577884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>16.5</td>\n",
       "      <td>20.340035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>17.8</td>\n",
       "      <td>11.641052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20.5</td>\n",
       "      <td>23.921329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>34.9</td>\n",
       "      <td>33.295468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>23.1</td>\n",
       "      <td>23.636584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>14.4</td>\n",
       "      <td>8.946510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>38.7</td>\n",
       "      <td>32.572501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>21.2</td>\n",
       "      <td>20.292460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>23.2</td>\n",
       "      <td>16.139947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.6</td>\n",
       "      <td>17.565318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>35.4</td>\n",
       "      <td>28.716857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>20.1</td>\n",
       "      <td>20.396242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>42.8</td>\n",
       "      <td>24.609283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>24.7</td>\n",
       "      <td>24.530273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     real    predict\n",
       "64   33.0  20.873263\n",
       "100  27.5  22.296060\n",
       "400   5.6  11.547405\n",
       "485  21.2  20.953713\n",
       "454  14.9  12.877872\n",
       "288  22.3  26.976483\n",
       "112  18.8  19.883253\n",
       "478  14.6  17.412548\n",
       "66   19.4  25.683863\n",
       "187  32.0  33.032632\n",
       "474  13.8  16.969114\n",
       "71   21.7  21.875249\n",
       "205  22.6  22.750782\n",
       "133  18.4  15.939197\n",
       "247  20.5  19.504920\n",
       "333  22.2  21.942568\n",
       "444  10.8  11.857166\n",
       "207  22.5  17.821404\n",
       "393  13.8  18.459672\n",
       "398   5.0   9.110466\n",
       "200  32.9  27.768911\n",
       "352  18.6  17.529857\n",
       "363  16.8  19.100385\n",
       "7    27.1  18.146384\n",
       "92   22.9  28.486638\n",
       "59   19.6  21.483810\n",
       "358  22.7  20.514364\n",
       "213  28.1  24.213563\n",
       "349  26.6  19.790243\n",
       "124  18.8  19.888380\n",
       "..    ...        ...\n",
       "350  22.9  19.441287\n",
       "191  30.5  29.378326\n",
       "239  23.3  27.549802\n",
       "109  19.4  18.380967\n",
       "219  23.0  29.076782\n",
       "221  21.7  21.431189\n",
       "375  15.0  19.943651\n",
       "450  13.4  13.786000\n",
       "163  50.0  39.776746\n",
       "402  12.1  15.835539\n",
       "378  13.1  13.517697\n",
       "0    24.0  29.607066\n",
       "315  16.2  21.027592\n",
       "437   8.7   9.584807\n",
       "194  29.1  31.630533\n",
       "486  19.1  18.577884\n",
       "342  16.5  20.340035\n",
       "148  17.8  11.641052\n",
       "51   20.5  23.921329\n",
       "189  34.9  33.295468\n",
       "347  23.1  23.636584\n",
       "141  14.4   8.946510\n",
       "97   38.7  32.572501\n",
       "78   21.2  20.292460\n",
       "391  23.2  16.139947\n",
       "21   19.6  17.565318\n",
       "55   35.4  28.716857\n",
       "104  20.1  20.396242\n",
       "253  42.8  24.609283\n",
       "43   24.7  24.530273\n",
       "\n",
       "[127 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba6c9f8518>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='real',y='predict',data=df1,height=8,aspect=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.784</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.776</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   102.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 07 Jun 2019</td> <th>  Prob (F-statistic):</th> <td>8.18e-113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:36:10</td>     <th>  Log-Likelihood:    </th> <td>  129.61</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   379</td>      <th>  AIC:               </th> <td>  -231.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   365</td>      <th>  BIC:               </th> <td>  -176.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    4.5727</td> <td>    0.223</td> <td>   20.530</td> <td> 0.000</td> <td>    4.135</td> <td>    5.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CRIM</th>      <td>   -0.0067</td> <td>    0.001</td> <td>   -4.686</td> <td> 0.000</td> <td>   -0.010</td> <td>   -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ZN</th>        <td>    0.0016</td> <td>    0.001</td> <td>    2.661</td> <td> 0.008</td> <td>    0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INDUS</th>     <td>    0.0012</td> <td>    0.003</td> <td>    0.444</td> <td> 0.657</td> <td>   -0.004</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CHAS</th>      <td>    0.1007</td> <td>    0.036</td> <td>    2.793</td> <td> 0.005</td> <td>    0.030</td> <td>    0.