ML_Project_Student_Grade_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_Project_Student_Grade_Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/ft-abhx/ML_Project/blob/main/ML_Project_Student_Grade_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lHLDIglfpjG8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "iqLex2NXqmRo",
        "outputId": "c8a7073b-4b74-46f3-8c67-5881494c69e7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-12a73b15-4eb8-41fc-ab0d-cbd65ae09130\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-12a73b15-4eb8-41fc-ab0d-cbd65ae09130\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving student-mat.csv to student-mat.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "df = pd.read_csv(io.BytesIO(uploaded['student-mat.csv']))"
      ],
      "metadata": {
        "id": "2LX1H6S0q7aB"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "hHzdFeYBwUd6",
        "outputId": "f0ec3c83-fc49-42c8-9a2b-46d6cda9e6a9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-318dc88d-4d35-46c7-b798-5c6e592baf91\">\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>reason</th>\n",
              "      <th>guardian</th>\n",
              "      <th>traveltime</th>\n",
              "      <th>studytime</th>\n",
              "      <th>failures</th>\n",
              "      <th>schoolsup</th>\n",
              "      <th>famsup</th>\n",
              "      <th>paid</th>\n",
              "      <th>activities</th>\n",
              "      <th>nursery</th>\n",
              "      <th>higher</th>\n",
              "      <th>internet</th>\n",
              "      <th>romantic</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>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
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              "      <td>no</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</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>course</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>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>other</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</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>home</td>\n",
              "      <td>mother</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</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>home</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</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",
              "      <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>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</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>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>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>course</td>\n",
              "      <td>other</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</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>course</td>\n",
              "      <td>mother</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>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>course</td>\n",
              "      <td>father</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</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-318dc88d-4d35-46c7-b798-5c6e592baf91')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      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-318dc88d-4d35-46c7-b798-5c6e592baf91 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-318dc88d-4d35-46c7-b798-5c6e592baf91');\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",
              "  "
            ],
            "text/plain": [
              "    school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "0       GP   F   18       U     GT3       A  ...     1       3        6   5   6   6\n",
              "1       GP   F   17       U     GT3       T  ...     1       3        4   5   5   6\n",
