Engagement.ipynb

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  "metadata": {
    "colab": {
      "name": "Engagement",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sZ6pP2vPqvD"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7S-XuQUS31X",
        "outputId": "0001983d-3cf5-49ea-a71a-dcdb07247cb8"
      },
      "source": [
        "#mouting the drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x429-0OlUw6U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6ec067a-998d-4fd6-931b-12594c8b92bc"
      },
      "source": [
        "#save the dataset files in a new folder in your gdrive, I've modified StudentAssessments\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUljjtcTwd85",
        "outputId": "d2a7976f-25b0-4f39-faf8-03f1202ea7b3"
      },
      "source": [
        "%cd drive/MyDrive/Datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1xLFDpoa_U9oW5qvFEvYTtCDqdMWucL1p/Datasets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYERiMAZ_wha"
      },
      "source": [
        "#importing CSVs \n",
        "studentInfo = pd.read_csv('studentInfo - studentInfo (1).csv', error_bad_lines = False, low_memory=False)\n",
        "studentRegistration = pd.read_csv('studentRegistration.csv', error_bad_lines = False, low_memory=False)\n",
        "studentVle = pd.read_csv('studentVle.csv', error_bad_lines = False, low_memory=False)\n",
        "assessments = pd.read_csv('assessments.csv', error_bad_lines = False, low_memory=False)\n",
        "courses = pd.read_csv('courses.csv', error_bad_lines = False, low_memory=False)\n",
        "vle = pd.read_csv('vle.csv', error_bad_lines = False, low_memory=False)\n",
        "lateSubmission = pd.read_csv('LateSubmission.csv', error_bad_lines = False, low_memory=False)\n",
        "studentAssessment = pd.read_csv('studentAssessmentwithquiz.csv', error_bad_lines = False, low_memory=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "zwAJoSDtXv_W",
        "outputId": "34bf9d79-8366-4e16-f9ff-9012c3a7a0dc"
      },
      "source": [
        "studentInfo.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_module</th>\n",
              "      <th>code_presentation</th>\n",
              "      <th>id_student</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>highest_education</th>\n",
              "      <th>imd_band</th>\n",
              "      <th>age_band</th>\n",
              "      <th>num_of_prev_attempts</th>\n",
              "      <th>studied_credits</th>\n",
              "      <th>disability</th>\n",
              "      <th>final_result</th>\n",
              "      <th>avg_absences_per_year</th>\n",
              "      <th>attendance</th>\n",
              "      <th>frequency_interaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>6516</td>\n",
              "      <td>M</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>HE Qualification</td>\n",
              "      <td>80-90%</td>\n",
              "      <td>55&lt;=</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>10</td>\n",
              "      <td>65</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>11391</td>\n",
              "      <td>M</td>\n",
              "      <td>East Anglian Region</td>\n",
              "      <td>HE Qualification</td>\n",
              "      <td>90-100%</td>\n",
              "      <td>55&lt;=</td>\n",
              "      <td>0</td>\n",
              "      <td>240</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>15</td>\n",
              "      <td>62</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>24734</td>\n",
              "      <td>F</td>\n",
              "      <td>South Region</td>\n",
              "      <td>Lower Than A Level</td>\n",
              "      <td>60-70%</td>\n",
              "      <td>0-35</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>10</td>\n",
              "      <td>88</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>26192</td>\n",
              "      <td>F</td>\n",
              "      <td>East Anglian Region</td>\n",
              "      <td>A Level or Equivalent</td>\n",
              "      <td>60-70%</td>\n",
              "      <td>35-55</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Distinction</td>\n",
              "      <td>7</td>\n",
              "      <td>112</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>28061</td>\n",
              "      <td>M</td>\n",
              "      <td>Yorkshire Region</td>\n",
              "      <td>Lower Than A Level</td>\n",
              "      <td>40-50%</td>\n",
              "      <td>35-55</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code_module code_presentation  ...  attendance frequency_interaction\n",
              "0         AAA             2014J  ...          65                    39\n",
              "1         AAA             2013J  ...          62                    15\n",
              "2         AAA             2014J  ...          88                    14\n",
              "3         AAA             2014J  ...         112                    31\n",
              "4         AAA             2014J  ...          92                    36\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k248AyemRLUp"
      },
      "source": [
        "studentRegistration['date_unregistration'].replace('NaN', '0', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLR7wki5g3sn"
      },
      "source": [
        "#normalizing (x-x.mean) / x.std scaling down (-1,1)\n",
        "def normalize(input):\n",
        "    return abs((input - np.mean(input))/np.std(input))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dKlgUBRgbp2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67639f56-0cc4-4f5e-de85-08cd72f0e824"
      },
      "source": [
        "#finding out engagement index\n",
        "student_info = studentInfo.drop(['age_band', 'highest_education', 'studied_credits'], axis = 1)\n",
        "k1 = k3 = 2\n",
        "k2 = 1\n",
        "k4 = 3\n",
        "k5 = 2\n",
        "last_submission = np.random.normal(0, 10) #remove replace with (last submission - curr_date)\n",
        "engagement_index = k1*normalize(studentInfo['frequency_interaction']) + k2*normalize(last_submission) + k3*normalize(studentAssessment['score'])\n",
