Untitled11.ipynb

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      "name": "Untitled11.ipynb",
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        "id": "LxkGFti_7egW"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df = pd.read_csv('student-mat.csv')"
      ],
      "metadata": {
        "id": "atH4WUK37rwg"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "raw_df.head()"
      ],
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              "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
              "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
              "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
              "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
              "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
              "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
              "\n",
              "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
              "0      4        3      4     1     1      3        6   5   6   6  \n",
              "1      5        3      3     1     1      3        4   5   5   6  \n",
              "2      4        3      2     2     3      3       10   7   8  10  \n",
              "3      3        2      2     1     1      5        2  15  14  15  \n",
              "4      4        3      2     1     2      5        4   6  10  10  \n",
              "\n",
              "[5 rows x 33 columns]"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 395 entries, 0 to 394\n",
            "Data columns (total 33 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   school      395 non-null    object\n",
            " 1   sex         395 non-null    object\n",
            " 2   age         395 non-null    int64 \n",
            " 3   address     395 non-null    object\n",
            " 4   famsize     395 non-null    object\n",
            " 5   Pstatus     395 non-null    object\n",
            " 6   Medu        395 non-null    int64 \n",
            " 7   Fedu        395 non-null    int64 \n",
            " 8   Mjob        395 non-null    object\n",
            " 9   Fjob        395 non-null    object\n",
            " 10  reason      395 non-null    object\n",
            " 11  guardian    395 non-null    object\n",
            " 12  traveltime  395 non-null    int64 \n",
            " 13  studytime   395 non-null    int64 \n",
            " 14  failures    395 non-null    int64 \n",
            " 15  schoolsup   395 non-null    object\n",
            " 16  famsup      395 non-null    object\n",
            " 17  paid        395 non-null    object\n",
            " 18  activities  395 non-null    object\n",
            " 19  nursery     395 non-null    object\n",
            " 20  higher      395 non-null    object\n",
            " 21  internet    395 non-null    object\n",
            " 22  romantic    395 non-null    object\n",
            " 23  famrel      395 non-null    int64 \n",
            " 24  freetime    395 non-null    int64 \n",
            " 25  goout       395 non-null    int64 \n",
            " 26  Dalc        395 non-null    int64 \n",
            " 27  Walc        395 non-null    int64 \n",
            " 28  health      395 non-null    int64 \n",
            " 29  absences    395 non-null    int64 \n",
            " 30  G1          395 non-null    int64 \n",
            " 31  G2          395 non-null    int64 \n",
            " 32  G3          395 non-null    int64 \n",
            "dtypes: int64(16), object(17)\n",
            "memory usage: 102.0+ KB\n"
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        "raw_df.describe()"
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              "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
              "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
              "mean    16.696203    2.749367    2.521519    1.448101    2.035443    0.334177   \n",
              "std      1.276043    1.094735    1.088201    0.697505    0.839240    0.743651   \n",
              "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
              "25%     16.000000    2.000000    2.000000    1.000000    1.000000    0.000000   \n",
              "50%     17.000000    3.000000    2.000000    1.000000    2.000000    0.000000   \n",
              "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
              "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
              "\n",
              "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
              "count  395.000000  395.000000  395.000000  395.000000  395.000000  395.000000   \n",
              "mean     3.944304    3.235443    3.108861    1.481013    2.291139    3.554430   \n",
              "std      0.896659    0.998862    1.113278    0.890741    1.287897    1.390303   \n",
              "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "25%      4.000000    3.000000    2.000000    1.000000    1.000000    3.000000   \n",
              "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
              "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
              "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
              "\n",
              "         absences          G1          G2          G3  \n",
              "count  395.000000  395.000000  395.000000  395.000000  \n",
              "mean     5.708861   10.908861   10.713924   10.415190  \n",
              "std      8.003096    3.319195    3.761505    4.581443  \n",
              "min      0.000000    3.000000    0.000000    0.000000  \n",
              "25%      0.000000    8.000000    9.000000    8.000000  \n",
              "50%      4.000000   11.000000   11.000000   11.000000  \n",
              "75%      8.000000   13.000000   13.000000   14.000000  \n",
              "max     75.000000   19.000000   19.000000   20.000000  "
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
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              "      <th>count</th>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
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              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
              "      <td>395.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.696203</td>\n",
              "      <td>2.749367</td>\n",
              "      <td>2.521519</td>\n",
              "      <td>1.448101</td>\n",
              "      <td>2.035443</td>\n",
              "      <td>0.334177</td>\n",
              "      <td>3.944304</td>\n",
              "      <td>3.235443</td>\n",
