student_grade_predictor.ipynb

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        "<a href=\"https://colab.research.google.com/github/abhirukth/Data-Sci.-File/blob/main/student_grade_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "pressing-contractor",
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      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "id": "pressing-contractor",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/kaggle/input/student-alcohol-consumption/student-mat.csv\n",
            "/kaggle/input/student-alcohol-consumption/student-merge.R\n",
            "/kaggle/input/student-alcohol-consumption/student-por.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "indirect-april",
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      "source": [
        "data = pd.read_csv('../input/student-alcohol-consumption/student-mat.csv')\n",
        "data"
      ],
      "id": "indirect-april",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "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",
              "     ... 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 33 columns]"
            ]
          },
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          },
          "execution_count": 2
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        "plt.figure(figsize = (14, 12))\n",
        "sns.heatmap(data.corr(), annot = True)\n",
        "plt.show()"
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\n",
            "text/plain": [
              "<Figure size 1008x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.206254Z",
          "iopub.status.busy": "2021-06-24T10:40:09.205159Z",
          "iopub.status.idle": "2021-06-24T10:40:09.213004Z",
          "shell.execute_reply": "2021-06-24T10:40:09.214013Z",
          "shell.execute_reply.started": "2021-06-24T09:48:14.246894Z"
        },
        "papermill": {
          "duration": 0.051487,
          "end_time": "2021-06-24T10:40:09.214285",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.162798",
          "status": "completed"
        },
        "tags": [],
        "id": "comparative-marshall",
        "outputId": "f6e15b92-56b8-4048-b1ec-76444905b2d6"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "id": "comparative-marshall",
      "execution_count": null,
      "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": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.283683Z",
          "iopub.status.busy": "2021-06-24T10:40:09.282700Z",
          "iopub.status.idle": "2021-06-24T10:40:09.291568Z",
          "shell.execute_reply": "2021-06-24T10:40:09.291031Z",
          "shell.execute_reply.started": "2021-06-24T09:48:59.048393Z"
        },
        "papermill": {
          "duration": 0.046144,
          "end_time": "2021-06-24T10:40:09.291704",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.245560",
          "status": "completed"
        },
        "tags": [],
        "id": "earned-inclusion",
        "outputId": "863c3a90-ca82-4fcb-d9ad-e0087633d279"
      },
      "source": [
        "data.dtypes"
      ],
      "id": "earned-inclusion",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        object\n",
              "sex           object\n",
              "age            int64\n",
              "address       object\n",
              "famsize       object\n",
              "Pstatus       object\n",
              "Medu           int64\n",
              "Fedu           int64\n",
              "Mjob          object\n",
              "Fjob          object\n",
              "reason        object\n",
              "guardian      object\n",
              "traveltime     int64\n",
              "studytime      int64\n",
              "failures       int64\n",
              "schoolsup     object\n",
              "famsup        object\n",
              "paid          object\n",
              "activities    object\n",
              "nursery       object\n",
              "higher        object\n",
              "internet      object\n",
              "romantic      object\n",
              "famrel         int64\n",
              "freetime       int64\n",
              "goout          int64\n",
              "Dalc           int64\n",
              "Walc           int64\n",
              "health         int64\n",
              "absences       int64\n",
              "G1             int64\n",
              "G2             int64\n",
              "G3             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.353961Z",
          "iopub.status.busy": "2021-06-24T10:40:09.352895Z",
          "iopub.status.idle": "2021-06-24T10:40:09.356705Z",
          "shell.execute_reply": "2021-06-24T10:40:09.357166Z",
          "shell.execute_reply.started": "2021-06-24T09:53:56.390356Z"
        },
        "papermill": {
          "duration": 0.038095,
          "end_time": "2021-06-24T10:40:09.357354",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.319259",
          "status": "completed"
        },
        "tags": [],
        "id": "thermal-editor",
        "outputId": "8c9f5629-4173-4ef6-e744-3930886d119d"
      },
      "source": [
        "object_col = [data.columns[index] for index, dtype in enumerate(data.dtypes) if dtype == 'object']\n",
        "object_col"
      ],
      "id": "thermal-editor",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['school',\n",
              " 'sex',\n",
              " 'address',\n",
              " 'famsize',\n",
              " 'Pstatus',\n",
              " 'Mjob',\n",
              " 'Fjob',\n",
              " 'reason',\n",
              " 'guardian',\n",
              " 'schoolsup',\n",
              " 'famsup',\n",
              " 'paid',\n",
              " 'activities',\n",
              " 'nursery',\n",
              " 'higher',\n",
              " 'internet',\n",
              " 'romantic']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.417025Z",
          "iopub.status.busy": "2021-06-24T10:40:09.416093Z",
          "iopub.status.idle": "2021-06-24T10:40:09.430409Z",
          "shell.execute_reply": "2021-06-24T10:40:09.430914Z",
          "shell.execute_reply.started": "2021-06-24T09:58:17.875859Z"
        },
        "papermill": {
          "duration": 0.045795,