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NOX</th>       <td>   -0.8741</td> <td>    0.163</td> <td>   -5.376</td> <td> 0.000</td> <td>   -1.194</td> <td>   -0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RM</th>        <td>    0.0429</td> <td>    0.018</td> <td>    2.345</td> <td> 0.020</td> <td>    0.007</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AGE</th>       <td>    0.0002</td> <td>    0.001</td> <td>    0.291</td> <td> 0.771</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIS</th>       <td>   -0.0614</td> <td>    0.009</td> <td>   -6.647</td> <td> 0.000</td> <td>   -0.080</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RAD</th>       <td>    0.0152</td> <td>    0.003</td> <td>    5.215</td> <td> 0.000</td> <td>    0.009</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TAX</th>       <td>   -0.0006</td> <td>    0.000</td> <td>   -3.704</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PTRATIO</th>   <td>   -0.0416</td> <td>    0.006</td> <td>   -7.474</td> <td> 0.000</td> <td>   -0.053</td> <td>   -0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>B</th>         <td>    0.0005</td> <td>    0.000</td> <td>    4.340</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LSTAT</th>     <td>   -0.0288</td> <td>    0.002</td> <td>  -13.868</td> <td> 0.000</td> <td>   -0.033</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>41.695</td> <th>  Durbin-Watson:     </th> <td>   1.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 167.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.362</td> <th>  Prob(JB):          </th> <td>4.72e-37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.173</td> <th>  Cond. No.          </th> <td>1.52e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.52e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.784\n",
       "Model:                            OLS   Adj. R-squared:                  0.776\n",
       "Method:                 Least Squares   F-statistic:                     102.0\n",
       "Date:                Fri, 07 Jun 2019   Prob (F-statistic):          8.18e-113\n",
       "Time:                        22:36:10   Log-Likelihood:                 129.61\n",
       "No. Observations:                 379   AIC:                            -231.2\n",
       "Df Residuals:                     365   BIC:                            -176.1\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      4.5727      0.223     20.530      0.000       4.135       5.011\n",
       "CRIM          -0.0067      0.001     -4.686      0.000      -0.010      -0.004\n",
       "ZN             0.0016      0.001      2.661      0.008       0.000       0.003\n",
       "INDUS          0.0012      0.003      0.444      0.657      -0.004       0.007\n",
       "CHAS           0.1007      0.036      2.793      0.005       0.030       0.172\n",
       "NOX           -0.8741      0.163     -5.376      0.000      -1.194      -0.554\n",
       "RM             0.0429      0.018      2.345      0.020       0.007       0.079\n",
       "AGE            0.0002      0.001      0.291      0.771      -0.001       0.001\n",
       "DIS           -0.0614      0.009     -6.647      0.000      -0.080      -0.043\n",
       "RAD            0.0152      0.003      5.215      0.000       0.009       0.021\n",
       "TAX           -0.0006      0.000     -3.704      0.000      -0.001      -0.000\n",
       "PTRATIO       -0.0416      0.006     -7.474      0.000      -0.053      -0.031\n",
       "B              0.0005      0.000      4.340      0.000       0.000       0.001\n",
       "LSTAT         -0.0288      0.002    -13.868      0.000      -0.033      -0.025\n",
       "==============================================================================\n",
       "Omnibus:                       41.695   Durbin-Watson:                   1.986\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              167.289\n",
       "Skew:                           0.362   Prob(JB):                     4.72e-37\n",
       "Kurtosis:                       6.173   Cond. No.                     1.52e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.52e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0250809923163\n",
      "2.71136700248414\n",
      "67.44945132374905\n",
      "134.9149815786189\n",
      "12.36920221003697\n",
      "14.190211266253757\n",
      "51.84367684615617\n",
      "86.85371775515524\n",
      "22.439674108941755\n",
