STUDENT_PERFORMANCE_ANALYSER.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/navyadua/Student-Performance-Analyser/blob/main/STUDENT_PERFORMANCE_ANALYSER.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "E3Sz3vCksQXJ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/navyadua/data/main/student-mat.csv')\n",
        "df"
      ],
      "metadata": {
        "id": "rY5ox7sOsmCS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "119d5b8e-8db3-445d-8690-f11378d92382"
      },
      "execution_count": 33,
      "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",
              "     ... 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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b01998e-38ec-4e4a-b4de-0bbd2dc19ecf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>at_home</td>\n",
              "      <td>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>at_home</td>\n",
              "      <td>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>at_home</td>\n",
              "      <td>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>health</td>\n",
              "      <td>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>other</td>\n",
              "      <td>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>services</td>\n",
              "      <td>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>services</td>\n",
              "      <td>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>other</td>\n",
              "      <td>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>services</td>\n",
              "      <td>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>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b01998e-38ec-4e4a-b4de-0bbd2dc19ecf')\"\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-4b01998e-38ec-4e4a-b4de-0bbd2dc19ecf 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-4b01998e-38ec-4e4a-b4de-0bbd2dc19ecf');\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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### EXPLORATORY DATA ANALYSIS"
      ],
      "metadata": {
        "id": "wk0LNUtIt7zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiumFh5NpFlr",
        "outputId": "08f4f480-4cd9-4167-d7a9-fac125600503"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(395, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THJKCExHfAhu",
        "outputId": "c5af9692-6526-475f-f95f-8f4ee3691f8f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
              "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
              "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
              "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
              "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6DQ5FF4ebCa",
        "outputId": "5cbaf0d9-6087-4d17-9118-88d6b9e5bff3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "by9eJ2hIemkH",
        "outputId": "e570a520-035c-476a-f100-eb042b00a9fe"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school         2\n",
              "sex            2\n",
              "age            8\n",
              "address        2\n",
              "famsize        2\n",
              "Pstatus        2\n",
              "Medu           5\n",
              "Fedu           5\n",
              "Mjob           5\n",
              "Fjob           5\n",
              "reason         4\n",
              "guardian       3\n",
              "traveltime     4\n",
              "studytime      4\n",
              "failures       4\n",
              "schoolsup      2\n",
              "famsup         2\n",
              "paid           2\n",
              "activities     2\n",
              "nursery        2\n",
              "higher         2\n",
              "internet       2\n",
              "romantic       2\n",
              "famrel         5\n",
              "freetime       5\n",
              "goout          5\n",
              "Dalc           5\n",
              "Walc           5\n",
              "health         5\n",
              "absences      34\n",
              "G1            17\n",
              "G2            17\n",
              "G3            18\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBWuZ-PupJii",
        "outputId": "988097f6-d178-4840-c5af-8c123946da75"
      },
      "execution_count": 38,
      "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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "backgrnd = pd.crosstab(index=df['G3'], columns=df['Pstatus'])"
      ],
      "metadata": {
        "id": "NlvJp0hOsmxk"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "backgrnd.plot.bar(colormap=\"PiYG\", fontsize=16, figsize=(8,8))\n",
        "plt.title(\"Parents relationship status of the students\", fontsize=18)\n",
        "plt.xlabel(\"Parents Status\", fontsize=16)\n",
        "plt.ylabel(\"No. of students\", fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "cZtLXXRKvoui",
        "outputId": "adce961d-0025-469e-93df-b8daf8fd55e5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outcome = pd.DataFrame(df.activities.value_counts())\n",
        "outcome"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "uMergZttv0x9",
        "outputId": "0f8adef7-f85c-439f-8cde-35688f64ce0b"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     activities\n",
              "yes         201\n",
              "no          194"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22939b11-287d-4e1f-8fbd-e38707444d25\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>activities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no</th>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22939b11-287d-4e1f-8fbd-e38707444d25')\"\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-22939b11-287d-4e1f-8fbd-e38707444d25 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-22939b11-287d-4e1f-8fbd-e38707444d25');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,10))\n",
        "x = outcome.index\n",
        "y = outcome.activities\n",
        "plt.bar(x,y,color = ['m','b'])\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "eXrEgE6Dv30f",
        "outputId": "7b02168c-4d42-4dce-bb88-9c8eb5b3be9b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tt_student = pd.crosstab(index=df['G3'], columns=df['traveltime'])\n",
        "plt.figure()\n",
        "tt_student.plot.bar(colormap=\"PiYG_r\", fontsize=16, figsize=(8,8))\n",
        "plt.title(\"Travel Time for the students\", fontsize=18)\n",
        "plt.ylabel(\"No:  Of Students\", fontsize=16)\n",
        "plt.xlabel(\"Marks\", fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "y-uhS6OLv6zf",
        "outputId": "103809ec-db00-49f5-edbd-9487f3b4b4d3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['G3'], columns=df['romantic'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "nWxvjg7jwFVv",
        "outputId": "cd76ea6f-d1df-4cef-bc5e-2b04cf858e98"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "romantic  no  yes\n",
              "G3               \n",
              "0         18   20\n",
              "4          0    1\n",
              "5          5    2\n",
              "6         13    2\n",
              "7          4    5\n",
              "8         22   10\n",
              "9         16   12\n",
              "10        45   11\n",
              "11        30   17\n",
              "12        17   14\n",
              "13        19   12\n",
              "14        19    8\n",
              "15        24    9\n",
              "16         9    7\n",
              "17         5    1\n",
              "18        11    1\n",
              "19         5    0\n",
              "20         1    0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-68a54025-ca19-4a90-894a-ef5eb2a6c4a9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>romantic</th>\n",
              "      <th>no</th>\n",
              "      <th>yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G3</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>30</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>17</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>19</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>24</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68a54025-ca19-4a90-894a-ef5eb2a6c4a9')\"\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-68a54025-ca19-4a90-894a-ef5eb2a6c4a9 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-68a54025-ca19-4a90-894a-ef5eb2a6c4a9');\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": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### CATEGORICAL VERIABLE ENCODING"
      ],
      "metadata": {
        "id": "6U4PjB71t0ed"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[ df['Pstatus'] == 'A', 'Pstatus'] = 1\n",
        "df.loc[ df['Pstatus'] == 'T', 'Pstatus'] = 2\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "n-_4RzvUCh_V",
        "outputId": "f5fda5d5-3792-4033-cd72-fb9fa2c72845"
      },
      "execution_count": 45,
      "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       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       U     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       U     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       U     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       U     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       U     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       U     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       R     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       R     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       U     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf220c0c-83c2-4193-a260-dc4a3296b7f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf220c0c-83c2-4193-a260-dc4a3296b7f8')\"\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-cf220c0c-83c2-4193-a260-dc4a3296b7f8 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-cf220c0c-83c2-4193-a260-dc4a3296b7f8');\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['address'] == 'U', 'address'] = 2\n",