              "2       GP   F   15       U     LE3       T  ...     3       3       10   7   8  10\n",
              "3       GP   F   15       U     GT3       T  ...     1       5        2  15  14  15\n",
              "4       GP   F   16       U     GT3       T  ...     2       5        4   6  10  10\n",
              "..     ...  ..  ...     ...     ...     ...  ...   ...     ...      ...  ..  ..  ..\n",
              "390     MS   M   20       U     LE3       A  ...     5       4       11   9   9   9\n",
              "391     MS   M   17       U     LE3       T  ...     4       2        3  14  16  16\n",
              "392     MS   M   21       R     GT3       T  ...     3       3        3  10   8   7\n",
              "393     MS   M   18       R     LE3       T  ...     4       5        0  11  12  10\n",
              "394     MS   M   19       U     LE3       T  ...     3       5        5   8   9   9\n",
              "\n",
              "[395 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImhgGm1VxDqY",
        "outputId": "e6e82663-d5bb-4c97-d659-670d2b797207"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of     school sex  age address famsize Pstatus  ...  Walc  health absences  G1  G2  G3\n",
              "0       GP   F   18       U     GT3       A  ...     1       3        6   5   6   6\n",
              "1       GP   F   17       U     GT3       T  ...     1       3        4   5   5   6\n",
              "2       GP   F   15       U     LE3       T  ...     3       3       10   7   8  10\n",
              "3       GP   F   15       U     GT3       T  ...     1       5        2  15  14  15\n",
              "4       GP   F   16       U     GT3       T  ...     2       5        4   6  10  10\n",
              "..     ...  ..  ...     ...     ...     ...  ...   ...     ...      ...  ..  ..  ..\n",
              "390     MS   M   20       U     LE3       A  ...     5       4       11   9   9   9\n",
              "391     MS   M   17       U     LE3       T  ...     4       2        3  14  16  16\n",
              "392     MS   M   21       R     GT3       T  ...     3       3        3  10   8   7\n",
              "393     MS   M   18       R     LE3       T  ...     4       5        0  11  12  10\n",
              "394     MS   M   19       U     LE3       T  ...     3       5        5   8   9   9\n",
              "\n",
              "[395 rows x 33 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6CicK3uyZG5",
        "outputId": "1a0de4df-c779-4984-def0-323e7c1ee230"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(395, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJmp7rPPyKzk",
        "outputId": "731deaa0-741d-48f8-d451-330b8e97d836"
      },
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Value Counts for Categorical Columns"
      ],
      "metadata": {
        "id": "5d4lqoNlxdNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "    if df[col].dtype == 'object':\n",
        "        print('\\nColumn Name:', col,)\n",
        "        print(df[col].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPw6PgCpxZVe",
        "outputId": "df4363f0-bbe0-497c-bb98-2297612c6997"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Column Name: school\n",
            "GP    349\n",
            "MS     46\n",
            "Name: school, dtype: int64\n",
            "\n",
            "Column Name: sex\n",
            "F    208\n",
            "M    187\n",
            "Name: sex, dtype: int64\n",
            "\n",
            "Column Name: address\n",
            "U    307\n",
            "R     88\n",
            "Name: address, dtype: int64\n",
            "\n",
            "Column Name: famsize\n",
            "GT3    281\n",
            "LE3    114\n",
            "Name: famsize, dtype: int64\n",
            "\n",
            "Column Name: Pstatus\n",
            "T    354\n",
            "A     41\n",