          "end_time": "2021-06-24T10:40:09.431078",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.385283",
          "status": "completed"
        },
        "tags": [],
        "id": "subsequent-batch",
        "outputId": "8ff692b8-6bd4-465a-8042-0bdb419a5d40"
      },
      "source": [
        "for column in object_col:\n",
        "    print(f\"{column} : {data[column].unique()}\")"
      ],
      "id": "subsequent-batch",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "school : ['GP' 'MS']\n",
            "sex : ['F' 'M']\n",
            "address : ['U' 'R']\n",
            "famsize : ['GT3' 'LE3']\n",
            "Pstatus : ['A' 'T']\n",
            "Mjob : ['at_home' 'health' 'other' 'services' 'teacher']\n",
            "Fjob : ['teacher' 'other' 'services' 'health' 'at_home']\n",
            "reason : ['course' 'other' 'home' 'reputation']\n",
            "guardian : ['mother' 'father' 'other']\n",
            "schoolsup : ['yes' 'no']\n",
            "famsup : ['no' 'yes']\n",
            "paid : ['no' 'yes']\n",
            "activities : ['no' 'yes']\n",
            "nursery : ['yes' 'no']\n",
            "higher : ['yes' 'no']\n",
            "internet : ['no' 'yes']\n",
            "romantic : ['no' 'yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.497072Z",
          "iopub.status.busy": "2021-06-24T10:40:09.496441Z",
          "iopub.status.idle": "2021-06-24T10:40:09.499583Z",
          "shell.execute_reply": "2021-06-24T10:40:09.499067Z",
          "shell.execute_reply.started": "2021-06-24T10:05:50.213769Z"
        },
        "papermill": {
          "duration": 0.040385,
          "end_time": "2021-06-24T10:40:09.499726",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.459341",
          "status": "completed"
        },
        "tags": [],
        "id": "radical-spray"
      },
      "source": [
        "data['Mjob'] = data['Mjob'].apply(lambda x : 'm_' + x)\n",
        "data['Fjob'] = data['Fjob'].apply(lambda x : 'f_' + x)\n",
        "data['reason'] = data['reason'].apply(lambda x : 'r_' + x)\n",
        "data['guardian'] = data['guardian'].apply(lambda x : 'g_' + x)"
      ],
      "id": "radical-spray",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.589290Z",
          "iopub.status.busy": "2021-06-24T10:40:09.577132Z",
          "iopub.status.idle": "2021-06-24T10:40:09.594864Z",
          "shell.execute_reply": "2021-06-24T10:40:09.595356Z",
          "shell.execute_reply.started": "2021-06-24T10:06:12.337067Z"
        },
        "papermill": {
          "duration": 0.067613,
          "end_time": "2021-06-24T10:40:09.595528",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.527915",
          "status": "completed"
        },
        "tags": [],
        "id": "freelance-greenhouse",
        "outputId": "b35e02ed-22ee-4724-9a4a-94b55358e56f"
      },
      "source": [
        "data"
      ],
      "id": "freelance-greenhouse",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>school</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>address</th>\n",
              "      <th>famsize</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>Mjob</th>\n",
              "      <th>Fjob</th>\n",
              "      <th>...</th>\n",
              "      <th>famrel</th>\n",
              "      <th>freetime</th>\n",
              "      <th>goout</th>\n",
              "      <th>Dalc</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>absences</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>m_at_home</td>\n",
              "      <td>f_teacher</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>m_at_home</td>\n",
              "      <td>f_other</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>m_at_home</td>\n",
              "      <td>f_other</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>m_health</td>\n",
              "      <td>f_services</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>m_other</td>\n",
              "      <td>f_other</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>10</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>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>m_services</td>\n",
              "      <td>f_services</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>m_services</td>\n",
              "      <td>f_services</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <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>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>m_other</td>\n",
              "      <td>f_other</td>\n",
              "      <td>...</td>\n",
              "      <td>5</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>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>m_services</td>\n",
              "      <td>f_other</td>\n",
              "      <td>...</td>\n",
              "      <td>4</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>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>m_other</td>\n",
              "      <td>f_at_home</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <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 × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu        Mjob  \\\n",
              "0       GP   F   18       U     GT3       A     4     4   m_at_home   \n",
              "1       GP   F   17       U     GT3       T     1     1   m_at_home   \n",
              "2       GP   F   15       U     LE3       T     1     1   m_at_home   \n",
              "3       GP   F   15       U     GT3       T     4     2    m_health   \n",
              "4       GP   F   16       U     GT3       T     3     3     m_other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...         ...   \n",
              "390     MS   M   20       U     LE3       A     2     2  m_services   \n",
              "391     MS   M   17       U     LE3       T     3     1  m_services   \n",
              "392     MS   M   21       R     GT3       T     1     1     m_other   \n",
              "393     MS   M   18       R     LE3       T     3     2  m_services   \n",
              "394     MS   M   19       U     LE3       T     1     1     m_other   \n",
              "\n",
              "           Fjob  ... famrel freetime  goout  Dalc  Walc health absences  G1  \\\n",
              "0     f_teacher  ...      4        3      4     1     1      3        6   5   \n",