        "df.loc[df['address'] == 'R', 'address'] = 1\n",
        "df"
      ],
      "metadata": {
        "id": "eyfGv_0PDN1W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "6e44f8fb-76c0-47ed-dd70-11dc92fe2472"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10ec141b-d366-45ec-986f-82ff9d71ce91\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10ec141b-d366-45ec-986f-82ff9d71ce91')\"\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-10ec141b-d366-45ec-986f-82ff9d71ce91 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-10ec141b-d366-45ec-986f-82ff9d71ce91');\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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['schoolsup'] == 'no', 'schoolsup'] = 1\n",
        "df.loc[df['schoolsup'] == 'yes', 'schoolsup'] = 2\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PpLtahUhEYET",
        "outputId": "0ec50e4a-f740-4d7d-f8c3-d93892608336"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b64d0957-e978-42cd-981c-d193baa6e36c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b64d0957-e978-42cd-981c-d193baa6e36c')\"\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-b64d0957-e978-42cd-981c-d193baa6e36c 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-b64d0957-e978-42cd-981c-d193baa6e36c');\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": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['famsup'] == 'no', 'famsup'] = 1\n",
        "df.loc[df['famsup'] == 'yes', 'famsup'] = 2\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ejEnea3WEYkE",
        "outputId": "19f912c0-7461-43d9-a075-966cf15a4a22"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4667df53-01cc-496e-a50e-41706e08a1db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4667df53-01cc-496e-a50e-41706e08a1db')\"\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-4667df53-01cc-496e-a50e-41706e08a1db 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-4667df53-01cc-496e-a50e-41706e08a1db');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['paid'] == 'yes', 'paid'] = 2\n",
        "df.loc[df['paid'] == 'no', 'paid'] = 1\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "b-GxPSBOF3Uk",
        "outputId": "021866c2-682c-486e-a49b-9701720584b7"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4527e5fe-85bc-40c4-8ec4-93ad80d271e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4527e5fe-85bc-40c4-8ec4-93ad80d271e4')\"\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-4527e5fe-85bc-40c4-8ec4-93ad80d271e4 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-4527e5fe-85bc-40c4-8ec4-93ad80d271e4');\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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['internet'] == 'yes', 'internet'] = 2\n",
        "df.loc[df['internet'] == 'no', 'internet'] = 1\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "3-LWGbPpF4-o",
        "outputId": "a741b81d-2f68-41ec-9091-9775d96064f5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-062444c4-7a87-4555-9906-3855e338ebdf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>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>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-062444c4-7a87-4555-9906-3855e338ebdf')\"\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-062444c4-7a87-4555-9906-3855e338ebdf 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-062444c4-7a87-4555-9906-3855e338ebdf');\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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['average'] = (df['G1'] + df['G2'] + df['G3'])/3\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "T9-SNZJ9Gi-G",
        "outputId": "6173f9ac-3249-424f-bc3f-cdb6dc3b9bff"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     1     1     other   at_home   \n",
              "\n",
              "     ... freetime goout  Dalc  Walc  health absences  G1  G2  G3    average  \n",
              "0    ...        3     4     1     1       3        6   5   6   6   5.666667  \n",
              "1    ...        3     3     1     1       3        4   5   5   6   5.333333  \n",
              "2    ...        3     2     2     3       3       10   7   8  10   8.333333  \n",
              "3    ...        2     2     1     1       5        2  15  14  15  14.666667  \n",
              "4    ...        3     2     1     2       5        4   6  10  10   8.666667  \n",
              "..   ...      ...   ...   ...   ...     ...      ...  ..  ..  ..        ...  \n",
              "390  ...        5     4     4     5       4       11   9   9   9   9.000000  \n",
              "391  ...        4     5     3     4       2        3  14  16  16  15.333333  \n",
              "392  ...        5     3     3     3       3        3  10   8   7   8.333333  \n",
              "393  ...        4     1     3     4       5        0  11  12  10  11.000000  \n",
              "394  ...        2     3     3     3       5        5   8   9   9   8.666667  \n",
              "\n",
              "[395 rows x 34 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c0c1998-05db-427f-bc5d-486b826915b5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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",
              "      <th>average</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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>teacher</td>\n",
              "      <td>...</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",
              "      <td>5.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>5.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>14.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.666667</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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>15.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 34 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c0c1998-05db-427f-bc5d-486b826915b5')\"\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-4c0c1998-05db-427f-bc5d-486b826915b5 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-4c0c1998-05db-427f-bc5d-486b826915b5');\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": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['sum'] = (df['G1'] + df['G2'] + df['G3'])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "bRhYc5Z8GyzG",
        "outputId": "9da32383-5f4a-4cab-f619-0dfc09e13f70"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     1     1     other   at_home   \n",
              "\n",
              "     ... goout Dalc  Walc  health  absences  G1  G2  G3    average sum  \n",
              "0    ...     4    1     1       3         6   5   6   6   5.666667  17  \n",
              "1    ...     3    1     1       3         4   5   5   6   5.333333  16  \n",
              "2    ...     2    2     3       3        10   7   8  10   8.333333  25  \n",
              "3    ...     2    1     1       5         2  15  14  15  14.666667  44  \n",
              "4    ...     2    1     2       5         4   6  10  10   8.666667  26  \n",
              "..   ...   ...  ...   ...     ...       ...  ..  ..  ..        ...  ..  \n",
              "390  ...     4    4     5       4        11   9   9   9   9.000000  27  \n",
              "391  ...     5    3     4       2         3  14  16  16  15.333333  46  \n",
              "392  ...     3    3     3       3         3  10   8   7   8.333333  25  \n",
              "393  ...     1    3     4       5         0  11  12  10  11.000000  33  \n",
              "394  ...     3    3     3       5         5   8   9   9   8.666667  26  \n",
              "\n",
              "[395 rows x 35 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afdea385-adb9-4fd2-b316-d62d12a936d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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",
              "      <th>average</th>\n",
              "      <th>sum</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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>teacher</td>\n",
              "      <td>...</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",
              "      <td>5.666667</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>5.333333</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>14.666667</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>9.000000</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>15.333333</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>11.000000</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 35 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afdea385-adb9-4fd2-b316-d62d12a936d8')\"\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-afdea385-adb9-4fd2-b316-d62d12a936d8 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-afdea385-adb9-4fd2-b316-d62d12a936d8');\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": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['percentage'] = (df['G1'] + df['G2'] + df['G3'])/60*100\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "q1JkYW2hG1jB",
        "outputId": "d66dd7a0-e86c-481f-a0fe-e9203d05a860"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18       2     GT3       1     4     4   at_home   teacher   \n",