              "      <td>3.108861</td>\n",
              "      <td>1.481013</td>\n",
              "      <td>2.291139</td>\n",
              "      <td>3.554430</td>\n",
              "      <td>5.708861</td>\n",
              "      <td>10.908861</td>\n",
              "      <td>10.713924</td>\n",
              "      <td>10.415190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.276043</td>\n",
              "      <td>1.094735</td>\n",
              "      <td>1.088201</td>\n",
              "      <td>0.697505</td>\n",
              "      <td>0.839240</td>\n",
              "      <td>0.743651</td>\n",
              "      <td>0.896659</td>\n",
              "      <td>0.998862</td>\n",
              "      <td>1.113278</td>\n",
              "      <td>0.890741</td>\n",
              "      <td>1.287897</td>\n",
              "      <td>1.390303</td>\n",
              "      <td>8.003096</td>\n",
              "      <td>3.319195</td>\n",
              "      <td>3.761505</td>\n",
              "      <td>4.581443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae6cd9b4-5f8d-42a2-b603-83654b83e064')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae6cd9b4-5f8d-42a2-b603-83654b83e064 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae6cd9b4-5f8d-42a2-b603-83654b83e064');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9_zkxtV9kFN",
        "outputId": "5ca8ed63-1db9-498d-cf1d-6ea9acfa70e6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        0\n",
              "sex           0\n",
              "age           0\n",
              "address       0\n",
              "famsize       0\n",
              "Pstatus       0\n",
              "Medu          0\n",
              "Fedu          0\n",
              "Mjob          0\n",
              "Fjob          0\n",
              "reason        0\n",
              "guardian      0\n",
              "traveltime    0\n",
              "studytime     0\n",
              "failures      0\n",
              "schoolsup     0\n",
              "famsup        0\n",
              "paid          0\n",
              "activities    0\n",
              "nursery       0\n",
              "higher        0\n",
              "internet      0\n",
              "romantic      0\n",
              "famrel        0\n",
              "freetime      0\n",
              "goout         0\n",
              "Dalc          0\n",
              "Walc          0\n",
              "health        0\n",
              "absences      0\n",
              "G1            0\n",
              "G2            0\n",
              "G3            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(raw_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "p2y8aMGv9uvW",
        "outputId": "bffcdb85-0d39-4fed-9706-4986e39ed15a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fb345bbe150>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x2880 with 272 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "zjGnLEDX91HT",
        "outputId": "bc0ddf71-732c-4f1b-8851-ae43cbb1c6cd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 age      Medu      Fedu  traveltime  studytime  failures  \\\n",
              "age         1.000000 -0.163658 -0.163438    0.070641  -0.004140  0.243665   \n",
              "Medu       -0.163658  1.000000  0.623455   -0.171639   0.064944 -0.236680   \n",
              "Fedu       -0.163438  0.623455  1.000000   -0.158194  -0.009175 -0.250408   \n",
              "traveltime  0.070641 -0.171639 -0.158194    1.000000  -0.100909  0.092239   \n",
              "studytime  -0.004140  0.064944 -0.009175   -0.100909   1.000000 -0.173563   \n",
              "failures    0.243665 -0.236680 -0.250408    0.092239  -0.173563  1.000000   \n",
              "famrel      0.053940 -0.003914 -0.001370   -0.016808   0.039731 -0.044337   \n",
              "freetime    0.016434  0.030891 -0.012846   -0.017025  -0.143198  0.091987   \n",
              "goout       0.126964  0.064094  0.043105    0.028540  -0.063904  0.124561   \n",
              "Dalc        0.131125  0.019834  0.002386    0.138325  -0.196019  0.136047   \n",
              "Walc        0.117276 -0.047123 -0.012631    0.134116  -0.253785  0.141962   \n",
              "health     -0.062187 -0.046878  0.014742    0.007501  -0.075616  0.065827   \n",
              "absences    0.175230  0.100285  0.024473   -0.012944  -0.062700  0.063726   \n",
              "G1         -0.064081  0.205341  0.190270   -0.093040   0.160612 -0.354718   \n",
              "G2         -0.143474  0.215527  0.164893   -0.153198   0.135880 -0.355896   \n",
              "G3         -0.161579  0.217147  0.152457   -0.117142   0.097820 -0.360415   \n",
              "\n",
              "              famrel  freetime     goout      Dalc      Walc    health  \\\n",
              "age         0.053940  0.016434  0.126964  0.131125  0.117276 -0.062187   \n",
              "Medu       -0.003914  0.030891  0.064094  0.019834 -0.047123 -0.046878   \n",
              "Fedu       -0.001370 -0.012846  0.043105  0.002386 -0.012631  0.014742   \n",
              "traveltime -0.016808 -0.017025  0.028540  0.138325  0.134116  0.007501   \n",
              "studytime   0.039731 -0.143198 -0.063904 -0.196019 -0.253785 -0.075616   \n",
              "failures   -0.044337  0.091987  0.124561  0.136047  0.141962  0.065827   \n",
              "famrel      1.000000  0.150701  0.064568 -0.077594 -0.113397  0.094056   \n",
              "freetime    0.150701  1.000000  0.285019  0.209001  0.147822  0.075733   \n",
              "goout       0.064568  0.285019  1.000000  0.266994  0.420386 -0.009577   \n",
              "Dalc       -0.077594  0.209001  0.266994  1.000000  0.647544  0.077180   \n",
              "Walc       -0.113397  0.147822  0.420386  0.647544  1.000000  0.092476   \n",
              "health      0.094056  0.075733 -0.009577  0.077180  0.092476  1.000000   \n",
              "absences   -0.044354 -0.058078  0.044302  0.111908  0.136291 -0.029937   \n",
              "G1          0.022168  0.012613 -0.149104 -0.094159 -0.126179 -0.073172   \n",
              "G2         -0.018281 -0.013777 -0.162250 -0.064120 -0.084927 -0.097720   \n",
              "G3          0.051363  0.011307 -0.132791 -0.054660 -0.051939 -0.061335   \n",
              "\n",
              "            absences        G1        G2        G3  \n",
              "age         0.175230 -0.064081 -0.143474 -0.161579  \n",
              "Medu        0.100285  0.205341  0.215527  0.217147  \n",