              "1       f_other  ...      5        3      3     1     1      3        4   5   \n",
              "2       f_other  ...      4        3      2     2     3      3       10   7   \n",
              "3    f_services  ...      3        2      2     1     1      5        2  15   \n",
              "4       f_other  ...      4        3      2     1     2      5        4   6   \n",
              "..          ...  ...    ...      ...    ...   ...   ...    ...      ...  ..   \n",
              "390  f_services  ...      5        5      4     4     5      4       11   9   \n",
              "391  f_services  ...      2        4      5     3     4      2        3  14   \n",
              "392     f_other  ...      5        5      3     3     3      3        3  10   \n",
              "393     f_other  ...      4        4      1     3     4      5        0  11   \n",
              "394   f_at_home  ...      3        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 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.669925Z",
          "iopub.status.busy": "2021-06-24T10:40:09.668993Z",
          "iopub.status.idle": "2021-06-24T10:40:09.674003Z",
          "shell.execute_reply": "2021-06-24T10:40:09.673405Z",
          "shell.execute_reply.started": "2021-06-24T10:07:01.978593Z"
        },
        "papermill": {
          "duration": 0.049759,
          "end_time": "2021-06-24T10:40:09.674132",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.624373",
          "status": "completed"
        },
        "tags": [],
        "id": "challenging-sheriff",
        "outputId": "465e6921-f1cf-4ede-8da4-4465ef21acfe"
      },
      "source": [
        "pd.get_dummies(data['Mjob'])"
      ],
      "id": "challenging-sheriff",
      "execution_count": null,
      "outputs": [
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            ],
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              "     m_at_home  m_health  m_other  m_services  m_teacher\n",
              "0            1         0        0           0          0\n",
              "1            1         0        0           0          0\n",
              "2            1         0        0           0          0\n",
              "3            0         1        0           0          0\n",
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              "..         ...       ...      ...         ...        ...\n",
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              "[395 rows x 5 columns]"
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        "id": "phantom-ordinance",
        "outputId": "55f4329f-c45f-415b-c1f9-5540e00026af"
      },
      "source": [
        "dummy = pd.concat([\n",
        "    pd.get_dummies(data['Mjob']),\n",
        "    pd.get_dummies(data['Fjob']),\n",
        "    pd.get_dummies(data['reason']),\n",
        "    pd.get_dummies(data['guardian'])],\n",
        "    axis = 1)\n",
        "\n",
        "dummy"
      ],
      "id": "phantom-ordinance",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "</table>\n",
              "<p>395 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     m_at_home  m_health  m_other  m_services  m_teacher  f_at_home  f_health  \\\n",
              "0            1         0        0           0          0          0         0   \n",
              "1            1         0        0           0          0          0         0   \n",
              "2            1         0        0           0          0          0         0   \n",
              "3            0         1        0           0          0          0         0   \n",
              "4            0         0        1           0          0          0         0   \n",
              "..         ...       ...      ...         ...        ...        ...       ...   \n",
              "390          0         0        0           1          0          0         0   \n",
              "391          0         0        0           1          0          0         0   \n",
              "392          0         0        1           0          0          0         0   \n",
              "393          0         0        0           1          0          0         0   \n",
              "394          0         0        1           0          0          1         0   \n",
              "\n",
              "     f_other  f_services  f_teacher  r_course  r_home  r_other  r_reputation  \\\n",
              "0          0           0          1         1       0        0             0   \n",
              "1          1           0          0         1       0        0             0   \n",
              "2          1           0          0         0       0        1             0   \n",
              "3          0           1          0         0       1        0             0   \n",
              "4          1           0          0         0       1        0             0   \n",
              "..       ...         ...        ...       ...     ...      ...           ...   \n",
              "390        0           1          0         1       0        0             0   \n",
              "391        0           1          0         1       0        0             0   \n",
              "392        1           0          0         1       0        0             0   \n",
              "393        1           0          0         1       0        0             0   \n",
              "394        0           0          0         1       0        0             0   \n",
              "\n",
              "     g_father  g_mother  g_other  \n",
              "0           0         1        0  \n",
              "1           1         0        0  \n",
              "2           0         1        0  \n",
              "3           0         1        0  \n",
              "4           1         0        0  \n",
              "..        ...       ...      ...  \n",
              "390         0         0        1  \n",
              "391         0         1        0  \n",
              "392         0         0        1  \n",
              "393         0         1        0  \n",
              "394         1         0        0  \n",
              "\n",
              "[395 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "shell.execute_reply": "2021-06-24T10:40:09.836161Z",