            "Name: Pstatus, dtype: int64\n",
            "\n",
            "Column Name: Mjob\n",
            "other       141\n",
            "services    103\n",
            "at_home      59\n",
            "teacher      58\n",
            "health       34\n",
            "Name: Mjob, dtype: int64\n",
            "\n",
            "Column Name: Fjob\n",
            "other       217\n",
            "services    111\n",
            "teacher      29\n",
            "at_home      20\n",
            "health       18\n",
            "Name: Fjob, dtype: int64\n",
            "\n",
            "Column Name: reason\n",
            "course        145\n",
            "home          109\n",
            "reputation    105\n",
            "other          36\n",
            "Name: reason, dtype: int64\n",
            "\n",
            "Column Name: guardian\n",
            "mother    273\n",
            "father     90\n",
            "other      32\n",
            "Name: guardian, dtype: int64\n",
            "\n",
            "Column Name: schoolsup\n",
            "no     344\n",
            "yes     51\n",
            "Name: schoolsup, dtype: int64\n",
            "\n",
            "Column Name: famsup\n",
            "yes    242\n",
            "no     153\n",
            "Name: famsup, dtype: int64\n",
            "\n",
            "Column Name: paid\n",
            "no     214\n",
            "yes    181\n",
            "Name: paid, dtype: int64\n",
            "\n",
            "Column Name: activities\n",
            "yes    201\n",
            "no     194\n",
            "Name: activities, dtype: int64\n",
            "\n",
            "Column Name: nursery\n",
            "yes    314\n",
            "no      81\n",
            "Name: nursery, dtype: int64\n",
            "\n",
            "Column Name: higher\n",
            "yes    375\n",
            "no      20\n",
            "Name: higher, dtype: int64\n",
            "\n",
            "Column Name: internet\n",
            "yes    329\n",
            "no      66\n",
            "Name: internet, dtype: int64\n",
            "\n",
            "Column Name: romantic\n",
            "no     263\n",
            "yes    132\n",
            "Name: romantic, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Final Grade"
      ],
      "metadata": {
        "id": "pSvj5KIZwlz7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['G1'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coroB3pPpuQX",
        "outputId": "60bbdc97-964e-4928-8d85-cc6a0b77062e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    395.000000\n",
              "mean      10.908861\n",
              "std        3.319195\n",
              "min        3.000000\n",
              "25%        8.000000\n",
              "50%       11.000000\n",
              "75%       13.000000\n",
              "max       19.000000\n",
              "Name: G1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "b = sns.countplot (df['G1'])\n",
        "b.axes.set_title( 'Distribution of Period I Grade of students', fontsize= 35)\n",
        "b.set_xlabel('Period I Grade', fontsize = 20)\n",
        "b.set_ylabel('Count', fontsize = 20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "DroDIpTRptC5",
        "outputId": "61347d0b-2ae6-416b-d64d-3a067678abd5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['G2'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiwMvkaIpxaP",
        "outputId": "7cb42129-74a7-4e45-c79b-5675efaef2dc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    395.000000\n",
              "mean      10.713924\n",
              "std        3.761505\n",
              "min        0.000000\n",
              "25%        9.000000\n",
              "50%       11.000000\n",
              "75%       13.000000\n",
              "max       19.000000\n",
              "Name: G2, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "b = sns.countplot (df['G2'])\n",