              "Fedu        0.024473  0.190270  0.164893  0.152457  \n",
              "traveltime -0.012944 -0.093040 -0.153198 -0.117142  \n",
              "studytime  -0.062700  0.160612  0.135880  0.097820  \n",
              "failures    0.063726 -0.354718 -0.355896 -0.360415  \n",
              "famrel     -0.044354  0.022168 -0.018281  0.051363  \n",
              "freetime   -0.058078  0.012613 -0.013777  0.011307  \n",
              "goout       0.044302 -0.149104 -0.162250 -0.132791  \n",
              "Dalc        0.111908 -0.094159 -0.064120 -0.054660  \n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "age, "
      ],
      "metadata": {
        "id": "rSqILLGBAY1A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['address', 'sex','famsize','Pstatus','Fedu','Mjob','Fjob'], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CNijBcpZ--kB",
        "outputId": "b909fbe5-4d3a-478d-bae9-417c8bdf498a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school  age  Medu  reason guardian  traveltime  studytime  failures  \\\n",
              "0       GP   18     4  course   mother           2          2         0   \n",
              "1       GP   17     1  course   father           1          2         0   \n",
              "2       GP   15     1   other   mother           1          2         3   \n",
              "3       GP   15     4    home   mother           1          3         0   \n",
              "4       GP   16     3    home   father           1          2         0   \n",
              "..     ...  ...   ...     ...      ...         ...        ...       ...   \n",
              "390     MS   20     2  course    other           1          2         2   \n",
              "391     MS   17     3  course   mother           2          1         0   \n",
              "392     MS   21     1  course    other           1          1         3   \n",
              "393     MS   18     3  course   mother           3          1         0   \n",
              "394     MS   19     1  course   father           1          1         0   \n",
              "\n",
              "    schoolsup famsup  ... famrel freetime goout Dalc Walc health  absences  \\\n",
              "0         yes     no  ...      4        3     4    1    1      3         6   \n",
              "1          no    yes  ...      5        3     3    1    1      3         4   \n",
              "2         yes     no  ...      4        3     2    2    3      3        10   \n",
              "3          no    yes  ...      3        2     2    1    1      5         2   \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['school', 'Medu','guardian','famrel','Fedu','Mjob','Fjob'], axis = 1)\n"
      ],
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      "execution_count": 10,
      "outputs": [
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          "data": {
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              "    sex  age address famsize Pstatus  reason  traveltime  studytime  failures  \\\n",
              "0     F   18       U     GT3       A  course           2          2         0   \n",
              "1     F   17       U     GT3       T  course           1          2         0   \n",
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              "..   ..  ...     ...     ...     ...     ...         ...        ...       ...   \n",
              "390   M   20       U     LE3       A  course           1          2         2   \n",
              "391   M   17       U     LE3       T  course           2          1         0   \n",
              "392   M   21       R     GT3       T  course           1          1         3   \n",
              "393   M   18       R     LE3       T  course           3          1         0   \n",
              "394   M   19       U     LE3       T  course           1          1         0   \n",
              "\n",
              "    schoolsup  ... romantic freetime goout Dalc Walc health absences  G1  G2  \\\n",
              "0         yes  ...       no        3     4    1    1      3        6   5   6   \n",
              "1          no  ...       no        3     3    1    1      3        4   5   5   \n",
              "2         yes  ...       no        3     2    2    3      3       10   7   8   \n",
              "3          no  ...      yes        2     2    1    1      5        2  15  14   \n",
              "4          no  ...       no        3     2    1    2      5        4   6  10   \n",
              "..        ...  ...      ...      ...   ...  ...  ...    ...      ...  ..  ..   \n",
              "390        no  ...       no        5     4    4    5      4       11   9   9   \n",
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              "394        no  ...       no        2     3    3    3      5        5   8   9   \n",
              "\n",
              "     G3  \n",
              "0     6  \n",
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              "3    15  \n",
              "4    10  \n",
              "..   ..  \n",
              "390   9  \n",
              "391  16  \n",
              "392   7  \n",
              "393  10  \n",
              "394   9  \n",
              "\n",
              "[395 rows x 26 columns]"
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              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
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              "      <td>18</td>\n",
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              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
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              "      <td>T</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 26 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['Dalc', 'Walc','G1','G2','G3'], axis = 1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GYsFSSGpMdGd",
        "outputId": "6de1fda2-c90d-4e66-8c74-edae7f7e8f22"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       U     GT3       A     4     4   at_home   teacher   \n",
              "1       GP   F   17       U     GT3       T     1     1   at_home     other   \n",
              "2       GP   F   15       U     LE3       T     1     1   at_home     other   \n",
              "3       GP   F   15       U     GT3       T     4     2    health  services   \n",
              "4       GP   F   16       U     GT3       T     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       U     LE3       A     2     2  services  services   \n",
              "391     MS   M   17       U     LE3       T     3     1  services  services   \n",