        "+k4*(normalize(studentRegistration['date_registration'] - studentRegistration['date_unregistration'])) + k5*normalize(student_info['attendance'])\n",
        "studentInfo = studentInfo.assign(Engagement_index= engagement_index) \n",
        "#if negative change to 0 number of school days student was enrolled in the course"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "-kaSsz_mg_Ty",
        "outputId": "b4321d45-6d6e-42a4-f32a-53c3b47b3819"
      },
      "source": [
        "studentInfo.drop(['highest_education', 'age_band'], axis =1)\n",
        "# studentInfo."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_module</th>\n",
              "      <th>code_presentation</th>\n",
              "      <th>id_student</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>imd_band</th>\n",
              "      <th>num_of_prev_attempts</th>\n",
              "      <th>studied_credits</th>\n",
              "      <th>disability</th>\n",
              "      <th>final_result</th>\n",
              "      <th>avg_absences_per_year</th>\n",
              "      <th>attendance</th>\n",
              "      <th>frequency_interaction</th>\n",
              "      <th>Engagement_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>6516</td>\n",
              "      <td>M</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>80-90%</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>11391</td>\n",
              "      <td>M</td>\n",
              "      <td>East Anglian Region</td>\n",
              "      <td>90-100%</td>\n",
              "      <td>0</td>\n",
              "      <td>240</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>15</td>\n",
              "      <td>62</td>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>24734</td>\n",
              "      <td>F</td>\n",
              "      <td>South Region</td>\n",
              "      <td>60-70%</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>10</td>\n",
              "      <td>88</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>26192</td>\n",
              "      <td>F</td>\n",
              "      <td>East Anglian Region</td>\n",
              "      <td>60-70%</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Distinction</td>\n",
              "      <td>7</td>\n",
              "      <td>112</td>\n",
              "      <td>31</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>28061</td>\n",
              "      <td>M</td>\n",
              "      <td>Yorkshire Region</td>\n",
              "      <td>40-50%</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>9</td>\n",
              "      <td>92</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32588</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>2679821</td>\n",
              "      <td>F</td>\n",
              "      <td>South East Region</td>\n",
              "      <td>90-100%</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>15</td>\n",
              "      <td>73</td>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32589</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014B</td>\n",
              "      <td>2682002</td>\n",
              "      <td>F</td>\n",
              "      <td>Yorkshire Region</td>\n",
              "      <td>80-90%</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>Distinction</td>\n",
              "      <td>7</td>\n",
              "      <td>80</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32590</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>2684003</td>\n",
              "      <td>F</td>\n",
              "      <td>Yorkshire Region</td>\n",
              "      <td>50-60%</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>Distinction</td>\n",
              "      <td>11</td>\n",
              "      <td>51</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32591</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014B</td>\n",
              "      <td>2689480</td>\n",
              "      <td>F</td>\n",
              "      <td>North Region</td>\n",
              "      <td>40-50%</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>11</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32592</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014B</td>\n",
              "      <td>2695362</td>\n",
              "      <td>F</td>\n",
              "      <td>East Midlands Region</td>\n",
              "      <td>0-10%</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>Distinction</td>\n",
              "      <td>4</td>\n",
              "      <td>81</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32593 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      code_module code_presentation  ...  frequency_interaction Engagement_index\n",
              "0             AAA             2014J  ...                     39              NaN\n",
              "1             AAA             2013J  ...                     15              NaN\n",
              "2             AAA             2014J  ...                     14              NaN\n",
              "3             AAA             2014J  ...                     31              NaN\n",
              "4             AAA             2014J  ...                     36              NaN\n",
              "...           ...               ...  ...                    ...              ...\n",
              "32588         GGG             2014J  ...                     17              NaN\n",
              "32589         GGG             2014B  ...                     25              NaN\n",
              "32590         GGG             2014J  ...                     27              NaN\n",
              "32591         GGG             2014B  ...                     37              NaN\n",
              "32592         GGG             2014B  ...                     25              NaN\n",
              "\n",
              "[32593 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHDxGfOTDo7X"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "pAFKr1CZAGwv",
        "outputId": "436da59b-9840-4072-b7c1-61bc12528743"
      },