              "1       GP   F   17       2     GT3       2     1     1   at_home     other   \n",
              "2       GP   F   15       2     LE3       2     1     1   at_home     other   \n",
              "3       GP   F   15       2     GT3       2     4     2    health  services   \n",
              "4       GP   F   16       2     GT3       2     3     3     other     other   \n",
              "..     ...  ..  ...     ...     ...     ...   ...   ...       ...       ...   \n",
              "390     MS   M   20       2     LE3       1     2     2  services  services   \n",
              "391     MS   M   17       2     LE3       2     3     1  services  services   \n",
              "392     MS   M   21       1     GT3       2     1     1     other     other   \n",
              "393     MS   M   18       1     LE3       2     3     2  services     other   \n",
              "394     MS   M   19       2     LE3       2     1     1     other   at_home   \n",
              "\n",
              "     ... Dalc Walc  health  absences  G1  G2  G3    average sum percentage  \n",
              "0    ...    1    1       3         6   5   6   6   5.666667  17  28.333333  \n",
              "1    ...    1    1       3         4   5   5   6   5.333333  16  26.666667  \n",
              "2    ...    2    3       3        10   7   8  10   8.333333  25  41.666667  \n",
              "3    ...    1    1       5         2  15  14  15  14.666667  44  73.333333  \n",
              "4    ...    1    2       5         4   6  10  10   8.666667  26  43.333333  \n",
              "..   ...  ...  ...     ...       ...  ..  ..  ..        ...  ..        ...  \n",
              "390  ...    4    5       4        11   9   9   9   9.000000  27  45.000000  \n",
              "391  ...    3    4       2         3  14  16  16  15.333333  46  76.666667  \n",
              "392  ...    3    3       3         3  10   8   7   8.333333  25  41.666667  \n",
              "393  ...    3    4       5         0  11  12  10  11.000000  33  55.000000  \n",
              "394  ...    3    3       5         5   8   9   9   8.666667  26  43.333333  \n",
              "\n",
              "[395 rows x 36 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd9e4e95-04d1-4d89-8428-ce58ca2750bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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",
              "      <th>average</th>\n",
              "      <th>sum</th>\n",
              "      <th>percentage</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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>teacher</td>\n",
              "      <td>...</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",
              "      <td>5.666667</td>\n",
              "      <td>17</td>\n",
              "      <td>28.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>5.333333</td>\n",
              "      <td>16</td>\n",
              "      <td>26.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "      <td>41.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>14.666667</td>\n",
              "      <td>44</td>\n",
              "      <td>73.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</td>\n",
              "      <td>43.333333</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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>9.000000</td>\n",
              "      <td>27</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>15.333333</td>\n",
              "      <td>46</td>\n",
              "      <td>76.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "      <td>41.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>11.000000</td>\n",
              "      <td>33</td>\n",
              "      <td>55.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</td>\n",
              "      <td>43.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 36 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd9e4e95-04d1-4d89-8428-ce58ca2750bd')\"\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-dd9e4e95-04d1-4d89-8428-ce58ca2750bd 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-dd9e4e95-04d1-4d89-8428-ce58ca2750bd');\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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/navyadua/data/main/student_new.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-4Ji8-DDH-gs",
        "outputId": "083063c1-af75-47cd-f183-43fecb5d1fb6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    school sex  age  address famsize  Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
              "0       GP   F   18        2     GT3        1     4     4   at_home   teacher   \n",
              "1       GP   F   17        2     GT3        2     1     1   at_home     other   \n",
              "2       GP   F   15        2     LE3        2     1     1   at_home     other   \n",
              "3       GP   F   15        2     GT3        2     4     2    health  services   \n",
              "4       GP   F   16        2     GT3        2     3     3     other     other   \n",
              "..     ...  ..  ...      ...     ...      ...   ...   ...       ...       ...   \n",
              "390     MS   M   20        2     LE3        1     2     2  services  services   \n",
              "391     MS   M   17        2     LE3        2     3     1  services  services   \n",
              "392     MS   M   21        1     GT3        2     1     1     other     other   \n",
              "393     MS   M   18        1     LE3        2     3     2  services     other   \n",
              "394     MS   M   19        2     LE3        2     1     1     other   at_home   \n",
              "\n",
              "     ... Dalc Walc  health  absences  G1  G2  G3    average sum percentage  \n",
              "0    ...    1    1       3         6   5   6   6   5.666667  17      28.33  \n",
              "1    ...    1    1       3         4   5   5   6   5.333333  16      26.67  \n",
              "2    ...    2    3       3        10   7   8  10   8.333333  25      41.67  \n",
              "3    ...    1    1       5         2  15  14  15  14.666670  44      73.33  \n",
              "4    ...    1    2       5         4   6  10  10   8.666667  26      43.33  \n",
              "..   ...  ...  ...     ...       ...  ..  ..  ..        ...  ..        ...  \n",
              "390  ...    4    5       4        11   9   9   9   9.000000  27      45.00  \n",
              "391  ...    3    4       2         3  14  16  16  15.333330  46      76.67  \n",
              "392  ...    3    3       3         3  10   8   7   8.333333  25      41.67  \n",
              "393  ...    3    4       5         0  11  12  10  11.000000  33      55.00  \n",
              "394  ...    3    3       5         5   8   9   9   8.666667  26      43.33  \n",
              "\n",
              "[395 rows x 36 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d2d162f-86c9-4c67-9482-7e0788d8c725\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>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",
              "      <th>average</th>\n",
              "      <th>sum</th>\n",
              "      <th>percentage</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>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>at_home</td>\n",
              "      <td>teacher</td>\n",
              "      <td>...</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",
              "      <td>5.666667</td>\n",
              "      <td>17</td>\n",
              "      <td>28.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>5.333333</td>\n",
              "      <td>16</td>\n",
              "      <td>26.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>at_home</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "      <td>41.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>health</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>14.666670</td>\n",
              "      <td>44</td>\n",
              "      <td>73.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GP</td>\n",
              "      <td>F</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</td>\n",
              "      <td>43.33</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>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>9.000000</td>\n",
              "      <td>27</td>\n",
              "      <td>45.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>services</td>\n",
              "      <td>services</td>\n",
              "      <td>...</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",
              "      <td>15.333330</td>\n",
              "      <td>46</td>\n",
              "      <td>76.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>GT3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>8.333333</td>\n",
              "      <td>25</td>\n",
              "      <td>41.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>services</td>\n",
              "      <td>other</td>\n",
              "      <td>...</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",
              "      <td>11.000000</td>\n",
              "      <td>33</td>\n",
              "      <td>55.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>MS</td>\n",
              "      <td>M</td>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>LE3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>other</td>\n",
              "      <td>at_home</td>\n",
              "      <td>...</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",
              "      <td>8.666667</td>\n",
