ALA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qrpoGmf9WRZk"
   },
   "source": [
    "### Accessible Learning Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qrpoGmf9WRZk"
   },
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3237
    },
    "colab_type": "code",
    "id": "nD-dNrEIG4Ka",
    "outputId": "100f4317-f248-4e65-f3e0-5ea5125f690b"
   },
   "outputs": [],
   "source": [
    "# #Unhide if you run the code from Google Colab (Select all line beloe, then (Ctrl + /))\n",
    "\n",
    "# !pip install graphviz\n",
    "# !apt-get install graphviz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cLg28ho_WUMc"
   },
   "outputs": [],
   "source": [
    "\n",
    "import io\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import re \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "import graphviz \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z8xHzQYQbriM"
   },
   "source": [
    "## Variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "jdb22j-yby_T"
   },
   "outputs": [],
   "source": [
    "# Semester Start Date:\n",
    "StartDate = '2017-09-10' \n",
    "StartDate = pd.to_datetime(StartDate)\n",
    "\n",
    "# Semester End Date:\n",
    "EndDate = '2018-01-15' \n",
    "EndDate = pd.to_datetime(EndDate)\n",
    "\n",
    "# Final grade column name in Grades file:\n",
    "FGradeCol = \"Course total (Real)\" \n",
    "\n",
    "\n",
    "# Student name column in Grade file:\n",
    "StdCol    = 'User full name' \n",
    "\n",
    "\n",
    "# Student Activity File Name  \n",
    "ActivityFile = 'MoodleLogs.csv' \n",
    "\n",
    "# Student Grades File Name\n",
    "GradeFile    = 'MoodleGrades.csv' \n",
    "\n",
    "# Grade A Start From:\n",
    "A_Start   = 75 \n",
    "\n",
    "# Grade B Start From:\n",
    "B_Start   = 45 \n",
    "\n",
    "# Please use the semicolon **\";\"** as your separator between names.\n",
    "InstructorNames= \"\" #@param {type:\"string\"}\n",
    "InstructorNames= pd.Series(InstructorNames).str.split(\";\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peeX23vWHxzh"
   },
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kTwvjtDA-xE-"
   },
   "source": [
    "**Import activity load:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iu7a0irO_MEg"
   },
   "outputs": [
    {
     "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>Time</th>\n",
       "      <th>User full name</th>\n",
       "      <th>Affected user</th>\n",
       "      <th>Event context</th>\n",
       "      <th>Component</th>\n",
       "      <th>Event name</th>\n",
       "      <th>Description</th>\n",
       "      <th>IP address</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28 Feb, 20:17</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14 Feb, 18:31</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14 Feb, 18:30</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>Overview report</td>\n",
       "      <td>Grade overview report viewed</td>\n",
       "      <td>The user with id '13485' viewed the overview r...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14 Feb, 18:29</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>User report</td>\n",
       "      <td>Grade user report viewed</td>\n",
       "      <td>The user with id '13485' viewed the user repor...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14 Feb, 18:29</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5 Feb, 22:14</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>User report</td>\n",
       "      <td>Grade user report viewed</td>\n",
       "      <td>The user with id '13485' viewed the user repor...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5 Feb, 22:14</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31 Jan, 18:01</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25 Jan, 14:10</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload classification p...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25 Jan, 14:10</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25 Jan, 14:08</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload clustering paper...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25 Jan, 14:08</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25 Jan, 14:07</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload practical report...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25 Jan, 14:07</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload practical report...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25 Jan, 14:07</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24 Jan, 17:20</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload classification p...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24 Jan, 17:15</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>User report</td>\n",
       "      <td>Grade user report viewed</td>\n",
       "      <td>The user with id '13485' viewed the user repor...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24 Jan, 17:15</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload practical report...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24 Jan, 17:15</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Turnitin Assignment 2: Upload clustering paper...</td>\n",
       "      <td>Turnitin Assignment 2</td>\n",
       "      <td>List Submissions</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24 Jan, 17:15</td>\n",
       "      <td>B77</td>\n",
       "      <td>-</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>System</td>\n",
       "      <td>Course viewed</td>\n",
       "      <td>The user with id '13485' viewed the course wit...</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Time User full name Affected user  \\\n",
       "0   28 Feb, 20:17            B77             -   \n",
       "1   14 Feb, 18:31            B77             -   \n",
       "2   14 Feb, 18:30            B77             -   \n",
       "3   14 Feb, 18:29            B77             -   \n",
       "4   14 Feb, 18:29            B77             -   \n",
       "5    5 Feb, 22:14            B77             -   \n",
       "6    5 Feb, 22:14            B77             -   \n",
       "7   31 Jan, 18:01            B77             -   \n",
       "8   25 Jan, 14:10            B77             -   \n",
       "9   25 Jan, 14:10            B77             -   \n",
       "10  25 Jan, 14:08            B77             -   \n",
       "11  25 Jan, 14:08            B77             -   \n",
       "12  25 Jan, 14:07            B77             -   \n",
       "13  25 Jan, 14:07            B77             -   \n",
       "14  25 Jan, 14:07            B77             -   \n",
       "15  24 Jan, 17:20            B77             -   \n",
       "16  24 Jan, 17:15            B77             -   \n",
       "17  24 Jan, 17:15            B77             -   \n",
       "18  24 Jan, 17:15            B77             -   \n",
       "19  24 Jan, 17:15            B77             -   \n",
       "\n",
       "                                        Event context              Component  \\\n",
       "0    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "1    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "2    Course: ADSA H6013 - Algorithms for Data Science        Overview report   \n",
       "3    Course: ADSA H6013 - Algorithms for Data Science            User report   \n",
       "4    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "5    Course: ADSA H6013 - Algorithms for Data Science            User report   \n",
       "6    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "7    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "8   Turnitin Assignment 2: Upload classification p...  Turnitin Assignment 2   \n",
       "9    Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "10  Turnitin Assignment 2: Upload clustering paper...  Turnitin Assignment 2   \n",
       "11   Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "12  Turnitin Assignment 2: Upload practical report...  Turnitin Assignment 2   \n",
       "13  Turnitin Assignment 2: Upload practical report...  Turnitin Assignment 2   \n",
       "14   Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "15  Turnitin Assignment 2: Upload classification p...  Turnitin Assignment 2   \n",
       "16   Course: ADSA H6013 - Algorithms for Data Science            User report   \n",
       "17  Turnitin Assignment 2: Upload practical report...  Turnitin Assignment 2   \n",
       "18  Turnitin Assignment 2: Upload clustering paper...  Turnitin Assignment 2   \n",
       "19   Course: ADSA H6013 - Algorithms for Data Science                 System   \n",
       "\n",
       "                      Event name  \\\n",
       "0                  Course viewed   \n",
       "1                  Course viewed   \n",
       "2   Grade overview report viewed   \n",
       "3       Grade user report viewed   \n",
       "4                  Course viewed   \n",
       "5       Grade user report viewed   \n",
       "6                  Course viewed   \n",
       "7                  Course viewed   \n",
       "8               List Submissions   \n",
       "9                  Course viewed   \n",
       "10              List Submissions   \n",
       "11                 Course viewed   \n",
       "12              List Submissions   \n",
       "13              List Submissions   \n",
       "14                 Course viewed   \n",
       "15              List Submissions   \n",
       "16      Grade user report viewed   \n",
       "17              List Submissions   \n",
       "18              List Submissions   \n",
       "19                 Course viewed   \n",
       "\n",
       "                                          Description  IP address Origin  \n",
       "0   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "1   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "2   The user with id '13485' viewed the overview r...  10.30.0.20    web  \n",
       "3   The user with id '13485' viewed the user repor...  10.30.0.20    web  \n",
       "4   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "5   The user with id '13485' viewed the user repor...  10.30.0.20    web  \n",
       "6   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "7   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "8   User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "9   The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "10  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "11  The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "12  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "13  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "14  The user with id '13485' viewed the course wit...  10.30.0.20    web  \n",
       "15  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "16  The user with id '13485' viewed the user repor...  10.30.0.20    web  \n",
       "17  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "18  User viewed their submission inbox at course: 264  10.30.0.20    web  \n",
       "19  The user with id '13485' viewed the course wit...  10.30.0.20    web  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the activity file\n",
    "Activity = pd.read_csv(ActivityFile,encoding = \"latin-1\",low_memory = False)\n",
    "\n",
    "Activity.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DKrY_Xj99oxe"
   },
   "source": [
    "**Let's see what the data set contains:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zUGNChqBYB4A",
    "outputId": "d77b169e-2b49-48fc-9896-be38bba537a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14811"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of records in the activity log.\n",
    "len(Activity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "colab_type": "code",
    "id": "I9OzIyNkbR4H",
    "outputId": "e27cba01-09b6-44e7-8f12-0d29b9506be5"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>14811</td>\n",
       "      <td>8779</td>\n",
       "      <td>5 Dec, 18:30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User full name</th>\n",
       "      <td>14811</td>\n",
       "      <td>37</td>\n",
       "      <td>B59</td>\n",
       "      <td>1529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Affected user</th>\n",
       "      <td>14811</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>14811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event context</th>\n",
       "      <td>14811</td>\n",
       "      <td>150</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>6104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <td>14811</td>\n",
       "      <td>9</td>\n",
       "      <td>System</td>\n",
       "      <td>5747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event name</th>\n",
       "      <td>14811</td>\n",
       "      <td>13</td>\n",
       "      <td>Course module viewed</td>\n",
       "      <td>7151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>14811</td>\n",
       "      <td>3283</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>1222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP address</th>\n",
       "      <td>14811</td>\n",
       "      <td>330</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>8840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Origin</th>\n",
       "      <td>14811</td>\n",
       "      <td>1</td>\n",
       "      <td>web</td>\n",
       "      <td>14811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count unique  \\\n",
       "Time            14811   8779   \n",
       "User full name  14811     37   \n",
       "Affected user   14811      1   \n",
       "Event context   14811    150   \n",
       "Component       14811      9   \n",
       "Event name      14811     13   \n",
       "Description     14811   3283   \n",
       "IP address      14811    330   \n",
       "Origin          14811      1   \n",
       "\n",
       "                                                              top   freq  \n",
       "Time                                                 5 Dec, 18:30     19  \n",
       "User full name                                                B59   1529  \n",
       "Affected user                                                   -  14811  \n",
       "Event context    Course: ADSA H6013 - Algorithms for Data Science   6104  \n",
       "Component                                                  System   5747  \n",
       "Event name                                   Course module viewed   7151  \n",
       "Description     User viewed their submission inbox at course: 264   1222  \n",
       "IP address                                             10.30.0.20   8840  \n",
       "Origin                                                        web  14811  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generates descriptive statistics\n",
    "Activity.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2xKGbmZg6KIO"
   },
   "source": [
    "Attribute Information: \n",
    "\n",
    "\n",
    "*   **Affected user:** It contains the name of the user if the action is related to them.\n",
    "*  **Time:** It contains the date, month and time stamp of the log entry when it was recorded. (Year infomration may or may not be included)\n",
    "*  **User full name:** Name of the user is listed in this column.\n",
    "*  **Event context:** It displays the name of the activity or resource. Ex: Lecture 4, Lab2 Upload\n",
    "*  **Component:** It displays the type of the activity or resource. Ex: File, System, Assignment, Quiz ...\n",
    "*  **Event name:** It displays the event name in relation to users' action. There are four actions that a user can do in Moodle - Create, View, Update and Delete.\n",
    "*  **Description:** It contains descriptive information about the action performed by the user.\n",
    "*  **IP address:** Shows the IP address of the user.\n",
    "*  **Origin:** It indicates whether the user performed an action using a desktop/laptop or mobile. Any user activity from a mobile device will be recorded as 'ws'.\n",
    "\n",
    "Reference: https://moodle.info.yorku.ca/instructor_resources/instructors/training/logs/#squelch-taas-toggle-shortcode-content-0\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3yc5SNY0amjw"
   },
   "outputs": [],
   "source": [
    "# Load the grade file\n",
    "\n",
    "Grades = pd.read_csv(GradeFile,encoding = \"latin-1\",low_memory = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "usYZaG1xa3Sc",
    "outputId": "f3bddce9-4689-4119-9938-263efc0be1a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of records in the grade file\n",
    "\n",
    "len(Grades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "JpP9yiQSbTga",
    "outputId": "5a6aab53-9311-4f6c-a3ee-dc6cb2858670"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paper Week7</th>\n",
       "      <td>37.0</td>\n",
       "      <td>14.943243</td>\n",
       "      <td>9.402557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper Week10</th>\n",
       "      <td>37.0</td>\n",
       "      <td>15.313514</td>\n",
       "      <td>9.878039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self evaluation Week10</th>\n",
       "      <td>37.0</td>\n",
       "      <td>2.859459</td>\n",
       "      <td>1.788460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Presentation Week 12</th>\n",
       "      <td>37.0</td>\n",
       "      <td>3.081081</td>\n",
       "      <td>1.951614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Practical Week 15</th>\n",
       "      <td>37.0</td>\n",
       "      <td>14.908108</td>\n",
       "      <td>10.185048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course total (Real)</th>\n",
       "      <td>37.0</td>\n",
       "      <td>51.137838</td>\n",
       "      <td>30.218504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61.9</td>\n",
       "      <td>77.3</td>\n",
       "      <td>90.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count       mean        std  min   25%   50%   75%  \\\n",
       "Paper Week7              37.0  14.943243   9.402557  0.0   9.0  17.0  23.4   \n",
       "Paper Week10             37.0  15.313514   9.878039  0.0   9.0  18.1  23.0   \n",
       "Self evaluation Week10   37.0   2.859459   1.788460  0.0   1.8   2.5   4.5   \n",
       "Presentation Week 12     37.0   3.081081   1.951614  0.0   0.0   3.9   4.7   \n",
       "Practical Week 15        37.0  14.908108  10.185048  0.0   2.0  19.1  23.4   \n",
       "Course total (Real)      37.0  51.137838  30.218504  0.0  30.0  61.9  77.3   \n",
       "\n",
       "                         max  \n",
       "Paper Week7             28.5  \n",
       "Paper Week10            29.0  \n",
       "Self evaluation Week10   5.0  \n",
       "Presentation Week 12     5.0  \n",
       "Practical Week 15       28.6  \n",
