Project 4.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, make_scorer, r2_score\n",
    "from sklearn.linear_model import LassoCV, RidgeCV\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Table-3-17.csv\").drop(51).set_index(\"State\")\n",
    "data = pd.concat([data.Sales, data.drop(\"Sales\", axis=1)], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consider the Cigarette Consumption data described in Exercise 3.15 and given in Table 3.17. The organization wanted to construct a regression equation that relates statewide cigarette consumption (per capita basis) to various socioeconomic and demographic variables, and to de- termine whether these variables were useful in predicting the consumption of cigarettes.\n",
    "- (a) Construct a linear regression model that explains the per capita sale of cigarettes in a given state. In your analysis, pay particular attention to outliers. See if the deletion of an outlier affects your findings. Look at residual plots before deciding on a final model. You need not include all the variables in the model if your analysis indicates otherwise. Your objective should be to find the smallest number of variables that describes the state sale of cigarettes meaningfully and adequately.\n",
    "- (b) Write a report describing your findings.\n",
    "- Variables <br>\n",
    "\n",
    "\n",
    "| Variable | Definition |\n",
    "|------|------|\n",
    "|   Age  |  Median age of a person living in a state |\n",
    "|   HS  | Percentage of people over 25 years of age in a state who had completed high school |\n",
    "|   Income  | Per capita personal income for a state(income in dollars)|\n",
    "|   Black  | Percentage of blacks living in a state|\n",
    "|   Female  | Percentage of females living in a state |\n",
    "|   Price  |Weighted average price(in cents) of a pack of cigarettes in a state |\n",
    "|   Sales  | Number of packs of cigarettes sold in a state on a per capita basis|\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### 파생변수 생각해볼것. \n",
    "- Price / Income : 담배 구매력 지수(IPP)\n",
    "- Female과 Black 의 경우 HS와 음의 상관관계가 있음 : 이 데이터가 구성된 지역들은 여성과 흑인이 차별받는 경향성 --> 그럼 어떤 파생변수를 만듦?\n",
    "- Female과 AGE는 양의 상관관계를 가지고 있음. Why?? 여자가 많으면 중위수가 올라간다 => 여성의 나이대가 더 높은 편이기때문이라는 추측 가능\n",
    "- 하지만 여성의 비율은 애초에 변동이 매우 작아 거의 degenerate 분포와 같은 형태를 보임. 이것의 문제는?\n",
    "- 여성 변수가 다른 변수들과 상관관계가 높은 경우가 많음. 그렇다면 여성을 어떻게 처리해야하는가?\n",
    "- 여성 관련한 파생변수... 여성비율에 따른 흡연율 ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "data": {
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       "<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>Sales</th>\n",
       "      <th>Age</th>\n",
       "      <th>HS</th>\n",
       "      <th>Income</th>\n",
       "      <th>Black</th>\n",
       "      <th>Female</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.54</td>\n",
       "      <td>27.47</td>\n",
       "      <td>53.15</td>\n",
       "      <td>3763.71</td>\n",
       "      <td>9.99</td>\n",
       "      <td>50.95</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.07</td>\n",
       "      <td>1.88</td>\n",
       "      <td>8.00</td>\n",
       "      <td>594.72</td>\n",
       "      <td>12.65</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>65.50</td>\n",
       "      <td>22.90</td>\n",
       "      <td>37.80</td>\n",
       "      <td>2626.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>45.70</td>\n",
       "      <td>29.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>105.35</td>\n",
       "      <td>26.40</td>\n",
       "      <td>48.30</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>1.60</td>\n",
       "      <td>50.75</td>\n",
       "      <td>34.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>119.00</td>\n",
       "      <td>27.40</td>\n",
       "      <td>53.30</td>\n",
       "      <td>3751.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>51.10</td>\n",
       "      <td>38.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>124.55</td>\n",
       "      <td>28.75</td>\n",
       "      <td>59.10</td>\n",
       "      <td>4116.50</td>\n",
       "      <td>13.55</td>\n",
       "      <td>51.50</td>\n",
       "      <td>41.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>265.70</td>\n",
       "      <td>32.30</td>\n",
       "      <td>67.30</td>\n",
       "      <td>5079.00</td>\n",
       "      <td>71.10</td>\n",
       "      <td>53.50</td>\n",
       "      <td>45.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales    Age     HS   Income  Black  Female  Price\n",
       "count   51.00  51.00  51.00    51.00  51.00   51.00  51.00\n",
       "mean   121.54  27.47  53.15  3763.71   9.99   50.95  38.07\n",
       "std     32.07   1.88   8.00   594.72  12.65    1.11   4.13\n",
       "min     65.50  22.90  37.80  2626.00   0.20   45.70  29.00\n",
       "25%    105.35  26.40  48.30  3271.00   1.60   50.75  34.70\n",
       "50%    119.00  27.40  53.30  3751.00   6.00   51.10  38.90\n",
       "75%    124.55  28.75  59.10  4116.50  13.55   51.50  41.35\n",
       "max    265.70  32.30  67.30  5079.00  71.10   53.50  45.50"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.describe(),2)"
   ]
  },
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       "        }</style><table id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Sales</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >HS</th>        <th class=\"col_heading level0 col3\" >Income</th>        <th class=\"col_heading level0 col4\" >Black</th>        <th class=\"col_heading level0 col5\" >Female</th>        <th class=\"col_heading level0 col6\" >Price</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row0\" class=\"row_heading level0 row0\" >Sales</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col1\" class=\"data row0 col1\" >0.23</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col2\" class=\"data row0 col2\" >0.07</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col3\" class=\"data row0 col3\" >0.33</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col4\" class=\"data row0 col4\" >0.19</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col5\" class=\"data row0 col5\" >0.15</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow0_col6\" class=\"data row0 col6\" >-0.3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col0\" class=\"data row1 col0\" >0.23</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col2\" class=\"data row1 col2\" >-0.1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col3\" class=\"data row1 col3\" >0.26</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col4\" class=\"data row1 col4\" >-0.04</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col5\" class=\"data row1 col5\" >0.55</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow1_col6\" class=\"data row1 col6\" >0.25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row2\" class=\"row_heading level0 row2\" >HS</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col0\" class=\"data row2 col0\" >0.07</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col1\" class=\"data row2 col1\" >-0.1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col3\" class=\"data row2 col3\" >0.53</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col4\" class=\"data row2 col4\" >-0.5</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col5\" class=\"data row2 col5\" >-0.42</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow2_col6\" class=\"data row2 col6\" >0.06</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row3\" class=\"row_heading level0 row3\" >Income</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col0\" class=\"data row3 col0\" >0.33</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col1\" class=\"data row3 col1\" >0.26</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col2\" class=\"data row3 col2\" >0.53</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col5\" class=\"data row3 col5\" >-0.07</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow3_col6\" class=\"data row3 col6\" >0.21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row4\" class=\"row_heading level0 row4\" >Black</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col1\" class=\"data row4 col1\" >-0.04</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col2\" class=\"data row4 col2\" >-0.5</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col5\" class=\"data row4 col5\" >0.45</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow4_col6\" class=\"data row4 col6\" >-0.15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row5\" class=\"row_heading level0 row5\" >Female</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col0\" class=\"data row5 col0\" >0.15</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col1\" class=\"data row5 col1\" >0.55</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col2\" class=\"data row5 col2\" >-0.42</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col3\" class=\"data row5 col3\" >-0.07</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col4\" class=\"data row5 col4\" >0.45</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow5_col6\" class=\"data row5 col6\" >0.02</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfblevel0_row6\" class=\"row_heading level0 row6\" >Price</th>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col0\" class=\"data row6 col0\" >-0.3</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col1\" class=\"data row6 col1\" >0.25</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col2\" class=\"data row6 col2\" >0.06</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col3\" class=\"data row6 col3\" >0.21</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col4\" class=\"data row6 col4\" >-0.15</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col5\" class=\"data row6 col5\" >0.02</td>\n",