          "shell.execute_reply.started": "2021-06-24T10:14:29.568580Z"
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          "start_time": "2021-06-24T10:40:09.793120",
          "status": "completed"
        },
        "tags": [],
        "id": "extreme-monitoring"
      },
      "source": [
        "data = pd.concat([data, dummy], axis = 1)\n",
        "data.drop(['Mjob', 'Fjob', 'reason', 'guardian'], axis = 1, inplace = True)"
      ],
      "id": "extreme-monitoring",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:09.906290Z",
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          "shell.execute_reply.started": "2021-06-24T10:14:33.105117Z"
        },
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          "exception": false,
          "start_time": "2021-06-24T10:40:09.867079",
          "status": "completed"
        },
        "tags": [],
        "id": "moving-childhood",
        "outputId": "de067e1f-e0ff-43a9-e2dc-46aad5d8428f"
      },
      "source": [
        "data"
      ],
      "id": "moving-childhood",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>school</th>\n",
              "      <th>sex</th>\n",
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              "      <th>studytime</th>\n",
              "      <th>...</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>18</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>...</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>U</td>\n",
              "      <td>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>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>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>20</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>U</td>\n",
              "      <td>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>GT3</td>\n",
              "      <td>T</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>LE3</td>\n",
              "      <td>T</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 46 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu  traveltime  \\\n",
              "0       GP   F   18       U     GT3       A     4     4           2   \n",
              "1       GP   F   17       U     GT3       T     1     1           1   \n",
              "2       GP   F   15       U     LE3       T     1     1           1   \n",
              "3       GP   F   15       U     GT3       T     4     2           1   \n",
              "4       GP   F   16       U     GT3       T     3     3           1   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...         ...   \n",
              "390     MS   M   20       U     LE3       A     2     2           1   \n",
              "391     MS   M   17       U     LE3       T     3     1           2   \n",
              "392     MS   M   21       R     GT3       T     1     1           1   \n",
              "393     MS   M   18       R     LE3       T     3     2           3   \n",
              "394     MS   M   19       U     LE3       T     1     1           1   \n",
              "\n",
              "     studytime  ...  f_other f_services f_teacher r_course r_home r_other  \\\n",
              "0            2  ...        0          0         1        1      0       0   \n",
              "1            2  ...        1          0         0        1      0       0   \n",
              "2            2  ...        1          0         0        0      0       1   \n",
              "3            3  ...        0          1         0        0      1       0   \n",
              "4            2  ...        1          0         0        0      1       0   \n",
              "..         ...  ...      ...        ...       ...      ...    ...     ...   \n",
              "390          2  ...        0          1         0        1      0       0   \n",
              "391          1  ...        0          1         0        1      0       0   \n",
              "392          1  ...        1          0         0        1      0       0   \n",
              "393          1  ...        1          0         0        1      0       0   \n",
              "394          1  ...        0          0         0        1      0       0   \n",
              "\n",
              "    r_reputation g_father g_mother  g_other  \n",
              "0              0        0        1        0  \n",
              "1              0        1        0        0  \n",
              "2              0        0        1        0  \n",
              "3              0        0        1        0  \n",
              "4              0        1        0        0  \n",
              "..           ...      ...      ...      ...  \n",
              "390            0        0        0        1  \n",
              "391            0        0        1        0  \n",
              "392            0        0        0        1  \n",
              "393            0        0        1        0  \n",
              "394            0        1        0        0  \n",
              "\n",
              "[395 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.006489Z",
          "iopub.status.busy": "2021-06-24T10:40:10.004250Z",
          "iopub.status.idle": "2021-06-24T10:40:10.012087Z",
          "shell.execute_reply": "2021-06-24T10:40:10.010691Z",
          "shell.execute_reply.started": "2021-06-24T10:16:23.767980Z"
        },
        "papermill": {
          "duration": 0.047009,
          "end_time": "2021-06-24T10:40:10.012248",
          "exception": false,
          "start_time": "2021-06-24T10:40:09.965239",
          "status": "completed"
        },
        "tags": [],
        "id": "liquid-winning",
        "outputId": "151b546f-1e16-4377-8f6f-21db1b3730e7"
      },
      "source": [
        "object_col = [data.columns[index] for index, dtype in enumerate(data.dtypes) if dtype == 'object']\n",
        "\n",
        "for column in object_col:\n",
        "    print(f\"{column} : {data[column].unique()}\")"
      ],
      "id": "liquid-winning",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "school : ['GP' 'MS']\n",
            "sex : ['F' 'M']\n",
            "address : ['U' 'R']\n",
            "famsize : ['GT3' 'LE3']\n",
            "Pstatus : ['A' 'T']\n",