       "Course total (Real)     90.8  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generates descriptive statistics\n",
    "\n",
    "Grades.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e06LfoiVeurB"
   },
   "source": [
    "# Load Functions Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run functions.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nI2o08F5bBRh"
   },
   "source": [
    "## Clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "rhQdcZ5cbLcT",
    "outputId": "8b8921ac-9e76-402c-c043-d594597ef0f8"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paper Week7</th>\n",
       "      <td>37.0</td>\n",
       "      <td>14.943243</td>\n",
       "      <td>9.402557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper Week10</th>\n",
       "      <td>37.0</td>\n",
       "      <td>15.313514</td>\n",
       "      <td>9.878039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self evaluation Week10</th>\n",
       "      <td>37.0</td>\n",
       "      <td>2.859459</td>\n",
       "      <td>1.788460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Presentation Week 12</th>\n",
       "      <td>37.0</td>\n",
       "      <td>3.081081</td>\n",
       "      <td>1.951614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Practical Week 15</th>\n",
       "      <td>37.0</td>\n",
       "      <td>14.908108</td>\n",
       "      <td>10.185048</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course total (Real)</th>\n",
       "      <td>37.0</td>\n",
       "      <td>51.137838</td>\n",
       "      <td>30.218504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>61.9</td>\n",
       "      <td>77.3</td>\n",
       "      <td>90.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count       mean        std  min   25%   50%   75%  \\\n",
       "Paper Week7              37.0  14.943243   9.402557  0.0   9.0  17.0  23.4   \n",
       "Paper Week10             37.0  15.313514   9.878039  0.0   9.0  18.1  23.0   \n",
       "Self evaluation Week10   37.0   2.859459   1.788460  0.0   1.8   2.5   4.5   \n",
       "Presentation Week 12     37.0   3.081081   1.951614  0.0   0.0   3.9   4.7   \n",
       "Practical Week 15        37.0  14.908108  10.185048  0.0   2.0  19.1  23.4   \n",
       "Course total (Real)      37.0  51.137838  30.218504  0.0  30.0  61.9  77.3   \n",
       "\n",
       "                         max  \n",
       "Paper Week7             28.5  \n",
       "Paper Week10            29.0  \n",
       "Self evaluation Week10   5.0  \n",
       "Presentation Week 12     5.0  \n",
       "Practical Week 15       28.6  \n",
       "Course total (Real)     90.8  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove columns with only one value \n",
    "\n",
    "Grades = Grades[[c for c\n",
    "                     in list(Grades)\n",
    "                     if len(Grades[c].unique()) > 1]]  \n",
    "\n",
    "\n",
    "Grades.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>14811</td>\n",
       "      <td>8779</td>\n",
       "      <td>5 Dec, 18:30</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User full name</th>\n",
       "      <td>14811</td>\n",
       "      <td>37</td>\n",
       "      <td>B59</td>\n",
       "      <td>1529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event context</th>\n",
       "      <td>14811</td>\n",
       "      <td>150</td>\n",
       "      <td>Course: ADSA H6013 - Algorithms for Data Science</td>\n",
       "      <td>6104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <td>14811</td>\n",
       "      <td>9</td>\n",
       "      <td>System</td>\n",
       "      <td>5747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event name</th>\n",
       "      <td>14811</td>\n",
       "      <td>13</td>\n",
       "      <td>Course module viewed</td>\n",
       "      <td>7151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>14811</td>\n",
       "      <td>3283</td>\n",
       "      <td>User viewed their submission inbox at course: 264</td>\n",
       "      <td>1222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP address</th>\n",
       "      <td>14811</td>\n",
       "      <td>330</td>\n",
       "      <td>10.30.0.20</td>\n",
       "      <td>8840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count unique  \\\n",
       "Time            14811   8779   \n",
       "User full name  14811     37   \n",
       "Event context   14811    150   \n",
       "Component       14811      9   \n",
       "Event name      14811     13   \n",
       "Description     14811   3283   \n",
       "IP address      14811    330   \n",
       "\n",
       "                                                              top  freq  \n",
       "Time                                                 5 Dec, 18:30    19  \n",
       "User full name                                                B59  1529  \n",
       "Event context    Course: ADSA H6013 - Algorithms for Data Science  6104  \n",
       "Component                                                  System  5747  \n",
       "Event name                                   Course module viewed  7151  \n",
       "Description     User viewed their submission inbox at course: 264  1222  \n",
       "IP address                                             10.30.0.20  8840  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove columns with only one value \n",
    "\n",
    "Activity = Activity[[c for c\n",
    "                     in list(Activity)\n",
    "                     if len(Activity[c].unique()) > 1]]  \n",
    "\n",
    "\n",
    "Activity.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PFXfDNNglaA1",
    "outputId": "e6ef7738-e606-4754-d9b3-3d7fd1656dd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current length: 14811 ... 0 Rows Deleted.\n"
     ]
    }
   ],
   "source": [
    "# Remove all rows with instructor name in the InstructorNames list\n",
    "\n",
    "originalLength = len(Activity)\n",
    "\n",
    "Activity = Activity[~Activity['User full name'].isin(InstructorNames)]\n",
    "\n",
    "print (\"Current length: \" + str(len(Activity)) + \" ... \" + str(originalLength - len(Activity)) + \" Rows Deleted.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NTxyO0a-pGRw"
   },
   "source": [
    "### Fix Time Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iS_bzmdh9_yU"
   },
   "source": [
    "1.   Assign DataTime data type to Time column (if not)\n",
    "\n",
    "2.   Add the year value (if not)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "EkkLPRs2nEQB",
    "outputId": "8208a568-65e6-473b-987d-ff49fc11e536"
   },
   "outputs": [
    {
     "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>Data Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User full name</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event context</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event name</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP address</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Data Type\n",
       "Time            datetime64[ns]\n",
       "User full name          object\n",
       "Event context           object\n",
       "Component               object\n",
       "Event name              object\n",
       "Description             object\n",
       "IP address              object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixTimeColumn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c4NXrseNbDG1"
   },
   "source": [
    "### Remove activity outside semester start and end dates range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DyHhErSxbDfW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13926"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (Activity['Time'] >= StartDate) & (Activity['Time'] <= EndDate)\n",
    "\n",
    "Activity = Activity.loc[mask]\n",
    "\n",
    "len(Activity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lSXMRNWa-3Tw"
   },
   "source": [
    "# **What we have in this course log file:**\n",
    "\n",
    " **Or Check Activity Patterns and Combinations:**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RmtzY6rrAM5J"
   },
   "source": [
    "- How? \n",
    "Hide changing values in the Description column to get all unique pattern of activity in your log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2959
    },
    "colab_type": "code",
    "id": "_2SGaFncYa07",
    "outputId": "6967b5d1-8ffc-4711-ee1d-eb4179e34883"
   },
   "outputs": [
    {
     "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></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <th>Event name</th>\n",
       "      <th>Pattern_Description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>File</th>\n",
       "      <th>Course module viewed</th>\n",
       "      <th>The user with  '#' viewed the 'resource' activity with course module  '#'.</th>\n",
       "      <td>4359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder</th>\n",
       "      <th>Course module viewed</th>\n",
       "      <th>The user with  '#' viewed the 'folder' activity with course module  '#'.</th>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Forum</th>\n",
       "      <th>Course module instance list viewed</th>\n",
       "      <th>The user with  '#' viewed the instance list for the module 'forum' in the course with  '#'.</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course module viewed</th>\n",
       "      <th>The user with  '#' viewed the 'forum' activity with course module  '#'.</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overview report</th>\n",
       "      <th>Grade overview report viewed</th>\n",
       "      <th>The user with  '#' viewed the overview report in the gradebook.</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page</th>\n",
       "      <th>Course module viewed</th>\n",
       "      <th>The user with  '#' viewed the 'page' activity with course module  '#'.</th>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">System</th>\n",
       "      <th>Course module instance list viewed</th>\n",
       "      <th>The user with  '#' viewed the list of resources in the course with  '#'.</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course user report viewed</th>\n",
       "      <th>The user with  '#' viewed the user report for the course with  '#' for user with  '#'.</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course viewed</th>\n",
       "      <th>The user with  '#' viewed the course with  '#'.</th>\n",
       "      <td>5095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role assigned</th>\n",
       "      <th>The user with  '#' assigned the role with  '#' to the user with  '#'.</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User enrolled in course</th>\n",
       "      <th>The user with  '#' enrolled the user with  '#' using the enrolment method 'self' in the course with  '#'.</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User graded</th>\n",
       "      <th>The user with  '#' updated the grade with  '#' for the user with  '#' for the grade item with  '#'.</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User list viewed</th>\n",
       "      <th>The user with  '#' viewed the list of users in the course with  '#'.</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User profile viewed</th>\n",
       "      <th>The user with  '#' viewed the profile for the user with  '#' in the course with  '#'.</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Turnitin Assignment 2</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Add Submission</th>\n",
       "      <th>User added  '#'</th>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User resubmission sent to  '#'</th>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User submission sent to  '#'</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>List Submissions</th>\n",
       "      <th>User viewed their submission inbox at course: 264</th>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL</th>\n",
       "      <th>Course module viewed</th>\n",
       "      <th>The user with  '#' viewed the 'url' activity with course module  '#'.</th>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User report</th>\n",
       "      <th>Grade user report viewed</th>\n",
       "      <th>The user with  '#' viewed the user report in the gradebook.</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                0\n",
       "Component             Event name                         Pattern_Description                                     \n",
       "File                  Course module viewed               The user with  '#' viewed the 'resource' activi...  4359\n",
       "Folder                Course module viewed               The user with  '#' viewed the 'folder' activity...   745\n",
       "Forum                 Course module instance list viewed The user with  '#' viewed the instance list for...    17\n",
       "                      Course module viewed               The user with  '#' viewed the 'forum' activity ...    14\n",
       "Overview report       Grade overview report viewed       The user with  '#' viewed the overview report i...    28\n",
       "Page                  Course module viewed               The user with  '#' viewed the 'page' activity w...  1297\n",
       "System                Course module instance list viewed The user with  '#' viewed the list of resources...    22\n",
       "                      Course user report viewed          The user with  '#' viewed the user report for t...    33\n",
       "                      Course viewed                      The user with  '#' viewed the course with  '#'.     5095\n",
       "                      Role assigned                      The user with  '#' assigned the role with  '#' ...     2\n",
       "                      User enrolled in course            The user with  '#' enrolled the user with  '#' ...     2\n",
       "                      User graded                        The user with  '#' updated the grade with  '#' ...    24\n",
       "                      User list viewed                   The user with  '#' viewed the list of users in ...    44\n",
       "                      User profile viewed                The user with  '#' viewed the profile for the u...    47\n",
       "Turnitin Assignment 2 Add Submission                     User added  '#'                                      183\n",
       "                                                         User resubmission sent to  '#'                        67\n",
       "                                                         User submission sent to  '#'                          84\n",
       "                      List Submissions                   User viewed their submission inbox at course: 264   1080\n",
       "URL                   Course module viewed               The user with  '#' viewed the 'url' activity wi...   582\n",
       "User report           Grade user report viewed           The user with  '#' viewed the user report in th...   201"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coud takes few minutes depends on the course log size.\n",
    "findPattern(  ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g6UCox9eFPg1"
   },
   "source": [
    "- Remove admin activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "R05riSxQFUnl",
    "outputId": "e085ab3b-704e-4041-f2de-e9da4845a29d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current length: 13855 ... 71 Rows Deleted.\n"
     ]
    }
   ],
   "source": [
    "# List of Event Name belong to admin\n",
    "toBeRemovedList = ['User graded','User profile viewed','Quiz attempt viewed','Quiz attempt summary viewed','Quiz attempt reviewed']\n",
    "\n",
    "beforeLength = len(Activity)\n",
    "\n",
    "Activity = Activity[~Activity['Event name'].isin(toBeRemovedList)]\n",
    "\n",
    "print (\"Current length: \" + str(len(Activity)) + \" ... \" + str(beforeLength - len(Activity)) + \" Rows Deleted.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZEl4Vot8o3rR"
   },
   "source": [
    "---\n",
    "\n",
    "# Resource usage Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YePjd4E-oTY8"
   },
   "source": [
    "Show the **Top** 5 most used resource for each component "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1346
    },
    "colab_type": "code",
    "id": "lPJU8t0OqH4O",
    "outputId": "f345ff1a-cfd8-4bda-ed20-1e8a277fa9e1"
   },
   "outputs": [
    {
     "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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <th>Event context</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">File</th>\n",
       "      <th>Other</th>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Theoretical Assessment Work (2 papers)</th>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Practical assessment work</th>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Lecture 1: Module introduction</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Template for practical report (self evaluation not required)</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Introductory Tutorial on RapidMiner</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Lect 7 and 8 Clustering</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: labsheet #1-gettingStartedwithRMiner</th>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Statistics for data mining</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Lecture 1: CRISP-DM methodology</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Folder</th>\n",
       "      <th>Folder: Datasets</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Papers from past students</th>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: RM processes for labsheet1</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Lab6 processes and dataset</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Lab sheet 3 -  files</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Illustration of over fitting and pruning</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: RM process and datasets - lab 7</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Case studies</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Latex template for papers, self evaulation, and bibtex file</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Lab 2 processes</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forum</th>\n",
       "      <th>Forum: News forum</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Page</th>\n",