              "      <td>26</td>\n",
              "      <td>43.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 36 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d2d162f-86c9-4c67-9482-7e0788d8c725')\"\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-0d2d162f-86c9-4c67-9482-7e0788d8c725 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-0d2d162f-86c9-4c67-9482-7e0788d8c725');\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": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Correlation with Heat Map"
      ],
      "metadata": {
        "id": "q48LNcs3uIP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (20,20))\n",
        "correlation_matrix = df.corr()\n",
        "sns.heatmap(data=correlation_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "S2RxAvhFIu67",
        "outputId": "9b903aaf-5fa9-46e3-d931-17423677b515"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2e0ca5a90>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "8z4AEVeoK3W0",
        "outputId": "64d8d47c-b34d-4ab8-ddca-da8ee4efca93"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 age   address   Pstatus      Medu      Fedu  traveltime  \\\n",
              "age         1.000000 -0.146722  0.029598 -0.163658 -0.163438    0.070641   \n",
              "address    -0.146722  1.000000 -0.042572  0.138804  0.072178   -0.328096   \n",
              "Pstatus     0.029598 -0.042572  1.000000 -0.123565 -0.088730    0.028265   \n",
              "Medu       -0.163658  0.138804 -0.123565  1.000000  0.623455   -0.171639   \n",
              "Fedu       -0.163438  0.072178 -0.088730  0.623455  1.000000   -0.158194   \n",
              "traveltime  0.070641 -0.328096  0.028265 -0.171639 -0.158194    1.000000   \n",
              "studytime  -0.004140 -0.020912  0.024294  0.064944 -0.009175   -0.100909   \n",
              "failures    0.243665 -0.078578 -0.003339 -0.236680 -0.250408    0.092239   \n",
              "schoolsup  -0.251811  0.024712 -0.042238 -0.036029  0.037530   -0.009246   \n",
              "famsup     -0.140609  0.023903  0.019067  0.183727  0.185496   -0.003286   \n",
              "paid       -0.035933  0.052800  0.046435  0.159700  0.086981   -0.066420   \n",
              "internet   -0.112094  0.216842  0.070074  0.201463  0.127507   -0.111302   \n",
              "famrel      0.053940  0.014258  0.025179 -0.003914 -0.001370   -0.016808   \n",
              "freetime    0.016434  0.034878  0.038717  0.030891 -0.012846   -0.017025   \n",
              "goout       0.126964  0.068835  0.003459  0.064094  0.043105    0.028540   \n",
              "Dalc        0.131125 -0.093494 -0.030590  0.019834  0.002386    0.138325   \n",
              "Walc        0.117276 -0.101126  0.006045 -0.047123 -0.012631    0.134116   \n",
              "health     -0.062187 -0.040355  0.022307 -0.046878  0.014742    0.007501   \n",
              "absences    0.175230 -0.027874 -0.134937  0.100285  0.024473   -0.012944   \n",
              "G1         -0.064081  0.069704 -0.016868  0.205341  0.190270   -0.093040   \n",
              "G2         -0.143474  0.126037 -0.041382  0.215527  0.164893   -0.153198   \n",
              "G3         -0.161579  0.105756 -0.058009  0.217147  0.152457   -0.117142   \n",
              "average    -0.134589  0.107297 -0.043048  0.224260  0.175852   -0.128197   \n",
              "sum        -0.134589  0.107297 -0.043048  0.224260  0.175852   -0.128197   \n",
              "percentage -0.134594  0.107301 -0.043044  0.224268  0.175858   -0.128208   \n",
              "\n",
              "            studytime  failures  schoolsup    famsup  ...      Dalc      Walc  \\\n",
              "age         -0.004140  0.243665  -0.251811 -0.140609  ...  0.131125  0.117276   \n",
              "address     -0.020912 -0.078578   0.024712  0.023903  ... -0.093494 -0.101126   \n",
              "Pstatus      0.024294 -0.003339  -0.042238  0.019067  ... -0.030590  0.006045   \n",
              "Medu         0.064944 -0.236680  -0.036029  0.183727  ...  0.019834 -0.047123   \n",
              "Fedu        -0.009175 -0.250408   0.037530  0.185496  ...  0.002386 -0.012631   \n",
              "traveltime  -0.100909  0.092239  -0.009246 -0.003286  ...  0.138325  0.134116   \n",
              "studytime    1.000000 -0.173563   0.037763  0.145228  ... -0.196019 -0.253785   \n",
              "failures    -0.173563  1.000000  -0.000437 -0.055075  ...  0.136047  0.141962   \n",
              "schoolsup    0.037763 -0.000437   1.000000  0.104681  ... -0.021485 -0.087152   \n",
              "famsup       0.145228 -0.055075   0.104681  1.000000  ... -0.031575 -0.086688   \n",
              "paid         0.167220 -0.188039  -0.020753  0.293184  ...  0.062465  0.060454   \n",
              "internet     0.059422 -0.063451  -0.009683  0.103581  ...  0.036210  0.011687   \n",
              "famrel       0.039731 -0.044337  -0.001345 -0.020436  ... -0.077594 -0.113397   \n",
              "freetime    -0.143198  0.091987  -0.045465  0.010538  ...  0.209001  0.147822   \n",
              "goout       -0.063904  0.124561  -0.037698 -0.015631  ...  0.266994  0.420386   \n",
              "Dalc        -0.196019  0.136047  -0.021485 -0.031575  ...  1.000000  0.647544   \n",
              "Walc        -0.253785  0.141962  -0.087152 -0.086688  ...  0.647544  1.000000   \n",
              "health      -0.075616  0.065827  -0.034124  0.029297  ...  0.077180  0.092476   \n",
              "absences    -0.062700  0.063726   0.022526  0.024353  ...  0.111908  0.136291   \n",
              "G1           0.160612 -0.354718  -0.212607 -0.084569  ... -0.094159 -0.126179   \n",
              "G2           0.135880 -0.355896  -0.117385 -0.059166  ... -0.064120 -0.084927   \n",
              "G3           0.097820 -0.360415  -0.082788 -0.039157  ... -0.054660 -0.051939   \n",
              "average      0.134565 -0.375759  -0.137644 -0.061553  ... -0.072508 -0.088025   \n",
              "sum          0.134565 -0.375759  -0.137644 -0.061553  ... -0.072508 -0.088025   \n",
              "percentage   0.134568 -0.375770  -0.137643 -0.061550  ... -0.072509 -0.088021   \n",
              "\n",
              "              health  absences        G1        G2        G3   average  \\\n",
              "age        -0.062187  0.175230 -0.064081 -0.143474 -0.161579 -0.134589   \n",
              "address    -0.040355 -0.027874  0.069704  0.126037  0.105756  0.107297   \n",
              "Pstatus     0.022307 -0.134937 -0.016868 -0.041382 -0.058009 -0.043048   \n",
              "Medu       -0.046878  0.100285  0.205341  0.215527  0.217147  0.224260   \n",
              "Fedu        0.014742  0.024473  0.190270  0.164893  0.152457  0.175852   \n",
              "traveltime  0.007501 -0.012944 -0.093040 -0.153198 -0.117142 -0.128197   \n",
              "studytime  -0.075616 -0.062700  0.160612  0.135880  0.097820  0.134565   \n",
              "failures    0.065827  0.063726 -0.354718 -0.355896 -0.360415 -0.375759   \n",
              "schoolsup  -0.034124  0.022526 -0.212607 -0.117385 -0.082788 -0.137644   \n",
              "famsup      0.029297  0.024353 -0.084569 -0.059166 -0.039157 -0.061553   \n",
              "paid       -0.078132  0.007435  0.039079  0.105198  0.101996  0.089511   \n",
              "internet   -0.080189  0.101701  0.071619  0.119439  0.098483  0.102628   \n",
              "famrel      0.094056 -0.044354  0.022168 -0.018281  0.051363  0.021653   \n",
              "freetime    0.075733 -0.058078  0.012613 -0.013777  0.011307  0.003773   \n",
              "goout      -0.009577  0.044302 -0.149104 -0.162250 -0.132791 -0.154511   \n",
              "Dalc        0.077180  0.111908 -0.094159 -0.064120 -0.054660 -0.072508   \n",
              "Walc        0.092476  0.136291 -0.126179 -0.084927 -0.051939 -0.088025   \n",
              "health      1.000000 -0.029937 -0.073172 -0.097720 -0.061335 -0.080380   \n",
              "absences   -0.029937  1.000000 -0.031003 -0.031777  0.034247 -0.005909   \n",
              "G1         -0.073172 -0.031003  1.000000  0.852118  0.801468  0.919386   \n",
              "G2         -0.097720 -0.031777  0.852118  1.000000  0.904868  0.967999   \n",
              "G3         -0.061335  0.034247  0.801468  0.904868  1.000000  0.959873   \n",
              "average    -0.080380 -0.005909  0.919386  0.967999  0.959873  1.000000   \n",
              "sum        -0.080380 -0.005909  0.919386  0.967999  0.959873  1.000000   \n",
              "percentage -0.080390 -0.005895  0.919387  0.968000  0.959871  1.000000   \n",
              "\n",
              "                 sum  percentage  \n",
              "age        -0.134589   -0.134594  \n",
              "address     0.107297    0.107301  \n",
              "Pstatus    -0.043048   -0.043044  \n",
              "Medu        0.224260    0.224268  \n",
              "Fedu        0.175852    0.175858  \n",
              "traveltime -0.128197   -0.128208  \n",
              "studytime   0.134565    0.134568  \n",
              "failures   -0.375759   -0.375770  \n",
              "schoolsup  -0.137644   -0.137643  \n",
              "famsup     -0.061553   -0.061550  \n",
              "paid        0.089511    0.089530  \n",
              "internet    0.102628    0.102641  \n",
              "famrel      0.021653    0.021657  \n",
              "freetime    0.003773    0.003773  \n",
              "goout      -0.154511   -0.154508  \n",
              "Dalc       -0.072508   -0.072509  \n",
              "Walc       -0.088025   -0.088021  \n",
              "health     -0.080380   -0.080390  \n",
              "absences   -0.005909   -0.005895  \n",