       "                        <td id=\"T_4d8cef06_8224_11e9_ae1b_f8633f14cbfbrow6_col6\" class=\"data row6 col6\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2403ff5ada0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.corr(),2).style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #799cf8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #86a9fc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Sales</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >HS</th>        <th class=\"col_heading level0 col3\" >Income</th>        <th class=\"col_heading level0 col4\" >Black</th>        <th class=\"col_heading level0 col5\" >Price</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row0\" class=\"row_heading level0 row0\" >Sales</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col1\" class=\"data row0 col1\" >0.23</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col2\" class=\"data row0 col2\" >0.07</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col3\" class=\"data row0 col3\" >0.33</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col4\" class=\"data row0 col4\" >0.19</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow0_col5\" class=\"data row0 col5\" >-0.3</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col0\" class=\"data row1 col0\" >0.23</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col2\" class=\"data row1 col2\" >-0.1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col3\" class=\"data row1 col3\" >0.26</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col4\" class=\"data row1 col4\" >-0.04</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow1_col5\" class=\"data row1 col5\" >0.25</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row2\" class=\"row_heading level0 row2\" >HS</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col0\" class=\"data row2 col0\" >0.07</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col1\" class=\"data row2 col1\" >-0.1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col3\" class=\"data row2 col3\" >0.53</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col4\" class=\"data row2 col4\" >-0.5</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow2_col5\" class=\"data row2 col5\" >0.06</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row3\" class=\"row_heading level0 row3\" >Income</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col0\" class=\"data row3 col0\" >0.33</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col1\" class=\"data row3 col1\" >0.26</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col2\" class=\"data row3 col2\" >0.53</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow3_col5\" class=\"data row3 col5\" >0.21</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row4\" class=\"row_heading level0 row4\" >Black</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col1\" class=\"data row4 col1\" >-0.04</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col2\" class=\"data row4 col2\" >-0.5</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow4_col5\" class=\"data row4 col5\" >-0.15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfblevel0_row5\" class=\"row_heading level0 row5\" >Price</th>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col0\" class=\"data row5 col0\" >-0.3</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col1\" class=\"data row5 col1\" >0.25</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col2\" class=\"data row5 col2\" >0.06</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col3\" class=\"data row5 col3\" >0.21</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col4\" class=\"data row5 col4\" >-0.15</td>\n",
       "                        <td id=\"T_4d968ba4_8224_11e9_aa59_f8633f14cbfbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24040093b00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.drop(\"Female\", axis=1).corr(),2).style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['IPP'] = data.Income/data.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Sales</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >HS</th>        <th class=\"col_heading level0 col3\" >Income</th>        <th class=\"col_heading level0 col4\" >Black</th>        <th class=\"col_heading level0 col5\" >Female</th>        <th class=\"col_heading level0 col6\" >Price</th>        <th class=\"col_heading level0 col7\" >IPP</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row0\" class=\"row_heading level0 row0\" >Sales</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col1\" class=\"data row0 col1\" >0.23</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col2\" class=\"data row0 col2\" >0.07</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col3\" class=\"data row0 col3\" >0.33</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col4\" class=\"data row0 col4\" >0.19</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col5\" class=\"data row0 col5\" >0.15</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col6\" class=\"data row0 col6\" >-0.3</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow0_col7\" class=\"data row0 col7\" >0.51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col0\" class=\"data row1 col0\" >0.23</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col2\" class=\"data row1 col2\" >-0.1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col3\" class=\"data row1 col3\" >0.26</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col4\" class=\"data row1 col4\" >-0.04</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col5\" class=\"data row1 col5\" >0.55</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col6\" class=\"data row1 col6\" >0.25</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow1_col7\" class=\"data row1 col7\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row2\" class=\"row_heading level0 row2\" >HS</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col0\" class=\"data row2 col0\" >0.07</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col1\" class=\"data row2 col1\" >-0.1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col3\" class=\"data row2 col3\" >0.53</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col4\" class=\"data row2 col4\" >-0.5</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col5\" class=\"data row2 col5\" >-0.42</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col6\" class=\"data row2 col6\" >0.06</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow2_col7\" class=\"data row2 col7\" >0.43</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row3\" class=\"row_heading level0 row3\" >Income</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col0\" class=\"data row3 col0\" >0.33</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col1\" class=\"data row3 col1\" >0.26</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col2\" class=\"data row3 col2\" >0.53</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col5\" class=\"data row3 col5\" >-0.07</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col6\" class=\"data row3 col6\" >0.21</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow3_col7\" class=\"data row3 col7\" >0.75</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row4\" class=\"row_heading level0 row4\" >Black</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col1\" class=\"data row4 col1\" >-0.04</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col2\" class=\"data row4 col2\" >-0.5</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col5\" class=\"data row4 col5\" >0.45</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col6\" class=\"data row4 col6\" >-0.15</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow4_col7\" class=\"data row4 col7\" >0.15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row5\" class=\"row_heading level0 row5\" >Female</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col0\" class=\"data row5 col0\" >0.15</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col1\" class=\"data row5 col1\" >0.55</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col2\" class=\"data row5 col2\" >-0.42</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col3\" class=\"data row5 col3\" >-0.07</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col4\" class=\"data row5 col4\" >0.45</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col6\" class=\"data row5 col6\" >0.02</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow5_col7\" class=\"data row5 col7\" >-0.05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row6\" class=\"row_heading level0 row6\" >Price</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col0\" class=\"data row6 col0\" >-0.3</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col1\" class=\"data row6 col1\" >0.25</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col2\" class=\"data row6 col2\" >0.06</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col3\" class=\"data row6 col3\" >0.21</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col4\" class=\"data row6 col4\" >-0.15</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col5\" class=\"data row6 col5\" >0.02</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow6_col7\" class=\"data row6 col7\" >-0.48</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfblevel0_row7\" class=\"row_heading level0 row7\" >IPP</th>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col0\" class=\"data row7 col0\" >0.51</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col1\" class=\"data row7 col1\" >0.09</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col2\" class=\"data row7 col2\" >0.43</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col3\" class=\"data row7 col3\" >0.75</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col4\" class=\"data row7 col4\" >0.15</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col5\" class=\"data row7 col5\" >-0.05</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col6\" class=\"data row7 col6\" >-0.48</td>\n",
       "                        <td id=\"T_4da99e34_8224_11e9_ac73_f8633f14cbfbrow7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24040093940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.corr(),2).style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x240400f8898>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(np.log(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Target = data.Sales\n",
    "Variables = data.drop(\"Sales\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(len(data.columns)*2, 6))\n",
    "idx = 1\n",
    "for li in data.columns :\n",
    "    ax = fig.add_subplot(2, len(data.columns)/2,idx)\n",
    "    sns.boxplot(data = data, x = li)\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(len(data.columns)*2, 6))\n",
    "idx = 1\n",
    "for li in data.columns :\n",
    "    ax = fig.add_subplot(2, len(data.columns)/2,idx)\n",
    "    sns.boxplot(data = np.log(data), x = li)\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col0 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col1 {\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col2 {\n",
       "            background-color:  #bed2f6;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col3 {\n",
       "            background-color:  #cad8ef;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col4 {\n",
       "            background-color:  #cfdaea;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col5 {\n",