            "schoolsup : ['yes' 'no']\n",
            "famsup : ['no' 'yes']\n",
            "paid : ['no' 'yes']\n",
            "activities : ['no' 'yes']\n",
            "nursery : ['yes' 'no']\n",
            "higher : ['yes' 'no']\n",
            "internet : ['no' 'yes']\n",
            "romantic : ['no' 'yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.084186Z",
          "iopub.status.busy": "2021-06-24T10:40:10.083555Z",
          "iopub.status.idle": "2021-06-24T10:40:10.089446Z",
          "shell.execute_reply": "2021-06-24T10:40:10.089950Z",
          "shell.execute_reply.started": "2021-06-24T10:19:11.490193Z"
        },
        "papermill": {
          "duration": 0.0462,
          "end_time": "2021-06-24T10:40:10.090118",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.043918",
          "status": "completed"
        },
        "tags": [],
        "id": "modified-limit"
      },
      "source": [
        "encoder = LabelEncoder()\n",
        "\n",
        "for column in object_col:\n",
        "    data[column] = encoder.fit_transform(data[column])"
      ],
      "id": "modified-limit",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.157125Z",
          "iopub.status.busy": "2021-06-24T10:40:10.156500Z",
          "iopub.status.idle": "2021-06-24T10:40:10.164532Z",
          "shell.execute_reply": "2021-06-24T10:40:10.165001Z",
          "shell.execute_reply.started": "2021-06-24T10:19:22.735063Z"
        },
        "papermill": {
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          "end_time": "2021-06-24T10:40:10.165178",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.122139",
          "status": "completed"
        },
        "tags": [],
        "id": "latest-bacteria",
        "outputId": "bebb2cfe-9fab-4dab-aa77-50087ef15a13"
      },
      "source": [
        "data.dtypes"
      ],
      "id": "latest-bacteria",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school          int64\n",
              "sex             int64\n",
              "age             int64\n",
              "address         int64\n",
              "famsize         int64\n",
              "Pstatus         int64\n",
              "Medu            int64\n",
              "Fedu            int64\n",
              "traveltime      int64\n",
              "studytime       int64\n",
              "failures        int64\n",
              "schoolsup       int64\n",
              "famsup          int64\n",
              "paid            int64\n",
              "activities      int64\n",
              "nursery         int64\n",
              "higher          int64\n",
              "internet        int64\n",
              "romantic        int64\n",
              "famrel          int64\n",
              "freetime        int64\n",
              "goout           int64\n",
              "Dalc            int64\n",
              "Walc            int64\n",
              "health          int64\n",
              "absences        int64\n",
              "G1              int64\n",
              "G2              int64\n",
              "G3              int64\n",
              "m_at_home       uint8\n",
              "m_health        uint8\n",
              "m_other         uint8\n",
              "m_services      uint8\n",
              "m_teacher       uint8\n",
              "f_at_home       uint8\n",
              "f_health        uint8\n",
              "f_other         uint8\n",
              "f_services      uint8\n",
              "f_teacher       uint8\n",
              "r_course        uint8\n",
              "r_home          uint8\n",
              "r_other         uint8\n",
              "r_reputation    uint8\n",
              "g_father        uint8\n",
              "g_mother        uint8\n",
              "g_other         uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.236553Z",
          "iopub.status.busy": "2021-06-24T10:40:10.235888Z",
          "iopub.status.idle": "2021-06-24T10:40:10.240242Z",
          "shell.execute_reply": "2021-06-24T10:40:10.240717Z",
          "shell.execute_reply.started": "2021-06-24T10:27:06.451747Z"
        },
        "papermill": {
          "duration": 0.042909,
          "end_time": "2021-06-24T10:40:10.240902",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.197993",
          "status": "completed"
        },
        "tags": [],
        "id": "nuclear-kennedy"
      },
      "source": [
        "y = data['G3']\n",
        "X = data.drop('G3', axis = 1)"
      ],
      "id": "nuclear-kennedy",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.311578Z",
          "iopub.status.busy": "2021-06-24T10:40:10.310805Z",
          "iopub.status.idle": "2021-06-24T10:40:10.315303Z",
          "shell.execute_reply": "2021-06-24T10:40:10.314741Z",
          "shell.execute_reply.started": "2021-06-24T10:27:07.080296Z"
        },
        "papermill": {
          "duration": 0.042669,
          "end_time": "2021-06-24T10:40:10.315442",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.272773",
          "status": "completed"
        },
        "tags": [],
        "id": "integrated-montreal",
        "outputId": "43426d86-884c-4115-8f72-a5bf4c850287"
      },
      "source": [
        "y"
      ],
      "id": "integrated-montreal",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       6\n",
              "1       6\n",
              "2      10\n",
              "3      15\n",
              "4      10\n",
              "       ..\n",
              "390     9\n",
              "391    16\n",
              "392     7\n",
              "393    10\n",
              "394     9\n",
              "Name: G3, Length: 395, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.384306Z",
          "iopub.status.busy": "2021-06-24T10:40:10.383659Z",
          "iopub.status.idle": "2021-06-24T10:40:10.405423Z",
          "shell.execute_reply": "2021-06-24T10:40:10.405921Z",
          "shell.execute_reply.started": "2021-06-24T10:27:10.644346Z"
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        "papermill": {
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          "end_time": "2021-06-24T10:40:10.406093",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.348255",
          "status": "completed"
        },
        "tags": [],
        "id": "structural-union",