       "      <th>Page: 2017 - recordings on clustering</th>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: Oct 3rd 2017, Evaluation and Decision Trees</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2017 Recordings: Classification, and getting started with Rapidminer</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2017 recordings, Oct 10th</th>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2017 - Lecture 1: Introduction and CRISP-DM</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: Oct 17th: past papers, ensembles and regression</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2017: Regression, SVM and intro to NN</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2017 recordings Clustering &amp;amp; Association rule mining</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: Papers on clustering and related topics</th>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: more classification papers</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">URL</th>\n",
       "      <th>URL: Recording for practical assessment (2017)</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: A selection of papers and resources on datamining</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: 2016 - Lecture 1 - Introduction and CRISP-DM</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Presentation recordings from a previous year</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Collection of papers on C5.0</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Rapidminer.com - getting started</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Oct 3rd, 2017, Evaluation - lecture 2.3</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: 2017: Finish Neural Networks &amp;amp; Deep Learning</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Minimise relative error rather than actual error</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: 2016, Sept 28th: Lecture 2 Classification</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              count\n",
       "Component Event context                                            \n",
       "File      Other                                                 751\n",
       "          File: Theoretical Assessment Work (2 papers)          205\n",
       "          File: Practical assessment work                       157\n",
       "          File: Lecture 1: Module introduction                  114\n",
       "          File: Template for practical report (self evalu...    113\n",
       "          File: Introductory Tutorial on RapidMiner             102\n",
       "          File: Lect 7 and 8 Clustering                          98\n",
       "          File: labsheet #1-gettingStartedwithRMiner             94\n",
       "          File: Statistics for data mining                       83\n",
       "          File: Lecture 1: CRISP-DM methodology                  76\n",
       "Folder    Folder: Datasets                                      140\n",
       "          Folder: Papers from past students                     101\n",
       "          Folder: RM processes for labsheet1                     99\n",
       "          Folder: Lab6 processes and dataset                     89\n",
       "          Folder: Lab sheet 3 -  files                           81\n",
       "          Folder: Illustration of over fitting and pruning       64\n",
       "          Folder: RM process and datasets - lab 7                60\n",
       "          Folder: Case studies                                   54\n",
       "          Folder: Latex template for papers, self evaulat...     53\n",
       "          Folder: Lab 2 processes                                 4\n",
       "Forum     Forum: News forum                                      14\n",
       "Page      Page: 2017 - recordings on clustering                 258\n",
       "          Page: Oct 3rd 2017, Evaluation and Decision Trees     163\n",
       "          Page: 2017 Recordings: Classification, and gett...    161\n",
       "          Page: 2017 recordings, Oct 10th                       139\n",
       "          Page: 2017 - Lecture 1: Introduction and CRISP-DM     134\n",
       "          Page: Oct 17th: past papers, ensembles and regr...    122\n",
       "          Page: 2017: Regression, SVM and intro to NN            93\n",
       "          Page: 2017 recordings Clustering &amp; Associat...     84\n",
       "          Page: Papers on clustering and related topics          61\n",
       "          Page: more classification papers                       39\n",
       "URL       URL: Recording for practical assessment (2017)        121\n",
       "          URL: A selection of papers and resources on dat...     80\n",
       "          URL: 2016 - Lecture 1 - Introduction and CRISP-DM      68\n",
       "          URL: Presentation recordings from a previous year      63\n",
       "          URL: Collection of papers on C5.0                      45\n",
       "          URL: Rapidminer.com - getting started                  44\n",
       "          URL: Oct 3rd, 2017, Evaluation - lecture 2.3           43\n",
       "          URL: 2017: Finish Neural Networks &amp; Deep Le...     32\n",
       "          URL: Minimise relative error rather than actual...     27\n",
       "          URL: 2016, Sept 28th: Lecture 2 Classification         10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use getCounts function  to get the n most used resources per resource category\n",
    "# Ex. getCounts(Activity, 5)\n",
    "getCounts(Activity, 10,'T') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BDzk9GA2HxtN"
   },
   "source": [
    "Show the **Least** 5 used resources for each component \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 911
    },
    "colab_type": "code",
    "id": "lidNrlDlHxA1",
    "outputId": "d6169dc7-cb98-4de1-8cdc-6a0f254b3917"
   },
   "outputs": [
    {
     "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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <th>Event context</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">File</th>\n",
       "      <th>File: 1996EsterEtAl Original proposers of DBScan</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Repeat and deferral instructions 2016-17</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: MSWord template for papers, including self evalutaion form</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: labsheet2 - evaluating classifiers</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>File: Shim 2012 Bibliography for MapReduce Algorithms for DataAnalysis</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Folder</th>\n",
       "      <th>Folder: Lab 2 processes</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Latex template for papers, self evaulation, and bibtex file</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Case studies</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: RM process and datasets - lab 7</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Folder: Illustration of over fitting and pruning</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forum</th>\n",
       "      <th>Forum: News forum</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Page</th>\n",
       "      <th>Page: 2016 clustering lecture 2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: Lecture recordings Oct 14th 2015, and 2016</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2016 recordings, Oct 6th: Decision Trees and Model Evaluation</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: 2016 Nov 24th Association Analysis</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page: Papers on Association Rule mining</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">URL</th>\n",
       "      <th>URL: 2016 recording - Oct 6th- Decision Trees</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: 2016-clustering lecture 1 (up to SOM)</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Guest lecture: IBMs perspective on Big Data Analytics</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: Guest lecture: Radoop - Rapidminer for Hadoop</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>URL: 2016 Dec: Practical Assessment and Big Data</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              count\n",
       "Component Event context                                            \n",
       "File      File: 1996EsterEtAl Original proposers of DBScan        1\n",
       "          File: Repeat and deferral instructions 2016-17          2\n",
       "          File: MSWord template for papers, including sel...      4\n",
       "          File: labsheet2 - evaluating classifiers                5\n",
       "          File: Shim 2012 Bibliography for MapReduce Algo...      6\n",
       "Folder    Folder: Lab 2 processes                                 4\n",
       "          Folder: Latex template for papers, self evaulat...     53\n",
       "          Folder: Case studies                                   54\n",
       "          Folder: RM process and datasets - lab 7                60\n",
       "          Folder: Illustration of over fitting and pruning       64\n",
       "Forum     Forum: News forum                                      14\n",
       "Page      Page: 2016 clustering lecture 2                         2\n",
       "          Page: Lecture recordings Oct 14th 2015, and 2016        3\n",
       "          Page: 2016 recordings, Oct 6th: Decision Trees ...      6\n",
       "          Page: 2016 Nov 24th Association Analysis               11\n",
       "          Page: Papers on Association Rule mining                21\n",
       "URL       URL: 2016 recording - Oct 6th- Decision Trees           3\n",
       "          URL: 2016-clustering lecture 1 (up to SOM)              4\n",
       "          URL: Guest lecture: IBMs perspective on Big Dat...      4\n",
       "          URL: Guest lecture: Radoop - Rapidminer for Hadoop      4\n",
       "          URL: 2016 Dec: Practical Assessment and Big Data        8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getCounts(Activity, 5,'L')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Number of resource viewed\n",
    "The number of resources (File, Folder, Forum, Page, URL) viewed by each student "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>lms_RescourceViewCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User full name  lms_RescourceViewCount\n",
       "0            B04                      50\n",
       "1            B11                      46\n",
       "2            B15                      72\n",
       "3            B18                      85\n",
       "4            B24                      40\n",
       "5            B25                      84\n",
       "6            B26                      84\n",
       "7            B30                      98\n",
       "8            B31                      55\n",
       "9            B32                      15"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StudentResourceView = resourceView()\n",
    "StudentResourceView.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consistency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>lms_ActiveWeekCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User full name  lms_ActiveWeekCount\n",
       "0            B04                   15\n",
       "1            B11                    8\n",
       "2            B15                   18\n",
       "3            B18                   18\n",
       "4            B24                   17\n",
       "5            B25                   15\n",
       "6            B26                   17\n",
       "7            B30                   16\n",
       "8            B31                   16\n",
       "9            B32                    8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WeeklyConsistency = WeeklyConsistency()\n",
    "\n",
    "WeeklyConsistency.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kequaecJEtkq"
   },
   "source": [
    "\n",
    "---\n",
    "\n",
    "\n",
    "# User Active Days Count\n",
    "How many unique days did the student interact with course page?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1969
    },
    "colab_type": "code",
    "id": "M4_ogZb4Esxt",
    "outputId": "69d52365-0a47-436e-febf-b4792b6ab9c9"
   },
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>lms_ActiveDaysCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User full name  lms_ActiveDaysCount\n",
       "0            B04                   28\n",
       "1            B11                   18\n",
       "2            B15                   81\n",
       "3            B18                   72\n",
       "4            B24                   48\n",
       "5            B25                   45\n",
       "6            B26                   53\n",
       "7            B30                   36\n",
       "8            B31                   37\n",
       "9            B32                   11"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ActiveDaysCount = accessDaysCount()\n",
    "# Display a sample\n",
    "ActiveDaysCount.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HE30Bnm3VOku"
   },
   "source": [
    "---\n",
    "\n",
    "# Access Duration: Time between first and last activity\n",
    "Number of days between the first and last activity recorded for each student.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1969
    },
    "colab_type": "code",
    "id": "eyMd4VbQVOwX",
    "outputId": "fb8103e5-8ff7-4178-9774-15283aa03e0a"
   },
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>lms_AccessDuration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User full name  lms_AccessDuration\n",
       "0            B04                 114\n",
       "1            B11                  79\n",
       "2            B15                 116\n",
       "3            B18                 120\n",
       "4            B24                 113\n",
       "5            B25                 110\n",
       "6            B26                 120\n",
       "7            B30                 117\n",
       "8            B31                 109\n",
       "9            B32                  55"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AccessDuration = firstlastGap()\n",
    "# Display a sample\n",
    "AccessDuration.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "56_PHL2ZVpBD"
   },
   "source": [
    "---\n",
    "\n",
    "# Longest period of inactivity\n",
    "Maximum time (in sec.) without any activity during the course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2057
    },
    "colab_type": "code",
    "id": "vyB6qjyHVpLg",
    "outputId": "6143f533-a2fb-466a-feff-144c5e2b21a5"
   },
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>lms_LInactivityPeriod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>17901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>60475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>11787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>10077.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>12913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>11813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>11535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>25540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>15752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>18940.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  User full name  lms_LInactivityPeriod\n",
       "0            B04                17901.0\n",
       "1            B11                60475.0\n",
       "2            B15                11787.0\n",
       "3            B18                10077.0\n",
       "4            B24                12913.0\n",
       "5            B25                11813.0\n",
       "6            B26                11535.0\n",
       "7            B30                25540.0\n",
       "8            B31                15752.0\n",
       "9            B32                18940.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LInactivityPeriod =  maxGap() \n",
    "LInactivityPeriod['lms_LInactivityPeriod']=LInactivityPeriod['lms_LInactivityPeriod']/60\n",
    "LInactivityPeriod.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vXFwQs8N-Lxw"
   },
   "source": [
    "---\n",
    "\n",
    "# Submission Analysis\n",
    "\n",
    "For each assignmnet alone calculate the difference between the submissin deadline and the actual submission date.\n",
    "\n",
    "\n",
    "\n",
    "*   Time is neglected, only date is considered.\n",
    "*   The most frequent submission date for each assignment is considered here as the deadline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2329
    },
    "colab_type": "code",
    "id": "8gjbBW2tXtmp",
    "outputId": "3fe31a17-d1d0-4acc-abd9-aa0d16b5404d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission deadline as calculated are:\n"
     ]
    },
    {
     "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>Event context</th>\n",
       "      <th>deadline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Turnitin Assignment 2: Upload classification p...</td>\n",
       "      <td>2017-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Turnitin Assignment 2: Upload clustering paper...</td>\n",
       "      <td>2017-12-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Turnitin Assignment 2: Upload practical report...</td>\n",
       "      <td>2018-01-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Event context   deadline\n",
       "0  Turnitin Assignment 2: Upload classification p... 2017-11-13\n",
       "1  Turnitin Assignment 2: Upload clustering paper... 2017-12-04\n",
       "2  Turnitin Assignment 2: Upload practical report... 2018-01-03"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n"
     ]
    },
    {
     "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>Click</th>\n",
       "      <th>lms_Assignment1_delay</th>\n",
       "      <th>lms_Assignment2_delay</th>\n",
       "      <th>lms_Assignment3_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User full name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B04</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B15</th>\n",
       "      <td>3</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B18</th>\n",
       "      <td>3</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B24</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B25</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B26</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B30</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B34</th>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B35</th>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B37</th>\n",
       "      <td>3</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B41</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B42</th>\n",
       "      <td>3</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B43</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B46</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B47</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B51</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B56</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B59</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B60</th>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B62</th>\n",