        "b.axes.set_title( 'Distribution of Period II Grade of students', fontsize= 35)\n",
        "b.set_xlabel('Period II Grade', fontsize = 20)\n",
        "b.set_ylabel('Count', fontsize = 20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "ZPmcBQpuqgsR",
        "outputId": "57532cdc-c23e-497a-8549-c46892dd9696"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['G3'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0qZawTIwki7",
        "outputId": "8db92bba-d631-49f9-a85d-2dfa5c3db4e7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    395.000000\n",
              "mean      10.415190\n",
              "std        4.581443\n",
              "min        0.000000\n",
              "25%        8.000000\n",
              "50%       11.000000\n",
              "75%       14.000000\n",
              "max       20.000000\n",
              "Name: G3, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "b = sns.countplot (df['G3'])\n",
        "b.axes.set_title( 'Distribution of Final grade of students', fontsize= 35)\n",
        "b.set_xlabel('Final Grade', fontsize = 20)\n",
        "b.set_ylabel('Count', fontsize = 20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "LDQ4xLO2w0Dh",
        "outputId": "a9510d33-464a-4318-9985-42d84253ec04"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "# Make one plot for each different location\n",
        "sns.kdeplot(df[(df['address']=='U')]['G3'], label = 'Urban', shade = True)\n",
        "sns.kdeplot(df[(df['address']=='R')]['G3'], label = 'Rural', shade = True)\n",
        "# Add labeling\n",
        "plt.xlabel('G3')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Density Plot of Final Grades by Location')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "vgZKihzxyv0L",
        "outputId": "8398d9e4-2e50-4cfb-e990-ff2847ed0914"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Density Plot of Final Grades by Location')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make one plot for each different location\n",
        "sns.kdeplot(df[(df['guardian']=='father')]['G3'], label = 'Father', shade = True)\n",
        "sns.kdeplot(df[(df['guardian']=='mother')]['G3'], label = 'Mother', shade = True)\n",
        "sns.kdeplot(df[(df['guardian']=='other')]['G3'], label = 'Other', shade = True)\n",
        "# Add labeling\n",
        "plt.xlabel('G3')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Density Plot of Final Grades by guardian')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "xLkmx2alz0TW",
        "outputId": "06af952e-4b4e-4f8e-a609-aa674bd2a16c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Density Plot of Final Grades by guardian')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make one plot for each different location\n",
        "sns.kdeplot(df[(df['school']=='GP')]['G3'], label = 'GP School', shade = True)\n",
        "sns.kdeplot(df[(df['school']=='MS')]['G3'], label = 'MS School', shade = True)\n",
        "# Add labeling\n",
        "plt.xlabel('G3')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Density Plot of Final Grades by School')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "swYbZf86z0cZ",
        "outputId": "7f77f218-742e-4d23-c168-be5d2a08ec96"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Density Plot of Final Grades by School')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make one plot for each different location\n",
        "sns.kdeplot(df[(df['sex']=='M')]['G3'], label = 'Male', shade = True)\n",
        "sns.kdeplot(df[(df['sex']=='F')]['G3'], label = 'Female', shade = True)\n",
        "# Add labeling\n",
        "plt.xlabel('G3')\n",
        "plt.ylabel('Density')\n",
        "plt.title('Density Plot of Final Grades by Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "T_K-yHxFz0nQ",