              "392     MS   M   21       R     GT3       T     1     1     other     other   \n",
              "393     MS   M   18       R     LE3       T     3     2  services     other   \n",
              "394     MS   M   19       U     LE3       T     1     1     other   at_home   \n",
              "\n",
              "     ... activities nursery  higher  internet  romantic famrel freetime goout  \\\n",
              "0    ...         no     yes     yes        no        no      4        3     4   \n",
              "1    ...         no      no     yes       yes        no      5        3     3   \n",
              "2    ...         no     yes     yes       yes        no      4        3     2   \n",
              "3    ...        yes     yes     yes       yes       yes      3        2     2   \n",
              "4    ...         no     yes     yes        no        no      4        3     2   \n",
              "..   ...        ...     ...     ...       ...       ...    ...      ...   ...   \n",
              "390  ...         no     yes     yes        no        no      5        5     4   \n",
              "391  ...         no      no     yes       yes        no      2        4     5   \n",
              "392  ...         no      no     yes        no        no      5        5     3   \n",
              "393  ...         no      no     yes       yes        no      4        4     1   \n",
              "394  ...         no     yes     yes       yes        no      3        2     3   \n",
              "\n",
              "    health absences  \n",
              "0        3        6  \n",
              "1        3        4  \n",
              "2        3       10  \n",
              "3        5        2  \n",
              "4        5        4  \n",
              "..     ...      ...  \n",
              "390      4       11  \n",
              "391      2        3  \n",
              "392      3        3  \n",
              "393      5        0  \n",
              "394      5        5  \n",
              "\n",
              "[395 rows x 28 columns]"
            ],
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              "      <td>no</td>\n",
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              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
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              "      <td>18</td>\n",
              "      <td>R</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 28 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['nursery', 'famrel','famsize','Pstatus','Medu'], axis = 1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PUc_IpGbM5Qt",
        "outputId": "f88d562e-0382-467a-b96b-88b9244244e6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address  Fedu      Mjob      Fjob  reason guardian  \\\n",
              "0       GP   F   18       U     4   at_home   teacher  course   mother   \n",
              "1       GP   F   17       U     1   at_home     other  course   father   \n",
              "2       GP   F   15       U     1   at_home     other   other   mother   \n",
              "3       GP   F   15       U     2    health  services    home   mother   \n",
              "4       GP   F   16       U     3     other     other    home   father   \n",
              "..     ...  ..  ...     ...   ...       ...       ...     ...      ...   \n",
              "390     MS   M   20       U     2  services  services  course    other   \n",
              "391     MS   M   17       U     1  services  services  course   mother   \n",
              "392     MS   M   21       R     1     other     other  course    other   \n",
              "393     MS   M   18       R     2  services     other  course   mother   \n",
              "394     MS   M   19       U     1     other   at_home  course   father   \n",
              "\n",
              "     traveltime  ...  romantic  freetime goout Dalc Walc health absences  G1  \\\n",
              "0             2  ...        no         3     4    1    1      3        6   5   \n",
              "1             1  ...        no         3     3    1    1      3        4   5   \n",
              "2             1  ...        no         3     2    2    3      3       10   7   \n",
              "3             1  ...       yes         2     2    1    1      5        2  15   \n",
              "4             1  ...        no         3     2    1    2      5        4   6   \n",
              "..          ...  ...       ...       ...   ...  ...  ...    ...      ...  ..   \n",
              "390           1  ...        no         5     4    4    5      4       11   9   \n",
              "391           2  ...        no         4     5    3    4      2        3  14   \n",
              "392           1  ...        no         5     3    3    3      3        3  10   \n",
              "393           3  ...        no         4     1    3    4      5        0  11   \n",
              "394           1  ...        no         2     3    3    3      5        5   8   \n",
              "\n",
              "     G2  G3  \n",
              "0     6   6  \n",
              "1     5   6  \n",
              "2     8  10  \n",
              "3    14  15  \n",
              "4    10  10  \n",
              "..   ..  ..  \n",
              "390   9   9  \n",
              "391  16  16  \n",
              "392   8   7  \n",
              "393  12  10  \n",
              "394   9   9  \n",
              "\n",
              "[395 rows x 28 columns]"
            ],
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>R</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>R</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>U</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>course</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 28 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['Fedu'], axis = 1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "A4eSbtpoNwaB",
        "outputId": "e6d9fa52-0c09-4d9f-bffc-4be99d2a6cdf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu      Mjob      Fjob  reason  \\\n",
              "0       GP   F   18       U     GT3       A     4   at_home   teacher  course   \n",
              "1       GP   F   17       U     GT3       T     1   at_home     other  course   \n",
              "2       GP   F   15       U     LE3       T     1   at_home     other   other   \n",
              "3       GP   F   15       U     GT3       T     4    health  services    home   \n",