       "      <td>3</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Click  lms_Assignment1_delay  lms_Assignment2_delay  \\\n",
       "User full name                                                        \n",
       "B04                 3                    0.0                    2.0   \n",
       "B15                 3                   -4.0                   -3.0   \n",
       "B18                 3                   -8.0                   -2.0   \n",
       "B24                 1                    0.0                    0.0   \n",
       "B25                 3                    0.0                    0.0   \n",
       "B26                 2                    0.0                    0.0   \n",
       "B30                 3                    0.0                    3.0   \n",
       "B34                 2                   27.0                   27.0   \n",
       "B35                 3                   16.0                    3.0   \n",
       "B37                 3                   51.0                   30.0   \n",
       "B41                 3                    0.0                    7.0   \n",
       "B42                 3                   -5.0                   -1.0   \n",
       "B43                 3                    0.0                    0.0   \n",
       "B46                 3                    0.0                    0.0   \n",
       "B47                 3                    1.0                    0.0   \n",
       "B51                 3                   -1.0                   12.0   \n",
       "B56                 1                    0.0                    0.0   \n",
       "B59                 3                    0.0                    0.0   \n",
       "B60                 3                   -1.0                   13.0   \n",
       "B62                 3                   -6.0                    0.0   \n",
       "\n",
       "                lms_Assignment3_delay  \n",
       "User full name                         \n",
       "B04                               0.0  \n",
       "B15                              -1.0  \n",
       "B18                              -2.0  \n",
       "B24                               0.0  \n",
       "B25                               0.0  \n",
       "B26                               0.0  \n",
       "B30                              -2.0  \n",
       "B34                               0.0  \n",
       "B35                               0.0  \n",
       "B37                               0.0  \n",
       "B41                               0.0  \n",
       "B42                              -1.0  \n",
       "B43                               0.0  \n",
       "B46                              -1.0  \n",
       "B47                               0.0  \n",
       "B51                               0.0  \n",
       "B56                               0.0  \n",
       "B59                              -1.0  \n",
       "B60                              -1.0  \n",
       "B62                               0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SA = SubmissionAnalysis()\n",
    "# Display a Sample\n",
    "SA.head(20)\n",
    "\n",
    "#column header full assignments name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uezH7aC3AwtT"
   },
   "source": [
    "# **Term-based views sum for each student per resource:**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description for the table summary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2057
    },
    "colab_type": "code",
    "id": "VAgXRKXrE8_I",
    "outputId": "22e6ee49-ac12-4407-dfe6-14625fafc486"
   },
   "outputs": [
    {
     "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>Component</th>\n",
       "      <th>User full name</th>\n",
       "      <th>lms_Count_File</th>\n",
       "      <th>lms_Count_Folder</th>\n",
       "      <th>lms_Count_Forum</th>\n",
       "      <th>lms_Count_Page</th>\n",
       "      <th>lms_Count_System</th>\n",
       "      <th>lms_Count_URL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B04</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B11</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B15</td>\n",
       "      <td>213.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B18</td>\n",
       "      <td>182.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B24</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B25</td>\n",
       "      <td>135.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B26</td>\n",
       "      <td>117.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B30</td>\n",
       "      <td>104.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B31</td>\n",
       "      <td>84.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B32</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B34</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B35</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B37</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B41</td>\n",
       "      <td>198.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>B42</td>\n",
       "      <td>179.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B43</td>\n",
       "      <td>239.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B46</td>\n",
       "      <td>140.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B47</td>\n",
       "      <td>61.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B51</td>\n",
       "      <td>98.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>B56</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Component User full name  lms_Count_File  lms_Count_Folder  lms_Count_Forum  \\\n",
       "0                    B04            57.0              16.0              0.0   \n",
       "1                    B11            65.0              14.0              1.0   \n",
       "2                    B15           213.0              28.0              0.0   \n",
       "3                    B18           182.0              23.0              0.0   \n",
       "4                    B24           118.0               7.0              0.0   \n",
       "5                    B25           135.0              25.0              0.0   \n",
       "6                    B26           117.0              27.0              0.0   \n",
       "7                    B30           104.0              11.0              1.0   \n",
       "8                    B31            84.0              12.0              0.0   \n",
       "9                    B32             9.0               3.0              0.0   \n",
       "10                   B34            21.0               4.0              1.0   \n",
       "11                   B35            94.0               6.0              0.0   \n",
       "12                   B37            61.0              12.0              0.0   \n",
       "13                   B41           198.0              23.0              0.0   \n",
       "14                   B42           179.0              20.0              1.0   \n",
       "15                   B43           239.0              33.0              1.0   \n",
       "16                   B46           140.0              29.0              1.0   \n",
       "17                   B47            61.0              19.0              0.0   \n",
       "18                   B51            98.0              13.0              0.0   \n",
       "19                   B56            30.0               6.0              1.0   \n",
       "\n",
       "Component  lms_Count_Page  lms_Count_System  lms_Count_URL  \n",
       "0                     8.0              92.0            3.0  \n",
       "1                     2.0              56.0           17.0  \n",
       "2                    61.0             261.0           17.0  \n",
       "3                    49.0             192.0           21.0  \n",
       "4                    22.0             139.0           21.0  \n",
       "5                    19.0             136.0            9.0  \n",
       "6                    48.0             171.0           28.0  \n",
       "7                    14.0              65.0            8.0  \n",
       "8                    11.0              88.0           22.0  \n",
       "9                    10.0              31.0            1.0  \n",
       "10                    3.0              81.0            1.0  \n",
       "11                    3.0              68.0            2.0  \n",
       "12                   16.0              76.0            7.0  \n",
       "13                   14.0             152.0           29.0  \n",
       "14                   39.0             209.0           21.0  \n",
       "15                   34.0             184.0           15.0  \n",
       "16                   37.0              98.0           29.0  \n",
       "17                   23.0              82.0            5.0  \n",
       "18                   30.0             123.0           14.0  \n",
       "19                    9.0              70.0           15.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Activity['Click']= 1 \n",
    "\n",
    "TermSummary = getTermSummary()\n",
    "\n",
    "TermSummary.head(20)\n",
    "\n",
    "# header FolderViewCount , ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t2ZEYkYqnwa2"
   },
   "source": [
    "# ***Weekly data for plot***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4vsJ5x6JGpH8"
   },
   "source": [
    "**Generate week based view sum:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eojDnLCbKI6W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e04406a20>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "WeeklySummary =  getWeeklySummary()    \n",
    "# print( WeeklySummary.head(20) )\n",
    "WeeklySummary.mean().transpose().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FdU-1orkkN-C"
   },
   "source": [
    "# **`Plot daily avarage click count`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5MrM-hR2hPV8"
   },
   "source": [
    "**Generate daily based view sum:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hXbmu5SThPWA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e04e7a518>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DailySummary = getDailySummary()\n",
    "# print( DailySummary.head(50) )\n",
    "DailySummary.mean().transpose().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nKCdplZoIDVY"
   },
   "source": [
    "# **Join Activity and Grades for further analysis **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PlSjGK928Da5"
   },
   "outputs": [
    {
     "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>Paper Week7</th>\n",
       "      <th>Paper Week10</th>\n",
       "      <th>Self evaluation Week10</th>\n",
       "      <th>Presentation Week 12</th>\n",
       "      <th>Practical Week 15</th>\n",
       "      <th>Course total (Real)</th>\n",
       "      <th>lms_Count_File</th>\n",
       "      <th>lms_Count_Folder</th>\n",
       "      <th>lms_Count_Forum</th>\n",
       "      <th>lms_Count_Page</th>\n",
       "      <th>...</th>\n",
       "      <th>lms_Count_URL</th>\n",
       "      <th>Click</th>\n",
       "      <th>lms_Assignment1_delay</th>\n",
       "      <th>lms_Assignment2_delay</th>\n",
       "      <th>lms_Assignment3_delay</th>\n",
       "      <th>lms_ActiveDaysCount</th>\n",
       "      <th>lms_LInactivityPeriod</th>\n",
       "      <th>lms_AccessDuration</th>\n",
       "      <th>lms_RescourceViewCount</th>\n",
       "      <th>lms_ActiveWeekCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>76.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>10965.0</td>\n",
       "      <td>107</td>\n",
       "      <td>43</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>28288.0</td>\n",
       "      <td>117</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>40.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54</td>\n",
       "      <td>10068.0</td>\n",
       "      <td>111</td>\n",
       "      <td>73</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>87.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>17901.0</td>\n",
       "      <td>114</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>20.4</td>\n",
       "      <td>81.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "      <td>20056.0</td>\n",
       "      <td>116</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>81.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>46</td>\n",
       "      <td>17712.0</td>\n",
       "      <td>121</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>30207.0</td>\n",
       "      <td>41</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>28.6</td>\n",
       "      <td>90.8</td>\n",
       "      <td>179.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>62</td>\n",
       "      <td>15864.0</td>\n",
       "      <td>118</td>\n",
       "      <td>103</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>67.9</td>\n",
       "      <td>154.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>64</td>\n",
       "      <td>8139.0</td>\n",
       "      <td>121</td>\n",
       "      <td>106</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>24.3</td>\n",
       "      <td>74.9</td>\n",
       "      <td>182.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>72</td>\n",
       "      <td>10077.0</td>\n",
       "      <td>120</td>\n",
       "      <td>85</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>16.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "      <td>14137.0</td>\n",
       "      <td>113</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>84.5</td>\n",
       "      <td>104.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>36</td>\n",
       "      <td>25540.0</td>\n",
       "      <td>117</td>\n",
       "      <td>98</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>109</td>\n",
       "      <td>56</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>112.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31</td>\n",
       "      <td>10054.0</td>\n",
       "      <td>75</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>8638.0</td>\n",
       "      <td>85</td>\n",
       "      <td>59</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>64.1</td>\n",
       "      <td>109.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>110</td>\n",
       "      <td>74</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>38.7</td>\n",
       "      <td>213.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>81</td>\n",
       "      <td>11787.0</td>\n",
       "      <td>116</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>198.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>13520.0</td>\n",
       "      <td>120</td>\n",
       "      <td>102</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>61.9</td>\n",
       "      <td>117.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53</td>\n",
       "      <td>11535.0</td>\n",
       "      <td>120</td>\n",
       "      <td>84</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>26.8</td>\n",
       "      <td>77.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>36</td>\n",
       "      <td>12923.0</td>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>21.5</td>\n",
       "      <td>77.1</td>\n",
       "      <td>128.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>31</td>\n",
       "      <td>19979.0</td>\n",
       "      <td>110</td>\n",
       "      <td>105</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>12913.0</td>\n",
       "      <td>113</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>89</td>\n",
       "      <td>5739.0</td>\n",
       "      <td>114</td>\n",
       "      <td>128</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>77.3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>18515.0</td>\n",
       "      <td>111</td>\n",
       "      <td>75</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>18940.0</td>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>13.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>11946.0</td>\n",
       "      <td>115</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>32.1</td>\n",
       "      <td>261.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55</td>\n",
       "      <td>8859.0</td>\n",
       "      <td>117</td>\n",
       "      <td>119</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25.5</td>\n",
       "      <td>18.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>77.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>43</td>\n",
       "      <td>27505.0</td>\n",
       "      <td>119</td>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>43.3</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28</td>\n",
       "      <td>18324.0</td>\n",
       "      <td>112</td>\n",
       "      <td>46</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>29715.0</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>25471.0</td>\n",
       "      <td>99</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>84.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>15752.0</td>\n",
       "      <td>109</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>60475.0</td>\n",
       "      <td>79</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>23.6</td>\n",
       "      <td>67.5</td>\n",
       "      <td>142.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>47</td>\n",
       "      <td>16040.0</td>\n",
       "      <td>113</td>\n",
       "      <td>68</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>23.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>80.3</td>\n",
       "      <td>239.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>9951.0</td>\n",
       "      <td>118</td>\n",
       "      <td>121</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>70.5</td>\n",
       "      <td>140.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>45</td>\n",
       "      <td>22625.0</td>\n",
       "      <td>118</td>\n",
       "      <td>93</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>135.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>11813.0</td>\n",
       "      <td>110</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Paper Week7  Paper Week10  Self evaluation Week10  Presentation Week 12  \\\n",
       "0          25.5          29.0                     2.0                   4.7   \n",
       "1           8.0           9.0                     1.3                   2.8   \n",
       "2           9.0          12.0                     2.0                   3.5   \n",
       "3          23.0          27.0                     5.0                   4.7   \n",
       "4          24.0          28.0                     5.0                   4.3   \n",
       "5          21.0          24.0                     4.5                   4.7   \n",