        "outputId": "20b1087b-80e3-4e8d-c527-09b0d2b44c0b"
      },
      "source": [
        "X"
      ],
      "id": "structural-union",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>r_other</th>\n",
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              "      <th>g_mother</th>\n",
              "      <th>g_other</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>392</th>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>393</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>394</th>\n",
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              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 45 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  traveltime  \\\n",
              "0         0    0   18        1        0        0     4     4           2   \n",
              "1         0    0   17        1        0        1     1     1           1   \n",
              "2         0    0   15        1        1        1     1     1           1   \n",
              "3         0    0   15        1        0        1     4     2           1   \n",
              "4         0    0   16        1        0        1     3     3           1   \n",
              "..      ...  ...  ...      ...      ...      ...   ...   ...         ...   \n",
              "390       1    1   20        1        1        0     2     2           1   \n",
              "391       1    1   17        1        1        1     3     1           2   \n",
              "392       1    1   21        0        0        1     1     1           1   \n",
              "393       1    1   18        0        1        1     3     2           3   \n",
              "394       1    1   19        1        1        1     1     1           1   \n",
              "\n",
              "     studytime  ...  f_other  f_services  f_teacher  r_course  r_home  \\\n",
              "0            2  ...        0           0          1         1       0   \n",
              "1            2  ...        1           0          0         1       0   \n",
              "2            2  ...        1           0          0         0       0   \n",
              "3            3  ...        0           1          0         0       1   \n",
              "4            2  ...        1           0          0         0       1   \n",
              "..         ...  ...      ...         ...        ...       ...     ...   \n",
              "390          2  ...        0           1          0         1       0   \n",
              "391          1  ...        0           1          0         1       0   \n",
              "392          1  ...        1           0          0         1       0   \n",
              "393          1  ...        1           0          0         1       0   \n",
              "394          1  ...        0           0          0         1       0   \n",
              "\n",
              "     r_other  r_reputation  g_father  g_mother  g_other  \n",
              "0          0             0         0         1        0  \n",
              "1          0             0         1         0        0  \n",
              "2          1             0         0         1        0  \n",
              "3          0             0         0         1        0  \n",
              "4          0             0         1         0        0  \n",
              "..       ...           ...       ...       ...      ...  \n",
              "390        0             0         0         0        1  \n",
              "391        0             0         0         1        0  \n",
              "392        0             0         0         0        1  \n",
              "393        0             0         0         1        0  \n",
              "394        0             0         1         0        0  \n",
              "\n",
              "[395 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
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    },
    {
      "cell_type": "code",
      "metadata": {
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          "iopub.status.busy": "2021-06-24T10:40:10.474754Z",
          "iopub.status.idle": "2021-06-24T10:40:10.484105Z",
          "shell.execute_reply": "2021-06-24T10:40:10.484613Z",
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          "status": "completed"
        },
        "tags": [],
        "id": "operational-patio"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "X = pd.DataFrame(scaler.fit_transform(X), columns = X.columns)"
      ],
      "id": "operational-patio",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.554107Z",
          "iopub.status.busy": "2021-06-24T10:40:10.553480Z",
          "iopub.status.idle": "2021-06-24T10:40:10.584973Z",
          "shell.execute_reply": "2021-06-24T10:40:10.585449Z",
          "shell.execute_reply.started": "2021-06-24T10:27:21.101263Z"
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          "exception": false,
          "start_time": "2021-06-24T10:40:10.517902",
          "status": "completed"
        },
        "tags": [],
        "id": "dietary-stylus",
        "outputId": "db538139-a19d-40b9-db4a-28d456ad5ce5"
      },
      "source": [
        "X"
      ],
      "id": "dietary-stylus",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>-0.948176</td>\n",
              "      <td>-0.546287</td>\n",
              "      <td>0.535392</td>\n",
              "      <td>-0.636941</td>\n",
              "      <td>0.340322</td>\n",
              "      <td>0.229234</td>\n",
              "      <td>0.440257</td>\n",
              "      <td>-0.643249</td>\n",
              "      <td>-0.042286</td>\n",
              "      <td>...</td>\n",
              "      <td>0.905691</td>\n",
              "      <td>-0.625176</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>-0.761577</td>\n",
              "      <td>1.619831</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>1.840894</td>\n",
              "      <td>-1.495896</td>\n",
              "      <td>-0.296908</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>2.754443</td>\n",
              "      <td>1.054656</td>\n",
              "      <td>2.592380</td>\n",
              "      <td>0.535392</td>\n",
              "      <td>1.570004</td>\n",