        "outputId": "01435763-ea48-4d08-ebe7-3f72db215853"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Density Plot of Final Grades by Gender')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4dxoLV9xqhO",
        "outputId": "86a7d189-0474-4249-b419-c3fd6925e9c0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "school        False\n",
              "sex           False\n",
              "age           False\n",
              "address       False\n",
              "famsize       False\n",
              "Pstatus       False\n",
              "Medu          False\n",
              "Fedu          False\n",
              "Mjob          False\n",
              "Fjob          False\n",
              "reason        False\n",
              "guardian      False\n",
              "traveltime    False\n",
              "studytime     False\n",
              "failures      False\n",
              "schoolsup     False\n",
              "famsup        False\n",
              "paid          False\n",
              "activities    False\n",
              "nursery       False\n",
              "higher        False\n",
              "internet      False\n",
              "romantic      False\n",
              "famrel        False\n",
              "freetime      False\n",
              "goout         False\n",
              "Dalc          False\n",
              "Walc          False\n",
              "health        False\n",
              "absences      False\n",
              "G1            False\n",
              "G2            False\n",
              "G3            False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpwzWIrnyVLJ",
        "outputId": "24b55e8a-7b7c-4ccc-f85b-6c422a6bbcc7"
      },
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "male_student= len(df[df['sex'] == 'M']) \n",
        "female_student = len(df[df['sex'] == 'F']) \n",
        "print('Number of male students:',male_student) \n",
        "print('Number of female students: ', female_student)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGcuF0JVyhI5",
        "outputId": "482e5f29-9476-4878-81e2-7e3bdfb60c95"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of male students: 187\n",
            "Number of female students:  208\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "þ = sns.countplot('age', hue='sex', data=df) \n",
        "b.axes.set_title('Number of students in different age groups', fontsize=30)"
      ],
      "metadata": {
        "id": "yg25VMdry4Xi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "5ee6654b-74f3-4ab1-ed26-5da11d60c8d3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0045989698307578, 'Number of students in different age groups')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEHCAYAAACp9y31AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVFElEQVR4nO3df5RdZX3v8fc3JDSAUQKM6cCQJqsVChpBMwV7tUDRGIlcQKssskDDj5r+UIFeTaHV5VVLu7C0cF3Sizf80FAVImoIBUqllEjtQjDBQAIBsTTVgUDSEANYogl87x9nR8dkZnKSzD57Js/7tdZZs/c++5zzYRI+58lz9t4nMhNJUjnGNB1AktRZFr8kFcbil6TCWPySVBiLX5IKY/FLUmHG1vnkEbE/cA3wOiCBc4HHgIXAFGA1cHpmbhjqeQ466KCcMmVKnVElaY+zbNmy/8rMrm23R53H8UfEAuBfM/OaiNgb2Bf4c+DZzLw0Ii4GJmbmRUM9T29vby5durS2nJK0J4qIZZnZu+322qZ6IuJVwHHAtQCZ+bPM/DFwKrCg2m0BcFpdGSRJ26tzjn8qsA74QkR8LyKuiYj9gEmZuaba52lgUo0ZJEnbqLP4xwJvBK7KzDcAPwEu7r9DtuaZBpxrioi5EbE0IpauW7euxpiSVJY6P9ztA/oy875q/Wu0iv+ZiOjOzDUR0Q2sHejBmTkfmA+tOf4ac6oAmzdvpq+vj02bNjUdpS3jx4+np6eHcePGNR1Fe6Daij8zn46IH0XE4Zn5GPBW4JHqNge4tPq5uK4M0lZ9fX1MmDCBKVOmEBFNxxlSZrJ+/Xr6+vqYOnVq03G0B6r1cE7gw8CXqyN6ngDOoTW99NWIOA/4T+D0mjNIbNq0aVSUPkBEcOCBB+IUp+pSa/Fn5nJgu0OJaI3+pY4aDaW/1WjKqtHHM3clqTAWvyQVpu45fo0C0+ddv1uPX3bZ+4cpiaROcMQvDeEnP/kJ73znOznqqKN43etex8KFC1m2bBnHH38806dPZ+bMmaxZs4aNGzdy+OGH89hjjwEwe/Zsrr766obTSwNzxC8N4Y477uDggw/mtttuA2Djxo2cdNJJLF68mK6uLhYuXMjHPvYxrrvuOq688krOPvtsLrjgAjZs2MAHPvCBhtNLA7P4pSFMmzaNj3zkI1x00UWcfPLJTJw4kZUrVzJjxgwAXnrpJbq7uwGYMWMGN910Ex/84Ad58MEHm4wtDcnil4Zw2GGH8cADD3D77bfz8Y9/nBNPPJHXvva13Hvvvdvt+/LLL7Nq1Sr23XdfNmzYQE9PTwOJpR1zjl8awlNPPcW+++7LWWedxbx587jvvvtYt27dz4t/8+bNPPzwwwBcccUVHHHEEXzlK1/hnHPOYfPmzU1GlwbliF8awooVK5g3bx5jxoxh3LhxXHXVVYwdO5bzzz+fjRs3smXLFi688ELGjh3LNddcw/3338+ECRM47rjjuOSSS/jUpz7V9H+CtB2LXxrCzJkzmTlz5nbb77nnnu22rVq16ufLl19+ea25pN3hVI8kFcbil6TCWPySVBiLX5IKY/FLUmEsfkkqjIdz1mh3r3oJXvmyLsPxZ9NfO39Oe+21F9OmTfv5+s0338yUKVOGNYfUDotf6pB99tmH5cuXNx1DcqpHkkrjiF/qkBdffJGjjz4agKlTp7Jo0aKGE6lUFr/UIU71aKRwqkeSCmPxS1JhnOpRkTxMViVzxC91yAsvvNB0BAmw+CWpOBa/JBWm1jn+iFgNPA+8BGzJzN6IOABYCEwBVgOnZ+aGOnNIkn6hEyP+383MozOzt1q/GLgrM18D3FWtS5I6pImpnlOBBdXyAuC0BjJIUrHqPpwzgW9GRAL/LzPnA5Myc011/9PApIEeGBFzgbkAkydPrjnmyPXDT0/b8U5DmPyJFcOURNKeou7if0tmPhkRrwbujIhH+9+ZmVm9KWynepOYD9Db2zvgPtKu2t031G218wYbEZx55pl86UtfAmDLli10d3dz7LHHcuuttw5rHmkotU71ZOaT1c+1wCLgGOCZiOgGqH6urTODNFLst99+rFy5khdffBGAO++8k0MOOaThVCpRbcUfEftFxISty8DbgZXALcCcarc5wOK6MkgjzaxZs7jtttsAuOGGG5g9e3bDiVSiOkf8k4BvR8SDwP3AbZl5B3ApMCMiHgfeVq1LRTjjjDO48cYb2bRpEw899BDHHnts05FUoNrm+DPzCeCoAbavB95a1+tKI9nrX/96Vq9ezQ033MCsWbOajqNCeZE2qcNOOeUUPvrRj7JkyRLWr1/fdBwVyOKXOuzcc89l//33Z9q0aSxZsqTpOCqQxa8iNXl+Q09PD+eff35jry9Z/FKHDHRZ5hNOOIETTjih82FUNK/OKUmFsfglqTAWv4qROXqu/DGasmr0sfhVhPHjx7N+/fpRUaiZyfr16xk/fnzTUbSH8sNdFaGnp4e+vj7WrVvXdJS2jB8/np6enqZjaA9l8asI48aNY+rUqU3HkEYEp3okqTAWvyQVxuKXpMJY/JJUGItfkgpj8UtSYYo9nHN3v2y7yas7StLucMQvSYWx+CWpMMVO9Wj0mT7v+t16/LLL3j9MSaTRzRG/JBXG4pekwlj8klQYi1+SCmPxS1JhLH5JKozFL0mFqb34I2KviPheRNxarU+NiPsi4gcRsTAi9q47gyTpFzox4r8AWNVv/TPAFZn5G8AG4LwOZJAkVWot/ojoAd4JXFOtB3Ai8LVqlwXAaXVmkCT9srov2fB/gD8FJlTrBwI/zswt1XofcMhAD4yIucBcgMmTJ9ccU7tjd690Cl7tVOqk2kb8EXEysDYzl+3K4zNzfmb2ZmZvV1fXMKeTpHLVOeJ/M3BKRMwCxgOvBD4L7B8RY6tRfw/wZI0ZJEnbqG3En5l/lpk9mTkFOAP4l8w8E7gbeE+12xxgcV0ZJEnba+KyzBcBN0bEJcD3gGsbyKAC+VmE1NKR4s/MJcCSavkJ4JhOvK4kaXueuStJhRmV38C1u9/EBLBowo73kaQ9kSN+SSqMxS9JhbH4JakwFr8kFcbil6TCWPySVBiLX5IKY/FLUmEsfkkqjMUvSYWx+CWpMBa/JBXG4pekwlj8klQYi1+SCtNW8UfEXe1skySNfEN+EUtEjAf2BQ6KiIlAVHe9Ejik5mySpBrs6Bu4/gC4EDgYWMYviv854Moac0mSajJk8WfmZ4HPRsSHM/NzHcokSapRW9+5m5mfi4j/AUzp/5jM3P0vv5UkdVRbxR8Rfw/8OrAceKnanIDFL0mjTFvFD/QCR2Zm1hlGklS/do/jXwn8ap1BJEmd0e6I/yDgkYi4H/jp1o2ZeUotqSRJtWm3+D+5s09cnQNwD/Ar1et8LTP/d0RMBW4EDqR1iOj7MvNnO/v8kqRd0+5RPd/ahef+KXBiZr4QEeOAb0fEPwL/C7giM2+MiM8D5wFX7cLzS5J2QbuXbHg+Ip6rbpsi4qWIeG6ox2TLC9XquOqWwInA16rtC4DTdjG7JGkXtDvin7B1OSICOBV4044eFxF70ZrO+Q3g74B/B36cmVuqXfrw0g+S1FE7fXXOaiR/MzCzjX1fysyjgR7gGOA3232diJgbEUsjYum6det2NqYkaRDtnsD17n6rY2gd17+p3RfJzB9HxN3AbwP7R8TYatTfAzw5yGPmA/MBent7PX9AkoZJu0f1/M9+y1uA1bSmewYVEV3A5qr09wFmAJ8B7gbeQ+vInjnA4p3MLEnaDe3O8Z+zC8/dDSyo5vnHAF/NzFsj4hHgxoi4BPgecO0uPLckaRe1O9XTA3wOeHO16V+BCzKzb7DHZOZDwBsG2P4Erfl+SVID2v1w9wvALbSuy38w8A/VNknSKNNu8Xdl5hcyc0t1+yLQVWMuSVJN2i3+9RFxVkTsVd3OAtbXGUySVI92i/9c4HTgaWANraNyzq4pkySpRu0ezvlpYE5mbgCIiAOAv6H1hiBJGkXaHfG/fmvpA2TmswxwxI4kaeRrt/jHRMTErSvViL/dfy1IkkaQdsv7b4F7I+Kmav29wF/WE0mSVKd2z9y9PiKW0rqkMsC7M/OR+mJJkurS9nRNVfSWvSSNcjt9WWZJ0uhm8UtSYSx+SSqMxS9JhbH4JakwFr8kFcbil6TCWPySVBiLX5IKY/FLUmEsfkkqjMUvSYWx+CWpMBa/JBXG4pekwlj8klQYi1+SClNb8UfEoRFxd0Q8EhEPR8QF1fYDIuLOiHi8+jlxR88lSRo+dY74twAfycwjgTcBH4yII4GLgbsy8zXAXdW6JKlDaiv+zFyTmQ9Uy88Dq4BDgFOBBdVuC4DT6sogSdpeR+b4I2IK8AbgPmBSZq6p7noamDTIY+ZGxNKIWLpu3bpOxJSkItRe/BHxCuDrwIWZ+Vz/+zIzgRzocZk5PzN7M7O3q6ur7piSVIxaiz8ixtEq/S9n5jeqzc9ERHd1fzewts4MkqRfVudRPQFcC6zKzMv73XULMKdangMsriuDJGl7Y2t87jcD7wNWRMTyatufA5cCX42I84D/BE6vMYMkaRu1FX9mfhuIQe5+a12vK0kammfuSlJhLH5JKozFL0mFsfglqTAWvyQVxuKXpMJY/JJUGItfkgpj8UtSYSx+SSqMxS9JhbH4JakwFr8kFcbil6TCWPySVBiLX5IKU+c3cEnFmT7v+t1+jmWXvX8YkkiDc8QvSYWx+CWpMBa/JBXG4pekwlj8klQYi1+SCmPxS1JhLH5JKozFL0mFsfglqTC1FX9EXBcRayNiZb9tB0TEnRHxePVzYl2vL0kaWJ0j/i8C79hm28XAXZn5GuCual2S1EG1FX9m3gM8u83mU4EF1fIC4LS6Xl+SNLBOX51zUmauqZafBiYNtmNEzAXmAkyePLkD0aSR4YefnrbbzzH5EyuGIYn2VI19uJuZCeQQ98/PzN7M7O3q6upgMknas3W6+J+JiG6A6ufaDr++JBWv08V/CzCnWp4DLO7w60tS8eo8nPMG4F7g8Ijoi4jzgEuBGRHxOPC2al2S1EG1fbibmbMHueutdb2mJGnHPHNXkgpj8UtSYSx+SSqMxS9JhbH4JakwFr8kFcbil6TCWPySVBiLX5IKY/FLUmEsfkkqjMUvSYWx+CWpMBa/JBXG4pekwlj8klQYi1+SCmPxS1JhLH