              "G1          0.919386    0.919387  \n",
              "G2          0.967999    0.968000  \n",
              "G3          0.959873    0.959871  \n",
              "average     1.000000    1.000000  \n",
              "sum         1.000000    1.000000  \n",
              "percentage  1.000000    1.000000  \n",
              "\n",
              "[25 rows x 25 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-583a96d5-cc42-4530-a894-02a8d762b279\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>age</th>\n",
              "      <th>address</th>\n",
              "      <th>Pstatus</th>\n",
              "      <th>Medu</th>\n",
              "      <th>Fedu</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>schoolsup</th>\n",
              "      <th>famsup</th>\n",
              "      <th>...</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",
              "      <th>average</th>\n",
              "      <th>sum</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.146722</td>\n",
              "      <td>0.029598</td>\n",
              "      <td>-0.163658</td>\n",
              "      <td>-0.163438</td>\n",
              "      <td>0.070641</td>\n",
              "      <td>-0.004140</td>\n",
              "      <td>0.243665</td>\n",
              "      <td>-0.251811</td>\n",
              "      <td>-0.140609</td>\n",
              "      <td>...</td>\n",
              "      <td>0.131125</td>\n",
              "      <td>0.117276</td>\n",
              "      <td>-0.062187</td>\n",
              "      <td>0.175230</td>\n",
              "      <td>-0.064081</td>\n",
              "      <td>-0.143474</td>\n",
              "      <td>-0.161579</td>\n",
              "      <td>-0.134589</td>\n",
              "      <td>-0.134589</td>\n",
              "      <td>-0.134594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>address</th>\n",
              "      <td>-0.146722</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.042572</td>\n",
              "      <td>0.138804</td>\n",
              "      <td>0.072178</td>\n",
              "      <td>-0.328096</td>\n",
              "      <td>-0.020912</td>\n",
              "      <td>-0.078578</td>\n",
              "      <td>0.024712</td>\n",
              "      <td>0.023903</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.093494</td>\n",
              "      <td>-0.101126</td>\n",
              "      <td>-0.040355</td>\n",
              "      <td>-0.027874</td>\n",
              "      <td>0.069704</td>\n",
              "      <td>0.126037</td>\n",
              "      <td>0.105756</td>\n",
              "      <td>0.107297</td>\n",
              "      <td>0.107297</td>\n",
              "      <td>0.107301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pstatus</th>\n",
              "      <td>0.029598</td>\n",
              "      <td>-0.042572</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.123565</td>\n",
              "      <td>-0.088730</td>\n",
              "      <td>0.028265</td>\n",
              "      <td>0.024294</td>\n",
              "      <td>-0.003339</td>\n",
              "      <td>-0.042238</td>\n",
              "      <td>0.019067</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.030590</td>\n",
              "      <td>0.006045</td>\n",
              "      <td>0.022307</td>\n",
              "      <td>-0.134937</td>\n",
              "      <td>-0.016868</td>\n",
              "      <td>-0.041382</td>\n",
              "      <td>-0.058009</td>\n",
              "      <td>-0.043048</td>\n",
              "      <td>-0.043048</td>\n",
              "      <td>-0.043044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medu</th>\n",
              "      <td>-0.163658</td>\n",
              "      <td>0.138804</td>\n",
              "      <td>-0.123565</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.623455</td>\n",
              "      <td>-0.171639</td>\n",
              "      <td>0.064944</td>\n",
              "      <td>-0.236680</td>\n",
              "      <td>-0.036029</td>\n",
              "      <td>0.183727</td>\n",
              "      <td>...</td>\n",
              "      <td>0.019834</td>\n",
              "      <td>-0.047123</td>\n",
              "      <td>-0.046878</td>\n",
              "      <td>0.100285</td>\n",
              "      <td>0.205341</td>\n",
              "      <td>0.215527</td>\n",
              "      <td>0.217147</td>\n",
              "      <td>0.224260</td>\n",
              "      <td>0.224260</td>\n",
              "      <td>0.224268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fedu</th>\n",
              "      <td>-0.163438</td>\n",
              "      <td>0.072178</td>\n",
              "      <td>-0.088730</td>\n",
              "      <td>0.623455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.158194</td>\n",
              "      <td>-0.009175</td>\n",
              "      <td>-0.250408</td>\n",
              "      <td>0.037530</td>\n",
              "      <td>0.185496</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002386</td>\n",
              "      <td>-0.012631</td>\n",
              "      <td>0.014742</td>\n",
              "      <td>0.024473</td>\n",
              "      <td>0.190270</td>\n",
              "      <td>0.164893</td>\n",
              "      <td>0.152457</td>\n",
              "      <td>0.175852</td>\n",
              "      <td>0.175852</td>\n",
              "      <td>0.175858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>traveltime</th>\n",
              "      <td>0.070641</td>\n",
              "      <td>-0.328096</td>\n",
              "      <td>0.028265</td>\n",
              "      <td>-0.171639</td>\n",
              "      <td>-0.158194</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.100909</td>\n",
              "      <td>0.092239</td>\n",
              "      <td>-0.009246</td>\n",
              "      <td>-0.003286</td>\n",
              "      <td>...</td>\n",
              "      <td>0.138325</td>\n",
              "      <td>0.134116</td>\n",
              "      <td>0.007501</td>\n",
              "      <td>-0.012944</td>\n",
              "      <td>-0.093040</td>\n",
              "      <td>-0.153198</td>\n",
              "      <td>-0.117142</td>\n",
              "      <td>-0.128197</td>\n",
              "      <td>-0.128197</td>\n",
              "      <td>-0.128208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>studytime</th>\n",
              "      <td>-0.004140</td>\n",
              "      <td>-0.020912</td>\n",
              "      <td>0.024294</td>\n",
              "      <td>0.064944</td>\n",
              "      <td>-0.009175</td>\n",
              "      <td>-0.100909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.173563</td>\n",
              "      <td>0.037763</td>\n",
              "      <td>0.145228</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.196019</td>\n",
              "      <td>-0.253785</td>\n",
              "      <td>-0.075616</td>\n",
              "      <td>-0.062700</td>\n",
              "      <td>0.160612</td>\n",
              "      <td>0.135880</td>\n",
              "      <td>0.097820</td>\n",
              "      <td>0.134565</td>\n",
              "      <td>0.134565</td>\n",
              "      <td>0.134568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>failures</th>\n",
              "      <td>0.243665</td>\n",
              "      <td>-0.078578</td>\n",
              "      <td>-0.003339</td>\n",
              "      <td>-0.236680</td>\n",
              "      <td>-0.250408</td>\n",
              "      <td>0.092239</td>\n",
              "      <td>-0.173563</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000437</td>\n",
              "      <td>-0.055075</td>\n",
              "      <td>...</td>\n",
              "      <td>0.136047</td>\n",
              "      <td>0.141962</td>\n",
              "      <td>0.065827</td>\n",
              "      <td>0.063726</td>\n",
              "      <td>-0.354718</td>\n",
              "      <td>-0.355896</td>\n",
              "      <td>-0.360415</td>\n",
              "      <td>-0.375759</td>\n",
              "      <td>-0.375759</td>\n",
              "      <td>-0.375770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>schoolsup</th>\n",
              "      <td>-0.251811</td>\n",
              "      <td>0.024712</td>\n",
              "      <td>-0.042238</td>\n",
              "      <td>-0.036029</td>\n",
              "      <td>0.037530</td>\n",
              "      <td>-0.009246</td>\n",
              "      <td>0.037763</td>\n",
              "      <td>-0.000437</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.104681</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.021485</td>\n",
              "      <td>-0.087152</td>\n",
              "      <td>-0.034124</td>\n",
              "      <td>0.022526</td>\n",
              "      <td>-0.212607</td>\n",
              "      <td>-0.117385</td>\n",
              "      <td>-0.082788</td>\n",
              "      <td>-0.137644</td>\n",
              "      <td>-0.137644</td>\n",
              "      <td>-0.137643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>famsup</th>\n",
              "      <td>-0.140609</td>\n",
              "      <td>0.023903</td>\n",
              "      <td>0.019067</td>\n",
              "      <td>0.183727</td>\n",
              "      <td>0.185496</td>\n",
              "      <td>-0.003286</td>\n",
              "      <td>0.145228</td>\n",