       "            background-color:  #bed2f6;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col6 {\n",
       "            background-color:  #5b7ae5;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col7 {\n",
       "            background-color:  #f7b194;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col0 {\n",
       "            background-color:  #d6dce4;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col1 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col2 {\n",
       "            background-color:  #a6c4fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col3 {\n",
       "            background-color:  #a9c6fd;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col4 {\n",
       "            background-color:  #cdd9ec;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col5 {\n",
       "            background-color:  #f7b396;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col6 {\n",
       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col7 {\n",
       "            background-color:  #bcd2f7;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col0 {\n",
       "            background-color:  #93b5fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col3 {\n",
       "            background-color:  #f3c7b1;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col4 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col5 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col6 {\n",
       "            background-color:  #b7cff9;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col7 {\n",
       "            background-color:  #f5c0a7;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col0 {\n",
       "            background-color:  #e3d9d3;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col1 {\n",
       "            background-color:  #a5c3fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col2 {\n",
       "            background-color:  #f5a081;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col3 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col4 {\n",
       "            background-color:  #c7d7f0;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col5 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col6 {\n",
       "            background-color:  #cedaeb;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col7 {\n",
       "            background-color:  #e36c55;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col0 {\n",
       "            background-color:  #aac7fd;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col1 {\n",
       "            background-color:  #7396f5;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col2 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col3 {\n",
       "            background-color:  #6f92f3;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col4 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col5 {\n",
       "            background-color:  #ecd3c5;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col6 {\n",
       "            background-color:  #b1cbfc;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col7 {\n",
       "            background-color:  #aec9fc;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col0 {\n",
       "            background-color:  #b2ccfb;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col1 {\n",
       "            background-color:  #f0cdbb;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col2 {\n",
       "            background-color:  #6282ea;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col3 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col4 {\n",
       "            background-color:  #f4c6af;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col5 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col6 {\n",
       "            background-color:  #a7c5fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col7 {\n",
       "            background-color:  #90b2fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col1 {\n",
       "            background-color:  #90b2fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col2 {\n",
       "            background-color:  #c5d6f2;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col3 {\n",
       "            background-color:  #8fb1fe;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col4 {\n",
       "            background-color:  #c0d4f5;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col5 {\n",
       "            background-color:  #98b9ff;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col6 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col7 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col0 {\n",
       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col1 {\n",
       "            background-color:  #6c8ff1;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col2 {\n",
       "            background-color:  #f7b89c;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col3 {\n",
       "            background-color:  #ee8468;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col4 {\n",
       "            background-color:  #bed2f6;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col5 {\n",
       "            background-color:  #7ea1fa;\n",
       "            color:  #000000;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col6 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col7 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Sales</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >HS</th>        <th class=\"col_heading level0 col3\" >Income</th>        <th class=\"col_heading level0 col4\" >Black</th>        <th class=\"col_heading level0 col5\" >Female</th>        <th class=\"col_heading level0 col6\" >Price</th>        <th class=\"col_heading level0 col7\" >IPP</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row0\" class=\"row_heading level0 row0\" >Sales</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col1\" class=\"data row0 col1\" >0.31</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col2\" class=\"data row0 col2\" >0.04</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col3\" class=\"data row0 col3\" >0.38</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col4\" class=\"data row0 col4\" >0.13</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col5\" class=\"data row0 col5\" >0.16</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col6\" class=\"data row0 col6\" >-0.31</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow0_col7\" class=\"data row0 col7\" >0.54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col0\" class=\"data row1 col0\" >0.31</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col2\" class=\"data row1 col2\" >-0.07</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col3\" class=\"data row1 col3\" >0.27</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col4\" class=\"data row1 col4\" >0.12</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col5\" class=\"data row1 col5\" >0.56</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col6\" class=\"data row1 col6\" >0.21</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow1_col7\" class=\"data row1 col7\" >0.1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row2\" class=\"row_heading level0 row2\" >HS</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col0\" class=\"data row2 col0\" >0.04</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col1\" class=\"data row2 col1\" >-0.07</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col3\" class=\"data row2 col3\" >0.58</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col4\" class=\"data row2 col4\" >-0.58</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col5\" class=\"data row2 col5\" >-0.38</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col6\" class=\"data row2 col6\" >0.08</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow2_col7\" class=\"data row2 col7\" >0.47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row3\" class=\"row_heading level0 row3\" >Income</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col0\" class=\"data row3 col0\" >0.38</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col1\" class=\"data row3 col1\" >0.27</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col2\" class=\"data row3 col2\" >0.58</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col4\" class=\"data row3 col4\" >0.09</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col5\" class=\"data row3 col5\" >-0.09</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col6\" class=\"data row3 col6\" >0.19</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow3_col7\" class=\"data row3 col7\" >0.78</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row4\" class=\"row_heading level0 row4\" >Black</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col0\" class=\"data row4 col0\" >0.13</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col1\" class=\"data row4 col1\" >0.12</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col2\" class=\"data row4 col2\" >-0.58</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col3\" class=\"data row4 col3\" >0.09</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col5\" class=\"data row4 col5\" >0.4</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col6\" class=\"data row4 col6\" >0.05</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow4_col7\" class=\"data row4 col7\" >0.04</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row5\" class=\"row_heading level0 row5\" >Female</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col0\" class=\"data row5 col0\" >0.16</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col1\" class=\"data row5 col1\" >0.56</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col2\" class=\"data row5 col2\" >-0.38</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col3\" class=\"data row5 col3\" >-0.09</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col4\" class=\"data row5 col4\" >0.4</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col6\" class=\"data row5 col6\" >0.01</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow5_col7\" class=\"data row5 col7\" >-0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row6\" class=\"row_heading level0 row6\" >Price</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col0\" class=\"data row6 col0\" >-0.31</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col1\" class=\"data row6 col1\" >0.21</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col2\" class=\"data row6 col2\" >0.08</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col3\" class=\"data row6 col3\" >0.19</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col4\" class=\"data row6 col4\" >0.05</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col5\" class=\"data row6 col5\" >0.01</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow6_col7\" class=\"data row6 col7\" >-0.47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfblevel0_row7\" class=\"row_heading level0 row7\" >IPP</th>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col0\" class=\"data row7 col0\" >0.54</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col1\" class=\"data row7 col1\" >0.1</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col2\" class=\"data row7 col2\" >0.47</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col3\" class=\"data row7 col3\" >0.78</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col4\" class=\"data row7 col4\" >0.04</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col5\" class=\"data row7 col5\" >-0.09</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col6\" class=\"data row7 col6\" >-0.47</td>\n",
