MachineLearningIntroductionWorkshop_16Apr2019.ipynb

{
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  "metadata": {
    "colab": {
      "name": "MachineLearningIntroductionWorkshop-16Apr2019",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "YvZazRASWWNr",
        "colab_type": "code",
        "outputId": "9d5c6620-e79b-42de-ae28-c74010453b45",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0eb796b3-1c36-493b-a946-a7a539473e3c\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-0eb796b3-1c36-493b-a946-a7a539473e3c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving wine.data to wine.data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EOmvx9m7XKlE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "names = ['label', 'alcohol', 'malic_acid', 'ash', 'alcalinity', 'magnesium', 'total_phenols', 'flavanoids', \n",
        "         'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', \n",
        "         'ods', 'proline']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cRyrZCmVXB0v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1rnO9rDeXLyZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('wine.data', names=names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8llmDAgA-kjv",
        "colab_type": "code",
        "outputId": "45a6cb60-d629-4826-c46c-a73bb697b027",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>ods</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "      <td>2.43</td>\n",
              "      <td>15.6</td>\n",
              "      <td>127</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>2.29</td>\n",
              "      <td>5.64</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.92</td>\n",
              "      <td>1065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2.14</td>\n",
              "      <td>11.2</td>\n",
              "      <td>100</td>\n",
              "      <td>2.65</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.38</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.40</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.67</td>\n",
              "      <td>18.6</td>\n",
              "      <td>101</td>\n",
              "      <td>2.80</td>\n",
              "      <td>3.24</td>\n",
              "      <td>0.30</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.68</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.17</td>\n",
              "      <td>1185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.50</td>\n",
              "      <td>16.8</td>\n",
              "      <td>113</td>\n",
              "      <td>3.85</td>\n",
              "      <td>3.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>2.18</td>\n",
              "      <td>7.80</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.45</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.87</td>\n",
              "      <td>21.0</td>\n",
              "      <td>118</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.82</td>\n",
              "      <td>4.32</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.93</td>\n",
              "      <td>735</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  alcohol  malic_acid   ash  alcalinity  magnesium  total_phenols  \\\n",
              "0      1    14.23        1.71  2.43        15.6        127           2.80   \n",
              "1      1    13.20        1.78  2.14        11.2        100           2.65   \n",
              "2      1    13.16        2.36  2.67        18.6        101           2.80   \n",
              "3      1    14.37        1.95  2.50        16.8        113           3.85   \n",
              "4      1    13.24        2.59  2.87        21.0        118           2.80   \n",
              "\n",
              "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
              "0        3.06                  0.28             2.29             5.64  1.04   \n",
              "1        2.76                  0.26             1.28             4.38  1.05   \n",
              "2        3.24                  0.30             2.81             5.68  1.03   \n",
              "3        3.49                  0.24             2.18             7.80  0.86   \n",
              "4        2.69                  0.39             1.82             4.32  1.04   \n",
              "\n",
              "    ods  proline  \n",
              "0  3.92     1065  \n",
              "1  3.40     1050  \n",
              "2  3.17     1185  \n",
              "3  3.45     1480  \n",
              "4  2.93      735  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "vM9CbNeoXQ8i",
        "colab_type": "code",
        "outputId": "b74e9eab-aa5e-44a8-c467-1763cd7f5ea2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "cell_type": "code",
      "source": [
        "df[['alcohol', 'malic_acid', 'ash', 'alcalinity', 'label']].tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>13.71</td>\n",
              "      <td>5.65</td>\n",
              "      <td>2.45</td>\n",
              "      <td>20.5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
              "      <td>2.48</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
              "      <td>2.26</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
              "      <td>2.37</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
              "      <td>2.74</td>\n",
              "      <td>24.5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     alcohol  malic_acid   ash  alcalinity  label\n",
              "173    13.71        5.65  2.45        20.5      3\n",
              "174    13.40        3.91  2.48        23.0      3\n",
              "175    13.27        4.28  2.26        20.0      3\n",
              "176    13.17        2.59  2.37        20.0      3\n",
              "177    14.13        4.10  2.74        24.5      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "J43lckkgyfMc",
        "colab_type": "code",
        "outputId": "4f64bc00-7c1d-431d-d46a-f8256cde44a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>malic_acid</th>\n",