              "      <td>-2.938392</td>\n",
              "      <td>-0.685387</td>\n",
              "      <td>-0.479857</td>\n",
              "      <td>-0.643249</td>\n",
              "      <td>-0.042286</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.104129</td>\n",
              "      <td>1.599549</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>1.313064</td>\n",
              "      <td>-0.617348</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>-0.543214</td>\n",
              "      <td>-1.495896</td>\n",
              "      <td>3.368048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>2.754443</td>\n",
              "      <td>1.054656</td>\n",
              "      <td>0.238380</td>\n",
              "      <td>0.535392</td>\n",
              "      <td>1.570004</td>\n",
              "      <td>0.340322</td>\n",
              "      <td>0.229234</td>\n",
              "      <td>-1.399970</td>\n",
              "      <td>0.792251</td>\n",
              "      <td>-1.235351</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.104129</td>\n",
              "      <td>1.599549</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>1.313064</td>\n",
              "      <td>-0.617348</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>-0.543214</td>\n",
              "      <td>0.668496</td>\n",
              "      <td>-0.296908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>2.754443</td>\n",
              "      <td>1.054656</td>\n",
              "      <td>3.377047</td>\n",
              "      <td>-1.867789</td>\n",
              "      <td>-0.636941</td>\n",
              "      <td>0.340322</td>\n",
              "      <td>-1.600009</td>\n",
              "      <td>-1.399970</td>\n",
              "      <td>-0.643249</td>\n",
              "      <td>-1.235351</td>\n",
              "      <td>...</td>\n",
              "      <td>0.905691</td>\n",
              "      <td>-0.625176</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>1.313064</td>\n",
              "      <td>-0.617348</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>-0.543214</td>\n",
              "      <td>-1.495896</td>\n",
              "      <td>3.368048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>2.754443</td>\n",
              "      <td>1.054656</td>\n",
              "      <td>1.023046</td>\n",
              "      <td>-1.867789</td>\n",
              "      <td>1.570004</td>\n",
              "      <td>0.340322</td>\n",
              "      <td>0.229234</td>\n",
              "      <td>-0.479857</td>\n",
              "      <td>2.227751</td>\n",
              "      <td>-1.235351</td>\n",
              "      <td>...</td>\n",
              "      <td>0.905691</td>\n",
              "      <td>-0.625176</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>1.313064</td>\n",
              "      <td>-0.617348</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>-0.543214</td>\n",
              "      <td>0.668496</td>\n",
              "      <td>-0.296908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>2.754443</td>\n",
              "      <td>1.054656</td>\n",
              "      <td>1.807713</td>\n",
              "      <td>0.535392</td>\n",
              "      <td>1.570004</td>\n",
              "      <td>0.340322</td>\n",
              "      <td>-1.600009</td>\n",
              "      <td>-1.399970</td>\n",
              "      <td>-0.643249</td>\n",
              "      <td>-1.235351</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.104129</td>\n",
              "      <td>-0.625176</td>\n",
              "      <td>-0.281487</td>\n",
              "      <td>1.313064</td>\n",
              "      <td>-0.617348</td>\n",
              "      <td>-0.316668</td>\n",
              "      <td>-0.601722</td>\n",
              "      <td>1.840894</td>\n",
              "      <td>-1.495896</td>\n",
              "      <td>-0.296908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 45 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       school       sex       age   address   famsize   Pstatus      Medu  \\\n",
              "0   -0.363050 -0.948176  1.023046  0.535392 -0.636941 -2.938392  1.143856   \n",
              "1   -0.363050 -0.948176  0.238380  0.535392 -0.636941  0.340322 -1.600009   \n",
              "2   -0.363050 -0.948176 -1.330954  0.535392  1.570004  0.340322 -1.600009   \n",
              "3   -0.363050 -0.948176 -1.330954  0.535392 -0.636941  0.340322  1.143856   \n",
              "4   -0.363050 -0.948176 -0.546287  0.535392 -0.636941  0.340322  0.229234   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "390  2.754443  1.054656  2.592380  0.535392  1.570004 -2.938392 -0.685387   \n",
              "391  2.754443  1.054656  0.238380  0.535392  1.570004  0.340322  0.229234   \n",
              "392  2.754443  1.054656  3.377047 -1.867789 -0.636941  0.340322 -1.600009   \n",
              "393  2.754443  1.054656  1.023046 -1.867789  1.570004  0.340322  0.229234   \n",
              "394  2.754443  1.054656  1.807713  0.535392  1.570004  0.340322 -1.600009   \n",
              "\n",
              "         Fedu  traveltime  studytime  ...   f_other  f_services  f_teacher  \\\n",
              "0    1.360371    0.792251  -0.042286  ... -1.104129   -0.625176   3.552561   \n",
              "1   -1.399970   -0.643249  -0.042286  ...  0.905691   -0.625176  -0.281487   \n",
              "2   -1.399970   -0.643249  -0.042286  ...  0.905691   -0.625176  -0.281487   \n",
              "3   -0.479857   -0.643249   1.150779  ... -1.104129    1.599549  -0.281487   \n",
              "4    0.440257   -0.643249  -0.042286  ...  0.905691   -0.625176  -0.281487   \n",
              "..        ...         ...        ...  ...       ...         ...        ...   \n",
              "390 -0.479857   -0.643249  -0.042286  ... -1.104129    1.599549  -0.281487   \n",
              "391 -1.399970    0.792251  -1.235351  ... -1.104129    1.599549  -0.281487   \n",
              "392 -1.399970   -0.643249  -1.235351  ...  0.905691   -0.625176  -0.281487   \n",
              "393 -0.479857    2.227751  -1.235351  ...  0.905691   -0.625176  -0.281487   \n",
              "394 -1.399970   -0.643249  -1.235351  ... -1.104129   -0.625176  -0.281487   \n",