       "                        <td id=\"T_533b3c78_8224_11e9_aeec_f8633f14cbfbrow7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24045af6d68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.log(data).corr(),2).style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #3b4cc0;\n",
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       "            background-color:  #f4c5ad;\n",
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       "            background-color:  #7295f4;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f4c6af;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f29274;\n",
       "            color:  #000000;\n",
       "        }    #T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col4 {\n",
       "            background-color:  #cad8ef;\n",
       "            color:  #000000;\n",
       "        }    #T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col5 {\n",
       "            background-color:  #90b2fe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col7 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Sales</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >HS</th>        <th class=\"col_heading level0 col3\" >Income</th>        <th class=\"col_heading level0 col4\" >Black</th>        <th class=\"col_heading level0 col5\" >Female</th>        <th class=\"col_heading level0 col6\" >Price</th>        <th class=\"col_heading level0 col7\" >IPP</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row0\" class=\"row_heading level0 row0\" >Sales</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col1\" class=\"data row0 col1\" >0.23</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col2\" class=\"data row0 col2\" >0.07</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col3\" class=\"data row0 col3\" >0.33</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col4\" class=\"data row0 col4\" >0.19</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col5\" class=\"data row0 col5\" >0.15</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col6\" class=\"data row0 col6\" >-0.3</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow0_col7\" class=\"data row0 col7\" >0.51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col0\" class=\"data row1 col0\" >0.23</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col2\" class=\"data row1 col2\" >-0.1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col3\" class=\"data row1 col3\" >0.26</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col4\" class=\"data row1 col4\" >-0.04</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col5\" class=\"data row1 col5\" >0.55</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col6\" class=\"data row1 col6\" >0.25</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow1_col7\" class=\"data row1 col7\" >0.09</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row2\" class=\"row_heading level0 row2\" >HS</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col0\" class=\"data row2 col0\" >0.07</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col1\" class=\"data row2 col1\" >-0.1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col3\" class=\"data row2 col3\" >0.53</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col4\" class=\"data row2 col4\" >-0.5</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col5\" class=\"data row2 col5\" >-0.42</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col6\" class=\"data row2 col6\" >0.06</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow2_col7\" class=\"data row2 col7\" >0.43</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row3\" class=\"row_heading level0 row3\" >Income</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col0\" class=\"data row3 col0\" >0.33</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col1\" class=\"data row3 col1\" >0.26</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col2\" class=\"data row3 col2\" >0.53</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col5\" class=\"data row3 col5\" >-0.07</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col6\" class=\"data row3 col6\" >0.21</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow3_col7\" class=\"data row3 col7\" >0.75</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row4\" class=\"row_heading level0 row4\" >Black</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col0\" class=\"data row4 col0\" >0.19</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col1\" class=\"data row4 col1\" >-0.04</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col2\" class=\"data row4 col2\" >-0.5</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col5\" class=\"data row4 col5\" >0.45</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col6\" class=\"data row4 col6\" >-0.15</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow4_col7\" class=\"data row4 col7\" >0.15</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row5\" class=\"row_heading level0 row5\" >Female</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col0\" class=\"data row5 col0\" >0.15</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col1\" class=\"data row5 col1\" >0.55</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col2\" class=\"data row5 col2\" >-0.42</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col3\" class=\"data row5 col3\" >-0.07</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col4\" class=\"data row5 col4\" >0.45</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col6\" class=\"data row5 col6\" >0.02</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow5_col7\" class=\"data row5 col7\" >-0.05</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row6\" class=\"row_heading level0 row6\" >Price</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col0\" class=\"data row6 col0\" >-0.3</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col1\" class=\"data row6 col1\" >0.25</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col2\" class=\"data row6 col2\" >0.06</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col3\" class=\"data row6 col3\" >0.21</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col4\" class=\"data row6 col4\" >-0.15</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col5\" class=\"data row6 col5\" >0.02</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow6_col7\" class=\"data row6 col7\" >-0.48</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfblevel0_row7\" class=\"row_heading level0 row7\" >IPP</th>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col0\" class=\"data row7 col0\" >0.51</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col1\" class=\"data row7 col1\" >0.09</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col2\" class=\"data row7 col2\" >0.43</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col3\" class=\"data row7 col3\" >0.75</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col4\" class=\"data row7 col4\" >0.15</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col5\" class=\"data row7 col5\" >-0.05</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col6\" class=\"data row7 col6\" >-0.48</td>\n",
       "                        <td id=\"T_534d0590_8224_11e9_a1fe_f8633f14cbfbrow7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24045b71860>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.corr(),2).style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2403b9d4438>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x216 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(len(Variables.columns)*4, 3))\n",
    "idx = 1\n",
    "for li in Variables.columns :\n",
    "    ax = fig.add_subplot(1, len(Variables.columns),idx)\n",
    "    sns.boxplot(data = Variables, x = li)\n",
    "\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Target = np.log(data).Sales\n",
    "Variables = np.log(data).drop(\"Sales\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Sales)</td>  <th>  R-squared:         </th> <td>   0.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   4.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>0.000562</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:42</td>     <th>  Log-Likelihood:    </th> <td>  16.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    51</td>      <th>  AIC:               </th> <td>  -19.31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    44</td>      <th>  BIC:               </th> <td>  -5.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</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>   -0.6036</td> <td>    6.396</td> <td>   -0.094</td> <td> 0.925</td> <td>  -13.494</td> <td>   12.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Age)</th>    <td>    0.7529</td> <td>    0.546</td> <td>    1.378</td> <td> 0.175</td> <td>   -0.348</td> <td>    1.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(HS)</th>     <td>   -0.4408</td> <td>    0.342</td> <td>   -1.287</td> <td> 0.205</td> <td>   -1.131</td> <td>    0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Income)</th> <td>    0.2711</td> <td>    0.215</td> <td>    1.258</td> <td> 0.215</td> <td>   -0.163</td> <td>    0.705</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Black)</th>  <td>   -0.0170</td> <td>    0.030</td> <td>   -0.570</td> <td> 0.571</td> <td>   -0.077</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Female)</th> <td>    0.2259</td> <td>    1.687</td> <td>    0.134</td> <td> 0.894</td> <td>   -3.175</td> <td>    3.627</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Price)</th>  <td>   -0.3078</td> <td>    0.191</td> <td>   -1.612</td> <td> 0.114</td> <td>   -0.693</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(IPP)</th>    <td>    0.5789</td> <td>    0.130</td> <td>    4.455</td> <td> 0.000</td> <td>    0.317</td> <td>    0.841</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>26.288</td> <th>  Durbin-Watson:     </th> <td>   1.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  64.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.390</td> <th>  Prob(JB):          </th> <td>1.20e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.737</td> <th>  Cond. No.          </th> <td>3.12e+16</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 smallest eigenvalue is 7.69e-30. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Sales)   R-squared:                       0.405\n",