              "      <th>ash</th>\n",
              "      <th>alcalinity</th>\n",
              "      <th>magnesium</th>\n",
              "      <th>total_phenols</th>\n",
              "      <th>flavanoids</th>\n",
              "      <th>nonflavanoid_phenols</th>\n",
              "      <th>proanthocyanins</th>\n",
              "      <th>color_intensity</th>\n",
              "      <th>hue</th>\n",
              "      <th>ods</th>\n",
              "      <th>proline</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>178.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.938202</td>\n",
              "      <td>13.000618</td>\n",
              "      <td>2.336348</td>\n",
              "      <td>2.366517</td>\n",
              "      <td>19.494944</td>\n",
              "      <td>99.741573</td>\n",
              "      <td>2.295112</td>\n",
              "      <td>2.029270</td>\n",
              "      <td>0.361854</td>\n",
              "      <td>1.590899</td>\n",
              "      <td>5.058090</td>\n",
              "      <td>0.957449</td>\n",
              "      <td>2.611685</td>\n",
              "      <td>746.893258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.775035</td>\n",
              "      <td>0.811827</td>\n",
              "      <td>1.117146</td>\n",
              "      <td>0.274344</td>\n",
              "      <td>3.339564</td>\n",
              "      <td>14.282484</td>\n",
              "      <td>0.625851</td>\n",
              "      <td>0.998859</td>\n",
              "      <td>0.124453</td>\n",
              "      <td>0.572359</td>\n",
              "      <td>2.318286</td>\n",
              "      <td>0.228572</td>\n",
              "      <td>0.709990</td>\n",
              "      <td>314.907474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.030000</td>\n",
              "      <td>0.740000</td>\n",
              "      <td>1.360000</td>\n",
              "      <td>10.600000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>1.270000</td>\n",
              "      <td>278.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.362500</td>\n",
              "      <td>1.602500</td>\n",
              "      <td>2.210000</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>1.742500</td>\n",
              "      <td>1.205000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>3.220000</td>\n",
              "      <td>0.782500</td>\n",
              "      <td>1.937500</td>\n",
              "      <td>500.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>13.050000</td>\n",
              "      <td>1.865000</td>\n",
              "      <td>2.360000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>2.355000</td>\n",
              "      <td>2.135000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>1.555000</td>\n",
              "      <td>4.690000</td>\n",
              "      <td>0.965000</td>\n",
              "      <td>2.780000</td>\n",
              "      <td>673.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>13.677500</td>\n",
              "      <td>3.082500</td>\n",
              "      <td>2.557500</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.875000</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>1.950000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.120000</td>\n",
              "      <td>3.170000</td>\n",
              "      <td>985.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.830000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.230000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>3.880000</td>\n",
              "      <td>5.080000</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>3.580000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1680.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            label     alcohol  malic_acid         ash  alcalinity   magnesium  \\\n",
              "count  178.000000  178.000000  178.000000  178.000000  178.000000  178.000000   \n",
              "mean     1.938202   13.000618    2.336348    2.366517   19.494944   99.741573   \n",
              "std      0.775035    0.811827    1.117146    0.274344    3.339564   14.282484   \n",
              "min      1.000000   11.030000    0.740000    1.360000   10.600000   70.000000   \n",
              "25%      1.000000   12.362500    1.602500    2.210000   17.200000   88.000000   \n",
              "50%      2.000000   13.050000    1.865000    2.360000   19.500000   98.000000   \n",
              "75%      3.000000   13.677500    3.082500    2.557500   21.500000  107.000000   \n",
              "max      3.000000   14.830000    5.800000    3.230000   30.000000  162.000000   \n",
              "\n",
              "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
              "count     178.000000  178.000000            178.000000       178.000000   \n",
              "mean        2.295112    2.029270              0.361854         1.590899   \n",
              "std         0.625851    0.998859              0.124453         0.572359   \n",
              "min         0.980000    0.340000              0.130000         0.410000   \n",
              "25%         1.742500    1.205000              0.270000         1.250000   \n",
              "50%         2.355000    2.135000              0.340000         1.555000   \n",
              "75%         2.800000    2.875000              0.437500         1.950000   \n",
              "max         3.880000    5.080000              0.660000         3.580000   \n",
              "\n",
              "       color_intensity         hue         ods      proline  \n",
              "count       178.000000  178.000000  178.000000   178.000000  \n",
              "mean          5.058090    0.957449    2.611685   746.893258  \n",
              "std           2.318286    0.228572    0.709990   314.907474  \n",
              "min           1.280000    0.480000    1.270000   278.000000  \n",