       "6           0.0           0.0                     0.0                   0.0   \n",
       "7          26.0          27.0                     4.8                   4.4   \n",
       "8          23.5          22.0                     4.0                   4.4   \n",
       "9          23.0          19.5                     4.3                   3.9   \n",
       "10          4.0           3.0                     1.5                   4.7   \n",
       "11         28.5          25.5                     4.8                   4.0   \n",
       "12         11.0          17.0                     2.5                   0.0   \n",
       "13         11.0           0.0                     1.8                   0.0   \n",
       "14         16.5          18.0                     3.5                   0.0   \n",
       "15         17.0          18.5                     3.5                   4.7   \n",
       "16         10.0          12.0                     1.0                   3.8   \n",
       "17         18.0          20.0                     1.8                   4.4   \n",
       "18         11.0          18.5                     5.0                   4.0   \n",
       "19         19.0          23.5                     4.8                   3.8   \n",
       "20         24.0          22.5                     4.4                   4.7   \n",
       "21          0.0           0.0                     0.0                   0.0   \n",
       "22         24.0          22.0                     5.0                   3.9   \n",
       "23         22.0          23.0                     5.0                   4.7   \n",
       "24          0.0           0.0                     0.0                   0.0   \n",
       "25         14.0          16.0                     2.3                   3.3   \n",
       "26          0.0          10.5                     1.8                   0.0   \n",
       "27         25.5          18.1                     4.3                   4.2   \n",
       "28         11.0          11.0                     2.3                   0.0   \n",
       "29          0.0           0.0                     0.0                   0.0   \n",
       "30         22.0           0.0                     2.3                   4.2   \n",
       "31          0.0           0.0                     0.0                   4.7   \n",
       "32          0.0           0.0                     0.0                   0.0   \n",
       "33         18.0          18.0                     4.0                   3.9   \n",
       "34         23.4          24.0                     5.0                   3.9   \n",
       "35         15.0          23.0                     3.8                   4.7   \n",
       "36         25.0          25.0                     2.5                   5.0   \n",
       "\n",
       "    Practical Week 15  Course total (Real)  lms_Count_File  lms_Count_Folder  \\\n",
       "0                15.4                 76.6            68.0              17.0   \n",
       "1                 2.0                 23.0            94.0               6.0   \n",
       "2                13.6                 40.1            98.0              13.0   \n",
       "3                27.3                 87.0            57.0              16.0   \n",
       "4                20.4                 81.7            21.0               4.0   \n",
       "5                26.9                 81.1            60.0              16.0   \n",
       "6                 0.0                  0.0            46.0              11.0   \n",
       "7                28.6                 90.8           179.0              20.0   \n",
       "8                14.0                 67.9           154.0              54.0   \n",
       "9                24.3                 74.9           182.0              23.0   \n",
       "10               16.8                 30.0           261.0              24.0   \n",
       "11               21.7                 84.5           104.0              11.0   \n",
       "12                6.5                 37.0            73.0              21.0   \n",
       "13                0.0                 12.8           112.0              33.0   \n",
       "14                0.0                 38.0           120.0              12.0   \n",
       "15               20.4                 64.1           109.0              10.0   \n",
       "16               11.9                 38.7           213.0              28.0   \n",
       "17               15.0                 60.2           198.0              23.0   \n",
       "18               23.4                 61.9           117.0              27.0   \n",
       "19               26.8                 77.8            38.0              20.0   \n",
       "20               21.5                 77.1           128.0              21.0   \n",
       "21                0.0                  0.0           118.0               7.0   \n",
       "22               20.2                 75.0           396.0              66.0   \n",
       "23               22.6                 77.3            93.0              15.0   \n",
       "24                0.0                  0.0             9.0               3.0   \n",
       "25               13.5                 50.0            61.0              19.0   \n",
       "26               19.8                 32.1           261.0              67.0   \n",
       "27               25.3                 77.3            41.0               7.0   \n",
       "28               19.1                 43.3            61.0              12.0   \n",
       "29                0.0                  0.0            52.0               3.0   \n",
       "30                0.0                 28.4            30.0               6.0   \n",
       "31                0.0                  4.7            84.0              12.0   \n",
       "32                0.0                  0.0            65.0              14.0   \n",
       "33               23.6                 67.5           142.0              17.0   \n",
       "34               24.0                 80.3           239.0              33.0   \n",
       "35               24.0                 70.5           140.0              29.0   \n",
       "36               23.0                 80.5           135.0              25.0   \n",
       "\n",
       "    lms_Count_Forum  lms_Count_Page         ...           lms_Count_URL  \\\n",
       "0               0.0            11.0         ...                     7.0   \n",
       "1               0.0             3.0         ...                     2.0   \n",
       "2               0.0            30.0         ...                    14.0   \n",
       "3               0.0             8.0         ...                     3.0   \n",
       "4               1.0             3.0         ...                     1.0   \n",
       "5               0.0            36.0         ...                     6.0   \n",
       "6               0.0             2.0         ...                     6.0   \n",
       "7               1.0            39.0         ...                    21.0   \n",
       "8               0.0            79.0         ...                    34.0   \n",
       "9               0.0            49.0         ...                    21.0   \n",
       "10              0.0           135.0         ...                    20.0   \n",
       "11              1.0            14.0         ...                     8.0   \n",
       "12              1.0            33.0         ...                    14.0   \n",
       "13              1.0             0.0         ...                     6.0   \n",
       "14              1.0            45.0         ...                    13.0   \n",
       "15              0.0            52.0         ...                    23.0   \n",
       "16              0.0            61.0         ...                    17.0   \n",
       "17              0.0            14.0         ...                    29.0   \n",
       "18              0.0            48.0         ...                    28.0   \n",
       "19              0.0            57.0         ...                    12.0   \n",
       "20              0.0            27.0         ...                    10.0   \n",
       "21              0.0            22.0         ...                    21.0   \n",
       "22              1.0           230.0         ...                    77.0   \n",
       "23              0.0             7.0         ...                     8.0   \n",
       "24              0.0            10.0         ...                     1.0   \n",
       "25              0.0            23.0         ...                     5.0   \n",
       "26              0.0            75.0         ...                    23.0   \n",
       "27              3.0            12.0         ...                    13.0   \n",
       "28              0.0            16.0         ...                     7.0   \n",
       "29              0.0             9.0         ...                    11.0   \n",
       "30              1.0             9.0         ...                    15.0   \n",
       "31              0.0            11.0         ...                    22.0   \n",
       "32              1.0             2.0         ...                    17.0   \n",
       "33              0.0            35.0         ...                    14.0   \n",
       "34              1.0            34.0         ...                    15.0   \n",
       "35              1.0            37.0         ...                    29.0   \n",
       "36              0.0            19.0         ...                     9.0   \n",
       "\n",
       "    Click  lms_Assignment1_delay  lms_Assignment2_delay  \\\n",
       "0     3.0                    0.0                    0.0   \n",
       "1     3.0                   16.0                    3.0   \n",
       "2     3.0                   -1.0                   12.0   \n",
       "3     3.0                    0.0                    2.0   \n",
       "4     2.0                   27.0                   27.0   \n",
       "5     3.0                   -7.0                   -7.0   \n",
       "6     NaN                    NaN                    NaN   \n",
       "7     3.0                   -5.0                   -1.0   \n",
       "8     3.0                  -11.0                    0.0   \n",
       "9     3.0                   -8.0                   -2.0   \n",
       "10    1.0                    0.0                    0.0   \n",
       "11    3.0                    0.0                    3.0   \n",
       "12    3.0                    0.0                   26.0   \n",
       "13    1.0                   -3.0                    0.0   \n",
       "14    2.0                    0.0                   -8.0   \n",
       "15    3.0                   -2.0                    3.0   \n",
       "16    3.0                   -4.0                   -3.0   \n",
       "17    3.0                    0.0                    7.0   \n",
       "18    2.0                    0.0                    0.0   \n",
       "19    3.0                   -2.0                    0.0   \n",
       "20    3.0                   13.0                    1.0   \n",
       "21    1.0                    0.0                    0.0   \n",
       "22    3.0                    0.0                    0.0   \n",
       "23    3.0                   -6.0                    0.0   \n",
       "24    NaN                    NaN                    NaN   \n",
       "25    3.0                    1.0                    0.0   \n",
       "26    2.0                    0.0                    3.0   \n",
       "27    3.0                   -1.0                   13.0   \n",
       "28    3.0                   51.0                   30.0   \n",
       "29    NaN                    NaN                    NaN   \n",
       "30    1.0                    0.0                    0.0   \n",
       "31    NaN                    NaN                    NaN   \n",
       "32    NaN                    NaN                    NaN   \n",
       "33    3.0                   28.0                   18.0   \n",
       "34    3.0                    0.0                    0.0   \n",
       "35    3.0                    0.0                    0.0   \n",
       "36    3.0                    0.0                    0.0   \n",
       "\n",
       "    lms_Assignment3_delay  lms_ActiveDaysCount  lms_LInactivityPeriod  \\\n",
       "0                     0.0                   49                10965.0   \n",
       "1                     0.0                   30                28288.0   \n",
       "2                     0.0                   54                10068.0   \n",
       "3                     0.0                   28                17901.0   \n",
       "4                     0.0                   36                20056.0   \n",
       "5                   -19.0                   46                17712.0   \n",
       "6                     NaN                   10                30207.0   \n",
       "7                    -1.0                   62                15864.0   \n",
       "8                    -2.0                   64                 8139.0   \n",
       "9                    -2.0                   72                10077.0   \n",
       "10                    0.0                   52                14137.0   \n",
       "11                   -2.0                   36                25540.0   \n",
       "12                    0.0                   50                11584.0   \n",
       "13                    0.0                   31                10054.0   \n",
       "14                    0.0                   49                 8638.0   \n",
       "15                    0.0                   68                 7010.0   \n",
       "16                   -1.0                   81                11787.0   \n",
       "17                    0.0                   60                13520.0   \n",
       "18                    0.0                   53                11535.0   \n",
       "19                  -10.0                   36                12923.0   \n",
       "20                   -2.0                   31                19979.0   \n",
       "21                    0.0                   48                12913.0   \n",
       "22                   -1.0                   89                 5739.0   \n",
       "23                    0.0                   30                18515.0   \n",
       "24                    NaN                   11                18940.0   \n",
       "25                    0.0                   45                11946.0   \n",
       "26                    0.0                   55                 8859.0   \n",
       "27                   -1.0                   43                27505.0   \n",
       "28                    0.0                   28                18324.0   \n",
       "29                    NaN                   23                29715.0   \n",
       "30                    0.0                   27                25471.0   \n",
       "31                    NaN                   37                15752.0   \n",
       "32                    NaN                   18                60475.0   \n",
       "33                   -1.0                   47                16040.0   \n",
       "34                    0.0                   71                 9951.0   \n",
       "35                   -1.0                   45                22625.0   \n",
       "36                    0.0                   45                11813.0   \n",
       "\n",
       "    lms_AccessDuration  lms_RescourceViewCount  lms_ActiveWeekCount  \n",
       "0                  107                      43                   16  \n",
       "1                  117                      38                   15  \n",
       "2                  111                      73                   17  \n",
       "3                  114                      50                   15  \n",
       "4                  116                      20                   17  \n",
       "5                  121                      49                   17  \n",
       "6                   41                      27                    5  \n",
       "7                  118                     103                   18  \n",
       "8                  121                     106                   18  \n",
       "9                  120                      85                   18  \n",
       "10                 113                      80                   17  \n",
       "11                 117                      98                   16  \n",
       "12                 109                      56                   17  \n",
       "13                  75                      48                   12  \n",
       "14                  85                      59                   13  \n",
       "15                 110                      74                   17  \n",
       "16                 116                      72                   18  \n",
       "17                 120                     102                   18  \n",
       "18                 120                      84                   17  \n",
       "19                 100                      45                   16  \n",
       "20                 110                     105                   15  \n",
       "21                 113                      40                   17  \n",
       "22                 114                     128                   17  \n",
       "23                 111                      75                   16  \n",
       "24                  55                      15                    8  \n",
       "25                 115                      47                   17  \n",
       "26                 117                     119                   18  \n",
       "27                 119                      40                   16  \n",
       "28                 112                      46                   16  \n",
       "29                  88                      24                   10  \n",
       "30                  99                      27                   14  \n",
       "31                 109                      55                   16  \n",
       "32                  79                      46                    8  \n",
       "33                 113                      68                   17  \n",
       "34                 118                     121                   18  \n",
       "35                 118                      93                   17  \n",
       "36                 110                      84                   15  \n",
       "\n",
       "[37 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final = Grades.merge(TermSummary, how='left', on=StdCol)\n",
    "Final = Final.merge(SA, how='left', on=StdCol)\n",
    "Final = Final.merge(ActiveDaysCount, how='left', on=StdCol)\n",
    "Final = Final.merge(LInactivityPeriod, how='left', on=StdCol)\n",