              "\n",
              "     r_course    r_home   r_other  r_reputation  g_father  g_mother   g_other  \n",
              "0    1.313064 -0.617348 -0.316668     -0.601722 -0.543214  0.668496 -0.296908  \n",
              "1    1.313064 -0.617348 -0.316668     -0.601722  1.840894 -1.495896 -0.296908  \n",
              "2   -0.761577 -0.617348  3.157883     -0.601722 -0.543214  0.668496 -0.296908  \n",
              "3   -0.761577  1.619831 -0.316668     -0.601722 -0.543214  0.668496 -0.296908  \n",
              "4   -0.761577  1.619831 -0.316668     -0.601722  1.840894 -1.495896 -0.296908  \n",
              "..        ...       ...       ...           ...       ...       ...       ...  \n",
              "390  1.313064 -0.617348 -0.316668     -0.601722 -0.543214 -1.495896  3.368048  \n",
              "391  1.313064 -0.617348 -0.316668     -0.601722 -0.543214  0.668496 -0.296908  \n",
              "392  1.313064 -0.617348 -0.316668     -0.601722 -0.543214 -1.495896  3.368048  \n",
              "393  1.313064 -0.617348 -0.316668     -0.601722 -0.543214  0.668496 -0.296908  \n",
              "394  1.313064 -0.617348 -0.316668     -0.601722  1.840894 -1.495896 -0.296908  \n",
              "\n",
              "[395 rows x 45 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.656777Z",
          "iopub.status.busy": "2021-06-24T10:40:10.656142Z",
          "iopub.status.idle": "2021-06-24T10:40:10.661015Z",
          "shell.execute_reply": "2021-06-24T10:40:10.661547Z",
          "shell.execute_reply.started": "2021-06-24T10:30:55.590709Z"
        },
        "papermill": {
          "duration": 0.042192,
          "end_time": "2021-06-24T10:40:10.661711",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.619519",
          "status": "completed"
        },
        "tags": [],
        "id": "racial-reform"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7)"
      ],
      "id": "racial-reform",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.732740Z",
          "iopub.status.busy": "2021-06-24T10:40:10.732096Z",
          "iopub.status.idle": "2021-06-24T10:40:10.761119Z",
          "shell.execute_reply": "2021-06-24T10:40:10.761937Z",
          "shell.execute_reply.started": "2021-06-24T10:30:55.841905Z"
        },
        "papermill": {
          "duration": 0.066534,
          "end_time": "2021-06-24T10:40:10.762210",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.695676",
          "status": "completed"
        },
        "tags": [],
        "id": "bright-nashville",
        "outputId": "70c698b4-ea5d-4f9f-f153-2a00bbd28b0f"
      },
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "id": "bright-nashville",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.886044Z",
          "iopub.status.busy": "2021-06-24T10:40:10.885145Z",
          "iopub.status.idle": "2021-06-24T10:40:10.888545Z",
          "shell.execute_reply": "2021-06-24T10:40:10.889063Z",
          "shell.execute_reply.started": "2021-06-24T10:30:56.017553Z"
        },
        "papermill": {
          "duration": 0.066705,
          "end_time": "2021-06-24T10:40:10.889271",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.822566",
          "status": "completed"
        },
        "tags": [],
        "id": "supported-colorado",
        "outputId": "e5c146cf-c9b5-4c3c-d4a3-f4ea94b9e7c4"
      },
      "source": [
        "print(f\"Model score : {model.score(X_test, y_test)}\")"
      ],
      "id": "supported-colorado",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model score : 0.7960651857921689\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:10.967068Z",
          "iopub.status.busy": "2021-06-24T10:40:10.966250Z",
          "iopub.status.idle": "2021-06-24T10:40:10.969351Z",
          "shell.execute_reply": "2021-06-24T10:40:10.968848Z",
          "shell.execute_reply.started": "2021-06-24T10:38:15.147262Z"
        },
        "papermill": {
          "duration": 0.045141,
          "end_time": "2021-06-24T10:40:10.969492",
          "exception": false,
          "start_time": "2021-06-24T10:40:10.924351",
          "status": "completed"
        },
        "tags": [],
        "id": "incredible-shame"
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "id": "incredible-shame",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-24T10:40:11.047568Z",
          "iopub.status.busy": "2021-06-24T10:40:11.046510Z",
          "iopub.status.idle": "2021-06-24T10:40:11.050799Z",
          "shell.execute_reply": "2021-06-24T10:40:11.051325Z",
          "shell.execute_reply.started": "2021-06-24T10:38:19.088883Z"
        },
        "papermill": {
          "duration": 0.046606,
          "end_time": "2021-06-24T10:40:11.051506",
          "exception": false,
          "start_time": "2021-06-24T10:40:11.004900",
          "status": "completed"
        },
        "tags": [],
        "id": "random-dodge",
        "outputId": "cafb8a85-e8ff-4618-be48-c5ab0c89638f"
      },
      "source": [
        "print(f'Explained variance regression score : {sklearn.metrics.explained_variance_score(y_test, y_pred)}')\n",
        "print(f'maximum residual error : {sklearn.metrics.max_error(y_test, y_pred)}')\n",
        "print(f'Mean absolute error : {sklearn.metrics.mean_absolute_error(y_test, y_pred)}')\n",
        "print(f'Mean squared error : {sklearn.metrics.mean_squared_error(y_test, y_pred)}')\n",
        "print(f'r2 score : {sklearn.metrics.r2_score(y_test, y_pred)}')"
      ],
      "id": "random-dodge",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Explained variance regression score : 0.7967883746310626\n",
            "maximum residual error : 8.28549941938372\n",
            "Mean absolute error : 1.436187171071627\n",
            "Mean squared error : 4.129503573509438\n",
            "r2 score : 0.7960651857921689\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "papermill": {
          "duration": 0.034329,
          "end_time": "2021-06-24T10:40:11.120787",
          "exception": false,
          "start_time": "2021-06-24T10:40:11.086458",
          "status": "completed"
        },
        "tags": [],
        "id": "damaged-monitoring"
      },
      "source": [
        ""
      ],
      "id": "damaged-monitoring",
      "execution_count": null,
      "outputs": []
    }
  ]
}