      "10.14403987883719\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(variance_inflation_factor(train.drop(['INDUS','AGE','CHAS'],axis=1).values,i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM      -0.454865\n",
       "ZN         0.379163\n",
       "CHAS       0.165890\n",
       "NOX       -0.498167\n",
       "RM         0.588220\n",
       "DIS        0.325043\n",
       "RAD       -0.459118\n",
       "TAX       -0.546714\n",
       "PTRATIO   -0.494519\n",
       "B          0.439701\n",
       "LSTAT     -0.800379\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(['INDUS','AGE','MEDV'],axis=1).corrwith(train.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers=model1.get_influence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "studentize=outliers.resid_studentized_external"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.index=range(train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([train.drop(['INDUS','AGE','CHAS'],axis=1),pd.Series(studentize,name='studentize')],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.047493403693931395"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[np.abs(train.studentize)>2,:].shape[0]/train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.loc[np.abs(train.studentize)<2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</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",
       "      <th>studentize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.03548</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.392</td>\n",
       "      <td>5.876</td>\n",
       "      <td>9.2203</td>\n",
       "      <td>1</td>\n",
       "      <td>315</td>\n",
       "      <td>16.4</td>\n",
       "      <td>395.18</td>\n",
       "      <td>9.25</td>\n",
       "      <td>3.086487</td>\n",
       "      <td>-0.163863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.17134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.547</td>\n",
       "      <td>5.928</td>\n",
       "      <td>2.4631</td>\n",
       "      <td>6</td>\n",
       "      <td>432</td>\n",
       "      <td>17.8</td>\n",
       "      <td>344.91</td>\n",
       "      <td>15.76</td>\n",
       "      <td>2.960105</td>\n",
       "      <td>-0.344527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.53700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.504</td>\n",
       "      <td>5.981</td>\n",
       "      <td>3.6715</td>\n",
       "      <td>8</td>\n",
       "      <td>307</td>\n",
       "      <td>17.4</td>\n",
       "      <td>378.35</td>\n",
       "      <td>11.65</td>\n",
       "      <td>3.230804</td>\n",
       "      <td>-0.025458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.09299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.581</td>\n",
       "      <td>5.961</td>\n",
       "      <td>2.0869</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>19.1</td>\n",
       "      <td>378.09</td>\n",
       "      <td>17.93</td>\n",
       "      <td>3.068053</td>\n",
       "      <td>0.247681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.44953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.605</td>\n",
       "      <td>6.402</td>\n",
       "      <td>2.2625</td>\n",
       "      <td>5</td>\n",
       "      <td>403</td>\n",
       "      <td>14.7</td>\n",
       "      <td>330.04</td>\n",
       "      <td>11.32</td>\n",
       "      <td>3.148453</td>\n",
       "      <td>-0.598208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN    NOX     RM     DIS  RAD  TAX  PTRATIO       B  LSTAT  \\\n",
       "0  0.03548  80.0  0.392  5.876  9.2203    1  315     16.4  395.18   9.25   \n",
       "1  0.17134   0.0  0.547  5.928  2.4631    6  432     17.8  344.91  15.76   \n",
       "2  0.53700   0.0  0.504  5.981  3.6715    8  307     17.4  378.35  11.65   \n",
       "3  0.09299   0.0  0.581  5.961  2.0869    2  188     19.1  378.09  17.93   \n",
       "4  2.44953   0.0  0.605  6.402  2.2625    5  403     14.7  330.04  11.32   \n",
       "\n",
       "       MEDV  studentize  \n",
       "0  3.086487   -0.163863  \n",
       "1  2.960105   -0.344527  \n",
       "2  3.230804   -0.025458  \n",
       "3  3.068053    0.247681  \n",
       "4  3.148453   -0.598208  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdas=np.logspace(-5,2,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_cv=linear_model.LassoCV(alphas=lambdas,normalize=True,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LassoCV(alphas=array([1.00000e-05, 1.08437e-05, ..., 9.22198e+01, 1.00000e+02]),\n",
       "    copy_X=True, cv=10, eps=0.001, fit_intercept=True, max_iter=1000,\n",
       "    n_alphas=100, n_jobs=None, normalize=True, positive=False,\n",