    "Final = Final.merge(AccessDuration, how='left', on=StdCol)\n",
    "Final = Final.merge(StudentResourceView, how='left', on=StdCol)\n",
    "Final = Final.merge(WeeklyConsistency, how='left', on=StdCol)\n",
    "\n",
    "\n",
    "\n",
    "# Final.sort_values(FGradeCol,ascending=False)\n",
    "\n",
    "#Grades\n",
    "\n",
    "Final._get_numeric_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'Click' in Final.columns:\n",
    "    Final.drop(['Click'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Original data length\n",
    "len(Final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many rows with missing data \n",
    "sum(Final.apply(lambda x: sum(x.isnull().values), axis = 1)>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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>User full name</th>\n",
       "      <th>Paper Week7</th>\n",
       "      <th>Paper Week10</th>\n",
       "      <th>Self evaluation Week10</th>\n",
       "      <th>Presentation Week 12</th>\n",
       "      <th>Practical Week 15</th>\n",
       "      <th>Course total (Real)</th>\n",
       "      <th>Grade</th>\n",
       "      <th>lms_Count_File</th>\n",
       "      <th>lms_Count_Folder</th>\n",
       "      <th>...</th>\n",
       "      <th>lms_Count_URL</th>\n",
       "      <th>Click</th>\n",
       "      <th>lms_Assignment1_delay</th>\n",
       "      <th>lms_Assignment2_delay</th>\n",
       "      <th>lms_Assignment3_delay</th>\n",
       "      <th>lms_ActiveDaysCount</th>\n",
       "      <th>lms_LInactivityPeriod</th>\n",
       "      <th>lms_AccessDuration</th>\n",
       "      <th>lms_RescourceViewCount</th>\n",
       "      <th>lms_ActiveWeekCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>46.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>30207.0</td>\n",
       "      <td>41</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>B32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>18940.0</td>\n",
       "      <td>55</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>B93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>29715.0</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>B31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>F</td>\n",
       "      <td>84.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>15752.0</td>\n",
       "      <td>109</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>B11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>65.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>60475.0</td>\n",
       "      <td>79</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   User full name  Paper Week7  Paper Week10  Self evaluation Week10  \\\n",
       "6             B61          0.0           0.0                     0.0   \n",
       "24            B32          0.0           0.0                     0.0   \n",
       "29            B93          0.0           0.0                     0.0   \n",
       "31            B31          0.0           0.0                     0.0   \n",
       "32            B11          0.0           0.0                     0.0   \n",
       "\n",
       "    Presentation Week 12  Practical Week 15  Course total (Real) Grade  \\\n",
       "6                    0.0                0.0                  0.0     F   \n",
       "24                   0.0                0.0                  0.0     F   \n",
       "29                   0.0                0.0                  0.0     F   \n",
       "31                   4.7                0.0                  4.7     F   \n",
       "32                   0.0                0.0                  0.0     F   \n",
       "\n",
       "    lms_Count_File  lms_Count_Folder         ...           lms_Count_URL  \\\n",
       "6             46.0              11.0         ...                     6.0   \n",
       "24             9.0               3.0         ...                     1.0   \n",
       "29            52.0               3.0         ...                    11.0   \n",
       "31            84.0              12.0         ...                    22.0   \n",
       "32            65.0              14.0         ...                    17.0   \n",
       "\n",
       "    Click  lms_Assignment1_delay  lms_Assignment2_delay  \\\n",
       "6     NaN                    NaN                    NaN   \n",
       "24    NaN                    NaN                    NaN   \n",
       "29    NaN                    NaN                    NaN   \n",
       "31    NaN                    NaN                    NaN   \n",
       "32    NaN                    NaN                    NaN   \n",
       "\n",
       "    lms_Assignment3_delay  lms_ActiveDaysCount  lms_LInactivityPeriod  \\\n",
       "6                     NaN                   10                30207.0   \n",
       "24                    NaN                   11                18940.0   \n",
       "29                    NaN                   23                29715.0   \n",
       "31                    NaN                   37                15752.0   \n",
       "32                    NaN                   18                60475.0   \n",
       "\n",
       "    lms_AccessDuration  lms_RescourceViewCount  lms_ActiveWeekCount  \n",
       "6                   41                      27                    5  \n",
       "24                  55                      15                    8  \n",
       "29                  88                      24                   10  \n",
       "31                 109                      55                   16  \n",
       "32                  79                      46                    8  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show Rows with missing data (\"for simplicity only\")\n",
    "null_data = Final[Final.isnull().any(axis=1)]\n",
    "null_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop all rows with missing values ()\n",
    "Final = Final.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d_iccWo8InGi"
   },
   "source": [
    "**Check Correlation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "colab_type": "code",
    "id": "FcJyKgoLSqwp",
    "outputId": "d31047da-03db-4412-a39b-aff4c6120781"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col0 {\n",
       "            background-color:  #8bcd8b;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col1 {\n",
       "            background-color:  #62b662;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col2 {\n",
       "            background-color:  #309a30;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col3 {\n",
       "            background-color:  #6fbd6f;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col4 {\n",
       "            background-color:  #6bbb6b;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col5 {\n",
       "            background-color:  #9ad59a;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col6 {\n",
       "            background-color:  #8dce8d;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col7 {\n",
       "            background-color:  #6bbb6b;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col8 {\n",
       "            background-color:  #e5ffe5;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col9 {\n",
       "            background-color:  #6abb6a;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col10 {\n",
       "            background-color:  #158b15;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col11 {\n",
       "            background-color:  #008000;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col12 {\n",
       "            background-color:  #128a12;\n",
       "        }    #T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col13 {\n",
       "            background-color:  #4caa4c;\n",
       "        }</style>  \n",
       "<table id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ec\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >lms_Count_File</th> \n",
       "        <th class=\"col_heading level0 col1\" >lms_Count_Folder</th> \n",
       "        <th class=\"col_heading level0 col2\" >lms_Count_Forum</th> \n",
       "        <th class=\"col_heading level0 col3\" >lms_Count_Page</th> \n",
       "        <th class=\"col_heading level0 col4\" >lms_Count_System</th> \n",
       "        <th class=\"col_heading level0 col5\" >lms_Count_URL</th> \n",
       "        <th class=\"col_heading level0 col6\" >lms_Assignment1_delay</th> \n",
       "        <th class=\"col_heading level0 col7\" >lms_Assignment2_delay</th> \n",
       "        <th class=\"col_heading level0 col8\" >lms_Assignment3_delay</th> \n",
       "        <th class=\"col_heading level0 col9\" >lms_ActiveDaysCount</th> \n",
       "        <th class=\"col_heading level0 col10\" >lms_LInactivityPeriod</th> \n",
       "        <th class=\"col_heading level0 col11\" >lms_AccessDuration</th> \n",
       "        <th class=\"col_heading level0 col12\" >lms_RescourceViewCount</th> \n",
       "        <th class=\"col_heading level0 col13\" >lms_ActiveWeekCount</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_e3635558_4bc1_11e9_8807_c85b765b37eclevel0_row0\" class=\"row_heading level0 row0\" >Course total (Real)</th> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col0\" class=\"data row0 col0\" >-0.16</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col1\" class=\"data row0 col1\" >-0.0239</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col2\" class=\"data row0 col2\" >0.14</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col3\" class=\"data row0 col3\" >-0.0636</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col4\" class=\"data row0 col4\" >-0.0518</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col5\" class=\"data row0 col5\" >-0.204</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col6\" class=\"data row0 col6\" >-0.165</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col7\" class=\"data row0 col7\" >-0.0538</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col8\" class=\"data row0 col8\" >-0.454</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col9\" class=\"data row0 col9\" >-0.0502</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col10\" class=\"data row0 col10\" >0.228</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col11\" class=\"data row0 col11\" >0.298</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col12\" class=\"data row0 col12\" >0.239</td> \n",
       "        <td id=\"T_e3635558_4bc1_11e9_8807_c85b765b37ecrow0_col13\" class=\"data row0 col13\" >0.0502</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29e04aa0b38>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx = pd.DataFrame( Final._get_numeric_data().corr(method='spearman')) \n",
    "\n",
    "filter_col = [col for col in xx if col.startswith('lms_')]\n",
    "\n",
    "xx = xx[filter_col]\n",
    "\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "\n",
    "xx.loc[xx.index == FGradeCol].style.format(\"{:.3}\").background_gradient(cmap=cm, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JVfaxa6iIY3l"
   },
   "source": [
    "**Plot pairwise relations between overall grade and views&marks columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(Final._get_numeric_data().corr(), annot=True, linewidths=2, fmt= '.1f')\n",
    "plt.show() # eliminates the black-white line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9C57gY6PFLtw"
   },
   "source": [
    "# Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avarage activity by group based on overall grade A is hiegest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WZbt4KHyOFyu"
   },
   "outputs": [],
   "source": [
    "Final['Group'] = ''\n",
    "Final[ \"Group Name\"]= ''\n",
    "Final = Final.copy()\n",
    "\n",
    "for item, row in Final.iterrows(): \n",
    "  if Final.loc[item, FGradeCol] >= 75 :\n",
    "     Final.loc[item, \"Group\"]= 1\n",
    "     Final.loc[item, \"Group Name\"]= 'A'\n",
    "  elif Final.loc[item, FGradeCol] >= 40 :\n",
    "     Final.loc[item, \"Group\"]= 2\n",
    "     Final.loc[item, \"Group Name\"]= 'B'\n",
    "  else:\n",
    "     Final.loc[item, \"Group\"]= 3\n",
    "     Final.loc[item, \"Group Name\"]= 'C'\n",
    "      \n",
    "#Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 196
    },
    "colab_type": "code",
    "id": "fwktmMJUIRdr",
    "outputId": "86f60f75-8659-4a2f-882a-2299950167b9"
   },
   "outputs": [
    {
     "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>Paper Week7</th>\n",
       "      <th>Paper Week10</th>\n",
       "      <th>Self evaluation Week10</th>\n",
       "      <th>Presentation Week 12</th>\n",
       "      <th>Practical Week 15</th>\n",
       "      <th>Course total (Real)</th>\n",
       "      <th>lms_Count_File</th>\n",
       "      <th>lms_Count_Folder</th>\n",
       "      <th>lms_Count_Forum</th>\n",
       "      <th>lms_Count_Page</th>\n",
       "      <th>...</th>\n",
       "      <th>lms_Assignment1_delay</th>\n",
       "      <th>lms_Assignment2_delay</th>\n",
       "      <th>lms_Assignment3_delay</th>\n",
       "      <th>lms_ActiveDaysCount</th>\n",
       "      <th>lms_LInactivityPeriod</th>\n",
       "      <th>lms_AccessDuration</th>\n",
       "      <th>lms_RescourceViewCount</th>\n",
       "      <th>lms_ActiveWeekCount</th>\n",
       "      <th>Group</th>\n",
       "      <th>lms_all</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>23.915385</td>\n",
       "      <td>24.507692</td>\n",
       "      <td>4.392308</td>\n",
       "      <td>4.384615</td>\n",
       "      <td>23.361538</td>\n",
       "      <td>80.538462</td>\n",
       "      <td>119.923077</td>\n",
       "      <td>20.846154</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>38.230769</td>\n",
       "      <td>...</td>\n",
       "      <td>1.461538</td>\n",
       "      <td>2.923077</td>\n",
       "      <td>-2.769231</td>\n",
       "      <td>46.307692</td>\n",
       "      <td>16497.153846</td>\n",
       "      <td>113.461538</td>\n",
       "      <td>73.923077</td>\n",
       "      <td>16.307692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>345.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>15.950000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>19.090000</td>\n",
       "      <td>60.040000</td>\n",
       "      <td>126.200000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>38.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>-0.600000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>12928.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>357.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>9.166667</td>\n",
       "      <td>7.722222</td>\n",
       "      <td>1.744444</td>\n",
       "      <td>1.722222</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>142.444444</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>42.555556</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>14636.777778</td>\n",
       "      <td>104.888889</td>\n",
       "      <td>59.888889</td>\n",
       "      <td>15.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>374.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Paper Week7  Paper Week10  Self evaluation Week10  \\\n",
       "Group Name                                                      \n",
       "A             23.915385     24.507692                4.392308   \n",
       "B             15.950000     17.850000                3.300000   \n",
       "C              9.166667      7.722222                1.744444   \n",
       "\n",
       "            Presentation Week 12  Practical Week 15  Course total (Real)  \\\n",
       "Group Name                                                                 \n",
       "A                       4.384615          23.361538            80.538462   \n",
       "B                       3.680000          19.090000            60.040000   \n",
       "C                       1.722222           6.333333            26.666667   \n",
       "\n",
       "            lms_Count_File  lms_Count_Folder  lms_Count_Forum  lms_Count_Page  \\\n",
       "Group Name                                                                      \n",
       "A               119.923077         20.846154         0.615385       38.230769   \n",
       "B               126.200000         22.700000         0.100000       38.300000   \n",
       "C               142.444444         22.666667         0.444444       42.555556   \n",
       "\n",
       "               ...      lms_Assignment1_delay  lms_Assignment2_delay  \\\n",
       "Group Name     ...                                                     \n",
       "A              ...                   1.461538               2.923077   \n",
       "B              ...                   5.800000               6.800000   \n",
       "C              ...                   1.000000               2.333333   \n",
       "\n",
       "            lms_Assignment3_delay  lms_ActiveDaysCount  lms_LInactivityPeriod  \\\n",
       "Group Name                                                                      \n",
       "A                       -2.769231            46.307692           16497.153846   \n",
       "B                       -0.600000            53.600000           12928.400000   \n",
       "C                       -0.111111            47.000000           14636.777778   \n",
       "\n",
       "            lms_AccessDuration  lms_RescourceViewCount  lms_ActiveWeekCount  \\\n",
       "Group Name                                                                    \n",
       "A                   113.461538               73.923077            16.307692   \n",
       "B                   116.000000               77.800000            17.200000   \n",
       "C                   104.888889               59.888889            15.666667   \n",
       "\n",
       "            Group     lms_all  \n",
       "Group Name                     \n",
       "A             1.0  345.230769  \n",
       "B             2.0  357.700000  \n",
       "C             3.0  374.111111  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (Final.groupby(['Group Name'], as_index=False).mean())\n",
    "# x['lms_all']= x['lms_C_File']+x['lms_C_Folder']+x['lms_C_Forum']+x['lms_C_URL']+x['lms_C_System']\n",
    "# x\n",
    "# pd.set_option('display.max_columns', 500)\n",
    "x['lms_all'] = x[[col for col in Final if col.startswith('lms_Count_')]].sum(axis=1)\n",
    "\n",
    "x.set_index(\"Group Name\",inplace=True)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RCutbJ4lX2gN"
   },
   "source": [
    "**Student Moodle Resources View by Student Group**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "colab_type": "code",
    "id": "4jDFNUipWlxl",