      "source": [
        "studentRegistration.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "      <th>date_unregistration</th>\n",
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              "      <td>11391</td>\n",
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              "      <td>-53.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
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              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
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            "text/plain": [
              "  code_module code_presentation  ...  date_registration  date_unregistration\n",
              "0         AAA             2013J  ...             -159.0                  NaN\n",
              "1         AAA             2013J  ...              -53.0                  NaN\n",
              "2         AAA             2013J  ...              -92.0                 12.0\n",
              "3         AAA             2013J  ...              -52.0                  NaN\n",
              "4         AAA             2013J  ...             -176.0                  NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 203
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        "id": "GcL8Y-GsAMFv",
        "outputId": "1638857d-0bac-42fa-9f16-683633279e3e"
      },
      "source": [
        "studentVle.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>code_presentation</th>\n",
              "      <th>id_student</th>\n",
              "      <th>id_site</th>\n",
              "      <th>date</th>\n",
              "      <th>sum_click</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>546652</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546614</td>\n",
              "      <td>-10</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546714</td>\n",
              "      <td>-10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code_module code_presentation  id_student  id_site  date  sum_click\n",
              "0         AAA             2013J       28400   546652   -10          4\n",
              "1         AAA             2013J       28400   546652   -10          1\n",
              "2         AAA             2013J       28400   546652   -10          1\n",
              "3         AAA             2013J       28400   546614   -10         11\n",
              "4         AAA             2013J       28400   546714   -10          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "K5vvj09TlmbA",
        "outputId": "7847731d-b9a5-4563-be3f-bd1e30fa72c2"
      },
      "source": [
        "studentVle.drop(['date'], axis =1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>code_presentation</th>\n",
              "      <th>id_student</th>\n",
              "      <th>id_site</th>\n",
              "      <th>sum_click</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
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              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546614</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546714</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655275</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>675811</td>\n",
              "      <td>896943</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655276</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>675578</td>\n",
              "      <td>896943</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655277</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>654064</td>\n",
              "      <td>896943</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655278</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>654064</td>\n",
              "      <td>896939</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655279</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>654064</td>\n",
              "      <td>896939</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10655280 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         code_module code_presentation  id_student  id_site  sum_click\n",
              "0                AAA             2013J       28400   546652          4\n",
              "1                AAA             2013J       28400   546652          1\n",
              "2                AAA             2013J       28400   546652          1\n",
              "3                AAA             2013J       28400   546614         11\n",
              "4                AAA             2013J       28400   546714          1\n",
              "...              ...               ...         ...      ...        ...\n",
              "10655275         GGG             2014J      675811   896943          3\n",
              "10655276         GGG             2014J      675578   896943          1\n",
              "10655277         GGG             2014J      654064   896943          3\n",
              "10655278         GGG             2014J      654064   896939          1\n",
              "10655279         GGG             2014J      654064   896939          1\n",
              "\n",
              "[10655280 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "Ef-inpbmAO1q",
        "outputId": "dc7b1e5c-2805-45f6-e413-3d9281d16eb6"
      },
      "source": [
        "assessments.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_module</th>\n",
              "      <th>code_presentation</th>\n",
              "      <th>id_assessment</th>\n",
              "      <th>assessment_type</th>\n",
              "      <th>date</th>\n",
              "      <th>weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>1752</td>\n",
              "      <td>TMA</td>\n",
              "      <td>19.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>1753</td>\n",
              "      <td>TMA</td>\n",
              "      <td>54.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>1754</td>\n",
              "      <td>TMA</td>\n",
              "      <td>117.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>1755</td>\n",
              "      <td>TMA</td>\n",
              "      <td>166.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>1756</td>\n",