              "4       GP   F   16       U     GT3       T     3     other     other    home   \n",
              "..     ...  ..  ...     ...     ...     ...   ...       ...       ...     ...   \n",
              "390     MS   M   20       U     LE3       A     2  services  services  course   \n",
              "391     MS   M   17       U     LE3       T     3  services  services  course   \n",
              "392     MS   M   21       R     GT3       T     1     other     other  course   \n",
              "393     MS   M   18       R     LE3       T     3  services     other  course   \n",
              "394     MS   M   19       U     LE3       T     1     other   at_home  course   \n",
              "\n",
              "     ... famrel  freetime  goout  Dalc Walc health absences  G1  G2  G3  \n",
              "0    ...      4         3      4     1    1      3        6   5   6   6  \n",
              "1    ...      5         3      3     1    1      3        4   5   5   6  \n",
              "2    ...      4         3      2     2    3      3       10   7   8  10  \n",
              "3    ...      3         2      2     1    1      5        2  15  14  15  \n",
              "4    ...      4         3      2     1    2      5        4   6  10  10  \n",
              "..   ...    ...       ...    ...   ...  ...    ...      ...  ..  ..  ..  \n",
              "390  ...      5         5      4     4    5      4       11   9   9   9  \n",
              "391  ...      2         4      5     3    4      2        3  14  16  16  \n",
              "392  ...      5         5      3     3    3      3        3  10   8   7  \n",
              "393  ...      4         4      1     3    4      5        0  11  12  10  \n",
              "394  ...      3         2      3     3    3      5        5   8   9   9  \n",
              "\n",
              "[395 rows x 32 columns]"
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              "<p>395 rows × 32 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['famsize', 'famsize', 'Pstatus', 'Medu', 'Mjob', 'Fjob', 'famrel', 'Dalc', 'Walc', 'G1', 'G2', 'G3'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "-T7OW8dUOyNM"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "tmFrGCYzP3fy",
        "outputId": "4d41675f-68b5-4db5-88e2-df530ca99ab1"
      },
      "execution_count": 15,
      "outputs": [
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              "    school sex  age address  Fedu  reason guardian  traveltime  studytime  \\\n",
              "0       GP   F   18       U     4  course   mother           2          2   \n",
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              "3       GP   F   15       U     2    home   mother           1          3   \n",
              "4       GP   F   16       U     3    home   father           1          2   \n",
              "..     ...  ..  ...     ...   ...     ...      ...         ...        ...   \n",
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              "391     MS   M   17       U     1  course   mother           2          1   \n",
              "392     MS   M   21       R     1  course    other           1          1   \n",
              "393     MS   M   18       R     2  course   mother           3          1   \n",
              "394     MS   M   19       U     1  course   father           1          1   \n",
              "\n",
              "     failures  ... paid activities nursery higher internet romantic freetime  \\\n",
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              "4           0  ...  yes         no     yes    yes       no       no        3   \n",
              "..        ...  ...  ...        ...     ...    ...      ...      ...      ...   \n",
              "390         2  ...  yes         no     yes    yes       no       no        5   \n",
              "391         0  ...   no         no      no    yes      yes       no        4   \n",
              "392         3  ...   no         no      no    yes       no       no        5   \n",
              "393         0  ...   no         no      no    yes      yes       no        4   \n",
              "394         0  ...   no         no     yes    yes      yes       no        2   \n",
              "\n",
              "    goout  health  absences  \n",
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              "..    ...     ...       ...  \n",
              "390     4       4        11  \n",
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              "[395 rows x 22 columns]"
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              "          const element = document.querySelector('#df-b5d2b4d0-9cb8-422e-abc6-f130de07491f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.drop(['Fedu', 'guardian', 'nursery'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "To5LsliSP42q"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tb1r1mPoQORH",
        "outputId": "08d75e38-fcbb-4dcb-9ff6-730a2bdead81"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address  reason  traveltime  studytime  failures  \\\n",
              "0       GP   F   18       U  course           2          2         0   \n",
              "1       GP   F   17       U  course           1          2         0   \n",
              "2       GP   F   15       U   other           1          2         3   \n",
              "3       GP   F   15       U    home           1          3         0   \n",
              "4       GP   F   16       U    home           1          2         0   \n",
              "..     ...  ..  ...     ...     ...         ...        ...       ...   \n",
              "390     MS   M   20       U  course           1          2         2   \n",
              "391     MS   M   17       U  course           2          1         0   \n",
              "392     MS   M   21       R  course           1          1         3   \n",
              "393     MS   M   18       R  course           3          1         0   \n",
              "394     MS   M   19       U  course           1          1         0   \n",
              "\n",
              "    schoolsup famsup paid activities higher internet romantic  freetime  \\\n",
              "0         yes     no   no         no    yes       no       no         3   \n",
              "1          no    yes   no         no    yes      yes       no         3   \n",
              "2         yes     no  yes         no    yes      yes       no         3   \n",
              "3          no    yes  yes        yes    yes      yes      yes         2   \n",