       "Model:                            OLS   Adj. R-squared:                  0.324\n",
       "Method:                 Least Squares   F-statistic:                     4.993\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           0.000562\n",
       "Time:                        00:13:42   Log-Likelihood:                 16.654\n",
       "No. Observations:                  51   AIC:                            -19.31\n",
       "Df Residuals:                      44   BIC:                            -5.785\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -0.6036      6.396     -0.094      0.925     -13.494      12.287\n",
       "np.log(Age)        0.7529      0.546      1.378      0.175      -0.348       1.854\n",
       "np.log(HS)        -0.4408      0.342     -1.287      0.205      -1.131       0.249\n",
       "np.log(Income)     0.2711      0.215      1.258      0.215      -0.163       0.705\n",
       "np.log(Black)     -0.0170      0.030     -0.570      0.571      -0.077       0.043\n",
       "np.log(Female)     0.2259      1.687      0.134      0.894      -3.175       3.627\n",
       "np.log(Price)     -0.3078      0.191     -1.612      0.114      -0.693       0.077\n",
       "np.log(IPP)        0.5789      0.130      4.455      0.000       0.317       0.841\n",
       "==============================================================================\n",
       "Omnibus:                       26.288   Durbin-Watson:                   1.723\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.104\n",
       "Skew:                           1.390   Prob(JB):                     1.20e-14\n",
       "Kurtosis:                       7.737   Cond. No.                     3.12e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 7.69e-30. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS.from_formula(\"np.log(Sales) ~ np.log(Age) + np.log(HS) + np.log(Income) + np.log(Black) + np.log(Female) + np.log(Price) + np.log(IPP)\", data=data).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2404921c518>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Target = np.log(data).Sales\n",
    "Variables = np.log(data).drop(\"Sales\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3935218125434194\n",
      "[ 0.54672098 -0.31293023  0.         -0.00610627  0.         -0.\n",
      "  0.77173612]\n",
      "0.001147644885583363\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24049283ba8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = LassoCV(cv=5, fit_intercept=True).fit(Variables, Target)\n",
    "print(L.score(Variables, Target))\n",
    "print(L.coef_)\n",
    "print(L.alpha_)\n",
    "resid = Target - L.predict(Variables)\n",
    "plt.scatter(range(len(resid)), resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3976201933753446\n",
      "[ 0.55243255 -0.34576691  0.24914727 -0.00919837  0.11386143 -0.28261595\n",
      "  0.53176322]\n",
      "0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hawi7\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2404930d1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PO912dbibqDeWOvbq2B3klr/VSt0G/FJ0v3Tb1ZHjCbIfOh17LxgarOWVlsPfaqVOs5VSdr9009VRtxNkXXQ69pb63o4qh7+7faxW6jRbqazul7JnxDRVp2Nv5scn25av+pWWW/5WO3WZrVRW90tVnhrZRGV9b0cvOPzNeqTM7pe6nCCboE5dkQsV6vaRdL6kOyU90vr/vDZlrpD0VUlHJH1N0puKrNOsLtz9koc6dUUuVOjBbpI+AvwgInZL2gmcFxF/tqjMi4GIiEckrQEOAi+JiJmlfrYf7GZN0NSbray6+vVgt23A1a3XtwBfBs4I/4j47wWvpyUdB1YDS4a/WRO4+8Wqquhsn4sj4nGA1v8XLVVY0pXAKuCbBddrZmYFLNvyl/Ql4IVtPnpPNyuSdAnwj8ANEfGTDmV2ADsA1q1b182PNzOzLiwb/hFxbafPJD0h6ZKIeLwV7sc7lHs+8AXgLyLiviXWtRfYC3N9/svVzczMVqZot88B4IbW6xuA2xcXkLQK+BzwDxHx6YLrMzOzBIqG/27gOkmPANe13iNpVNLHWmXeCPw68DZJD7b+XVFwvWZmVoC/w9fMrEHOdqqnn+1jZpYhh7+ZWYYc/mZmGXL4m5llyOFvZpYhh7+ZWYYc/mZmGXL4m5llyOFvZpYhh7+ZWYYc/mZmGXL4m5llyOFvZpYhh7+ZWYYc/mZmGXL4m5llaNnv8DUza6L9h6bYMz7J9Mwsa4aHGNu8ke2bRsquVt84/M0sO/sPTbFr32FmT54CYGpmll37DgNkcwJwt4+ZZWfP+OSzwT9v9uQp9oxPllSj/nP4m1l2pmdmu1reRA5/M8vOmuGhrpY3kcPfzLIztnkjQ4MDZywbGhxgbPPGkmrUfx7wNbPszA/qeraPmVlmtm8aySrsFyvU7SPpfEl3Snqk9f95S5R9vqQpSX9bZJ1mZlZc0T7/ncBdEbEBuKv1vpMPAP9WcH1mZpZA0fDfBtzSen0LsL1dIUmvBC4GvlhwfWZmlkDR8L84Ih4HaP1/0eICks4B/goYK7guMzNLZNkBX0lfAl7Y5qP3nOU63gHcERGPSVpuXTuAHQDr1q07yx9vZmbdWjb8I+LaTp9JekLSJRHxuKRLgONtiv0K8GuS3gE8F1gl6UcR8VPjAxGxF9gLMDo6Gme7EWZm1p2iUz0PADcAu1v/3764QES8Zf61pLcBo+2C38zM+qdon/9u4DpJjwDXtd4jaVTSx4pWzszMekMR1exdGR0djYmJibKrYWZWK5IORsTocuX8bB8zsww5/M3MMuRn+zRY7l9TZ2adOfwbyl9TZ2ZLcbdPQ/lr6sxsKQ7/hvLX1JnZUhz+DeWvqTOzpTj8G8pfU2dmS/GAb0P5a+rMbCkO/wbL/WvqzKwzd/uYmWXI4W9mliGHv5lZhhz+ZmYZcvibmWXI4W9mliGHv5lZhir7TV6STgDfKfAjLgS+l6g6dZHbNue2veBtzkWRbX5RRKxerlBlw78oSRNn81VmTZLbNue2veBtzkU/ttndPmZmGXL4m5llqMnhv7fsCpQgt23ObXvB25yLnm9zY/v8zcyssya3/M3MrIPGhb+kLZImJR2VtLPs+vSCpE9IOi7p6wuWnS/pTkmPtP4/r8w6pibpUkn3SHpY0hFJ72wtb+x2S/oZSf8h6b9a2/z+1vLLJN3f2uZPSVpVdl1TkjQg6ZCkz7feN317vy3psKQHJU20lvX8uG5U+EsaAG4GXgNcDrxZ0uXl1qon/h7YsmjZTuCuiNgA3NV63yTPAO+OiJcArwL+oLVvm7zdTwHXRMQvAVcAWyS9Cvgw8NHWNj8JvL3EOvbCO4GHF7xv+vYCvDoirlgwvbPnx3Wjwh+4EjgaEY9GxNPArcC2kuuUXET8O/CDRYu3Abe0Xt8CbO9rpXosIh6PiP9svf5f5sJhhAZvd8z5UevtYOtfANcAn2ktb9Q2S1oLvBb4WOu9aPD2LqHnx3XTwn8EeGzB+2OtZTm4OCIeh7mgBC4quT49I2k9sAm4n4Zvd6sL5EHgOHAn8E1gJiKeaRVp2jH+N8CfAj9pvb+AZm8vzJ3QvyjpoKQdrWU9P66b9jWOarPM05kaRNJzgc8CfxQR/zPXMGyuiDgFXCFpGPgc8JJ2xfpbq96Q9DrgeEQclHT1/OI2RRuxvQtcFRHTki4C7pT0jX6stGkt/2PApQverwWmS6pLvz0h6RKA1v/HS65PcpIGmQv+f4qIfa3Fjd9ugIiYAb7M3HjHsKT5hluTjvGrgK2Svs1cl+01zF0JNHV7AYiI6db/x5k7wV9JH47rpoX/A8CG1uyAVcD1wIGS69QvB4AbWq9vAG4vsS7Jtfp+Pw48HBF/veCjxm63pNWtFj+ShoBrmRvruAd4Q6tYY7Y5InZFxNqIWM/c3+7dEfEWGrq9AJJ+TtLz5l8DvwV8nT4c1427yUvSbzPXWhgAPhERHyy5SslJ+mfgauae/PcE8JfAfuA2YB3wXeB3I2LxoHBtSfpV4CvAYU73B/85c/3+jdxuSa9gbrBvgLmG2m0RcZOkn2euZXw+cAh4a0Q8VV5N02t1+/xJRLyuydvb2rbPtd6eC3wyIj4o6QJ6fFw3LvzNzGx5Tev2MTOzs+DwNzPLkMPfzCxDDn8zsww5/M3MMuTwNzPLkMPfzCxDDn8zswz9P6vUxMiPqMeHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = RidgeCV(cv=5, fit_intercept=True).fit(Variables, Target)\n",
    "print(R.score(Variables, Target))\n",
    "print(R.coef_)\n",
    "print(R.alpha_)\n",
    "resid = Target - R.predict(Variables)\n",
    "plt.scatter(range(len(resid)), resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "crit = np.log(data.Sales).std()\n",
    "data_out = data[np.abs(LR.resid) < crit * 1]\n",
    "data_outlier = data[np.abs(LR.resid) > crit * 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Target = np.log(data_out).Sales\n",
    "Variables = np.log(data_out).drop(\"Sales\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Sales)</td>  <th>  R-squared:         </th> <td>   0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   13.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>4.23e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:43</td>     <th>  Log-Likelihood:    </th> <td>  44.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    46</td>      <th>  AIC:               </th> <td>  -75.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    39</td>      <th>  BIC:               </th> <td>  -62.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</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>    0.4575</td> <td>    3.715</td> <td>    0.123</td> <td> 0.903</td> <td>   -7.057</td> <td>    7.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Age)</th>    <td>    0.2088</td> <td>    0.322</td> <td>    0.648</td> <td> 0.521</td> <td>   -0.443</td> <td>    0.861</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(HS)</th>     <td>   -0.4174</td> <td>    0.199</td> <td>   -2.095</td> <td> 0.043</td> <td>   -0.820</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Income)</th> <td>    0.2372</td> <td>    0.122</td> <td>    1.941</td> <td> 0.060</td> <td>   -0.010</td> <td>    0.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Black)</th>  <td>   -0.0131</td> <td>    0.017</td> <td>   -0.759</td> <td> 0.452</td> <td>   -0.048</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Female)</th> <td>    0.4906</td> <td>    1.018</td> <td>    0.482</td> <td> 0.633</td> <td>   -1.569</td> <td>    2.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Price)</th>  <td>   -0.3313</td> <td>    0.104</td> <td>   -3.183</td> <td> 0.003</td> <td>   -0.542</td> <td>   -0.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(IPP)</th>    <td>    0.5685</td> <td>    0.075</td> <td>    7.614</td> <td> 0.000</td> <td>    0.417</td> <td>    0.720</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.867</td> <th>  Durbin-Watson:     </th> <td>   1.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.239</td> <th>  Jarque-Bera (JB):  </th> <td>   2.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.415</td> <th>  Prob(JB):          </th> <td>   0.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.282</td> <th>  Cond. No.          </th> <td>1.31e+16</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 smallest eigenvalue is 3.96e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Sales)   R-squared:                       0.670\n",
       "Model:                            OLS   Adj. R-squared:                  0.619\n",
       "Method:                 Least Squares   F-statistic:                     13.20\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           4.23e-08\n",
       "Time:                        00:13:43   Log-Likelihood:                 44.662\n",
       "No. Observations:                  46   AIC:                            -75.32\n",
       "Df Residuals:                      39   BIC:                            -62.52\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          0.4575      3.715      0.123      0.903      -7.057       7.972\n",
       "np.log(Age)        0.2088      0.322      0.648      0.521      -0.443       0.861\n",
       "np.log(HS)        -0.4174      0.199     -2.095      0.043      -0.820      -0.014\n",
       "np.log(Income)     0.2372      0.122      1.941      0.060      -0.010       0.484\n",
       "np.log(Black)     -0.0131      0.017     -0.759      0.452      -0.048       0.022\n",