              "      <td>TMA</td>\n",
              "      <td>215.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code_module code_presentation  id_assessment assessment_type   date  weight\n",
              "0         AAA             2013J           1752             TMA   19.0    10.0\n",
              "1         AAA             2013J           1753             TMA   54.0    20.0\n",
              "2         AAA             2013J           1754             TMA  117.0    20.0\n",
              "3         AAA             2013J           1755             TMA  166.0    20.0\n",
              "4         AAA             2013J           1756             TMA  215.0    30.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "S5LmaywbkNJX",
        "outputId": "a4702d95-80a2-4d9a-8c0d-984bee445014"
      },
      "source": [
        "courses.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_module</th>\n",
              "      <th>code_presentation</th>\n",
              "      <th>module_presentation_length</th>\n",
              "      <th>students_per_teacher</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>268</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2014J</td>\n",
              "      <td>269</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BBB</td>\n",
              "      <td>2013J</td>\n",
              "      <td>268</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BBB</td>\n",
              "      <td>2014J</td>\n",
              "      <td>262</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BBB</td>\n",
              "      <td>2013B</td>\n",
              "      <td>240</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code_module  ... students_per_teacher\n",
              "0         AAA  ...                   17\n",
              "1         AAA  ...                   50\n",
              "2         BBB  ...                   39\n",
              "3         BBB  ...                   36\n",
              "4         BBB  ...                   37\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHKg-mnZkRzj",
        "outputId": "dd2232e2-7997-44da-939f-ae17d0201626"
      },
      "source": [
        "lateSubmission.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(173912, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MTvYMPSAUyh",
        "outputId": "9d422897-9962-4f31-f55d-3a23f340f229"
      },
      "source": [
        "courses.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZawHaskFAXv0",
        "outputId": "237092b0-776e-4751-9b3e-9c9cecb6eb1d"
      },
      "source": [
        "vle.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6364, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njHbJ6zeAbs2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc64104e-a473-4492-d773-f40c7d95494d"
      },
      "source": [
        "studentAssessment.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(173912, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hs5dxmeRDHwL"
      },
      "source": [
        "# Majority of students are giving their 1st attempt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "FGHR9hhlCuEj",
        "outputId": "f2d0b655-086c-4e1f-ecdb-4e5f46782789"
      },
      "source": [
        "sns.pairplot(student_info, x_vars=[\"num_of_prev_attempts\"], y_vars=[\"code_module\"], dropna=True, size=5,kind=\"hist\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:1969: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f57114c0a50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQdWJp8PDLrw"
      },
      "source": [
        "# Majority of students fall under 0-35 age band"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Un5yYliru6h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "outputId": "b54b9c03-559b-46bc-89af-7c26e7caae4b"
      },
      "source": [
        "studentInfo.groupby(['age_band','final_result']).size().unstack().plot(kind='bar', title='Final results by age',figsize=(8,8))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKabt6ukFgax",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "c842bc5f-9811-47e2-e85b-43bb2a8ef820"
      },
      "source": [
        "student_info['final_result'].value_counts().plot(kind='bar', title='Final Result count',color='brown', figsize=(6,6))\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHAJ--6lWATq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "outputId": "d3541be2-7d57-420d-cdd2-4c8deedab9a5"
      },
      "source": [
        "student_vle_all = studentVle.merge(vle, on = 'id_site', how = 'left')\n",
        "plt.figure(figsize=(8,10))\n",
        "plt.xticks(rotation = 90)\n",
        "a=student_vle_all['activity_type'].value_counts()[:10]\n",
        "sns.barplot(a,a.index)\n",
        "plt.xlabel('Count')\n",
        "plt.title(\"Activity types\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: 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",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Activity types')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snE2WhayYshf"
      },
      "source": [
        "#number of quizes displayed according to score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdniqExKwvf-",
        "outputId": "d962fd50-f919-410b-c741-5072355f9432"
      },
      "source": [
        "studentAssessment['Quiz'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    86049\n",
              "3    53569\n",
              "5    18813\n",
              "2    12024\n",
              "1     3457\n",
              "Name: Quiz, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e57dTGoDFc8o"
      },
      "source": [
        "# id_meandate = assignment_date.groupby('id_assessment')[['date_submitted']].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsyuZcxCGnRc"
      },
      "source": [
        "# id_meandate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNdWfXAJNCpV"
      },
      "source": [
        "# merged = pd.merge(id_meandate, assignment_date, on = \"id_assessment\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msT0JLH4NcBC"
      },
      "source": [
        "#comparision with mean date and date submitted for individual student ID will give us status of submission\n",