              "4          no    yes  yes         no    yes       no       no         3   \n",
              "..        ...    ...  ...        ...    ...      ...      ...       ...   \n",
              "390        no    yes  yes         no    yes       no       no         5   \n",
              "391        no     no   no         no    yes      yes       no         4   \n",
              "392        no     no   no         no    yes       no       no         5   \n",
              "393        no     no   no         no    yes      yes       no         4   \n",
              "394        no     no   no         no    yes      yes       no         2   \n",
              "\n",
              "     goout  health  absences  \n",
              "0        4       3         6  \n",
              "1        3       3         4  \n",
              "2        2       3        10  \n",
              "3        2       5         2  \n",
              "4        2       5         4  \n",
              "..     ...     ...       ...  \n",
              "390      4       4        11  \n",
              "391      5       2         3  \n",
              "392      3       3         3  \n",
              "393      1       5         0  \n",
              "394      3       5         5  \n",
              "\n",
              "[395 rows x 19 columns]"
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              "      <td>3</td>\n",
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              "      <td>MS</td>\n",
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              "      <td>R</td>\n",
              "      <td>course</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "      <td>no</td>\n",
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              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccdcd3ff-4f91-4994-9248-35e30648c1f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ccdcd3ff-4f91-4994-9248-35e30648c1f0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ccdcd3ff-4f91-4994-9248-35e30648c1f0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_df.studytime.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZ_wy7UXQPQ-",
        "outputId": "127e6e85-ddb9-4669-c71d-36bbde494ad8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 3, 1, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = pd.get_dummies(raw_df, columns = ['school', 'sex', 'address', 'reason', 'schoolsup', 'famsup', 'paid', 'activities', 'higher', 'internet', 'romantic'])"
      ],
      "metadata": {
        "id": "SJ0hyoWtQhXj"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "RhwtqtHGRjMd",
        "outputId": "1cd58bbd-233b-42f2-f419-b5f337e7934d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  traveltime  studytime  failures  freetime  goout  health  absences  \\\n",
              "0     18           2          2         0         3      4       3         6   \n",
              "1     17           1          2         0         3      3       3         4   \n",
              "2     15           1          2         3         3      2       3        10   \n",
              "3     15           1          3         0         2      2       5         2   \n",
              "4     16           1          2         0         3      2       5         4   \n",
              "..   ...         ...        ...       ...       ...    ...     ...       ...   \n",
              "390   20           1          2         2         5      4       4        11   \n",
              "391   17           2          1         0         4      5       2         3   \n",
              "392   21           1          1         3         5      3       3         3   \n",
              "393   18           3          1         0         4      1       5         0   \n",
              "394   19           1          1         0         2      3       5         5   \n",
              "\n",
              "     school_GP  school_MS  ...  paid_no  paid_yes  activities_no  \\\n",
              "0            1          0  ...        1         0              1   \n",
              "1            1          0  ...        1         0              1   \n",
              "2            1          0  ...        0         1              1   \n",
              "3            1          0  ...        0         1              0   \n",
              "4            1          0  ...        0         1              1   \n",
              "..         ...        ...  ...      ...       ...            ...   \n",
              "390          0          1  ...        0         1              1   \n",
              "391          0          1  ...        1         0              1   \n",
              "392          0          1  ...        1         0              1   \n",
              "393          0          1  ...        1         0              1   \n",
              "394          0          1  ...        1         0              1   \n",
              "\n",
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      "outputs": []
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              "     age  traveltime  studytime  failures  freetime  goout  health  absences  \\\n",
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              "      <th>internet_yes</th>\n",
              "      <th>romantic_no</th>\n",
              "      <th>romantic_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17d8fe28-49fb-4343-8d39-56400299d727')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-17d8fe28-49fb-4343-8d39-56400299d727 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-17d8fe28-49fb-4343-8d39-56400299d727');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "30H84ZU2R8of"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "tFqg4xtPTaQt"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(new_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KeEsc4nTfvE",
        "outputId": "41964c48-cca2-466a-f726-39958103a53d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "iEVWMBs2Tiaj",
        "outputId": "d5f0a214-bb5c-4da7-d8f3-e111c7e55814"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  traveltime  studytime  failures  freetime  goout  health  absences  \\\n",
              "0     18           2          2         0         3      4       3         6   \n",
              "1     17           1          2         0         3      3       3         4   \n",
              "2     15           1          2         3         3      2       3        10   \n",