5JKozFL0mFsfglqTAWvyQVxuKXpMKMbTqApNHph5+ettvPMfkTK4YhiXaWI35JKozFL0mFaWSqJyLeAXwW2Au4JjMvbSKHVKrp867f7edYNGEYgnTAaJmS2t2cO5Ox4yP+iNgL+DvgJOBIYHZEHNnpHJJUqiameo4BfpCZT2Tmz4AbgVMbyCFJRWqi+A8BftRvva/aJknqgMjMzr5gxHuAd2Tm71fr7wOOzcwPbbPfXGButXo48NgwRzkI+K9hfs46jIacoyEjmHO4mXN41ZHz1zKza9uNTXy4+yRwaL/1nmrbL8nM+cD8ukJExNLM7K3r+YfLaMg5GjKCOYebOYdXJ3M2MdXzXeA1ETE1IvYGzgBuaSCHJBWp4yP+zNwSER8C/onW4ZzXZebDnc4hSaVq5Dj+zLwduL2J1+6ntmmkYTYaco6GjGDO4WbO4dWxnB3/cFeS1Cwv2SBJhdnjiz8irouItRGxst+2T0bEkxGxvLrNajJjlWm7nNX2D0fEoxHxcET8dVP5+uUZ6Pe5sN/vcnVELG8yY5VpoJxHR8R3qpxLI+KYJjNWmQbKeVRE3BsRKyLiHyLilQ1nPDQi7o6IR6q/hxdU2w+IiDsj4vHq58QRmvO91frLEdH40T1D5Lys+n/9oYhYFBH71xYiM/foG3Ac8EZgZb9tnwQ+2nS2NnL+LvDPwK9U668eiTm3uf9vgU+MxJzAN4GTquVZwJIRmvO7wPHV8rnAXzScsRt4Y7U8Afg+rcut/DVwcbX9YuAzIzTnEbTOBVoC9I6AP/PBcr4dGFtt/0ydv889fsSfmfcAzzadY0cGyflHwKWZ+dNqn7UdD7aNoX6fERHA6cANHQ01gEFyJrB19Pwq4KmOhhrAIDkPA+6plu8Efq+jobaRmWsy84Fq+XlgFa2z7U8FFlS7LQBOayZhy2A5M3NVZg73CaC7bIic38zMLdVu36F1jlMt9vjiH8KHqn9SXdf0P1GHcBjwOxFxX0R8KyJ+q+lAO/A7wDOZ+XjTQQZxIXBZRPwI+BvgzxrOM5iH+cX1q97LL5/w2KiImAK8AbgPmJSZa6q7ngYmNRRrO9vkHLGGyHku8I91vW6pxX8V8OvA0cAaWtMTI9FY4ADgTcA84KvVqHqkms0IGO0P4Y+AP8nMQ4E/Aa5tOM9gzgX+OCKW0ZoK+FnDeQCIiFcAXwcuzMzn+t+XrfmJEXGI4FA5R5LBckbEx4AtwJfreu0iiz8zn8nMlzLzZeBqWlcMHYn6gG9ky/3Ay7Su5zHiRMRY4N3AwqazDGEO8I1q+SZG6J97Zj6amW/PzOm03kj/velMETGOVkl9OTO3/g6fiYju6v5uoPGpyEFyjjiD5YyIs4GTgTOrN9NaFFn8W/+yVt4FrBxs34bdTOsDXiLiMGBvRu7Fpt4GPJqZfU0HGcJTwPHV8onAiJySiohXVz/HAB8HPt9wnqD1r6NVmXl5v7tuofVmSvVzcaez9TdEzhFlsJzVF1T9KXBKZv53rSGa/oS77hutEdMaYDOtEfR5wN8DK4CHaP3l7R6hOfcGvkTrjekB4MSRmLPa/kXgD5vOt4Pf51uAZcCDtOZUp4/QnBfQOtLj+8ClVCdaNpjxLbSmcR4Clle3WcCBwF203kD/GThghOZ8V/W7/SnwDPBPIzTnD2hdsn7rts/XlcEzdyWpMEVO9UhSySx+SSqMxS9JhbH4JakwFr8kFcbil6TCWPySVBiLX9qBiLg5IpZV106fW207LyK+HxH3R8TVEXFltb0rIr4eEd+tbm9uNr20PU/gknYgIg7IzGcjYh9a18qfCfwbrevoPw/8C/BgZn4oIr4C/N/M/HZETKZ1lugRjYWXBtDIl61Lo8z5EfGuavlQ4H3AtzLzWYCIuInWJbShdc2iI/tdRPWVEfGKzHyhk4GloVj80hAi4gRaZf7bmfnfEbEEeJTWtzoNZAzwpszc1JmE0s5zjl8a2quADVXp/yat70bYDzg+IiZWl6Pu/w1Z3wQ+vHUlIo7uaFqpDRa/NLQ7gLERsYrWlTK/AzwJ/BVwP625/tXAxmr/84He6tvdHgH+sOOJpR3ww11pF2ydt69G/IuA6zJzUdO5pHY44pd2zScjYjmt70r4D1pfmiONCo74JakwjvglqTAWvyQVxuKXpMJY/JJUGItfkgpj8UtSYf4/+l09q7PvVlAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['GradeAvg'] = (df[ 'G1'] + df['G2'] + df[ 'G3']) / 3\n",
        "df['GradeAvg']"
      ],
      "metadata": {
        "id": "npAHJO_OrtWz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03d90691-3b02-4f23-d292-efb601340768"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5.666667\n",
              "1       5.333333\n",
              "2       8.333333\n",
              "3      14.666667\n",
              "4       8.666667\n",
              "         ...    \n",
              "390     9.000000\n",
              "391    15.333333\n",
              "392     8.333333\n",
              "393    11.000000\n",
              "394     8.666667\n",
              "Name: GradeAvg, Length: 395, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_grade (data):\n",
        "    # Create a list to store the data \n",
        "    grades = []\n",
        "\n",
        "    # For each row in the column,\n",
        "    for row in data['GradeAvg']: \n",
        "        # if more than a value,\n",
        "        if row >= (0.9 * df['GradeAvg'].max()): \n",
        "          # Append a letter grade \n",
        "          grades.append('1')\n",
        "        #else, if more than a value,\n",
        "        elif row >= (0.7 * df['GradeAvg'].max()):\n",
        "        # Append a letter grade\n",
        "            grades.append('2') \n",
        "        #else, if more than a value, \n",
        "        elif row < (0.7 * df['GradeAvg' ].max()):\n",
        "      # Append a letter grade\n",
        "            grades.append('3')\n",
        "\n",
        "     # Create a column from the list \n",
        "    df['GradeAvg'] = grades\n",