        "# merged"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYd04n1gZdta"
      },
      "source": [
        "withlatesub = pd.read_csv('/content/drive/MyDrive/Datasets/LateSubmission.csv', error_bad_lines = False, low_memory=False)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pZ_otayaJn4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "86b8e13c-4a0a-4860-8c37-95d29da11c62"
      },
      "source": [
        "#After manipulation added column to mention whether submission was made after mean date of submission\n",
        "withlatesub"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "        Unnamed: 0  id_assessment  ...  date_submitted  late_submission\n",
              "0                0           1752  ...              18                N\n",
              "1                1           1752  ...              22                Y\n",
              "2                2           1752  ...              17                N\n",
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              "4                4           1752  ...              19                N\n",
              "...            ...            ...  ...             ...              ...\n",
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              "173910      173910          37443  ...             230                Y\n",
              "173911      173911          37443  ...             224                Y\n",
              "\n",
              "[173912 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "sxrJJqgNODzc",
        "outputId": "4cc906bd-3fdb-43be-d54f-3ac794d7e715"
      },
      "source": [
        "studentVle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<p>10655280 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         code_module code_presentation  id_student  id_site  date  sum_click\n",
              "0                AAA             2013J       28400   546652   -10          4\n",
              "1                AAA             2013J       28400   546652   -10          1\n",
              "2                AAA             2013J       28400   546652   -10          1\n",
              "3                AAA             2013J       28400   546614   -10         11\n",
              "4                AAA             2013J       28400   546714   -10          1\n",
              "...              ...               ...         ...      ...   ...        ...\n",
              "10655275         GGG             2014J      675811   896943   269          3\n",
              "10655276         GGG             2014J      675578   896943   269          1\n",
              "10655277         GGG             2014J      654064   896943   269          3\n",
              "10655278         GGG             2014J      654064   896939   269          1\n",
              "10655279         GGG             2014J      654064   896939   269          1\n",
              "\n",
              "[10655280 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "rtYQWoeNzSCR",
        "outputId": "6bb2e3ad-1d03-4ad9-f101-51e1ae0a2480"
      },
      "source": [
        "site_count = studentVle.groupby(['code_module', 'code_presentation','id_student']).agg({'id_student':['size']})\n",
        "site_count.reset_index(level=[0,1,2], inplace=True)\n",
        "site_count.columns = ['code_module', 'code_presentation','id_student','site_count']\n",
        "site_count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</table>\n",
              "<p>29228 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      code_module code_presentation  id_student  site_count\n",
              "0             AAA             2013J       11391         196\n",
              "1             AAA             2013J       28400         430\n",
              "2             AAA             2013J       30268          76\n",
              "3             AAA             2013J       31604         663\n",
              "4             AAA             2013J       32885         352\n",
              "...           ...               ...         ...         ...\n",
              "29223         GGG             2014J     2640965          19\n",
              "29224         GGG             2014J     2645731         237\n",
              "29225         GGG             2014J     2648187         108\n",
              "29226         GGG             2014J     2679821          61\n",
              "29227         GGG             2014J     2684003         181\n",
              "\n",
              "[29228 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Lqia5KNd0SpU",
        "outputId": "7d953848-2b75-482c-ca75-642c6e26d7f3"
      },
      "source": [
        "vle_new = pd.merge(studentVle, site_count, how = 'outer', on=['code_module','code_presentation','id_student'] )\n",
        "vle_new"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546614</td>\n",
              "      <td>-10</td>\n",
              "      <td>11</td>\n",
              "      <td>430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546714</td>\n",
              "      <td>-10</td>\n",
              "      <td>1</td>\n",
              "      <td>430</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655275</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>624767</td>\n",
              "      <td>897068</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655276</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>624767</td>\n",
              "      <td>896959</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655277</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>624767</td>\n",
              "      <td>896974</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655278</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>624767</td>\n",
              "      <td>896943</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10655279</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>624767</td>\n",
              "      <td>896961</td>\n",
              "      <td>225</td>\n",
              "      <td>17</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10655280 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         code_module code_presentation  id_student  ...  date  sum_click  site_count\n",