              "3     15           1          3         0         2      2       5         2   \n",
              "4     16           1          2         0         3      2       5         4   \n",
              "..   ...         ...        ...       ...       ...    ...     ...       ...   \n",
              "390   20           1          2         2         5      4       4        11   \n",
              "391   17           2          1         0         4      5       2         3   \n",
              "392   21           1          1         3         5      3       3         3   \n",
              "393   18           3          1         0         4      1       5         0   \n",
              "394   19           1          1         0         2      3       5         5   \n",
              "\n",
              "     school_GP  school_MS  ...  paid_no  paid_yes  activities_no  \\\n",
              "0            1          0  ...        1         0              1   \n",
              "1            1          0  ...        1         0              1   \n",
              "2            1          0  ...        0         1              1   \n",
              "3            1          0  ...        0         1              0   \n",
              "4            1          0  ...        0         1              1   \n",
              "..         ...        ...  ...      ...       ...            ...   \n",
              "390          0          1  ...        0         1              1   \n",
              "391          0          1  ...        1         0              1   \n",
              "392          0          1  ...        1         0              1   \n",
              "393          0          1  ...        1         0              1   \n",
              "394          0          1  ...        1         0              1   \n",
              "\n",
              "     activities_yes  higher_no  higher_yes  internet_no  internet_yes  \\\n",
              "0                 0          0           1            1             0   \n",
              "1                 0          0           1            0             1   \n",
              "2                 0          0           1            0             1   \n",
              "3                 1          0           1            0             1   \n",
              "4                 0          0           1            1             0   \n",
              "..              ...        ...         ...          ...           ...   \n",
              "390               0          0           1            1             0   \n",
              "391               0          0           1            0             1   \n",
              "392               0          0           1            1             0   \n",
              "393               0          0           1            0             1   \n",
              "394               0          0           1            0             1   \n",
              "\n",
              "     romantic_no  romantic_yes  \n",
              "0              1             0  \n",
              "1              1             0  \n",
              "2              1             0  \n",
              "3              0             1  \n",
              "4              1             0  \n",
              "..           ...           ...  \n",
              "390            1             0  \n",
              "391            1             0  \n",
              "392            1             0  \n",
              "393            1             0  \n",
              "394            1             0  \n",
              "\n",
              "[395 rows x 32 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>traveltime</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
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              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 32 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def minmax(df):\n",
        "  for i in df.columns:\n",
        "    df[i] = scaler.fit_transform(df[[i]])\n",
        "  return df\n"
      ],
      "metadata": {
        "id": "xF_LcxeTTnV-"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df2 = minmax(new_df)"
      ],
      "metadata": {
        "id": "1GiAFhd7Tv2m"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "VSTEzdoaT1_e",
        "outputId": "9e437007-b42b-41f5-eed9-1d8a1e37c0a0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          age  traveltime  studytime  failures  freetime  goout  health  \\\n",
              "0    0.428571    0.333333   0.333333  0.000000      0.50   0.75    0.50   \n",
              "1    0.285714    0.000000   0.333333  0.000000      0.50   0.50    0.50   \n",
              "2    0.000000    0.000000   0.333333  1.000000      0.50   0.25    0.50   \n",
              "3    0.000000    0.000000   0.666667  0.000000      0.25   0.25    1.00   \n",
              "4    0.142857    0.000000   0.333333  0.000000      0.50   0.25    1.00   \n",
              "..        ...         ...        ...       ...       ...    ...     ...   \n",
              "390  0.714286    0.000000   0.333333  0.666667      1.00   0.75    0.75   \n",
              "391  0.285714    0.333333   0.000000  0.000000      0.75   1.00    0.25   \n",
              "392  0.857143    0.000000   0.000000  1.000000      1.00   0.50    0.50   \n",
              "393  0.428571    0.666667   0.000000  0.000000      0.75   0.00    1.00   \n",
              "394  0.571429    0.000000   0.000000  0.000000      0.25   0.50    1.00   \n",
              "\n",
              "     absences  school_GP  school_MS  ...  paid_no  paid_yes  activities_no  \\\n",
              "0    0.080000        1.0        0.0  ...      1.0       0.0            1.0   \n",
              "1    0.053333        1.0        0.0  ...      1.0       0.0            1.0   \n",
              "2    0.133333        1.0        0.0  ...      0.0       1.0            1.0   \n",
              "3    0.026667        1.0        0.0  ...      0.0       1.0            0.0   \n",
              "4    0.053333        1.0        0.0  ...      0.0       1.0            1.0   \n",
              "..        ...        ...        ...  ...      ...       ...            ...   \n",
              "390  0.146667        0.0        1.0  ...      0.0       1.0            1.0   \n",
              "391  0.040000        0.0        1.0  ...      1.0       0.0            1.0   \n",
              "392  0.040000        0.0        1.0  ...      1.0       0.0            1.0   \n",
              "393  0.000000        0.0        1.0  ...      1.0       0.0            1.0   \n",