       "np.log(Female)     0.4906      1.018      0.482      0.633      -1.569       2.550\n",
       "np.log(Price)     -0.3313      0.104     -3.183      0.003      -0.542      -0.121\n",
       "np.log(IPP)        0.5685      0.075      7.614      0.000       0.417       0.720\n",
       "==============================================================================\n",
       "Omnibus:                        2.867   Durbin-Watson:                   1.940\n",
       "Prob(Omnibus):                  0.239   Jarque-Bera (JB):                2.310\n",
       "Skew:                           0.415   Prob(JB):                        0.315\n",
       "Kurtosis:                       2.282   Cond. No.                     1.31e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 3.96e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS.from_formula(\"np.log(Sales) ~ np.log(Age) + np.log(HS) + np.log(Income) + np.log(Black) + np.log(Female) + np.log(Price) + np.log(IPP)\", data=data_out).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x240493aacc0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SL3QRONVovSjS1vv9ppLWAtsjYgmwPTlOSYLHDcD7gaXADZJOaSnyqYg4P3lkHhSgOF37qk5zLUr7FkWnVGNRUgVWPEXqafYbGFYCG5PnG4FVbcosB7ZFxOGkN7ENWNHn+85IURq8qh+gRWnfophqQ0Sgkl8MLBtFWtfR7xjDGRFxECAiDko6vU2ZBcDzLcejyblx35X0JvBDmmmmtvcalbQGWAMwPDw8o0oWpWtf1Q/QorRvkbRLNS5bv6MwqQIrniJtvd+1xyDpAUlPtHmsnOZ7qM258Q//T0XEecAHk8dnpvohEbEhIhoR0RgaGprmWzcVZRZRkb4RZKko7Vt0Vf1iYNko0tb7XXsMEXHxVK9JekHSmUlv4Uyg3RjBKPCRluOFwEPJzx5L/n1N0n/QHIP412nXfpqKMouoSN8IslSU9i0696ysm6Ks69AUmZvpXSzdArwcEeslrQXmR8SXJ5SZD+wC/iw59SjwXuBVYF5EvCTpROD7wAMR8a1u79toNGJkZKTneuepirOSbHomzt6C5hcD35DJBkXSrohodCvX7xjDemCTpKuB54ArkzdvAJ+LiGsi4rCkbwA7k2vWJed+H9iaBIU5wAPAv/RZn8IryjcCGzz3rKws+uox5KXMPQYzs7xMt8fgLTHMzCylNltimPXCY0JWRw4MZlOo61YfDobmVJLZFKq6Ur2Tqm7bYjPjwGA2hTouSKtjMLTJHBjMplDVleqd1DEY2mQODGZTqONWH3UMhjaZA4PZFIq0d82g1DEY2mSelWTWQVFWqg9qppBXZxs4MJgV3qCnzRYlGFp+nEoyKzjPFLJBc2AwKzjPFLJBc2AwKzjPFLJBc2AwKzjPFLJB8+CzWcF5ppANmgODWQl4ppANkgODmVWGd4bNhgODmVVCXbdJnw19DT5Lmi9pm6Snk39PmaLcjyUdkfSjCecXS3okuf4uSSf1Ux8zqy+v98hOv7OS1gLbI2IJsD05bucW4DNtzt8M3Jpc/wpwdZ/1MbPE5t1jLFu/g8Vr72PZ+h2Vv6eC13tkp9/AsBLYmDzfCKxqVygitgOvtZ6TJOBC4O5u11tv6vbBYMfV8YY7Xu+RnX4DwxkRcRAg+ff0GVx7KnAkIo4lx6PAlIlASWskjUgaOXToUM8VrotuHwwOGtVWx7SK13tkp+vgs6QHgD9s89JX+3xvtTkXUxWOiA3ABoBGozFlOWvq9sHgQbpqq2Naxes9stM1METExVO9JukFSWdGxEFJZwIvzuC9XwLmSToh6TUsBA7M4HrroNMHQ6eg4T+iajhr3lzG2vwOVD2t4vUe2eg3lbQFWJ08Xw3cO90LIyKAB4ErerneOuuUb63jt8m6cVrF+tFvYFgPXCLpaeCS5BhJDUnfGS8k6WHgB8BFkkYlLU9e+grwRUn7aI453N5nfSzR6YPBg3TVV8e7z1l2+lrgFhEvAxe1OT8CXNNy/MEprt8PLO2nDtZet3xr6xgD+NtkFTmtYr3yyucKm+qDwYN0ZtaJA0NN+dukmU3F92MwM7MU9xjMrFS8g+rsc2Aws9LwDqqD4cBgZqVRlsWZZe/VODCYWWmUYXFmFXo1Hnw2s9Iow+LMKmxg6MBgZqVRhq0+ytCr6caBwcxKowxbfZShV9ONxxisUso+6GfdFX1x5nXLzyn9ljMODFYZZR/0c1CrhipsOePAYJVRlqmM7ZQ9qFla0Xs13XiMwSqjzIN+VZjJYtXhwGCVUeZBvzIHNaseBwarjDJMZZxKmYOaVY8Dg1VGGaYyTqXMQc2qx4PPVillHfSrwkwWq46+AoOk+cBdwCLgWeATEfFKm3I/Bj4A/HdEfLzl/PeADwO/Sk59NiIe66dOZmVV1qBWV1WeXtxvKmktsD0ilgDbk+N2bgE+M8Vr10XE+cnDQcHMCm98evHYkaMEx6cXb949lnfVMtFvYFgJbEyebwRWtSsUEduB1/p8LzOzQqj69OJ+A8MZEXEQIPn39B5+xo2Sfi7pVkknT1VI0hpJI5JGDh061Gt9zcz6VvXpxV0Dg6QHJD3R5rEyg/e/Hvhj4H3AfOArUxWMiA0R0YiIxtDQUAZvbWbWm6pPL+4aGCLi4oj4kzaPe4EXJJ0JkPz74kzePCIORtPrwHeBpb38J8zMBqnq04v7TSVtAVYnz1cD987k4pagIprjE0/0WR8zs1lX5jUz06GI6P1i6VRgEzAMPAdcGRGHJTWAz0XENUm5h2mmjN4KvAxcHRFbJe0AhgABjyXX/Lrb+zYajRgZGem53mZmdSRpV0Q0upXrax1DRLwMXNTm/AhwTcvxB6e4/sJ+3t/MzLLnLTHMzCzFgcHMzFIcGMzMLMWBwczMUhwYzMwsxYHBzMxSHBjMzCzFgcHMzFIcGMzMLMWBwczMUhwYzMwsxYHBzMxS+tpdNS+SDgG/7PHy04CXMqxO2bk9jnNbpLk90qrQHm+PiK53OitlYOiHpJHpbDtbF26P49wWaW6PtDq1h1NJZmaW4sBgZmYpdQwMG/KuQMG4PY5zW6S5PdJq0x61G2MwM7PO6thjMDOzDmoVGCStkLRX0j5Ja/Ouz6BJukPSi5KeaDk3X9I2SU8n/56SZx0HRdLZkh6U9JSkPZK+kJyvXXtIeoukn0r6WdIWf5ecXyzpkaQt7pJ0Ut51HSRJcyTtlvSj5Lg27VGbwCBpDnAb8FHgXOCTks7Nt1YD9z1gxYRza4HtEbEE2J4c18Ex4G8i4l3AB4DPJ78PdWyP14ELI+JPgfOBFZI+ANwM3Jq0xSvA1TnWMQ9fAJ5qOa5Ne9QmMABLgX0RsT8i3gDuBFbmXKeBioj/Ag5POL0S2Jg83wisGmilchIRByPi0eT5azQ/ABZQw/aIpl8nhycmjwAuBO5OzteiLcZJWgh8DPhOcixq1B51CgwLgOdbjkeTc3V3RkQchOaHJXB6zvUZOEmLgAuAR6hpeyRpk8eAF4FtwDPAkYg4lhSp29/LN4EvA79Njk+lRu1Rp8CgNuc8JavmJL0V+CHw1xHxat71yUtEvBkR5wMLafau39Wu2GBrlQ9JHwdejIhdrafbFK1se5yQdwUGaBQ4u+V4IXAgp7oUyQuSzoyIg5LOpPmNsRYknUgzKPx7RNyTnK5tewBExBFJD9Ecd5kn6YTkW3Kd/l6WAZdJ+gvgLcAf0OxB1KY96tRj2AksSWYWnARcBWzJuU5FsAVYnTxfDdybY10GJskZ3w48FRH/1PJS7dpD0pCkecnzucDFNMdcHgSuSIrVoi0AIuL6iFgYEYtofk7siIhPUaP2qNUCt+QbwDeBOcAdEXFjzlUaKEnfBz5Cc5fIF4AbgM3AJmAYeA64MiImDlBXjqQ/Bx4GHud4HvlvaY4z1Ko9JL2H5mDqHJpfFjdFxDpJ76A5SWM+sBv4dES8nl9NB0/SR4AvRcTH69QetQoMZmbWXZ1SSWZmNg0ODGZmluLAYGZmKQ4MZmaW4sBgZmYpDgxmZpbiwGBmZikODGZmlvL/pCVPn3xWJnUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6607244774449125\n",
      "[ 0.15724018 -0.33413347  0.         -0.00432363  0.         -0.08829858\n",
      "  0.75012593]\n",
      "0.0006069217900888012\n",
      "2.4492064426795617\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x240496225f8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = LassoCV(cv=5, fit_intercept=True).fit(Variables, Target)\n",
    "print(L.score(Variables, Target))\n",
    "print(L.coef_)\n",
    "print(L.alpha_)\n",
    "print(L.intercept_)\n",
    "resid = Target - L.predict(Variables)\n",
    "plt.scatter(range(len(resid)), resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6602331271917548\n",
      "[ 0.20881077 -0.28046384  0.18350666 -0.00150744  0.08127688 -0.32382604\n",
      "  0.5073327 ]\n",
      "0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hawi7\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2404968c9b0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = RidgeCV(cv=5, fit_intercept=True).fit(Variables, Target)\n",
    "print(R.score(Variables, Target))\n",
    "print(R.coef_)\n",
    "print(R.alpha_)\n",
    "resid = Target - R.predict(Variables)\n",
    "plt.scatter(range(len(resid)), resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Sales)</td>  <th>  R-squared:         </th> <td>   0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.627</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   16.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>1.10e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:44</td>     <th>  Log-Likelihood:    </th> <td>  44.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    46</td>      <th>  AIC:               </th> <td>  -77.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    40</td>      <th>  BIC:               </th> <td>  -66.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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>    2.1831</td> <td>    0.976</td> <td>    2.238</td> <td> 0.031</td> <td>    0.211</td> <td>    4.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Age)</th>    <td>    0.3082</td> <td>    0.245</td> <td>    1.257</td> <td> 0.216</td> <td>   -0.187</td> <td>    0.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(HS)</th>     <td>   -0.4183</td> <td>    0.197</td> <td>   -2.120</td> <td> 0.040</td> <td>   -0.817</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Income)</th> <td>    0.2267</td> <td>    0.119</td> <td>    1.903</td> <td> 0.064</td> <td>   -0.014</td> <td>    0.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Black)</th>  <td>   -0.0104</td> <td>    0.016</td> <td>   -0.644</td> <td> 0.524</td> <td>   -0.043</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Price)</th>  <td>   -0.3396</td> <td>    0.102</td> <td>   -3.342</td> <td> 0.002</td> <td>   -0.545</td> <td>   -0.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(IPP)</th>    <td>    0.5663</td> <td>    0.074</td> <td>    7.673</td> <td> 0.000</td> <td>    0.417</td> <td>    0.716</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.295</td> <th>  Durbin-Watson:     </th> <td>   1.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.317</td> <th>  Jarque-Bera (JB):  </th> <td>   2.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.423</td> <th>  Prob(JB):          </th> <td>   0.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.396</td> <th>  Cond. No.          </th> <td>1.23e+16</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 smallest eigenvalue is 3.98e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Sales)   R-squared:                       0.668\n",
       "Model:                            OLS   Adj. R-squared:                  0.627\n",
       "Method:                 Least Squares   F-statistic:                     16.10\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           1.10e-08\n",
       "Time:                        00:13:44   Log-Likelihood:                 44.525\n",
       "No. Observations:                  46   AIC:                            -77.05\n",
       "Df Residuals:                      40   BIC:                            -66.08\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          2.1831      0.976      2.238      0.031       0.211       4.155\n",
       "np.log(Age)        0.3082      0.245      1.257      0.216      -0.187       0.804\n",
       "np.log(HS)        -0.4183      0.197     -2.120      0.040      -0.817      -0.020\n",
       "np.log(Income)     0.2267      0.119      1.903      0.064      -0.014       0.467\n",
       "np.log(Black)     -0.0104      0.016     -0.644      0.524      -0.043       0.022\n",
       "np.log(Price)     -0.3396      0.102     -3.342      0.002      -0.545      -0.134\n",
       "np.log(IPP)        0.5663      0.074      7.673      0.000       0.417       0.716\n",
       "==============================================================================\n",
       "Omnibus:                        2.295   Durbin-Watson:                   1.942\n",
       "Prob(Omnibus):                  0.317   Jarque-Bera (JB):                2.074\n",
       "Skew:                           0.423   Prob(JB):                        0.354\n",