              "0                AAA             2013J       28400  ...   -10          4         430\n",
              "1                AAA             2013J       28400  ...   -10          1         430\n",
              "2                AAA             2013J       28400  ...   -10          1         430\n",
              "3                AAA             2013J       28400  ...   -10         11         430\n",
              "4                AAA             2013J       28400  ...   -10          1         430\n",
              "...              ...               ...         ...  ...   ...        ...         ...\n",
              "10655275         GGG             2014J      624767  ...   217          1          24\n",
              "10655276         GGG             2014J      624767  ...   217          1          24\n",
              "10655277         GGG             2014J      624767  ...   217          1          24\n",
              "10655278         GGG             2014J      624767  ...   225          1          24\n",
              "10655279         GGG             2014J      624767  ...   225         17          24\n",
              "\n",
              "[10655280 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoInBfCHxSnD"
      },
      "source": [
        "studentVle_new = vle_new.groupby(['id_student', 'code_module', 'code_presentation', 'site_count']).agg({'sum_click':['mean','sum']})\n",
        "studentVle_new.reset_index(level=[0,1,2,3], inplace=True)\n",
        "studentVle_new.columns = ['id_student', 'code_module', 'code_presentation','site_count', 'mean_clicks', 'sum_clicks']\n",
        "vle_interactions = pd.merge(studentInfo,studentVle_new, how='outer', on=['id_student', 'code_module', 'code_presentation'])\n",
        "vle_interactions = vle_interactions[['code_module','code_presentation','id_student','gender','region','highest_education','imd_band','age_band','num_of_prev_attempts','studied_credits','disability','mean_clicks','sum_clicks','site_count','final_result']]\n",
        "vle_interactions.reset_index(level=[0], inplace=True)\n",
        "vle_interactions = vle_interactions.drop(['highest_education'], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTPk-YFE0_Dm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "ce0bbc4c-91a9-4f01-f560-3e871ade9993"
      },
      "source": [
        "plt.figure(figsize=(20,12))\n",
        "sns.catplot(x='final_result', y = 'sum_clicks', data = vle_interactions)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x864 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "J_FJ9EkHKwst",
        "outputId": "a237610d-2649-43e2-e40c-96b6171a7793"
      },
      "source": [
        "plt.subplots(figsize=(10, 5))\n",
        "sns.violinplot(data=vle_interactions, x='final_result', y = 'sum_clicks', hue='final_result',\n",
        "                inner=\"quart\", linewidth=1,\n",
        "               )\n",
        "sns.despine(left=True)\n",
        "plt.xticks(rotation = 90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3]), <a list of 4 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgKSUXdPIzGy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "78a7162a-9a53-4850-f2cd-4a7da2ef8ee1"
      },
      "source": [
        "final = pd.merge(studentVle, student_info,how='outer', on=['id_student', 'code_module', 'code_presentation'])\n",
        "final = final.fillna(0)\n",
        "final = final.assign(Engagement_index= engagement_index) \n",
        "\n",
        "final"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_module</th>\n",
              "      <th>code_presentation</th>\n",
              "      <th>id_student</th>\n",
              "      <th>id_site</th>\n",
              "      <th>date</th>\n",
              "      <th>sum_click</th>\n",
              "      <th>gender</th>\n",
              "      <th>region</th>\n",
              "      <th>imd_band</th>\n",
              "      <th>num_of_prev_attempts</th>\n",
              "      <th>disability</th>\n",
              "      <th>final_result</th>\n",
              "      <th>avg_absences_per_year</th>\n",
              "      <th>attendance</th>\n",
              "      <th>frequency_interaction</th>\n",
              "      <th>Engagement_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546652.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546652.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546652.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546614.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAA</td>\n",
              "      <td>2013J</td>\n",
              "      <td>28400</td>\n",
              "      <td>546714.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>Scotland</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Pass</td>\n",
              "      <td>7</td>\n",
              "      <td>70</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10658640</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2014J</td>\n",
              "      <td>2239862</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>M</td>\n",
              "      <td>North Western Region</td>\n",
              "      <td>50-60%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>44</td>\n",
              "      <td>104</td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10658641</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2013J</td>\n",
              "      <td>2440133</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>London Region</td>\n",
              "      <td>60-70%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Withdrawn</td>\n",
              "      <td>13</td>\n",
              "      <td>91</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10658642</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2013J</td>\n",
              "      <td>2464917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>M</td>\n",
              "      <td>East Anglian Region</td>\n",
              "      <td>20-30%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Fail</td>\n",
              "      <td>14</td>\n",