              "394  0.066667        0.0        1.0  ...      1.0       0.0            1.0   \n",
              "\n",
              "     activities_yes  higher_no  higher_yes  internet_no  internet_yes  \\\n",
              "0               0.0        0.0         1.0          1.0           0.0   \n",
              "1               0.0        0.0         1.0          0.0           1.0   \n",
              "2               0.0        0.0         1.0          0.0           1.0   \n",
              "3               1.0        0.0         1.0          0.0           1.0   \n",
              "4               0.0        0.0         1.0          1.0           0.0   \n",
              "..              ...        ...         ...          ...           ...   \n",
              "390             0.0        0.0         1.0          1.0           0.0   \n",
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              "392             0.0        0.0         1.0          1.0           0.0   \n",
              "393             0.0        0.0         1.0          0.0           1.0   \n",
              "394             0.0        0.0         1.0          0.0           1.0   \n",
              "\n",
              "     romantic_no  romantic_yes  \n",
              "0            1.0           0.0  \n",
              "1            1.0           0.0  \n",
              "2            1.0           0.0  \n",
              "3            0.0           1.0  \n",
              "4            1.0           0.0  \n",
              "..           ...           ...  \n",
              "390          1.0           0.0  \n",
              "391          1.0           0.0  \n",
              "392          1.0           0.0  \n",
              "393          1.0           0.0  \n",
              "394          1.0           0.0  \n",
              "\n",
              "[395 rows x 32 columns]"
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.142857</td>\n",
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              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.75</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>0.857143</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
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          },
          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "cols = [i for i in new_df2.columns if i != 'failures']"
      ],
      "metadata": {
        "id": "zIPltPyMUzTQ"
      },
      "execution_count": 42,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "X = new_df2[cols]"
      ],
      "metadata": {
        "id": "T_P6W2b4U5Xk"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
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        "id": "_FJ0v33cVLAX",
        "outputId": "f2e4b24d-1979-4a65-b7c7-3ae1a8604401"
      },
      "execution_count": 46,
      "outputs": [
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          "output_type": "execute_result",
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              "          age  traveltime  studytime  freetime  goout  health  absences  \\\n",
              "0    0.428571    0.333333   0.333333      0.50   0.75    0.50  0.080000   \n",
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              "4    0.142857    0.000000   0.333333      0.50   0.25    1.00  0.053333   \n",
              "..        ...         ...        ...       ...    ...     ...       ...   \n",
              "390  0.714286    0.000000   0.333333      1.00   0.75    0.75  0.146667   \n",
              "391  0.285714    0.333333   0.000000      0.75   1.00    0.25  0.040000   \n",
              "392  0.857143    0.000000   0.000000      1.00   0.50    0.50  0.040000   \n",
              "393  0.428571    0.666667   0.000000      0.75   0.00    1.00  0.000000   \n",
              "394  0.571429    0.000000   0.000000      0.25   0.50    1.00  0.066667   \n",
              "\n",
              "     school_GP  school_MS  sex_F  ...  paid_no  paid_yes  activities_no  \\\n",
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              "390        0.0        1.0    0.0  ...      0.0       1.0            1.0   \n",
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              "\n",
              "     activities_yes  higher_no  higher_yes  internet_no  internet_yes  \\\n",
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              "..              ...        ...         ...          ...           ...   \n",
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              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.000000</td>\n",
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              "<p>395 rows × 31 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = new_df2[['failures']]"
      ],
      "metadata": {
        "id": "I7F1bOt6Vlqf"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
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      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "v16q92GPVqLa",
        "outputId": "2d0834ad-4e18-4454-a54b-c41ab9ddfa5a"
      },
      "execution_count": 48,
      "outputs": [
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          "data": {
            "text/plain": [
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fb0e181f-9d3f-4fd8-8770-92cba2a7e8b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "LE7hzHbkVrDs"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, train_size = 0.7, random_state=42)"
      ],
      "metadata": {
        "id": "X50_AEz1Vzwy"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_A_kk80WA8y",
        "outputId": "6137b660-4f6c-4677-be6f-7855f054a20c"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(276, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "x0vPGR7SWC5q"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "EKz3iGfuWIB9"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ORwpVW9OWKq4",
        "outputId": "d8a095b3-537d-48af-a2f4-1b1b080e5c72"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "Z1e2-rY5WOE0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "Y-5wYB3QWSx8"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "2b7T5fTTWTud"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZqWCsZ9WqWL",
        "outputId": "eb67f7a5-eed2-45c8-8f7a-3c7c4db23968"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.053839039999878914"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lGh5bxoGWriq"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0kFHVa_aW8ww"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}