        "    return df"
      ],
      "metadata": {
        "id": "Ocnj_HYszeF4"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['GradeAvg']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FOIjONcs9FB",
        "outputId": "fb607337-0ba0-4a1d-f898-b64087a59f99"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5.666667\n",
              "1       5.333333\n",
              "2       8.333333\n",
              "3      14.666667\n",
              "4       8.666667\n",
              "         ...    \n",
              "390     9.000000\n",
              "391    15.333333\n",
              "392     8.333333\n",
              "393    11.000000\n",
              "394     8.666667\n",
              "Name: GradeAvg, Length: 395, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dum = find_grade (df)"
      ],
      "metadata": {
        "id": "3gEn1JNEzFoI"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "#creating labelEncoder\n",
        "le = preprocessing.LabelEncoder()\n",
        "for i in df.loc[:,df.dtypes==object].columns:\n",
        "  df[i]=le.fit_transform(df[i])"
      ],
      "metadata": {
        "id": "bSMsfspVL7w4"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_dum.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "f5WWWroX2bOA",
        "outputId": "263c410a-1bdf-4b25-8960-3307e23adb85"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c73bd476-568d-4102-8c34-4b9a77f9ec64')\"\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-c73bd476-568d-4102-8c34-4b9a77f9ec64 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-c73bd476-568d-4102-8c34-4b9a77f9ec64');\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",
              "  "
            ],
            "text/plain": [
              "   school  sex  age  address  famsize  ...  absences  G1  G2  G3  GradeAvg\n",
              "0       0    0   18        1        0  ...         6   5   6   6         2\n",
              "1       0    0   17        1        0  ...         4   5   5   6         2\n",
              "2       0    0   15        1        1  ...        10   7   8  10         2\n",
              "3       0    0   15        1        0  ...         2  15  14  15         1\n",
              "4       0    0   16        1        0  ...         4   6  10  10         2\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x=df_dum.drop(\"G3\", axis=1)\n",
        "y=df_dum['G3']"
      ],
      "metadata": {
        "id": "XiWz_wvMynZg"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test= train_test_split(x,y, test_size= 0.25, random_state=42)"
      ],
      "metadata": {
        "id": "DhATnic4yIlI"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "Z3o0ax0Isb3Z"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L=LinearRegression() "
      ],
      "metadata": {
        "id": "yAEWcMkfvzjg"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "L.fit(X_train, y_train)\n",
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XI88_DGBx8ub",
        "outputId": "bac65723-0437-4aad-d994-d41bf0acca6c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=L.predict(X_test)"
      ],
      "metadata": {
        "id": "efbpOrVJECdO"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,mean_absolute_error,mean_squared_error,r2_score\n",
        "#print(accuracy_score(y_test,y_pred))\n",
        "print(mean_absolute_error(y_test,y_pred))\n",
        "print(mean_squared_error(y_test,y_pred))\n",
        "print(r2_score(y_test,y_pred))\n",
        "print(L.score(X_test,y_test))"
      ],
      "metadata": {
        "id": "NmQ_fqC2EdCe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d174cd05-ae60-4a18-ebac-9547930febcd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3831445871196204\n",
            "4.578002634519738\n",
            "0.8012834538520601\n",
            "0.8012834538520601\n"
          ]
        }
      ]
    }
  ]
}