       "    precompute='auto', random_state=None, selection='cyclic', tol=0.0001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_cv.fit(train.drop(['MEDV','studentize'],axis=1),train.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_alpha=lasso_cv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=linear_model.Lasso(alpha=best_alpha,normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1e-05, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=True, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(train.drop(['MEDV','studentize'],axis=1),train.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8988085830383286"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=pd.Series(model2.coef_,index=['CRIM', 'ZN', 'NOX', 'RM', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM      -0.006317\n",
       "ZN         0.001277\n",
       "NOX       -0.598930\n",
       "RM         0.095010\n",
       "DIS       -0.049469\n",
       "RAD        0.009790\n",
       "TAX       -0.000460\n",
       "PTRATIO   -0.036941\n",
       "B          0.000573\n",
       "LSTAT     -0.024919\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'NOX', 'RM', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT',\n",
       "       'MEDV', 'studentize'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2=model2.predict(test.drop(['MEDV','INDUS','AGE','CHAS'],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred2=model2.predict(train.drop(['MEDV','studentize'],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.582913915507906"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(np.expm1(train.MEDV),np.expm1(train_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.914138663879516"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(np.expm1(test.MEDV),np.expm1(pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.DataFrame({'real':np.expm1(test.MEDV),'predict':np.expm1(pred2)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>real</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>33.0</td>\n",
       "      <td>22.523818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>27.5</td>\n",
       "      <td>23.037821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>5.6</td>\n",
       "      <td>11.911977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>21.2</td>\n",
       "      <td>20.536631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>14.9</td>\n",
       "      <td>13.113334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>22.3</td>\n",
       "      <td>26.374594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>18.8</td>\n",
       "      <td>19.913451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>14.6</td>\n",
       "      <td>17.120115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>19.4</td>\n",
       "      <td>24.701114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.594479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>13.8</td>\n",
       "      <td>15.871452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>21.7</td>\n",
       "      <td>21.520880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>22.6</td>\n",
       "      <td>22.350499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>18.4</td>\n",
       "      <td>16.407427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>20.5</td>\n",
       "      <td>19.782797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>22.2</td>\n",
       "      <td>22.381532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>10.8</td>\n",
       "      <td>11.990435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>22.5</td>\n",
       "      <td>17.838913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>13.8</td>\n",
       "      <td>18.305408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.309659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>32.9</td>\n",
       "      <td>29.068839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>18.6</td>\n",
       "      <td>18.347601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>16.8</td>\n",
       "      <td>16.950497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.1</td>\n",
       "      <td>18.815907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>22.9</td>\n",
       "      <td>27.576116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>19.6</td>\n",
       "      <td>21.118451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>22.7</td>\n",
       "      <td>18.585224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>28.1</td>\n",
       "      <td>24.212828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>26.6</td>\n",
       "      <td>21.679801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>18.8</td>\n",
       "      <td>19.414118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>22.9</td>\n",
       "      <td>20.767156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>30.5</td>\n",
       "      <td>29.704580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>23.3</td>\n",