              "      <td>-0.055075</td>\n",
              "      <td>0.104681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.031575</td>\n",
              "      <td>-0.086688</td>\n",
              "      <td>0.029297</td>\n",
              "      <td>0.024353</td>\n",
              "      <td>-0.084569</td>\n",
              "      <td>-0.059166</td>\n",
              "      <td>-0.039157</td>\n",
              "      <td>-0.061553</td>\n",
              "      <td>-0.061553</td>\n",
              "      <td>-0.061550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paid</th>\n",
              "      <td>-0.035933</td>\n",
              "      <td>0.052800</td>\n",
              "      <td>0.046435</td>\n",
              "      <td>0.159700</td>\n",
              "      <td>0.086981</td>\n",
              "      <td>-0.066420</td>\n",
              "      <td>0.167220</td>\n",
              "      <td>-0.188039</td>\n",
              "      <td>-0.020753</td>\n",
              "      <td>0.293184</td>\n",
              "      <td>...</td>\n",
              "      <td>0.062465</td>\n",
              "      <td>0.060454</td>\n",
              "      <td>-0.078132</td>\n",
              "      <td>0.007435</td>\n",
              "      <td>0.039079</td>\n",
              "      <td>0.105198</td>\n",
              "      <td>0.101996</td>\n",
              "      <td>0.089511</td>\n",
              "      <td>0.089511</td>\n",
              "      <td>0.089530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>internet</th>\n",
              "      <td>-0.112094</td>\n",
              "      <td>0.216842</td>\n",
              "      <td>0.070074</td>\n",
              "      <td>0.201463</td>\n",
              "      <td>0.127507</td>\n",
              "      <td>-0.111302</td>\n",
              "      <td>0.059422</td>\n",
              "      <td>-0.063451</td>\n",
              "      <td>-0.009683</td>\n",
              "      <td>0.103581</td>\n",
              "      <td>...</td>\n",
              "      <td>0.036210</td>\n",
              "      <td>0.011687</td>\n",
              "      <td>-0.080189</td>\n",
              "      <td>0.101701</td>\n",
              "      <td>0.071619</td>\n",
              "      <td>0.119439</td>\n",
              "      <td>0.098483</td>\n",
              "      <td>0.102628</td>\n",
              "      <td>0.102628</td>\n",
              "      <td>0.102641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>famrel</th>\n",
              "      <td>0.053940</td>\n",
              "      <td>0.014258</td>\n",
              "      <td>0.025179</td>\n",
              "      <td>-0.003914</td>\n",
              "      <td>-0.001370</td>\n",
              "      <td>-0.016808</td>\n",
              "      <td>0.039731</td>\n",
              "      <td>-0.044337</td>\n",
              "      <td>-0.001345</td>\n",
              "      <td>-0.020436</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.077594</td>\n",
              "      <td>-0.113397</td>\n",
              "      <td>0.094056</td>\n",
              "      <td>-0.044354</td>\n",
              "      <td>0.022168</td>\n",
              "      <td>-0.018281</td>\n",
              "      <td>0.051363</td>\n",
              "      <td>0.021653</td>\n",
              "      <td>0.021653</td>\n",
              "      <td>0.021657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freetime</th>\n",
              "      <td>0.016434</td>\n",
              "      <td>0.034878</td>\n",
              "      <td>0.038717</td>\n",
              "      <td>0.030891</td>\n",
              "      <td>-0.012846</td>\n",
              "      <td>-0.017025</td>\n",
              "      <td>-0.143198</td>\n",
              "      <td>0.091987</td>\n",
              "      <td>-0.045465</td>\n",
              "      <td>0.010538</td>\n",
              "      <td>...</td>\n",
              "      <td>0.209001</td>\n",
              "      <td>0.147822</td>\n",
              "      <td>0.075733</td>\n",
              "      <td>-0.058078</td>\n",
              "      <td>0.012613</td>\n",
              "      <td>-0.013777</td>\n",
              "      <td>0.011307</td>\n",
              "      <td>0.003773</td>\n",
              "      <td>0.003773</td>\n",
              "      <td>0.003773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>goout</th>\n",
              "      <td>0.126964</td>\n",
              "      <td>0.068835</td>\n",
              "      <td>0.003459</td>\n",
              "      <td>0.064094</td>\n",
              "      <td>0.043105</td>\n",
              "      <td>0.028540</td>\n",
              "      <td>-0.063904</td>\n",
              "      <td>0.124561</td>\n",
              "      <td>-0.037698</td>\n",
              "      <td>-0.015631</td>\n",
              "      <td>...</td>\n",
              "      <td>0.266994</td>\n",
              "      <td>0.420386</td>\n",
              "      <td>-0.009577</td>\n",
              "      <td>0.044302</td>\n",
              "      <td>-0.149104</td>\n",
              "      <td>-0.162250</td>\n",
              "      <td>-0.132791</td>\n",
              "      <td>-0.154511</td>\n",
              "      <td>-0.154511</td>\n",
              "      <td>-0.154508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dalc</th>\n",
              "      <td>0.131125</td>\n",
              "      <td>-0.093494</td>\n",
              "      <td>-0.030590</td>\n",
              "      <td>0.019834</td>\n",
              "      <td>0.002386</td>\n",
              "      <td>0.138325</td>\n",
              "      <td>-0.196019</td>\n",
              "      <td>0.136047</td>\n",
              "      <td>-0.021485</td>\n",
              "      <td>-0.031575</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.647544</td>\n",
              "      <td>0.077180</td>\n",
              "      <td>0.111908</td>\n",
              "      <td>-0.094159</td>\n",
              "      <td>-0.064120</td>\n",
              "      <td>-0.054660</td>\n",
              "      <td>-0.072508</td>\n",
              "      <td>-0.072508</td>\n",
              "      <td>-0.072509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Walc</th>\n",
              "      <td>0.117276</td>\n",
              "      <td>-0.101126</td>\n",
              "      <td>0.006045</td>\n",
              "      <td>-0.047123</td>\n",
              "      <td>-0.012631</td>\n",
              "      <td>0.134116</td>\n",
              "      <td>-0.253785</td>\n",
              "      <td>0.141962</td>\n",
              "      <td>-0.087152</td>\n",
              "      <td>-0.086688</td>\n",
              "      <td>...</td>\n",
              "      <td>0.647544</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.092476</td>\n",
              "      <td>0.136291</td>\n",
              "      <td>-0.126179</td>\n",
              "      <td>-0.084927</td>\n",
              "      <td>-0.051939</td>\n",
              "      <td>-0.088025</td>\n",
              "      <td>-0.088025</td>\n",
              "      <td>-0.088021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>health</th>\n",
              "      <td>-0.062187</td>\n",
              "      <td>-0.040355</td>\n",
              "      <td>0.022307</td>\n",
              "      <td>-0.046878</td>\n",
              "      <td>0.014742</td>\n",
              "      <td>0.007501</td>\n",
              "      <td>-0.075616</td>\n",
              "      <td>0.065827</td>\n",
              "      <td>-0.034124</td>\n",
              "      <td>0.029297</td>\n",
              "      <td>...</td>\n",
              "      <td>0.077180</td>\n",
              "      <td>0.092476</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.029937</td>\n",
              "      <td>-0.073172</td>\n",
              "      <td>-0.097720</td>\n",
              "      <td>-0.061335</td>\n",
              "      <td>-0.080380</td>\n",
              "      <td>-0.080380</td>\n",
              "      <td>-0.080390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>absences</th>\n",
              "      <td>0.175230</td>\n",
              "      <td>-0.027874</td>\n",
              "      <td>-0.134937</td>\n",
              "      <td>0.100285</td>\n",
              "      <td>0.024473</td>\n",
              "      <td>-0.012944</td>\n",
              "      <td>-0.062700</td>\n",
              "      <td>0.063726</td>\n",
              "      <td>0.022526</td>\n",
              "      <td>0.024353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.111908</td>\n",
              "      <td>0.136291</td>\n",
              "      <td>-0.029937</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031003</td>\n",
              "      <td>-0.031777</td>\n",
              "      <td>0.034247</td>\n",
              "      <td>-0.005909</td>\n",
              "      <td>-0.005909</td>\n",
              "      <td>-0.005895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G1</th>\n",
              "      <td>-0.064081</td>\n",
              "      <td>0.069704</td>\n",
              "      <td>-0.016868</td>\n",
              "      <td>0.205341</td>\n",
              "      <td>0.190270</td>\n",
              "      <td>-0.093040</td>\n",
              "      <td>0.160612</td>\n",
              "      <td>-0.354718</td>\n",
              "      <td>-0.212607</td>\n",
              "      <td>-0.084569</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.094159</td>\n",
              "      <td>-0.126179</td>\n",