       "Kurtosis:                       2.396   Cond. No.                     1.23e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 3.98e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS.from_formula(\"np.log(Sales) ~ np.log(Age) + np.log(HS) + np.log(Income) + np.log(Black) + np.log(Price) + np.log(IPP)\", data=data_out).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x240496f9f98>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Sales)</td>  <th>  R-squared:         </th> <td>   0.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   20.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>2.74e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:44</td>     <th>  Log-Likelihood:    </th> <td>  44.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    46</td>      <th>  AIC:               </th> <td>  -78.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    41</td>      <th>  BIC:               </th> <td>  -69.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</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>    2.2634</td> <td>    0.961</td> <td>    2.356</td> <td> 0.023</td> <td>    0.323</td> <td>    4.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Age)</th>    <td>    0.3374</td> <td>    0.239</td> <td>    1.411</td> <td> 0.166</td> <td>   -0.146</td> <td>    0.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(HS)</th>     <td>   -0.3180</td> <td>    0.120</td> <td>   -2.647</td> <td> 0.011</td> <td>   -0.561</td> <td>   -0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Income)</th> <td>    0.1790</td> <td>    0.093</td> <td>    1.934</td> <td> 0.060</td> <td>   -0.008</td> <td>    0.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Price)</th>  <td>   -0.3624</td> <td>    0.095</td> <td>   -3.830</td> <td> 0.000</td> <td>   -0.553</td> <td>   -0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(IPP)</th>    <td>    0.5413</td> <td>    0.062</td> <td>    8.686</td> <td> 0.000</td> <td>    0.415</td> <td>    0.667</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.193</td> <th>  Durbin-Watson:     </th> <td>   1.998</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.334</td> <th>  Jarque-Bera (JB):  </th> <td>   2.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.459</td> <th>  Prob(JB):          </th> <td>   0.353</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.505</td> <th>  Cond. No.          </th> <td>1.57e+16</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 smallest eigenvalue is 2.4e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Sales)   R-squared:                       0.665\n",
       "Model:                            OLS   Adj. R-squared:                  0.632\n",
       "Method:                 Least Squares   F-statistic:                     20.32\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           2.74e-09\n",
       "Time:                        00:13:44   Log-Likelihood:                 44.289\n",
       "No. Observations:                  46   AIC:                            -78.58\n",
       "Df Residuals:                      41   BIC:                            -69.43\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          2.2634      0.961      2.356      0.023       0.323       4.204\n",
       "np.log(Age)        0.3374      0.239      1.411      0.166      -0.146       0.820\n",
       "np.log(HS)        -0.3180      0.120     -2.647      0.011      -0.561      -0.075\n",
       "np.log(Income)     0.1790      0.093      1.934      0.060      -0.008       0.366\n",
       "np.log(Price)     -0.3624      0.095     -3.830      0.000      -0.553      -0.171\n",
       "np.log(IPP)        0.5413      0.062      8.686      0.000       0.415       0.667\n",
       "==============================================================================\n",
       "Omnibus:                        2.193   Durbin-Watson:                   1.998\n",
       "Prob(Omnibus):                  0.334   Jarque-Bera (JB):                2.085\n",
       "Skew:                           0.459   Prob(JB):                        0.353\n",
       "Kurtosis:                       2.505   Cond. No.                     1.57e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.4e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS.from_formula(\"np.log(Sales) ~ np.log(Age) + np.log(HS) + np.log(Income) + np.log(Price) + np.log(IPP)\", data=data_out).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24049770c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>np.log(Sales)</td>  <th>  R-squared:         </th> <td>   0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   25.82</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>1.26e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:44</td>     <th>  Log-Likelihood:    </th> <td>  43.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    46</td>      <th>  AIC:               </th> <td>  -78.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    42</td>      <th>  BIC:               </th> <td>  -71.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>    2.8868</td> <td>    0.863</td> <td>    3.345</td> <td> 0.002</td> <td>    1.145</td> <td>    4.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(HS)</th>     <td>   -0.3499</td> <td>    0.119</td> <td>   -2.931</td> <td> 0.005</td> <td>   -0.591</td> <td>   -0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Income)</th> <td>    0.2307</td> <td>    0.086</td> <td>    2.686</td> <td> 0.010</td> <td>    0.057</td> <td>    0.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(Price)</th>  <td>   -0.3192</td> <td>    0.091</td> <td>   -3.524</td> <td> 0.001</td> <td>   -0.502</td> <td>   -0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(IPP)</th>    <td>    0.5499</td> <td>    0.063</td> <td>    8.763</td> <td> 0.000</td> <td>    0.423</td> <td>    0.677</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.135</td> <th>  Durbin-Watson:     </th> <td>   2.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.344</td> <th>  Jarque-Bera (JB):  </th> <td>   1.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.484</td> <th>  Prob(JB):          </th> <td>   0.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.676</td> <th>  Cond. No.          </th> <td>1.50e+16</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 smallest eigenvalue is 2.41e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:          np.log(Sales)   R-squared:                       0.648\n",
       "Model:                            OLS   Adj. R-squared:                  0.623\n",
       "Method:                 Least Squares   F-statistic:                     25.82\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           1.26e-09\n",
       "Time:                        00:13:44   Log-Likelihood:                 43.198\n",
       "No. Observations:                  46   AIC:                            -78.40\n",
       "Df Residuals:                      42   BIC:                            -71.08\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          2.8868      0.863      3.345      0.002       1.145       4.628\n",
       "np.log(HS)        -0.3499      0.119     -2.931      0.005      -0.591      -0.109\n",
       "np.log(Income)     0.2307      0.086      2.686      0.010       0.057       0.404\n",
       "np.log(Price)     -0.3192      0.091     -3.524      0.001      -0.502      -0.136\n",
       "np.log(IPP)        0.5499      0.063      8.763      0.000       0.423       0.677\n",
       "==============================================================================\n",
       "Omnibus:                        2.135   Durbin-Watson:                   2.046\n",
       "Prob(Omnibus):                  0.344   Jarque-Bera (JB):                1.999\n",
       "Skew:                           0.484   Prob(JB):                        0.368\n",
       "Kurtosis:                       2.676   Cond. No.                     1.50e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.41e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS.from_formula(\"np.log(Sales) ~ np.log(HS) + np.log(Income) + np.log(Price) + np.log(IPP)\", data=data_out).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2404a7b07f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = sm.PCA(Variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108.87\n",
       "1     88.83\n",
       "2     65.93\n",
       "3     39.15\n",
       "4     13.74\n",
       "5      5.49\n",
       "6      0.00\n",
       "Name: eigenvals, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(pca.eigenvals,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = pca.factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = pc.drop(\"comp_6\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   13.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 30 May 2019</td> <th>  Prob (F-statistic):</th> <td>2.54e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:13:44</td>     <th>  Log-Likelihood:    </th> <td> -39.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    46</td>      <th>  AIC:               </th> <td>   90.53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    40</td>      <th>  BIC:               </th> <td>   101.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</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>comp_0</th> <td>    3.0410</td> <td>    0.609</td> <td>    4.991</td> <td> 0.000</td> <td>    1.810</td> <td>    4.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>comp_1</th> <td>   -2.4729</td> <td>    0.609</td> <td>   -4.059</td> <td> 0.000</td> <td>   -3.704</td> <td>   -1.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>comp_2</th> <td>    3.5034</td> <td>    0.609</td> <td>    5.750</td> <td> 0.000</td> <td>    2.272</td> <td>    4.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>comp_3</th> <td>    0.7444</td> <td>    0.609</td> <td>    1.222</td> <td> 0.229</td> <td>   -0.487</td> <td>    1.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>comp_4</th> <td>   -0.7885</td> <td>    0.609</td> <td>   -1.294</td> <td> 0.203</td> <td>   -2.020</td> <td>    0.443</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>comp_5</th> <td>   -1.1574</td> <td>    0.609</td> <td>   -1.900</td> <td> 0.065</td> <td>   -2.389</td> <td>    0.074</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.867</td> <th>  Durbin-Watson:     </th> <td>   1.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.239</td> <th>  Jarque-Bera (JB):  </th> <td>   2.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.415</td> <th>  Prob(JB):          </th> <td>   0.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.282</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.670\n",
       "Model:                            OLS   Adj. R-squared:                  0.621\n",
       "Method:                 Least Squares   F-statistic:                     13.54\n",
       "Date:                Thu, 30 May 2019   Prob (F-statistic):           2.54e-08\n",
       "Time:                        00:13:44   Log-Likelihood:                -39.263\n",
       "No. Observations:                  46   AIC:                             90.53\n",
       "Df Residuals:                      40   BIC:                             101.5\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "comp_0         3.0410      0.609      4.991      0.000       1.810       4.272\n",