       "      <td>27.366705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>19.4</td>\n",
       "      <td>18.852394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>23.0</td>\n",
       "      <td>25.782399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>21.7</td>\n",
       "      <td>19.354278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>15.0</td>\n",
       "      <td>20.699931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>13.4</td>\n",
       "      <td>13.929136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>50.0</td>\n",
       "      <td>38.479369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>12.1</td>\n",
       "      <td>16.149373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>13.1</td>\n",
       "      <td>13.943533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>29.984097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>16.2</td>\n",
       "      <td>20.720756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>8.7</td>\n",
       "      <td>9.781933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>29.1</td>\n",
       "      <td>31.236291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>19.1</td>\n",
       "      <td>18.162035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>16.5</td>\n",
       "      <td>22.005350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>17.8</td>\n",
       "      <td>12.445451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>20.5</td>\n",
       "      <td>23.622652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>34.9</td>\n",
       "      <td>33.744058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>23.1</td>\n",
       "      <td>24.274335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>14.4</td>\n",
       "      <td>9.434886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>38.7</td>\n",
       "      <td>34.704455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>21.2</td>\n",
       "      <td>20.531953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>23.2</td>\n",
       "      <td>16.204612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.6</td>\n",
       "      <td>17.948727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>35.4</td>\n",
       "      <td>29.612764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>20.1</td>\n",
       "      <td>20.551199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>42.8</td>\n",
       "      <td>27.809030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>24.7</td>\n",
       "      <td>24.482745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     real    predict\n",
       "64   33.0  22.523818\n",
       "100  27.5  23.037821\n",
       "400   5.6  11.911977\n",
       "485  21.2  20.536631\n",
       "454  14.9  13.113334\n",
       "288  22.3  26.374594\n",
       "112  18.8  19.913451\n",
       "478  14.6  17.120115\n",
       "66   19.4  24.701114\n",
       "187  32.0  32.594479\n",
       "474  13.8  15.871452\n",
       "71   21.7  21.520880\n",
       "205  22.6  22.350499\n",
       "133  18.4  16.407427\n",
       "247  20.5  19.782797\n",
       "333  22.2  22.381532\n",
       "444  10.8  11.990435\n",
       "207  22.5  17.838913\n",
       "393  13.8  18.305408\n",
       "398   5.0   9.309659\n",
       "200  32.9  29.068839\n",
       "352  18.6  18.347601\n",
       "363  16.8  16.950497\n",
       "7    27.1  18.815907\n",
       "92   22.9  27.576116\n",
       "59   19.6  21.118451\n",
       "358  22.7  18.585224\n",
       "213  28.1  24.212828\n",
       "349  26.6  21.679801\n",
       "124  18.8  19.414118\n",
       "..    ...        ...\n",
       "350  22.9  20.767156\n",
       "191  30.5  29.704580\n",
       "239  23.3  27.366705\n",
       "109  19.4  18.852394\n",
       "219  23.0  25.782399\n",
       "221  21.7  19.354278\n",
       "375  15.0  20.699931\n",
       "450  13.4  13.929136\n",
       "163  50.0  38.479369\n",
       "402  12.1  16.149373\n",
       "378  13.1  13.943533\n",
       "0    24.0  29.984097\n",
       "315  16.2  20.720756\n",
       "437   8.7   9.781933\n",
       "194  29.1  31.236291\n",
       "486  19.1  18.162035\n",
       "342  16.5  22.005350\n",
       "148  17.8  12.445451\n",
       "51   20.5  23.622652\n",
       "189  34.9  33.744058\n",
       "347  23.1  24.274335\n",
       "141  14.4   9.434886\n",
       "97   38.7  34.704455\n",
       "78   21.2  20.531953\n",
       "391  23.2  16.204612\n",
       "21   19.6  17.948727\n",
       "55   35.4  29.612764\n",
       "104  20.1  20.551199\n",
       "253  42.8  27.809030\n",
       "43   24.7  24.482745\n",
       "\n",
       "[127 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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