              "      <td>-0.073172</td>\n",
              "      <td>-0.031003</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.852118</td>\n",
              "      <td>0.801468</td>\n",
              "      <td>0.919386</td>\n",
              "      <td>0.919386</td>\n",
              "      <td>0.919387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G2</th>\n",
              "      <td>-0.143474</td>\n",
              "      <td>0.126037</td>\n",
              "      <td>-0.041382</td>\n",
              "      <td>0.215527</td>\n",
              "      <td>0.164893</td>\n",
              "      <td>-0.153198</td>\n",
              "      <td>0.135880</td>\n",
              "      <td>-0.355896</td>\n",
              "      <td>-0.117385</td>\n",
              "      <td>-0.059166</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.064120</td>\n",
              "      <td>-0.084927</td>\n",
              "      <td>-0.097720</td>\n",
              "      <td>-0.031777</td>\n",
              "      <td>0.852118</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904868</td>\n",
              "      <td>0.967999</td>\n",
              "      <td>0.967999</td>\n",
              "      <td>0.968000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G3</th>\n",
              "      <td>-0.161579</td>\n",
              "      <td>0.105756</td>\n",
              "      <td>-0.058009</td>\n",
              "      <td>0.217147</td>\n",
              "      <td>0.152457</td>\n",
              "      <td>-0.117142</td>\n",
              "      <td>0.097820</td>\n",
              "      <td>-0.360415</td>\n",
              "      <td>-0.082788</td>\n",
              "      <td>-0.039157</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.054660</td>\n",
              "      <td>-0.051939</td>\n",
              "      <td>-0.061335</td>\n",
              "      <td>0.034247</td>\n",
              "      <td>0.801468</td>\n",
              "      <td>0.904868</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.959873</td>\n",
              "      <td>0.959873</td>\n",
              "      <td>0.959871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>average</th>\n",
              "      <td>-0.134589</td>\n",
              "      <td>0.107297</td>\n",
              "      <td>-0.043048</td>\n",
              "      <td>0.224260</td>\n",
              "      <td>0.175852</td>\n",
              "      <td>-0.128197</td>\n",
              "      <td>0.134565</td>\n",
              "      <td>-0.375759</td>\n",
              "      <td>-0.137644</td>\n",
              "      <td>-0.061553</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.072508</td>\n",
              "      <td>-0.088025</td>\n",
              "      <td>-0.080380</td>\n",
              "      <td>-0.005909</td>\n",
              "      <td>0.919386</td>\n",
              "      <td>0.967999</td>\n",
              "      <td>0.959873</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>-0.134589</td>\n",
              "      <td>0.107297</td>\n",
              "      <td>-0.043048</td>\n",
              "      <td>0.224260</td>\n",
              "      <td>0.175852</td>\n",
              "      <td>-0.128197</td>\n",
              "      <td>0.134565</td>\n",
              "      <td>-0.375759</td>\n",
              "      <td>-0.137644</td>\n",
              "      <td>-0.061553</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.072508</td>\n",
              "      <td>-0.088025</td>\n",
              "      <td>-0.080380</td>\n",
              "      <td>-0.005909</td>\n",
              "      <td>0.919386</td>\n",
              "      <td>0.967999</td>\n",
              "      <td>0.959873</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percentage</th>\n",
              "      <td>-0.134594</td>\n",
              "      <td>0.107301</td>\n",
              "      <td>-0.043044</td>\n",
              "      <td>0.224268</td>\n",
              "      <td>0.175858</td>\n",
              "      <td>-0.128208</td>\n",
              "      <td>0.134568</td>\n",
              "      <td>-0.375770</td>\n",
              "      <td>-0.137643</td>\n",
              "      <td>-0.061550</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.072509</td>\n",
              "      <td>-0.088021</td>\n",
              "      <td>-0.080390</td>\n",
              "      <td>-0.005895</td>\n",
              "      <td>0.919387</td>\n",
              "      <td>0.968000</td>\n",
              "      <td>0.959871</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25 rows × 25 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-583a96d5-cc42-4530-a894-02a8d762b279')\"\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-583a96d5-cc42-4530-a894-02a8d762b279 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-583a96d5-cc42-4530-a894-02a8d762b279');\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": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_mat = df.corr()\n",
        "sorted_mat = corr_mat.unstack().sort_values()\n",
        "val = sorted_mat.to_frame()\n",
        "val.reset_index(drop=False, inplace=True)\n",
        "values = val[val.level_0 == 'average'] \n",
        "positive = values[values[0] > 0.05] \n",
        "print(positive.level_1.values)\n",
        "positive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "_cGhdlK4wiCK",
        "outputId": "6770e925-b42d-4320-957e-2c72768efea6"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['paid' 'internet' 'address' 'studytime' 'Fedu' 'Medu' 'G1' 'G3' 'G2'\n",
            " 'percentage' 'sum' 'average']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     level_0     level_1         0\n",
              "426  average        paid  0.089511\n",
              "451  average    internet  0.102628\n",
              "464  average     address  0.107297\n",
              "489  average   studytime  0.134565\n",
              "527  average        Fedu  0.175852\n",
              "550  average        Medu  0.224260\n",
              "576  average          G1  0.919386\n",
              "586  average          G3  0.959873\n",
              "590  average          G2  0.967999\n",
              "595  average  percentage  1.000000\n",
              "598  average         sum  1.000000\n",
              "602  average     average  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0eaf224-58ee-4175-8699-458babf34667\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>level_0</th>\n",
              "      <th>level_1</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>average</td>\n",
              "      <td>paid</td>\n",
              "      <td>0.089511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>average</td>\n",
              "      <td>internet</td>\n",
              "      <td>0.102628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>average</td>\n",
              "      <td>address</td>\n",
              "      <td>0.107297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>average</td>\n",
              "      <td>studytime</td>\n",
              "      <td>0.134565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>average</td>\n",
              "      <td>Fedu</td>\n",
              "      <td>0.175852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>550</th>\n",
              "      <td>average</td>\n",
              "      <td>Medu</td>\n",
              "      <td>0.224260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>average</td>\n",
              "      <td>G1</td>\n",
              "      <td>0.919386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>average</td>\n",
              "      <td>G3</td>\n",
              "      <td>0.959873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>average</td>\n",
              "      <td>G2</td>\n",
              "      <td>0.967999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>average</td>\n",
              "      <td>percentage</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>average</td>\n",
              "      <td>sum</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>average</td>\n",
              "      <td>average</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0eaf224-58ee-4175-8699-458babf34667')\"\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-f0eaf224-58ee-4175-8699-458babf34667 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-f0eaf224-58ee-4175-8699-458babf34667');\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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "negative = values[values[0] < -0.05]\n",
        "print(negative.level_1.values)\n",
        "negative"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "onh0jTQQwrdi",
        "outputId": "36708c49-8076-49ce-8313-a89c9e3a0810"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['failures' 'goout' 'schoolsup' 'age' 'traveltime' 'Walc' 'health' 'Dalc'\n",
            " 'famsup']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     level_0     level_1         0\n",
              "4    average    failures -0.375759\n",
              "42   average       goout -0.154511\n",
              "62   average   schoolsup -0.137644\n",
              "70   average         age -0.134589\n",
              "80   average  traveltime -0.128197\n",
              "110  average        Walc -0.088025\n",
              "131  average      health -0.080380\n",
              "148  average        Dalc -0.072508\n",
              "167  average      famsup -0.061553"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ecbbaca-a57d-4928-8a6a-aa889a43ae7b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>level_0</th>\n",