              "25%           3.220000    0.782500    1.937500   500.500000  \n",
              "50%           4.690000    0.965000    2.780000   673.500000  \n",
              "75%           6.200000    1.120000    3.170000   985.000000  \n",
              "max          13.000000    1.710000    4.000000  1680.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "XaBRuwFH_8RM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qv3d3VuZbaGc",
        "colab_type": "code",
        "outputId": "4a9630da-11fd-4735-ef56-7076b8cfc882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "df['label'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    71\n",
              "1    59\n",
              "3    48\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "mI9CS2jWbjO2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YIxlvgz6x10t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y = df['label']\n",
        "X = df.drop('label', axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b6ePWrocyCKp",
        "colab_type": "code",
        "outputId": "ded5e5f4-a75c-4f45-c4a6-ea66b63431b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "cell_type": "code",
      "source": [
        "X.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['alcohol', 'malic_acid', 'ash', 'alcalinity', 'magnesium',\n",
              "       'total_phenols', 'flavanoids', 'nonflavanoid_phenols',\n",
              "       'proanthocyanins', 'color_intensity', 'hue', 'ods', 'proline'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "Zj5sEH42Boet",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "BtfudYGmBo2e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5uGx297Uws5U",
        "colab_type": "code",
        "outputId": "f45af969-214b-4a95-9809-03a0e7b0aab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "M3IehPewxURn",
        "colab_type": "code",
        "outputId": "4b1d0dcb-c487-4fd3-f11c-ba08e38cb01f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "142"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "L5ilRxs3xYre",
        "colab_type": "code",
        "outputId": "c8b534e5-8f6f-4291-8cb3-b01d28a9aa4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "uvfS6W2gxaBb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg = LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1PU2OGxtyTcQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg = reg.fit(X_train, Y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XjR-158RyVng",
        "colab_type": "code",
        "outputId": "e2a524f9-b5ba-4cdb-8082-ea5db16c4b3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "reg.score(X_test, Y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8439696726193413"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "kPJaR92dyb3X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pxgVQCGMyoqy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JOl5pmeMzAmN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "kf = KFold(n_splits=5, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qeeidFVR0bQx",
        "colab_type": "code",
        "outputId": "61929fd6-3cca-4669-decb-d82d39267299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        }
      },
      "cell_type": "code",
      "source": [
        "X = X.values\n",
        "Y = Y.values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-edc78e1fd9ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "CzK9kKjCy6XN",
        "colab_type": "code",
        "outputId": "d8c7ef15-0914-47ce-c5ef-49d321e2e2b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "cell_type": "code",
      "source": [
        "scores = []\n",
        "for train, test in kf.split(X):\n",
        "  reg = LinearRegression()\n",
        "  reg = reg.fit(X[train], Y[train])\n",
        "  scores.append(reg.score(X[test], Y[test]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 13  14  33  35  36  38  40  41  44  45  46  54  61  62  65  66  67  70\n",
            "  79  83 102 103 108 109 116 127 129 138 140 148 150 156 162 169 170 172]\n",
            "[  0   2   3   8  11  17  18  26  29  39  42  43  55  71  77  85  97 101\n",
            " 104 106 107 114 117 120 121 124 132 136 149 152 154 157 160 165 168 177]\n",
            "[  4   5   7  16  24  31  34  68  74  88  89  90  91  92  94 100 105 110\n",
            " 111 115 122 126 128 131 133 139 144 151 158 159 161 167 171 173 174 176]\n",
            "[  6  10  20  21  22  23  25  27  28  37  47  51  52  53  57  59  60  64\n",
            "  69  72  73  76  80  93  95  96 113 123 134 137 143 145 163 164 166]\n",
            "[  1   9  12  15  19  30  32  48  49  50  56  58  63  75  78  81  82  84\n",
            "  86  87  98  99 112 118 119 125 130 135 141 142 146 147 153 155 175]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "97T8ifsEzFYP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YBqDRq3ezLeR",
        "colab_type": "code",
        "outputId": "1c27319f-dd6e-46db-97d6-12150655330e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "cell_type": "code",
      "source": [
        "scores"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8188842265065561,\n",
              " 0.8555576527601886,\n",
              " 0.8887910166008696,\n",
              " 0.8412797085830016,\n",
              " 0.8865709461949899]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "metadata": {
        "id": "y3sm443vzHK0",
        "colab_type": "code",
        "outputId": "d27400ed-b68b-49e8-8400-7fa23badf39e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "np.mean(scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8582167101291212"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "gGnPXFhszIge",
        "colab_type": "code",
        "outputId": "c54afdeb-ed69-4ba6-c166-9b61e96b8b4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "cell_type": "code",
      "source": [
        "np.std(scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026757159021433066"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "hU2ukcVXzKZV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}