       "comp_1        -2.4729      0.609     -4.059      0.000      -3.704      -1.242\n",
       "comp_2         3.5034      0.609      5.750      0.000       2.272       4.735\n",
       "comp_3         0.7444      0.609      1.222      0.229      -0.487       1.976\n",
       "comp_4        -0.7885      0.609     -1.294      0.203      -2.020       0.443\n",
       "comp_5        -1.1574      0.609     -1.900      0.065      -2.389       0.074\n",
       "==============================================================================\n",
       "Omnibus:                        2.867   Durbin-Watson:                   1.940\n",
       "Prob(Omnibus):                  0.239   Jarque-Bera (JB):                2.310\n",
       "Skew:                           0.415   Prob(JB):                        0.315\n",
       "Kurtosis:                       2.282   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = sm.OLS((Target-Target.mean())/Target.std(), pc).fit()\n",
    "LR.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2404a81e6d8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gOU3YtqIJs5NhCP4AktOEbSuaMDsZhpw/gCSlvkhx99aNjd5CheCPxmlyhYXU/Pajp+lbqBD80ShNr7BoevuxUOqzk2HI+aNRml5h0fT2oz0I/miUpldYNL39aA+CPxql6RUWTW8/2oPgj0ZpQv33ME1vP9qDB75olKZXWDS9/WgPx4CDvuvW6XSi2+3W3QwAaBTbxyKis9R1pH0AIEOkfQBgiLYuyiP4A8AAbV6UR9oHAAZo86I8gj8ADNDmRXkEfwAYoM2L8sYK/rbvtv2y7Z/aHlhaZHub7ZO2T9neM849AaAqbV6UN+7I/yVJOyU9N+gC2yskPSzpDkk3SvqE7RvHvC8ATFwTDpVZrrGqfSLihCTZHnbZzZJORcSrxbWPSdou6ZVx7g0AVWjyts3DVJHzn5H02rzXp4v3AAA1WXLkb/spSR/s86nPRMTXRrhHv2lB3z0lbO+StEuS1q1bN8KXBgAsx5LBPyJuG/MepyVdN+/1WklnBtxrv6T9Um9vnzHvCwAYoIq0zwuSrre9wfZVku6RdLCC+wIABhi31PPXbJ+W9MuSDtk+Ury/xvZhSYqIdyTdJ+mIpBOSnoiIl8drNgBgHONW+3xV0lf7vH9G0p3zXh+WdHicewEAypPsfv62z0n63hhfYqWkH5TUnKajLxaiPxaiP+a0oS9+PiJWLXVRssF/XLa7oxxokAP6YiH6YyH6Y05OfcHePgCQIYI/AGSozcF/f90NSAh9sRD9sRD9MSebvmhtzh8AMFibR/4AgAFaF/xzPzvA9iO237D90rz3rrH9ddvfKf5+X51trIrt62w/Y/tEce7Ep4v3c+2Pq23/q+1vFf3xF8X7G2w/X/TH48VK/CzYXmH7uO1/Ll5n0xetCv6cHSBJ+pKkbYve2yPp6Yi4XtLTxescvCPpjyLiBkm3SPq94uch1/74saSPRMQvSrpJ0jbbt0h6UNLniv54S9Knamxj1T6t3s4DF2XTF60K/pp3dkBEvC3p4tkB2YiI5yS9uejt7ZIeLT5+VNKOShtVk4g4GxH/Vnz83+r9ks8o3/6IiPhR8fLK4k9I+oikJ4v3s+kP22slfVTSF4rXVkZ90bbgz9kB/X0gIs5KvYAo6f01t6dyttdL2izpeWXcH0Wa45uS3pD0dUnflXS+2INLyut35vOS/kTST4vX1yqjvmhb8B/57ADkw/Z7Jf2jpD+IiP+quz11ioh3I+Im9bZWv1nSDf0uq7ZV1bP9MUlvRMSx+W/3ubS1fTHWxm4JGvnsgMy8bnt1RJy1vVq9UV8WbF+pXuD/h4j4SvF2tv1xUUSct/2ses9Cpm1fUYx4c/md2SLpLtt3Srpa0s+qNxPIpi/aNvLn7ID+Dkq6t/j4XkmjnMDWeEUO94uSTkTEX837VK79scr2dPHxlKTb1HsO8oykjxeXZdEfEXF/RKyNiPXqxYmjEfGbyqgvWrfIq/if/POSVkh6JCL+suYmVcr2lyXdqt7uhK9L+qykA5KekLRO0vcl3R0Rix8Kt47tX5H0L5Je1Fxe98/Uy/vn2B8fVu8h5gr1Bn5PRMQDtj+kXnHENZKOS/qtiPhxfS2tlu1bJf1xRHwsp75oXfAHACytbWkfAMAICP4AkCGCPwBkiOAPABki+ANAhgj+AJAhgj8AZIjgDwAZ+j+kSPOaXxMOfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(LR.resid)), LR.resid )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Age</th>\n",
       "      <th>HS</th>\n",
       "      <th>Income</th>\n",
       "      <th>Black</th>\n",
       "      <th>Female</th>\n",
       "      <th>Price</th>\n",
       "      <th>IPP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>155.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>54.6</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>41.3</td>\n",
       "      <td>109.539952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>82.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>61.9</td>\n",
       "      <td>4623.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>125.967302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV</th>\n",
       "      <td>189.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4563.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>103.704545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>265.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>109.589443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>65.5</td>\n",
       "      <td>23.1</td>\n",
       "      <td>67.3</td>\n",
       "      <td>3227.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>88.169399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sales   Age    HS  Income  Black  Female  Price         IPP\n",
       "State                                                             \n",
       "DE     155.0  26.8  54.6  4524.0   14.3    51.3   41.3  109.539952\n",
       "HI      82.1  25.0  61.9  4623.0    1.0    48.0   36.7  125.967302\n",
       "NV     189.5  27.8  65.2  4563.0    5.7    49.3   44.0  103.704545\n",
       "NH     265.7  28.0  57.6  3737.0    0.3    51.1   34.1  109.589443\n",
       "UT      65.5  23.1  67.3  3227.0    0.6    50.6   36.6   88.169399"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_outlier.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Age</th>\n",
       "      <th>HS</th>\n",
       "      <th>Income</th>\n",
       "      <th>Black</th>\n",
       "      <th>Female</th>\n",
       "      <th>Price</th>\n",
       "      <th>IPP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121.54</td>\n",
       "      <td>27.47</td>\n",
       "      <td>53.15</td>\n",
       "      <td>3763.71</td>\n",
       "      <td>9.99</td>\n",
       "      <td>50.95</td>\n",
       "      <td>38.07</td>\n",
       "      <td>99.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.07</td>\n",
       "      <td>1.88</td>\n",
       "      <td>8.00</td>\n",
       "      <td>594.72</td>\n",
       "      <td>12.65</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.13</td>\n",
       "      <td>17.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>65.50</td>\n",
       "      <td>22.90</td>\n",
       "      <td>37.80</td>\n",
       "      <td>2626.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>45.70</td>\n",
       "      <td>29.00</td>\n",
       "      <td>69.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>105.35</td>\n",
       "      <td>26.40</td>\n",
       "      <td>48.30</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>1.60</td>\n",
       "      <td>50.75</td>\n",
       "      <td>34.70</td>\n",
       "      <td>86.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>119.00</td>\n",
       "      <td>27.40</td>\n",
       "      <td>53.30</td>\n",
       "      <td>3751.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>51.10</td>\n",
       "      <td>38.90</td>\n",
       "      <td>100.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>124.55</td>\n",
       "      <td>28.75</td>\n",
       "      <td>59.10</td>\n",
       "      <td>4116.50</td>\n",
       "      <td>13.55</td>\n",
       "      <td>51.50</td>\n",
       "      <td>41.35</td>\n",
       "      <td>110.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>265.70</td>\n",
       "      <td>32.30</td>\n",
       "      <td>67.30</td>\n",
       "      <td>5079.00</td>\n",
       "      <td>71.10</td>\n",
       "      <td>53.50</td>\n",
       "      <td>45.50</td>\n",
       "      <td>155.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales    Age     HS   Income  Black  Female  Price     IPP\n",
       "count   51.00  51.00  51.00    51.00  51.00   51.00  51.00   51.00\n",
       "mean   121.54  27.47  53.15  3763.71   9.99   50.95  38.07   99.74\n",
       "std     32.07   1.88   8.00   594.72  12.65    1.11   4.13   17.55\n",
       "min     65.50  22.90  37.80  2626.00   0.20   45.70  29.00   69.04\n",
       "25%    105.35  26.40  48.30  3271.00   1.60   50.75  34.70   86.51\n",
       "50%    119.00  27.40  53.30  3751.00   6.00   51.10  38.90  100.57\n",
       "75%    124.55  28.75  59.10  4116.50  13.55   51.50  41.35  110.10\n",
       "max    265.70  32.30  67.30  5079.00  71.10   53.50  45.50  155.80"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(data.describe(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([66.87975457, 76.94380752, 58.91666754, 66.79944761, 46.49810215])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.predict(data_outlier.drop(\"Sales\", axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([pd.Series(np.exp(L.predict(np.log(data_outlier.drop(\"Sales\", axis=1))))),data_outlier.reset_index()], axis=1)\n",
    "df = df.rename({0 : \"Predicted\"}, axis=1).set_index(\"State\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Age</th>\n",
       "      <th>HS</th>\n",
       "      <th>Income</th>\n",
       "      <th>Black</th>\n",
       "      <th>Female</th>\n",
       "      <th>Price</th>\n",
       "      <th>IPP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>123.04</td>\n",
       "      <td>155.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>54.6</td>\n",
       "      <td>4524.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>41.3</td>\n",
       "      <td>109.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HI</th>\n",
       "      <td>132.47</td>\n",
       "      <td>82.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>61.9</td>\n",
       "      <td>4623.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>125.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV</th>\n",
       "      <td>111.75</td>\n",
       "      <td>189.5</td>\n",
       "      <td>27.8</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4563.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>49.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>103.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>125.90</td>\n",
       "      <td>265.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>57.6</td>\n",
       "      <td>3737.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>109.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UT</th>\n",
       "      <td>97.60</td>\n",
       "      <td>65.5</td>\n",
       "      <td>23.1</td>\n",
       "      <td>67.3</td>\n",
       "      <td>3227.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>36.6</td>\n",
       "      <td>88.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Predicted  Sales   Age    HS  Income  Black  Female  Price     IPP\n",
       "State                                                                    \n",
       "DE        123.04  155.0  26.8  54.6  4524.0   14.3    51.3   41.3  109.54\n",
       "HI        132.47   82.1  25.0  61.9  4623.0    1.0    48.0   36.7  125.97\n",
       "NV        111.75  189.5  27.8  65.2  4563.0    5.7    49.3   44.0  103.70\n",
       "NH        125.90  265.7  28.0  57.6  3737.0    0.3    51.1   34.1  109.59\n",
       "UT         97.60   65.5  23.1  67.3  3227.0    0.6    50.6   36.6   88.17"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(df,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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