              "      <th>level_1</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>average</td>\n",
              "      <td>failures</td>\n",
              "      <td>-0.375759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>average</td>\n",
              "      <td>goout</td>\n",
              "      <td>-0.154511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>average</td>\n",
              "      <td>schoolsup</td>\n",
              "      <td>-0.137644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>average</td>\n",
              "      <td>age</td>\n",
              "      <td>-0.134589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>average</td>\n",
              "      <td>traveltime</td>\n",
              "      <td>-0.128197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>average</td>\n",
              "      <td>Walc</td>\n",
              "      <td>-0.088025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>average</td>\n",
              "      <td>health</td>\n",
              "      <td>-0.080380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>average</td>\n",
              "      <td>Dalc</td>\n",
              "      <td>-0.072508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>average</td>\n",
              "      <td>famsup</td>\n",
              "      <td>-0.061553</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ecbbaca-a57d-4928-8a6a-aa889a43ae7b')\"\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-2ecbbaca-a57d-4928-8a6a-aa889a43ae7b 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-2ecbbaca-a57d-4928-8a6a-aa889a43ae7b');\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": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### K- Means Clustering"
      ],
      "metadata": {
        "id": "AfOGIvV-uOgm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QZAEyhRgAmLr",
        "outputId": "b87ee13b-bf3c-459b-d68a-fbaf11e8fb65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# importing libraries    \n",
        "import numpy as nm    \n",
        "import matplotlib.pyplot as mtp    \n",
        "import pandas as pd \n",
        "import seaborn as sns  \n",
        "# G1,G2,G3 \n",
        "# Importing the dataset  \n",
        "dataset = pd.read_csv('https://raw.githubusercontent.com/navyadua/data/main/student_new.csv')\n",
        "x = dataset.iloc[:, [29,35]].values\n",
        "#finding optimal number of clusters using the elbow method  \n",
        "from sklearn.cluster import KMeans  \n",
        "wcss_list= []  #Initializing the list for the values of WCSS\n",
        "\n",
        "#Using for loop for iterations from 1 to 10.  \n",
        "for i in range(1, 11):  \n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 0)  \n",
        "    kmeans.fit(x)  \n",
        "    wcss_list.append(kmeans.inertia_)  \n",
        "mtp.plot(range(1, 11), wcss_list)  \n",
        "mtp.title('The Elobw Method Graph')  \n",
        "mtp.xlabel('Number of clusters(k)')  \n",
        "mtp.ylabel('wcss_list')  \n",
        "mtp.show()  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting K-Means to the dataset\n",
        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
        "y_kmeans = kmeans.fit_predict(x)\n",
        "\n",
        "# Visualising the clusters\n",
        "mtp.figure(figsize=(15,7))\n",
        "sns.scatterplot(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], color = 'yellow', label = 'Cluster 1',s=50)\n",
        "sns.scatterplot(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], color = 'blue', label = 'Cluster 2',s=50)\n",
        "sns.scatterplot(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], color = 'green', label = 'Cluster 3',s=50)\n",
        "sns.scatterplot(x[y_kmeans == 3, 0], x[y_kmeans == 3, 1], color = 'grey', label = 'Cluster 4',s=50)\n",
        "sns.scatterplot(x[y_kmeans == 4, 0], x[y_kmeans == 4, 1], color = 'pink', label = 'Cluster 5',s=50)\n",
        "sns.scatterplot(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], color = 'red', label = 'Centroids',s=300,marker=',')\n",
        "mtp.grid(False)\n",
        "mtp.title('Clusters of students')\n",
        "mtp.xlabel('Absences')\n",
        "mtp.ylabel('Average')\n",
        "mtp.legend()\n",
        "mtp.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "MvB9itL0Ax2G",
        "outputId": "4865dce4-7647-4aa3-f294-657d064bed62"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "X = pd.read_csv('https://raw.githubusercontent.com/navyadua/data/main/student_new.csv')\n",
        "corr_columns = ['paid' , 'internet' , 'address',  'studytime', 'traveltime', 'Walc','health','average']\n",
        "df = pd.DataFrame(X, columns=corr_columns)\n",
        "\n",
        "kmeans = KMeans(n_clusters=5 , random_state = 990)\n",
        "\n",
        "y = kmeans.fit_predict(df.values)\n",
        "\n",
        "df['Cluster'] = y\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "jRwg0GYFugU1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "be60e368-373e-4262-f308-6f92a9f00028"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     paid  internet  address  studytime  traveltime  Walc  health    average  \\\n",
              "0       1         1        2          2           2     1       3   5.666667   \n",
              "1       1         2        2          2           1     1       3   5.333333   \n",
              "2       2         2        2          2           1     3       3   8.333333   \n",
              "3       2         2        2          3           1     1       5  14.666670   \n",
              "4       2         1        2          2           1     2       5   8.666667   \n",
              "..    ...       ...      ...        ...         ...   ...     ...        ...   \n",
              "390     2         1        2          2           1     5       4   9.000000   \n",
              "391     1         2        2          1           2     4       2  15.333330   \n",
              "392     1         1        1          1           1     3       3   8.333333   \n",
              "393     1         2        1          1           3     4       5  11.000000   \n",
              "394     1         2        2          1           1     3       5   8.666667   \n",
              "\n",
              "     Cluster  \n",
              "0          2  \n",
              "1          2  \n",
              "2          3  \n",
              "3          1  \n",
              "4          3  \n",
              "..       ...  \n",
              "390        3  \n",
              "391        1  \n",
              "392        3  \n",
              "393        0  \n",
              "394        3  \n",
              "\n",
              "[395 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88d3f99a-f3f3-4762-a776-a5dfad4f177d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>paid</th>\n",
              "      <th>internet</th>\n",
              "      <th>address</th>\n",
              "      <th>studytime</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>average</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>14.666670</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>3</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>15.333330</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>395 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88d3f99a-f3f3-4762-a776-a5dfad4f177d')\"\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-88d3f99a-f3f3-4762-a776-a5dfad4f177d 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-88d3f99a-f3f3-4762-a776-a5dfad4f177d');\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": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "df_col = dataset[corr_columns]\n",
        "n = random.randint(0,394)\n",
        "args = (df_col.loc[[n]])\n",
        "args"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "lKEPdeefxhdo",
        "outputId": "4337a4a0-843d-450e-8cb8-e9e5f779a51d"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   paid  internet  address  studytime  traveltime  Walc  health   average\n",
              "0     1         1        2          2           2     1       3  5.666667"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f9d1f03-710b-4620-91ed-a0081c4c8efc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>paid</th>\n",
              "      <th>internet</th>\n",
              "      <th>address</th>\n",
              "      <th>studytime</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>Walc</th>\n",
              "      <th>health</th>\n",
              "      <th>average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f9d1f03-710b-4620-91ed-a0081c4c8efc')\"\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-9f9d1f03-710b-4620-91ed-a0081c4c8efc 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-9f9d1f03-710b-4620-91ed-a0081c4c8efc');\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": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster = {0:'Good',1:'Excellent',2:'Can be Improved',3:'Sufficient',4:'Satisfactory'}\n",
        "import numpy as np\n",
        "prediction = kmeans.predict(args)\n",
        "print(\"It belongs to\",cluster.get(prediction[0]) , \"cluster\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLm1PBggyg97",
        "outputId": "0f749e5b-75b0-4f4c-ff30-e12707aa9c98"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It belongs to Can be Improved cluster\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:443: UserWarning: X has feature names, but KMeans was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}