              "      <td>82</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10658643</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2013J</td>\n",
              "      <td>2540288</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>South Region</td>\n",
              "      <td>50-60%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Fail</td>\n",
              "      <td>13</td>\n",
              "      <td>80</td>\n",
              "      <td>23</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10658644</th>\n",
              "      <td>GGG</td>\n",
              "      <td>2013J</td>\n",
              "      <td>2649341</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>F</td>\n",
              "      <td>West Midlands Region</td>\n",
              "      <td>50-60%</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Fail</td>\n",
              "      <td>19</td>\n",
              "      <td>68</td>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10658645 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         code_module code_presentation  ...  frequency_interaction  Engagement_index\n",
              "0                AAA             2013J  ...                     13               NaN\n",
              "1                AAA             2013J  ...                     13               NaN\n",
              "2                AAA             2013J  ...                     13               NaN\n",
              "3                AAA             2013J  ...                     13               NaN\n",
              "4                AAA             2013J  ...                     13               NaN\n",
              "...              ...               ...  ...                    ...               ...\n",
              "10658640         GGG             2014J  ...                     24               NaN\n",
              "10658641         GGG             2013J  ...                     22               NaN\n",
              "10658642         GGG             2013J  ...                     13               NaN\n",
              "10658643         GGG             2013J  ...                     23               NaN\n",
              "10658644         GGG             2013J  ...                     32               NaN\n",
              "\n",
              "[10658645 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRynCOOW9MeC"
      },
      "source": [
        "final.info();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iA41T8e8vUE"
      },
      "source": [
        "\n",
        "plt.matshow(final.corr());"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6Ey6MBbH9Bl"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelEncoder = LabelEncoder()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITK8IcbWINNL"
      },
      "source": [
        "final.code_module = labelEncoder.fit_transform(final.code_module)\n",
        "final.code_presentation = labelEncoder.fit_transform(final.code_presentation)\n",
        "final.gender = labelEncoder.fit_transform(final.gender)\n",
        "final.region = labelEncoder.fit_transform(final.region)\n",
        "final.disability = labelEncoder.fit_transform(final.disability)\n",
        "final.final_result = labelEncoder.fit_transform(final.final_result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBqmmBVqIPkw"
      },
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.heatmap(final.corr(), annot = True, vmin = 0, cmap = 'GnBu')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClcuuXDuKL-g"
      },
      "source": [
        "final1 = final.drop(['id_student', 'code_module', 'code_presentation','gender','region'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzN88HV5K1y7"
      },
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.heatmap(final1.corr(), annot = True, vmin = 0, cmap = 'GnBu')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzuCdFSsOjox"
      },
      "source": [
        "x = final1.drop(['final_result','num_of_prev_attempts','disability', 'imd_band'], axis=1).values\n",
        "y = final1['final_result']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HX1l_pldPuoh"
      },
      "source": [
        "final1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STmWNbStPkun"
      },
      "source": [
        "x[10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxOafPTlK39u"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn import preprocessing\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestClassifier as rfc\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAiJ4RWtOe0d"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x[:10000, :], y[:10000], test_size = 0.20)\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lp8VEKVO5R2"
      },
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=2)\n",
        "knn.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPb1_SL9O673"
      },
      "source": [
        "y_pred=knn.predict(x_train)\n",
        "acc_score=metrics.accuracy_score(y_train,y_pred)\n",
        "print(\"accuracy of train:\", acc_score*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Lecf0s_Uu2u"
      },
      "source": [
        "y_pred=knn.predict(x_test)\n",
        "acc_score=metrics.accuracy_score(y_test,y_pred)\n",
        "print(\"accuracy of test:\", acc_score*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSwDkG8ZUxJ2"
      },
      "source": [
        "rfc_ = rfc(n_estimators=100,criterion='gini', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0)\n",
        "rfc_.fit(x_train, y_train)\n",
        "print(\"accuracy of train:\", acc_score*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hYmHH0SGtvd"
      },
      "source": [
        "y_pred = rfc_.predict(x_test)\n",
        "acc_score = metrics.accuracy_score(y_test, y_pred)\n",
        "print(\"accuracy of test:\", acc_score*100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3JWODWMfh48"
      },
      "source": [
        "print(y_pred[6]) # 3 lowest risk 2 lower risk 1 high risk\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulaCS22gfkMC"
      },
      "source": [
        "print(y_train[6])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iamVCmyoj0oA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}