    "outputId": "54220444-ac22-41bb-e837-a233d7796f0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e04ab4470>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAFKCAYAAAAuUkrvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xm8XWV97/HPl8GEAjIGGhIwqFhBkIhh8KIyXia1oJbpVgVFqRVrvddakHorcB1wKqJSLVbLUMSCSqVgCwjiUAUMyByVAIEcgxBGGQQh/O4fax3cHM5EclbOOeTzfr3266z1rGev/Vtr75z9zbOevU+qCkmSJI2tlca7AEmSpOciQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZWiEkuTTJO8e7jq70Hl+SQ5P8eLxrei5LcnSSf+74MSrJi7t8jLGQZOckfeNdx1h6Lh6TxochSxNCklcn+UmSB5Lcm+S/k2zbbpswoWE0tbSBp5JsPaD939v2nTstchm0tT+a5KEkdyf5dpLp413X8pTkn5KcNkj7y5M8lmTdqvp4VU2K0J7keUk+m6SvfV5vTXJCz/YFSXYfzxr7jaaWJGsm+Ye278NJbk/yzSTbLa86pdEyZGncJXk+cB7wBWBdYAZwLPDYeNa1jH4FvK1/Jcl6wA7A4nGraPTeW1VrAC8G1gA+M871PCXJKsvhYU4B3pRk9QHtbwPOq6p7l0MNY+lDwBxgO2BNYBfg5+Na0VJKMgW4BNgKeD3wfGBz4BvAPkPcZ3m8ZqRBGbI0EbwEoKrOrKolVfW7qrqwqq5NsjnwZeBV7f/C74dnXv4bOMKU5H8m+UU7MvZFIL0PmOQdSeYluS/JBUle0LOtkrw7yU3t9pPSGLSWIZwBHJhk5Xb9YOAc4Pc9jzMlyeeSLGpvn2vfRPq3vyvJ/HZk79wkG432+AYc60uTXNTu55dJDhim7qdU1f3AvwOze/a1UpKjktyc5J4kZyVZt902Ncm/tu33J/lZkg3bbRu1x3Bve0zv6tnnKUk+2rP+tEs17YjFkUmuBR5OskqSjdtRtsXt432xp/+gz237HJ6Q5K72vF2bZMtBjvunwK+BN/fsc2XgfwGntuvHJPnXnu07pBmJvT/JNWlHK5PskuS6nn7fS3JFz/qPk+w3zNOwT5Jb0owqfro9/1Pa87hVz342SPK7JNMG2ce2wDlVtagaC6rqtPZ+pwObAP/Rvqb/duD573kOdm+XV2ufs/uS3Njuv7fvRkm+1T43tyZ5X8+2Y9rXzGlJHkxyQ5I5Q9UyyLG8FZgJ7FdV17e/Lx6uqm9W1TE9j1NJjkhyE3BT23ZikoVJfpvkyiSv6em/1MckDceQpYngV8CSJKcm2TvJOv0bqmoe8G7gp1W1RlWtPdLOkqwPfAv4MLA+cDOwY8/2/YCjgTcB04AfAWcO2M3raX7Rbg0cAOz5LGtZBNwI7NGuvw0YeAnq72hGt2a3j7NdWzNJdgU+0T72dOA2mv+tj3h8A87F6sBFwNeBDWjC3j8medkwtfffdz2aczS/p/l9wH7ATsBGwH3ASe22Q4C1gI2B9WjO1e/abWcCfe19/gz4eJLdRqqhx8HA64C1gaIZ+bwNmEUz8tl/boZ7bvcAXksT6tcGDgTuGeLxTqNnJBLYHVgV+M+BHZPMAM4HPkozEvs3wLfawPNT4MVJ1k8zorIlMDPNJa/VgFe2NQ7ljTSjUNsA+wLvqKrH2uN9y4Dz872qGmyk9DLg/yR5T5KtkjwVyKvqrcDtwBva1/Snhqml30eAF7W3PWme9/5zsRLwH8A1NM/LbsD7k+zZc/8/betfGzgX+OKzqGV34IKqengUde4HbA9s0a7/jObf2ro0/x7OTjJ1jI5JGlxVefM27jeaIf9TaN6In6D55bthu+1Q4McD+l8KvLNn/ak+NG+Ol/VsS7vfd7br/wkc1rN9JeAR4AXtegGv7tl+FnDUULUMciyXAu+keRM8E/gT4Ffttj5g53b5ZmCfnvvtCSxol78KfKpn2xrA4zShYqTj6z0XBwI/GlDfPwEfGab2R4AH2vNwNbBJz/Z5wG4969PbulYB3gH8BHj5gH1uDCwB1uxp+wRwSrt8CvDRnm07A3096wtowkX/+qtoLruuMkj9Qz63wK40gX4HYKURnsNN2uOa2a6fAZzYs/0Y4F/b5SOB0wfc/wLgkHb5RzShbwfgwvb1tBfNZbtrh6mhgL161t8DXNwubw8s7D8OYC5wwBD7WRk4Avhvmkvwi/pr6zm/uw91/gf2AW4ZUNfh/f3bum4fcN8PAf/Sc96+17NtC+B3Q9UyyLF8Dzi+Z302cD/wW+CXA87driM8x/cBWy/rMXnzNtzNkSxNCFU1r6oOraqZNP/b3wj43FLubiOaN6D+fVfvOs0b7ontpZ37gXtpgsqMnj6/6Vl+hCbkPFvfpnlj/yvg9CHqvK1n/ba27RnbquohmlGXGYx8fL1eAGzff6zt8f458MfD1P2+qloLeDmwDs3lmd79ndOzr3k0AWrD9hgvAL6R5vLnp5Ks2tZ7b1U9OOBYe8/3SHqPb2Pgtqp6YojjHfS5rapLaEZNTgLuTHJymvmAz1BVtwM/BN6SZA2aUZFTh6jtBcD+A87xq2kCKMAPaILLa9vlS2lGAndq10d73E+9PqrqcuBhYKckL6WZP3fuEMeypKpOqqodaUaPPgZ8Lc3l76XxtNcfT38NvwDYaMC5OJrm9dFv4L+tqRn9vKl7+MN5paqurmZE+U3AlAF9n/ZvIskH0lxGfqCtay2akeCxOCZpUIYsTThV9Qua0Y3++TI1SLeHgT/qWe8NDXfQvBEDzVyc3nWaX6Z/UVVr99xWq6qfjKa8UfRpOlY9QjOy8pcMHrIW0fwC77dJ2/aMbe1lv/Vo5gqNdHy9FgI/GHCsa1TVX46i/utoLoGd1HOJaSGw94D9Ta2qX1fV41V1bFVtAfwPmkuub2uPZd0kaw441l+3y8M9l0+VM+CYNhnijXnY57aqPl9VrwReRnPZ8IPDnIJT2/rfDNxaVVcN0W8hzUhW72OuXlXHt9sHhqwfMPqQ1fu89r4++ut7C808pW9W1aMj7Itq5jueRDOK038ZbeBr+mnPR5r5aL1zvZ72+mvr6reQ5lz1nos1q2rQSemDlTjC9ouBPfLMDyUMu692/tWRNJff12mD2QP8YS5jl8ekFZghS+MuzcTsDySZ2a5vTDPH5LK2y50081ie13O3q2k+AfZHab5L6LCebecDL0vypvaN+H08/Y37y8CH+uclJVkryf6jLHewWoZzNLBTVS0YZNuZwIeTTGvnWf090D+Z+uvA25PMTjMZ/uPA5e1+Rjq+XucBL0ny1iSrtrdtn8Uoxqk0c7n+tF3/MvCx/GEy+bQk+7bLu7RzflamuXzzOLCkqhbSXEb8RJrJ8S+neb7OaPd5Nc0E73WT/DHw/hFquoLmTfH4JKu3++yfkzbkc9se9/bt6NrDwKM0o3BD+RbNG++xDD2KBc1z9oYkeyZZua1n5/7Xc3vsf0Iz5+6KqrqBdoSRZrRsOB9Msk77b+KvgX/r2XY6zZytt/DM+X5PSfL+tp7V0nxo4BCaTxn2f8LwTuCFPXf5Fc3o0uvac/Vhnj5KdBbNOV6nPca/6tl2BfDbNB9UWK09H1um/TqWURhYy0Cn0Tz357T7XbmdVzVnhP2uSTMNYTGwSpK/p/lk4vI4Jq3ADFmaCB6kecO5PMnDNOHqeuAD7fZLgBuA3yS5u207geaTenfSvAH2v2FTVXcD+wPH01xe2IxmPkr/9nOAT9Jc1vpt+1h7j7LWwWoZUjWf6Brqe7U+SjOX5lrgOuCqto2quhj4vzRv9HfQTMg9aDTHN+DxH6SZ8H0QzSjIb2iOfeCllaHq/z3w+bYWgBNpLktdmORBmudq+3bbHwPfpAlY82hGafpD48E088kW0XzK8iNVdVG77XSaScULaOYs9QaJwWpaAryB5hLZ7TTz0Q5stw333D4f+ArNKM5tNOduyK+nqGZydX/QOmOYfgtpJqUfTfMmvpBmhGylnv1cBdzQnk9oJsTfVlV3DXeswHeAK2mC6Pk0c/X6H7ev3W8x/OT53wGfpXnu76aZn/Xmqrql3f4JmrB/f5K/qaoHaOZ//TPNaOPDNOe437E05+9WmufrqVHanudmdrv97nY/a41wnP2eVsvAje1o3S40Hyo5n3YuFs2HVIb71OwFNKPKv2prf5SnXx7s8pi0AksznUOSNNkk+RqwqKo+PN61SHomv6RNkiahJLNoJny/YnwrkTQULxdK0iST5P/RXAr9dFXdOt71SBqclwslSZI64EiWJElSBwxZkiRJHZgQE9/XX3/9mjVr1niXIUmSNKIrr7zy7qoa7A+yP82ECFmzZs1i7ty5412GJEnSiJLcNnIvLxdKkiR1wpAlSZLUAUOWJElSBybEnKzBPP744/T19fHooyP+YfkVytSpU5k5cyarrrrqeJciSZKGMWFDVl9fH2uuuSazZs0iyXiXMyFUFffccw99fX1suumm412OJEkaxoS9XPjoo4+y3nrrGbB6JGG99dZzdE+SpElgwoYswIA1CM+JJEmTw4QOWZIkSZPVhJ2TNdCso84f0/0tOP51I/ZZY401eOihh8b0cYezYMECXv/613P99ddz6aWX8pnPfIbzzjtvuT2+JEkaO45kSZIkdcCQNQqXXnopO+20EwcccAAveclLOOqoozjjjDPYbrvt2Gqrrbj55psBOPvss9lyyy3Zeuutee1rXzvk/hYsWMBrXvMattlmG7bZZht+8pOfLK9DkSRJy8mkuVw43q655hrmzZvHuuuuywtf+ELe+c53csUVV3DiiSfyhS98gc997nMcd9xxXHDBBcyYMYP7779/yH1tsMEGXHTRRUydOpWbbrqJgw8+2L/dKEnjbKtTtxrvElY41x1y3XiX0ClHskZp2223Zfr06UyZMoUXvehF7LHHHgBstdVWLFiwAIAdd9yRQw89lK985SssWbJkyH09/vjjvOtd72KrrbZi//3358Ybb1wehyBJkpYjR7JGacqUKU8tr7TSSk+tr7TSSjzxxBMAfPnLX+byyy/n/PPPZ/bs2Vx99dWst956z9jXCSecwIYbbsg111zDk08+ydSpU5fPQUiSpOXGkawxdPPNN7P99ttz3HHHsf7667Nw4cJB+z3wwANMnz6dlVZaidNPP33YUS9JkjQ5TZqRrNF85cJ4++AHP8hNN91EVbHbbrux9dZbD9rvPe95D29+85s5++yz2WWXXVh99dWXc6WSJKlrqarxroE5c+bUwInf8+bNY/PNNx+niiY2z40kjT0nvi9/k3Xie5Irq2rOSP28XChJktSBSXO5cDK64IILOPLII5/Wtummm3LOOeeMU0WSJGl5MWR1aM8992TPPfcc7zIkSdI4mNCXCyfCfLGJxnMiSdLkMGFD1tSpU7nnnnsMFT2qinvuucfv1ZIkaRKYsJcLZ86cSV9fH4sXLx7vUiaUqVOnMnPmzPEuQyuaY9Ya7wpWPMc8MN4VSFpGEzZkrbrqqmy66abjXYYkSdJSGfFyYZKpSa5Ick2SG5Ic27afkuTWJFe3t9lte5J8Psn8JNcm2abrg5AkSZpoRjOS9Riwa1U9lGRV4MdJ/rPd9sGq+uaA/nsDm7W37YEvtT8lSZJWGCOOZFXjoXZ11fY23Gz0fYHT2vtdBqydZPqylypJkjR5jOrThUlWTnI1cBdwUVVd3m76WHtJ8IQkU9q2GUDvX0bua9skSZJWGKMKWVW1pKpmAzOB7ZJsCXwIeCmwLbAu0P/V5hlsFwMbkhyeZG6SuX6CUJIkPdc8q+/Jqqr7gUuBvarqjvaS4GPAvwDbtd36gI177jYTWDTIvk6uqjlVNWfatGlLVbwkSdJENZpPF05Lsna7vBqwO/CL/nlWSQLsB1zf3uVc4G3tpwx3AB6oqjs6qV6SJGmCGs2nC6cDpyZZmSaUnVVV5yW5JMk0msuDVwPvbvt/F9gHmA88Arx97MueOGYddf54l7DCWXD868a7BEmSRjRiyKqqa4FXDNK+6xD9Czhi2UuTJEmavCbs3y6UJEmazAxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSB0YMWUmmJrkiyTVJbkhybNu+aZLLk9yU5N+SPK9tn9Kuz2+3z+r2ECRJkiae0YxkPQbsWlVbA7OBvZLsAHwSOKGqNgPuAw5r+x8G3FdVLwZOaPtJkiStUEYMWdV4qF1dtb0VsCvwzbb9VGC/dnnfdp12+25JMmYVS5IkTQKjmpOVZOUkVwN3ARcBNwP3V9UTbZc+YEa7PANYCNBufwBYbyyLliRJmuhGFbKqaklVzQZmAtsBmw/Wrf052KhVDWxIcniSuUnmLl68eLT1SpIkTQrP6tOFVXU/cCmwA7B2klXaTTOBRe1yH7AxQLt9LeDeQfZ1clXNqao506ZNW7rqJUmSJqjRfLpwWpK12+XVgN2BecD3gT9rux0CfKddPrddp91+SVU9YyRLkiTpuWyVkbswHTg1yco0oeysqjovyY3AN5J8FPg58NW2/1eB05PMpxnBOqiDuiVJkia0EUNWVV0LvGKQ9lto5mcNbH8U2H9MqpMkSZqk/MZ3SZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA4YsSZKkDhiyJEmSOjBiyEqycZLvJ5mX5IYkf922H5Pk10mubm/79NznQ0nmJ/llkj27PABJkqSJaJVR9HkC+EBVXZVkTeDKJBe1206oqs/0dk6yBXAQ8DJgI+B7SV5SVUvGsnBJkqSJbMSRrKq6o6quapcfBOYBM4a5y77AN6rqsaq6FZgPbDcWxUqSJE0Wz2pOVpJZwCuAy9um9ya5NsnXkqzTts0AFvbcrY/hQ5kkSdJzzqhDVpI1gG8B76+q3wJfAl4EzAbuAD7b33WQu9cg+zs8ydwkcxcvXvysC5ckSZrIRhWykqxKE7DOqKpvA1TVnVW1pKqeBL7CHy4J9gEb99x9JrBo4D6r6uSqmlNVc6ZNm7YsxyBJkjThjObThQG+Csyrqn/oaZ/e0+2NwPXt8rnAQUmmJNkU2Ay4YuxKliRJmvhG8+nCHYG3AtclubptOxo4OMlsmkuBC4C/AKiqG5KcBdxI88nEI/xkoSRJWtGMGLKq6scMPs/qu8Pc52PAx5ahLkmSpEnNb3yXJEnqgCFLkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpAyOGrCQbJ/l+knlJbkjy1237ukkuSnJT+3Odtj1JPp9kfpJrk2zT9UFIkiRNNKMZyXoC+EBVbQ7sAByRZAvgKODiqtoMuLhdB9gb2Ky9HQ58acyrliRJmuBGDFlVdUdVXdUuPwjMA2YA+wKntt1OBfZrl/cFTqvGZcDaSaaPeeWSJEkT2LOak5VkFvAK4HJgw6q6A5ogBmzQdpsBLOy5W1/bJkmStMIYdchKsgbwLeD9VfXb4boO0laD7O/wJHOTzF28ePFoy5AkSZoURhWykqxKE7DOqKpvt8139l8GbH/e1bb3ARv33H0msGjgPqvq5KqaU1Vzpk2btrT1S5IkTUij+XRhgK8C86rqH3o2nQsc0i4fAnynp/1t7acMdwAe6L+sKEmStKJYZRR9dgTeClyX5Oq27WjgeOCsJIcBtwP7t9u+C+wDzAceAd4+phVLkiRNAiOGrKr6MYPPswLYbZD+BRyxjHVJkiRNan7juyRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgdGDFlJvpbkriTX97Qdk+TXSa5ub/v0bPtQkvlJfplkz64KlyRJmshGM5J1CrDXIO0nVNXs9vZdgCRbAAcBL2vv849JVh6rYiVJkiaLEUNWVf0QuHeU+9sX+EZVPVZVtwLzge2WoT5JkqRJaVnmZL03ybXt5cR12rYZwMKePn1tmyRJ0gplaUPWl4AXAbOBO4DPtu0ZpG8NtoMkhyeZm2Tu4sWLl7IMSZKkiWmpQlZV3VlVS6rqSeAr/OGSYB+wcU/XmcCiIfZxclXNqao506ZNW5oyJEmSJqylCllJpvesvhHo/+ThucBBSaYk2RTYDLhi2UqUJEmafFYZqUOSM4GdgfWT9AEfAXZOMpvmUuAC4C8AquqGJGcBNwJPAEdU1ZJuSpckSZq4RgxZVXXwIM1fHab/x4CPLUtRkiRJk53f+C5JktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHRgxZSb6W5K4k1/e0rZvkoiQ3tT/XaduT5PNJ5ie5Nsk2XRYvSZI0UY1mJOsUYK8BbUcBF1fVZsDF7TrA3sBm7e1w4EtjU6YkSdLkMmLIqqofAvcOaN4XOLVdPhXYr6f9tGpcBqydZPpYFStJkjRZLO2crA2r6g6A9ucGbfsMYGFPv762TZIkaYUy1hPfM0hbDdoxOTzJ3CRzFy9ePMZlSJIkja+lDVl39l8GbH/e1bb3ARv39JsJLBpsB1V1clXNqao506ZNW8oyJEmSJqalDVnnAoe0y4cA3+lpf1v7KcMdgAf6LytKkiStSFYZqUOSM4GdgfWT9AEfAY4HzkpyGHA7sH/b/bvAPsB84BHg7R3ULEmSNOGNGLKq6uAhNu02SN8CjljWoiRJkiY7v/FdkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA4YsSZKkDhiyJEmSOmDIkiRJ6oAhS5IkqQOGLEmSpA4YsiRJkjpgyJIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA6ssy52TLAAeBJYAT1TVnCTrAv8GzAIWAAdU1X3LVqYkSdLkMhYjWbtU1eyqmtOuHwVcXFWbARe365IkSSuULi4X7guc2i6fCuzXwWNIkiRNaMsasgq4MMmVSQ5v2zasqjsA2p8bLONjSJIkTTrLNCcL2LGqFiXZALgoyS9Ge8c2lB0OsMkmmyxjGZIkSRPLMo1kVdWi9uddwDnAdsCdSaYDtD/vGuK+J1fVnKqaM23atGUpQ5IkacJZ6pCVZPUka/YvA3sA1wPnAoe03Q4BvrOsRUqSJE02y3K5cEPgnCT9+/l6Vf1Xkp8BZyU5DLgd2H/Zy5QkSZpcljpkVdUtwNaDtN8D7LYsRUmSJE12fuO7JElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHTBkSZIkdcCQJUmS1AFDliRJUgcMWZIkSR0wZEmSJHXAkCVJktQBQ5YkSVIHDFmSJEkdMGRJkiR1wJAlSZLUAUOWJElSBwxZkiRJHegsZCXZK8kvk8xPclRXjyNJkjQRdRKykqwMnATsDWwBHJxkiy4eS5IkaSLqaiRrO2B+Vd1SVb8HvgHs29FjSZIkTThdhawZwMKe9b62TZIkaYWwSkf7zSBt9bQOyeHA4e3qQ0l+2VEtGtz6wN3jXcTSyCfHuwJNIpP2dc6xg/0alQY1aV/nOXTSvs5fMJpOXYWsPmDjnvWZwKLeDlV1MnByR4+vESSZW1VzxrsOqUu+zrUi8HU+cXV1ufBnwGZJNk3yPOAg4NyOHkuSJGnC6WQkq6qeSPJe4AJgZeBrVXVDF48lSZI0EXV1uZCq+i7w3a72r2XmpVqtCHyda0Xg63yCSlWN3EuSJEnPin9WR5IkqQOGLEmSpA4YslZQSXZMctJ41yFJGr0kL06y4yDtr0nyovGoSUMzZK1AksxO8qkkC4CPAr8Y55KkTiVZP8mk/bZDaRCfAx4cpP137TZNIIas57gkL0ny90nmAV+k+XNHqapdquoL41yeNGaS7JDk0iTfTvKKJNcD1wN3JtlrvOuTxsisqrp2YGNVzQVmLf9yNJzOvsJBE8YvgB8Bb6iq+QBJ/vf4liR14ovA0cBawCXA3lV1WZKXAmcC/zWexUljZOow21ZbblVoVBzJeu57M/Ab4PtJvpJkNwb/25LSZLdKVV1YVWcDv6mqywCqysviei75WZJ3DWxMchhw5TjUo2E4kvUcV1XnAOckWR3YD/jfwIZJvgScU1UXjmuB0th5smf5dwO2+YWAeq54P83v9D/nD6FqDvA84I3jVpUG5ZeRroCSrAvsDxxYVbuOdz3SWEiyBHiYZqQzO2J8AAADCElEQVR2NeCR/k3A1Kpadbxqk8Zakl2ALdvVG6rqkvGsR4MzZEmSJHXAOVmSJEkdMGRJkiR1wJAlqXNJNkzy9SS3JLkyyU+TLNdJukkOTfJkkpf3tF2fZNbyrEPSisOQJalT7Teu/zvww6p6YVW9EjgImDlI364/8dwH/F3HjyFJgCFLUvd2BX5fVV/ub6iq2/r/4kA7wnR2kv8ALkzj0+0o03VJDmz77ZzkvP59JPlikkPb5QVJPpnkivb24iFqOQ94WZI/GbghyZeSzE1yQ5Jje9oXJPl4O/o2N8k2SS5IcnOSd/f0+2CSnyW5tvf+klZchixJXXsZcNUIfV4FHNJ+pcibgNnA1sDuwKeTTB/F4/y2qraj+eb3of6G25PAp2i+GX6gv6uqOcDLgZ16LysCC6vqVTR/PeEU4M+AHYDjAJLsAWwGbNfW/sokrx1FzZKewwxZkparJCcluSbJz3qaL6qqe9vlVwNnVtWSqroT+AGw7Sh2fWbPz1cN0+/rwA5JNh3QfkCSq4Cf0wTDLXq2ndv+vA64vKoerKrFwKNJ1gb2aG8/pwmUL6UJXZJWYH7ju6Su3UDz550AqKojkqwPzO3p83DP8lB/9ukJnv4fw4F/w62GWH56p6onknwWOPKpB2wC198A21bVfUlOGbD/x9qfT/Ys96+v0tb8iar6p6EeV9KKx5EsSV27BJia5C972v5omP4/BA5MsnKSacBrgSuA24AtkkxJshaw24D7Hdjz86cj1HQKzaXIae3682mC3gNJNgT2HuH+A10AvCPJGgBJZiTZ4FnuQ9JzjCNZkjpVVZVkP+CEJH8LLKYJNEcOcZdzaC73XUMzIvW3VfUbgCRnAdcCN9Fcmus1JcnlNP95PHiEmn6f5PPAie36NUl+TjPqdgvw38/yGC9Msjnw0+bDlDwEvAW469nsR9Jzi39WR9Kkl2QBMKeq7h7vWiSpn5cLJUmSOuBIliRJUgccyZIkSeqAIUuSJKkDhixJkqQOGLIkSZI6YMiSJEnqgCFLkiSpA/8f+j6k6eKS9YkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot.bar( y='lms_all' ,figsize=(10,5), title='Student Moodle Resources View by Student Grade'  )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OrgtESecX4Ox"
   },
   "source": [
    "**Student Moodle Course Access Rate by Student Grade**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "colab_type": "code",
    "id": "nbYSLVpuX4X5",
    "outputId": "c69b7ab7-0967-4911-ef9a-2af8090e0235"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e05f89240>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot.bar(y='lms_ActiveDaysCount' ,figsize=(10,5), title='Student Moodle Course Access Rate by Student Grade'  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 366
    },
    "colab_type": "code",
    "id": "nbYSLVpuX4X5",
    "outputId": "c69b7ab7-0967-4911-ef9a-2af8090e0235"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e0555bb00>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot.bar(y='lms_RescourceViewCount' ,figsize=(10,5), title='Student Moodle Course Access Rate by Student Grade'  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e055b9cf8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAFKCAYAAAAnueqVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XmYFfWd7/H3l0VAcQeNCgZiojEgouJu1ESDSxwxalweFzAxxFxzJ5M7OlHHGx0zYyYTMzERH7foaEYlTnT0mriAE+OgwQ0VlQwaFDsBcUHcd9Hv/aOq8dCcXujTVNPwfj3PefpU1a+qvlWnus+nf1WnTmQmkiRJWrF6dXcBkiRJqwNDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF3q8SLirog4sbvrWFFqty8iJkTEPd1dk1ZuEdEUEft2dx3tiYhhEZER0ae7a+kqq+I2qesYurTCRcQeETE9Il6LiJcj4g8RsWM5baUJER2ppQxAGRHbthh/Uzl+7xVaZIMiYr+ImBYRb0TEwoj474g4uLvr6ohy378SEf26u5b2RMSVEfF+RLxZHvN3RMRnl2P+lSY0RcQZEfFMuS3zI+K6mmkrzT88HaklItaIiO9HxJMR8VZEPBsRt0XE2Krq1OrN0KUVKiLWAX4LXABsAGwG/APwXnfW1aA/Acc3D0TEhsAuwMJuq6gDIuJw4NfAL4EhwMbA94G/WgHr6tL/8iNiGPB5IIEeERKBf8nMgRTH/LPA5d1cz3KLiPHAccC+5baMAX7XvVU15HpgHMXv7/rAcOBnwJfrNba3Sl0uM334WGEPij/Sr7YybWvgXeBD4M3mdsBdwIk17SYA99QMfwl4AngNmAT8d4v2XwNmA68AU4BP1kxL4CRgTjn9QiBaq6VOzXdRBJX5QO9y3LeBi8pxe5fj+gHnAwvKx/lAv5rlfAN4CngZuBnYtCPbV2dffBa4o1zOk8ARrdQdwF+AU9t4rXoBZwJ/Bl6kCGfrltP2Bua3aN9E8WYMcDbFG9rVwOvAicBOwIxy+AXgX2vm3QWYDrwKPNq839qo7fvAH4B/BX7bYtoA4Cdl3a8B9wADyml71KxnHjCh5vU5r9wnLwAX18wziOIfhVfL/Xo30Kuc9j2KAPVGub/3aaXeK4F/rBk+EHirZngL4E5gEfAScA2wXjnt34GPgHcojsW/W959Vr42pwP/Q3Gc/xvQv5w2C/irmrZ9yxpG11nOJOD8VtbxTxS/L++WdU4ChlH8jvVp8TvTfPz2Lvf7S8Bc4OTa9sC6FOH0uXI//yMf/55NKF/b88ptegY4oLVa6tS7b7lPh7RzrDWVr/NjFP8c9gFOA54uX/f/Ab5S077T2+Rj9Xt0ewE+Vu0HsE75xnIVcACwfovpE6gJEeW4JX+kW7aheEN8HTi8fLP4LrC45o/6IRRhZuvyj+WZwPSaZSXFG+p6wOYUvVP7t1ZLne25iyJQTK35g/8AsCtLh65zgPuAjYDBFG+WPyinfbH8A709xZv/BcC0Dm5f7b5YiyJInFBu6/blckfUqfuz5bYPb2Pbvlbuu08BA4H/BP69nLY37YeuD8r934siCN0LHFdOHwjsUj7frDwmDizbfqkcHtxGbU8B/wvYoVzPxjXTLixfl80o3gB3K/fr5hRvkkeX+3JDymBBEYJvpuh9XRv4DfDDctoPKUJY3/LxeYrQulW5vzct2w0Dtmil3ispQ1f5Ov078GjN9E+X292vPD6mURNuavdtZ/ZZOf8sYGi5jX+oqefvgOtq2o4DHm9lOcdSBM9TKf6B6t1i+l0s/bs6jLZD10kU/1A01/V7lg4oNwGXlPtsI4rfrW/WHPsfUPzD0hv4FsU/NFGvljrb8s/AXR34m9UEzCxrbA7iXwU2Lff9kcBbwCaNbpOP1e/R7QX4WPUfFAHoSopQspjizW7jctoEli90HQ/cVzMtyuU2/1G/Dfh6zfRewNuUvV3lH8M9aqb/B3Baa7XU2Za7KELXscBkijfiP5XTakPX08CBNfPtBzSVzy+nOPXUPG1g+WYyrAPbV7svjgTublHfJcBZderevdz2/m1s2++A/1UzvFVZVx86FrqmtZg+jeJU8qAW479HGeZqxk0BxrdS1x5lHYPK4SeA79a8vu8A29aZ73Tgxjrjg+JNc4uacbsCz5TPzwH+H/DpFvN9mqIHcF+gbzvHyZUUvS6vUvRaPQOMaqP9IcAj9fZtJ/dZE3BSzfCBwNPl800pwug65fD1lL1prSzrGOC/yn22iPL3pZXf1WG0HbrubFHX2Ob2FKe736MMOuX0o4Hf1xz7T9VMW7Oc9xP1aqmzHb8AflUzvEH5+rwGvNti332tndd3JjCu0W3ysfo9vKZLK1xmzs7MCZk5BBhJ8Uf//E4ublOK3obmZWftMPBJ4GcR8WpENJ8eCoqegmbP1zx/myL0LK//pOix+t8UvRj16vxzzfCfy3HLTMvMNynezDaj/e2r9Ulg5+ZtLbf3GOATddouKn9u0sY21au5+Y2jI1rW+XVgS+CJiHgwIg6qqfurLereo43axgNTM/OlcvjachwUPYP9KUJuS0NbGT+Y4g37oZr1316OB/gxRc/a1IiYGxGnAWTmU8DfUATMFyPiVxGx6TJL/9h5mbkeRRB5hyLEAhARG5XzPxsRr1Oclh3UxrKWd5/B0q/HkuMvMxdQ9HwdFhHrUfRAX9PaQjLzmszcl6J3+CTgnIjYr431tmWp45ulj7dPUvQsPlezjZdQ9A41W/K7m5lvl087+vu7iJr9lZkvl6/PDhQ9jrWWOpYj4viImFlT10g+fr0a3SatRgxdqlRmPkHRCzCyeVSdZm9RvCk2qw0Rz1G8mQIQEVE7TPHH75uZuV7NY0BmTu9IeR1oUzQs/uDfRnGKo17oWkDxB7fZ5uW4ZaZFxFoUp76epf3tqzUP+O8W2zowM79Vp+2TZfvD2tisejUvprjmaanXJCJ683FIabbU/svMOZl5NMUbzI+A68ttnUfRa1Nb91qZ+c8tC4qIAcARwF4R8XxEPE9xynXb8hOkL1H0KG1RZ3vmtTL+JYoQNKJm/etmcaE4mflGZv5tZn6K4kMG/yci9imnXZuZe5T7KcvtalNm/gX4DsU/AwPK0T8s5x+VmetQ9JxG7Wx1tqVD+6xG7XFTe/xBcbr/WIrTZvdm5rMd2I4PMvPXFNc6tfb7+1b5s0O/v2VdzeZR9AoNqtnGdTJzRHu1tVJLS78DdoyIIcuzrIj4JHAZxbWbG5ZBbRYfv14rcpu0ijF0aYWKiM9GxN82/6GLiKEU3ev3lU1eAIZExBo1s80EDo2INSPi0xQ9Js1uAUZExKHlJ4v+mqX/qF8MnB4RI8r1rRsRX+1gufVqacsZwF6Z2VRn2mTgzIgYHBGDKC4Ev7qcdi1wQkSMLm9/cC5wf7mc9rav1m+BLSPiuIjoWz52jIitWzYse8z+D/B/I+KEiFgnInpFcTuPS2tq/m5EDI+IgWVd12XmYopPbPaPiC9HRF+Ka+XavHVDRBwbEYMz8yOK0zhQXOx8NfBX5e0rekdE/4jYu5U3w0PKeT4HjC4fW1Nc3H58uewrgH+NiE3L5e1a7tdrgH0j4oiI6BMRG0bE6HKey4CfRsRGZa2bNffeRMRBEfHpMvC+Xq7/w4jYKiK+WC77XYrg9mFb+6Bm/99BEXomlqPWpvzARkRsRnHNVK0XKK6ta7Y8+6zZyRExJCI2oDhWr6uZdhPFNYDfofjARF1R3EblyxGxdnm8HACMAO6vV2dmLqT45+HYss6vsXTw/Q/gr8u61qe4QL153ucorpX8Sc3xuUVE7NXGNtZquc+WkplTKa63uikido7i9hF9KT6g0Ja1KELYQoCIOIGPQ+eK3iatarr7/KaPVftBccrsPyj+EL9V/ryEj68nWYMiaLwMvFSOG0Txh+oNitMgZ7P0J/b2pwgBrX168TjgcYo3zHnAFTXTkpprdVj6gudlaqmzPXfRynUjLH1NV3/g5xT/BT9XPu9f0/YkilNfL1OEpyEd2T6W/fTiVmXNCylOn9xJnU+htVj23RRv+AvL7flyOa0XRTicV067mpoPPpTrfo7iuqZTWPaarqtbrOvqsu2bwB+BQ2qm7Vxu18vlum4BNq9T7+3AT+qMP4LiVFMfiov2z6c4tl6juJas+QLoz1MEhOZjYXzN63MuxafNXqf4tOtfl9O+W27bW+Vr+n/L8aMoLoJ+o+Z127SV/bzkuKoZd2RZYz+K4PJQuW9mAn9LzTVzFBe3/4UirJ6yPPusbNvEx59efJWiZ2vNFm1+UW7jwDaOl0MpfgdfKffT45SfAC2n70pxrL4C/LwcdwDFNWyvUnyqtPb47QP8lOJYfYb6n/Rr/iTwa8AjwFH1jv2Wv8/1aqmzPf0ojtU5FJcWzKfosd6vxb7bt8V8/1Tu95coPkHbJdvkY/V7NH/qQ5K0GomI7wNbZuax3V2LtLrwxm+StJopTzl+naJXWFJFvKZLklYjEfENilOtt2XmtO6uR1qdeHpRkiSpAvZ0SZIkVcDQJUmSVIGV8kL6QYMG5bBhw7q7DEmSpHY99NBDL2VmyxtGL2OlDF3Dhg1jxowZ3V2GJElSuyLiz+238vSiJElSJQxdkiRJFTB0SZIkVaDda7oi4grgIODFzBxZjruO4jvfANYDXs3M0XXmbaL4nrIPgcWZOaaL6pYkqUM++OAD5s+fz7vvvtvdpaiH69+/P0OGDKFv376dmr8jF9JfSfGlu0u+iT4zj2x+HhE/ofgSz9Z8ITNf6lR1kiQ1aP78+ay99toMGzaMiOjuctRDZSaLFi1i/vz5DB8+vFPLaPf0Yvk1ES/XmxbF0XsEMLlTa5ckaQV799132XDDDQ1cakhEsOGGGzbUY9roNV2fB17IzDmtTE9gakQ8FBETG1yXJEmdYuBSV2j0OGr0Pl1H03Yv1+6ZuSAiNgLuiIgnWvuC1TKUTQTYfPPNGyxLkiRp5dLp0BURfYBDgR1aa5OZC8qfL0bEjcBOQN3QlZmXApcCjBkzxm/hliStEMNOu6VLl9f0z19ut83AgQN58803u3S9tb7zne9w/fXXM2/ePHr1avsk1rnnnssZZ5yxZHi33XZj+vTpy73Or3zlK4wfP55DDjkEgK222orjjjuOM888E4DDDjuMY445hkMPPXS5ltvU1MRBBx3ErFmzlpn2wAMPcMopp/DCCy8QEeyxxx78/Oc/Z80111zu+ltz5ZVXMnbsWDbddNMuW2azRk4v7gs8kZnz602MiLUiYu3m58BYYNk9KEmSOu2jjz7ixhtvZOjQoUybVrdfYynnnnvuUsOdCVywdFhbtGgRAwcO5N57710y/d5772W33Xbr1LLreeGFF/jqV7/Kj370I5588klmz57N/vvvzxtvvNFl64AidC1YsKBLl9ms3dAVEZOBe4GtImJ+RHy9nHQULU4tRsSmEXFrObgxcE9EPAo8ANySmbd3XemSJPUsd911F3vttRdHHHEEW265JaeddhrXXHMNO+20E9tssw1PP/00AL/+9a8ZOXIk2267LXvuuWeby/z973/PyJEj+da3vsXkyR+/Lb/55puccMIJbLPNNowaNYobbriB0047jXfeeYfRo0dzzDHHAEUvHMCRRx7JrbfeumT+CRMmcMMNN/Dhhx9y6qmnsuOOOzJq1CguueQSAHbfffcloWv69OkcdNBBLFy4kMzkmWeeYcCAAXziE59odX6AH//4x0vGn3XWWcts29y5c9luu+148MEHufDCCxk/fjy77rorUFxfdfjhh7Pxxhvz8ssvc8ghhzBq1Ch22WUXHnvsMQDOPvtszjvvvCXLGzlyJE1NTTQ1NbH11lvzjW98gxEjRjB27Fjeeecdrr/+embMmMExxxzD6NGjeeeddzr4ynZMu6cXM/PoVsZPqDNuAXBg+XwusG2D9Ula2Zy9bndXsPo5u6278qinefTRR5k9ezYbbLABn/rUpzjxxBN54IEH+NnPfsYFF1zA+eefzznnnMOUKVPYbLPNePXVV9tc3uTJkzn66KMZN24cZ5xxBh988AF9+/blBz/4Aeuuuy6PP/44AK+88gqHHXYYkyZNYubMmcss56ijjuK6667jwAMP5P333+d3v/sdF110EZdffjnrrrsuDz74IO+99x677747Y8eOZYcddmDWrFm8//77TJ8+nb322ou5c+cye/ZsHnnkEXbffXeAVuefM2cOc+bM4YEHHiAzOfjgg5k2bdqS67qffPJJjjrqKP7t3/6N0aNH88Mf/pDx48fX3QdnnXUW2223HTfddBN33nknxx9/fN1trDVnzhwmT57MZZddxhFHHMENN9zAsccey6RJkzjvvPMYM6brby3qHeklSarQjjvuyCabbEK/fv3YYostGDt2LADbbLMNTU1NQNGLNGHCBC677DI+/PDDVpf1/vvvc+utt3LIIYewzjrrsPPOOzN16lQA/uu//ouTTz55Sdv111+/zboOOOAA7rzzTt577z1uu+029txzTwYMGMDUqVP55S9/yejRo9l5551ZtGgRc+bMoV+/fowYMYKHH36Y++67j5133pldd92V6dOnM3369CWnFlubf+rUqUydOpXtttuO7bffnieeeII5c4qbISxcuJBx48Zx9dVXM3r0MvdeX8Y999zDcccdB8AXv/hFFi1axGuvtf3PyvDhw5cse4cddliy71ekRj+9KEmSlkO/fv2WPO/Vq9eS4V69erF48WIALr74Yu6//35uueUWRo8ezcyZM9lwww2XWdbtt9/Oa6+9xjbbbAPA22+/zZprrsmXv/xlMnO5bnHQv39/9t57b6ZMmcJ1113H0UcXJ7oykwsuuID99ttvmXl22203pk2bxhtvvMH666/PLrvswqRJk3jkkUc46aST2px/ypQpnH766Xzzm99canxTUxPrrrsuQ4cO5Q9/+AMjRowAYMSIETz00EOMGzdumToyl/38XUTQp08fPvrooyXjau+xVfs69O7du8tPJdZjT5ckSSuZp59+mp133plzzjmHQYMGMW/evLrtJk+ezC9+8Ysl1yk988wzTJ06lbfffpuxY8cyadKkJW1feeUVAPr27csHH3xQd3nNp/PuvvvuJSFpv/3246KLLloyz5/+9CfeeustoOiRu+SSS9h22+JqolGjRnHffffxl7/8ZUlYam3+/fbbjyuuuGLJpzqfffZZXnzxRQDWWGMNbrrpJn75y19y7bXXAvDtb3+bq666ivvvv39JvVdffTXPP/88e+65J9dccw1QXDc3aNAg1llnHYYNG8bDDz8MwMMPP8wzzzzT7r5fe+21u/zi/Gb2dEmSVisducVDdzv11FOZM2cOmck+++yzJNTUevvtt5kyZcpSF6avtdZa7LHHHvzmN7/hzDPP5OSTT2bkyJH07t2bs846i0MPPZSJEycyatQott9++yVBpdnYsWM5/vjjOfjgg1ljjTUAOPHEE2lqamL77bcnMxk8eDA33XQTUPR0zZ07l9NPPx2APn36sNFGGzF06NAlt65obf6xY8cye/bsJRfGDxw4kKuvvprevXsv2Zbf/va3fOlLX2KttdZi3Lhx/OpXv+KUU07hxRdfpFevXuy5554ceuihnH322ZxwwgmMGjWKNddck6uuugooblvRfGpzxx13ZMstt2x330+YMIGTTjqJAQMGcO+99zJgwIDleu3aEvW65LrbmDFjcsaMGd1dhqR6vJC+el5I35DZs2ez9dZbd3cZWkXUO54i4qHMbPfKe08vSpIkVcDTi5IkreSmTJnC9773vaXGDR8+nBtvvLGbKlJnGLokSVrJ7bfffnU/PaiexdOLkqRV3sp4/bJ6nkaPI0OXJGmV1r9/fxYtWmTwUkMyk0WLFtG/f/9OL8PTi5KkVdqQIUOYP38+Cxcu7O5S1MP179+fIUOGdHp+Q5ckaZXWt29fhg8f3t1lSIaurjTstFu6u4TVTk+4yaEkSeA1XZIkSZUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFfCO9JIktbDNVdt0dwmrncfHP97dJaxw9nRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVaDd0BURV0TEixExq2bc2RHxbETMLB8HtjLv/hHxZEQ8FRGndWXhkiRJPUlHerquBPavM/6nmTm6fNzacmJE9AYuBA4APgccHRGfa6RYSZKknqrd0JWZ04CXO7HsnYCnMnNuZr4P/AoY14nlSJIk9XiNXNP17Yh4rDz9uH6d6ZsB82qG55fjJEmSVjudDV0XAVsAo4HngJ/UaRN1xmVrC4yIiRExIyJmLFy4sJNlSZIkrZw6Fboy84XM/DAzPwIuoziV2NJ8YGjN8BBgQRvLvDQzx2TmmMGDB3emLEmSpJVWp0JXRGxSM/gVYFadZg8Cn4mI4RGxBnAUcHNn1idJktTTtfuF1xExGdgbGBQR84GzgL0jYjTF6cIm4Jtl202BX2TmgZm5OCK+DUwBegNXZOYfV8hWSJIkreTaDV2ZeXSd0Ze30nYBcGDN8K3AMreTkCRJWt14R3pJkqQKGLokSZIqYOiSJEmqgKFLkiSpAoYuSZKkChi6JEmSKmDokiRJqoChS5IkqQKGLkmSpAoYuiRJkipg6JIkSaqAoUuSJKkChi5JkqQKGLokSZIqYOiSJEmqgKFLkiSpAoYuSZKkChi6JEmSKmDokiRJqoChS5IkqQKGLkmSpAoYuiRJkipg6JIkSaqAoUuSJKkChi5JkqQKGLokSZIqYOiSJEmqgKFLkiSpAoYuSZKkCrQbuiLiioh4MSJm1Yz7cUQ8ERGPRcSNEbFeK/M2RcTjETEzImZ0ZeGSJEk9SUd6uq4E9m8x7g5gZGaOAv4EnN7G/F/IzNGZOaZzJUqSJPV87YauzJwGvNxi3NTMXFwO3gcMWQG1SZIkrTK64pqurwG3tTItgakR8VBETOyCdUmSJPVIfRqZOSL+HlgMXNNKk90zc0FEbATcERFPlD1n9ZY1EZgIsPnmmzdSliRJ0kqn0z1dETEeOAg4JjOzXpvMXFD+fBG4EdipteVl5qWZOSYzxwwePLizZUmSJK2UOhW6ImJ/4HvAwZn5ditt1oqItZufA2OBWfXaSpIkreo6csuIycC9wFYRMT8ivg5MAtamOGU4MyIuLttuGhG3lrNuDNwTEY8CDwC3ZObtK2QrJEmSVnLtXtOVmUfXGX15K20XAAeWz+cC2zZUnSRJ0irCO9JLkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVaBDoSsiroiIFyNiVs24DSLijoiYU/5cv5V5x5dt5kTE+K4qXJIkqSfpaE/XlcD+LcadBvwuMz8D/K4cXkpEbACcBewM7ASc1Vo4kyRJWpV1KHRl5jTg5RajxwFXlc+vAg6pM+t+wB2Z+XJmvgLcwbLhTZIkaZXXyDVdG2fmcwDlz43qtNkMmFczPL8ct4yImBgRMyJixsKFCxsoS5IkaeWzoi+kjzrjsl7DzLw0M8dk5pjBgwev4LIkSZKq1UjoeiEiNgEof75Yp818YGjN8BBgQQPrlCRJ6pEaCV03A82fRhwP/L86baYAYyNi/fIC+rHlOEmSpNVKR28ZMRm4F9gqIuZHxNeBfwa+FBFzgC+Vw0TEmIj4BUBmvgz8AHiwfJxTjpMkSVqt9OlIo8w8upVJ+9RpOwM4sWb4CuCKTlUnSZK0ivCO9JIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFOh26ImKriJhZ83g9Iv6mRZu9I+K1mjbfb7xkSZKknqdPZ2fMzCeB0QAR0Rt4FrixTtO7M/Ogzq5HkiRpVdBVpxf3AZ7OzD930fIkSZJWKV0Vuo4CJrcybdeIeDQibouIEV20PkmSpB6l4dAVEWsABwO/rjP5YeCTmbktcAFwUxvLmRgRMyJixsKFCxstS5IkaaXSFT1dBwAPZ+YLLSdk5uuZ+Wb5/Fagb0QMqreQzLw0M8dk5pjBgwd3QVmSJEkrj64IXUfTyqnFiPhERET5fKdyfYu6YJ2SJEk9Sqc/vQgQEWsCXwK+WTPuJIDMvBg4HPhWRCwG3gGOysxsZJ2SJEk9UUOhKzPfBjZsMe7imueTgEmNrEOSJGlV4B3pJUmSKmDokiRJqoChS5IkqQKGLkmSpAoYuiRJkipg6JIkSaqAoUuSJKkChi5JkqQKGLokSZIqYOiSJEmqgKFLkiSpAoYuSZKkChi6JEmSKmDokiRJqoChS5IkqQKGLkmSpAoYuiRJkipg6JIkSaqAoUuSJKkChi5JkqQKGLokSZIqYOiSJEmqgKFLkiSpAoYuSZKkChi6JEmSKmDokiRJqoChS5IkqQKGLkmSpAoYuiRJkirQcOiKiKaIeDwiZkbEjDrTIyJ+HhFPRcRjEbF9o+uUJEnqafp00XK+kJkvtTLtAOAz5WNn4KLypyRJ0mqjitOL44BfZuE+YL2I2KSC9UqSJK00uiJ0JTA1Ih6KiIl1pm8GzKsZnl+OkyRJWm10xenF3TNzQURsBNwREU9k5rSa6VFnnmw5ogxsEwE233zzLihLkiRp5dFwT1dmLih/vgjcCOzUosl8YGjN8BBgQZ3lXJqZYzJzzODBgxstS5IkaaXSUOiKiLUiYu3m58BYYFaLZjckRSPiAAAIwElEQVQDx5efYtwFeC0zn2tkvZIkST1No6cXNwZujIjmZV2bmbdHxEkAmXkxcCtwIPAU8DZwQoPrlCRJ6nEaCl2ZORfYts74i2ueJ3ByI+uRJEnq6bwjvSRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklSBToeuiBgaEb+PiNkR8ceI+E6dNntHxGsRMbN8fL+xciVJknqmPg3Muxj428x8OCLWBh6KiDsy839atLs7Mw9qYD2SJEk9Xqd7ujLzucx8uHz+BjAb2KyrCpMkSVqVdMk1XRExDNgOuL/O5F0j4tGIuC0iRnTF+iRJknqaRk4vAhARA4EbgL/JzNdbTH4Y+GRmvhkRBwI3AZ9pZTkTgYkAm2++eaNlSZIkrVQa6umKiL4UgeuazPzPltMz8/XMfLN8fivQNyIG1VtWZl6amWMyc8zgwYMbKUuSJGml08inFwO4HJidmf/aSptPlO2IiJ3K9S3q7DolSZJ6qkZOL+4OHAc8HhEzy3FnAJsDZObFwOHAtyJiMfAOcFRmZgPrlCRJ6pE6Hboy8x4g2mkzCZjU2XVIkiStKrwjvSRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRVwNAlSZJUAUOXJElSBQxdkiRJFTB0SZIkVcDQJUmSVAFDlyRJUgUMXZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFGgpdEbF/RDwZEU9FxGl1pveLiOvK6fdHxLBG1idJktRTdTp0RURv4ELgAOBzwNER8bkWzb4OvJKZnwZ+Cvyos+uTJEnqyRrp6doJeCoz52bm+8CvgHEt2owDriqfXw/sExHRwDolSZJ6pEZC12bAvJrh+eW4um0yczHwGrBhA+uUJEnqkfo0MG+9HqvsRJuiYcREYGI5+GZEPNlAbVp+g4CXuruI5RWesNby6ZHHOf/gCQItlx55nMeEHn2cf7IjjRoJXfOBoTXDQ4AFrbSZHxF9gHWBl+stLDMvBS5toB41ICJmZOaY7q5DWpE8zrU68DhfeTVyevFB4DMRMTwi1gCOAm5u0eZmYHz5/HDgzsys29MlSZK0Kut0T1dmLo6IbwNTgN7AFZn5x4g4B5iRmTcDlwP/HhFPUfRwHdUVRUuSJPU0YceToLimrjzFK62yPM61OvA4X3kZuiRJkirg1wBJkiRVwNAlSZJUAUOXAIiI3SPiwu6uQ5LUMRHx6YjYvc74z0fEFt1Rk9pm6FqNRcToiPiXiGgC/hF4optLklaoiBjkV5FpFXI+8Ead8e+U07SSMXStZiJiy4j4fkTMBiZRfE1TZOYXMvOCbi5P6jIRsUtE3BUR/xkR20XELGAW8EJE7N/d9UldYFhmPtZyZGbOAIZVX47a08gd6dUzPQHcDfxVZj4FEBHf7d6SpBViEnAGxTdh3AkckJn3RcRngcnA7d1ZnNQF+rcxbUBlVajD7Ola/RwGPA/8PiIui4h9qP8dmVJP1yczp2bmr4HnM/M+gMz0NLpWFQ9GxDdajoyIrwMPdUM9aoc9XauZzLwRuDEi1gIOAb4LbBwRFwE3ZubUbi1Q6jof1Tx/p8U0b1CoVcHfUPw9P4aPQ9YYYA3gK91WlVrlzVFFRGwAfBU4MjO/2N31SF0hIj4E3qLoyR0AvN08CeifmX27qzapK0XEF4CR5eAfM/PO7qxHrTN0SZIkVcBruiRJkipg6JIkSaqAoUtSpSJi44i4NiLmRsRDEXFvRFR60W9ETIiIjyJiVM24WRExrMo6JK1eDF2SKlPeDf4mYFpmfiozdwCOAobUabuiP109H/j7FbwOSVrC0CWpSl8E3s/Mi5tHZOafm78NoeyB+nVE/AaYGoUfl71Qj0fEkWW7vSPit83LiIhJETGhfN4UET+KiAfKx6dbqeW3wIiI2KrlhIi4KCJmRMQfI+IfasY3RcS5Ze/cjIjYPiKmRMTTEXFSTbtTI+LBiHisdn5JqzdDl6QqjQAebqfNrsD48vYlhwKjgW2BfYEfR8QmHVjP65m5E8Vd6Vv7DrqPgH+huGt9S3+fmWOAUcBetachgXmZuSvFNztcCRwO7AKcAxARY4HPADuVte8QEXt2oGZJqzhDl6RuExEXRsSjEfFgzeg7MvPl8vkewOTM/DAzXwD+G9ixA4ueXPNz1zbaXQvsEhHDW4w/IiIeBh6hCIqfq5l2c/nzceD+zHwjMxcC70bEesDY8vEIRcD8LEUIk7Sa8470kqr0R4qvogIgM0+OiEHAjJo2b9U8b+0rqhaz9D+NLb+DLlt5vnSjzMUR8RPge0tWWASwU4AdM/OViLiyxfLfK39+VPO8ebhPWfMPM/OS1tYrafVkT5ekKt0J9I+Ib9WMW7ON9tOAIyOid0QMBvYEHgD+DHwuIvpFxLrAPi3mO7Lm573t1HQlxanLweXwOhTB77WI2Bg4oJ35W5oCfC0iBgJExGYRsdFyLkPSKsieLkmVycyMiEOAn0bE3wELKQLO91qZ5UaK04OPUvRY/V1mPg8QEf8BPAbMoTiVV6tfRNxP8Y/l0e3U9H5E/Bz4WTn8aEQ8QtErNxf4w3Ju49SI2Bq4t/iwJm8CxwIvLs9yJK16/BogSauUiGgCxmTmS91diyTV8vSiJElSBezpkiRJqoA9XZIkSRUwdEmSJFXA0CVJklQBQ5ckSVIFDF2SJEkVMHRJkiRV4P8DJ8zqKH3i0+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot.bar(y='lms_ActiveWeekCount' ,figsize=(10,5), title='Student Moodle Course Access Rate by Student Grade'  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n4AdVnt1WBSf"
   },
   "outputs": [],
   "source": [
    "#Final[['Group',FGradeCol, 'ExamGrade','CAgrade',\t'File',\t'Folder',\t'Page',\t'System','URL','Quiz','diff1','AF']].corr(method='spearman').style.format(\"{:.5}\").background_gradient(cmap=plt.get_cmap('coolwarm'), axis=1)\n",
    "\n",
    "#Final[Final.columns.difference(['Grade'])].corr(method='spearman').style.format(\"{:.5}\").background_gradient(cmap=plt.get_cmap('coolwarm'), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pRPy8dqoHoM0"
   },
   "outputs": [],
   "source": [
    "# (Final.groupby(['Group'], as_index=False).mean()).plot(x='Group','all')\n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "# for key, grp in Final.groupby(['Group']):\n",
    "#    ax = grp.plot(ax=ax, kind='line', x='x', y='y', c=key, label=key)\n",
    "\n",
    "# plt.legend(loc='best')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nb_XVIfoB2t-"
   },
   "source": [
    "# Decision Tree Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "cEgb3KIrB5C9",
    "outputId": "1beb42d5-87e5-4f1b-9b0f-214f16e74576"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import RepeatedKFold \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "colab_type": "code",
    "id": "xHyTpnrZDOCm",
    "outputId": "d7af97e1-7305-4d00-ae84-f39b43cd0244"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lms_Count_File',\n",
       " 'lms_Count_Folder',\n",
       " 'lms_Count_Forum',\n",
       " 'lms_Count_Page',\n",
       " 'lms_Count_System',\n",
       " 'lms_Count_URL',\n",
       " 'lms_Assignment1_delay',\n",
       " 'lms_Assignment2_delay',\n",
       " 'lms_Assignment3_delay',\n",
       " 'lms_ActiveDaysCount',\n",
       " 'lms_LInactivityPeriod',\n",
       " 'lms_AccessDuration',\n",
       " 'lms_RescourceViewCount',\n",
       " 'lms_ActiveWeekCount']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#X = pd.DataFrame(Final[[\t'CAgrade', 'ExamGrade', 'File', 'Folder',\t'Forum', 'Page', 'Quiz', 'System', 'URL', 'diff']])\n",
    "#XX = pd.DataFrame(Final[[\t 'File', 'Folder',\t'Forum', 'Page', 'Quiz', 'System', 'URL', 'diff1','AF']])\n",
    "\n",
    "# All clumns incuding grades\n",
    "# filter_col = Final._get_numeric_data().columns.difference(['Grade','Group',FGradeCol])\n",
    "# X = pd.DataFrame(Final[filter_col])\n",
    "\n",
    "\n",
    "# only LMS data\n",
    "filter_col = [col for col in Final if col.startswith('lms_') and not col.startswith('lms_d')]\n",
    "X = Final[filter_col]\n",
    "\n",
    "\n",
    "Y = Final['Group']\n",
    "\n",
    "X.fillna('-20', inplace=True)\n",
    "filter_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kf = RepeatedKFold(n_splits=5, n_repeats=10, random_state=None) \n",
    "\n",
    "# for train_index, test_index in kf.split(X):\n",
    "#      print(\"Train:\", train_index, \"Validation:\",test_index)\n",
    "#      X_train, X_test = X[train_index], X[test_index] \n",
    "#      y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import KFold \n",
    "# X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])\n",
    "# kf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=None) \n",
    "\n",
    "# for train_index, test_index in kf.split(X):\n",
    "#       print(\"Train:\", train_index, \"Validation:\",test_index)\n",
    "#       X_train, X_test = X[train_index], X[test_index] \n",
    "#       y_train, y_test = Y[train_index], Y[test_index]\n",
    "#       break;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paper Week7               float64\n",
       "Paper Week10              float64\n",
       "Self evaluation Week10    float64\n",
       "Presentation Week 12      float64\n",
       "Practical Week 15         float64\n",
       "Course total (Real)       float64\n",
       "lms_Count_File            float64\n",
       "lms_Count_Folder          float64\n",
       "lms_Count_Forum           float64\n",
       "lms_Count_Page            float64\n",
       "lms_Count_System          float64\n",
       "lms_Count_URL             float64\n",
       "Click                     float64\n",
       "lms_Assignment1_delay     float64\n",
       "lms_Assignment2_delay     float64\n",
       "lms_Assignment3_delay     float64\n",
       "lms_ActiveDaysCount       float64\n",
       "lms_LInactivityPeriod     float64\n",
       "lms_AccessDuration        float64\n",
       "lms_RescourceViewCount    float64\n",
       "lms_ActiveWeekCount       float64\n",
       "Group                     float64\n",
       "lms_all                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ScO6bShCEVnZ"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.6, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "f1bjpCYwEY_m",
    "outputId": "7e02952e-2bbe-49c0-ad4f-fbc1715be485"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clf_gini = DecisionTreeClassifier(criterion = \"gini\", random_state = 100, max_depth=10, min_samples_leaf=5)\n",
    "\n",
    "clf_gini = DecisionTreeClassifier()\n",
    "clf_gini.fit(X_train, Y_train)\n",
    "\n",
    "\n",
    "#classifier = DecisionTreeClassifier()  \n",
    "#classifier.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1093
    },
    "colab_type": "code",
    "id": "QFWWSNJKgZ4R",
    "outputId": "a89066a6-3db3-4d98-e1f8-1ea07b403fee"
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
       " -->\r\n",
       "<!-- Title: Tree Pages: 1 -->\r\n",
       "<svg width=\"561pt\" height=\"373pt\"\r\n",
       " viewBox=\"0.00 0.00 561.00 373.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 369)\">\r\n",
       "<title>Tree</title>\r\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-369 557,-369 557,4 -4,4\"/>\r\n",
       "<!-- 0 -->\r\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.125490\" stroke=\"black\" d=\"M287,-365C287,-365 106,-365 106,-365 100,-365 94,-359 94,-353 94,-353 94,-309 94,-309 94,-303 100,-297 106,-297 106,-297 287,-297 287,-297 293,-297 299,-303 299,-309 299,-309 299,-353 299,-353 299,-359 293,-365 287,-365\"/>\r\n",
       "<text text-anchor=\"start\" x=\"102\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">lms_ActiveWeekCount ≤ 16.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"159\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.653</text>\r\n",
       "<text text-anchor=\"start\" x=\"153\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 12</text>\r\n",
       "<text text-anchor=\"start\" x=\"148\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 5, 3]</text>\r\n",
       "</g>\r\n",
       "<!-- 1 -->\r\n",
       "<g id=\"node2\" class=\"node\"><title>1</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M162,-253.5C162,-253.5 73,-253.5 73,-253.5 67,-253.5 61,-247.5 61,-241.5 61,-241.5 61,-212.5 61,-212.5 61,-206.5 67,-200.5 73,-200.5 73,-200.5 162,-200.5 162,-200.5 168,-200.5 174,-206.5 174,-212.5 174,-212.5 174,-241.5 174,-241.5 174,-247.5 168,-253.5 162,-253.5\"/>\r\n",
       "<text text-anchor=\"start\" x=\"88.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"78\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"start\" x=\"69\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 0, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;1 -->\r\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M170.851,-296.884C162.077,-285.556 152.26,-272.88 143.462,-261.521\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"146.156,-259.283 137.265,-253.52 140.622,-263.569 146.156,-259.283\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"134.114\" y=\"-274.621\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\r\n",
       "</g>\r\n",
       "<!-- 2 -->\r\n",
       "<g id=\"node3\" class=\"node\"><title>2</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.333333\" stroke=\"black\" d=\"M348.5,-261C348.5,-261 204.5,-261 204.5,-261 198.5,-261 192.5,-255 192.5,-249 192.5,-249 192.5,-205 192.5,-205 192.5,-199 198.5,-193 204.5,-193 204.5,-193 348.5,-193 348.5,-193 354.5,-193 360.5,-199 360.5,-205 360.5,-205 360.5,-249 360.5,-249 360.5,-255 354.5,-261 348.5,-261\"/>\r\n",
       "<text text-anchor=\"start\" x=\"200.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">lms_Count_File ≤ 205.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"239\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.568</text>\r\n",
       "<text text-anchor=\"start\" x=\"237\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\r\n",
       "<text text-anchor=\"start\" x=\"228\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 5, 3]</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\"><title>0&#45;&gt;2</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M222.474,-296.884C229.39,-288.065 236.947,-278.43 244.159,-269.235\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"246.965,-271.328 250.383,-261.299 241.457,-267.008 246.965,-271.328\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"253.383\" y=\"-282.419\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n",
       "</g>\r\n",
       "<!-- 3 -->\r\n",
       "<g id=\"node4\" class=\"node\"><title>3</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.800000\" stroke=\"black\" d=\"M240.5,-157C240.5,-157 86.5,-157 86.5,-157 80.5,-157 74.5,-151 74.5,-145 74.5,-145 74.5,-101 74.5,-101 74.5,-95 80.5,-89 86.5,-89 86.5,-89 240.5,-89 240.5,-89 246.5,-89 252.5,-95 252.5,-101 252.5,-101 252.5,-145 252.5,-145 252.5,-151 246.5,-157 240.5,-157\"/>\r\n",
       "<text text-anchor=\"start\" x=\"82.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">lms_Count_Folder ≤ 10.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"126\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.278</text>\r\n",
       "<text text-anchor=\"start\" x=\"124\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n",
       "<text text-anchor=\"start\" x=\"115\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5, 1]</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
       "<g id=\"edge3\" class=\"edge\"><title>2&#45;&gt;3</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M239.812,-192.884C229.648,-183.709 218.505,-173.65 207.95,-164.123\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"210.159,-161.402 200.39,-157.299 205.468,-166.598 210.159,-161.402\"/>\r\n",
       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M496,-157C496,-157 283,-157 283,-157 277,-157 271,-151 271,-145 271,-145 271,-101 271,-101 271,-95 277,-89 283,-89 283,-89 496,-89 496,-89 502,-89 508,-95 508,-101 508,-101 508,-145 508,-145 508,-151 502,-157 496,-157\"/>\r\n",
       "<text text-anchor=\"start\" x=\"279\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">lms_RescourceViewCount ≤ 123.5</text>\r\n",
       "<text text-anchor=\"start\" x=\"352\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"start\" x=\"350\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"start\" x=\"341\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0, 2]</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>2&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M313.188,-192.884C323.352,-183.709 334.495,-173.65 345.05,-164.123\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"347.532,-166.598 352.61,-157.299 342.841,-161.402 347.532,-166.598\"/>\r\n",
       "</g>\r\n",
       "<!-- 4 -->\r\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M101,-53C101,-53 12,-53 12,-53 6,-53 0,-47 0,-41 0,-41 0,-12 0,-12 0,-6 6,-0 12,-0 12,-0 101,-0 101,-0 107,-0 113,-6 113,-12 113,-12 113,-41 113,-41 113,-47 107,-53 101,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"27.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"17\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>3&#45;&gt;4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M126.077,-88.9485C115.408,-79.526 103.799,-69.2731 93.175,-59.8906\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"95.4533,-57.2331 85.641,-53.2367 90.8195,-62.4798 95.4533,-57.2331\"/>\r\n",
       "</g>\r\n",
       "<!-- 5 -->\r\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M232,-53C232,-53 143,-53 143,-53 137,-53 131,-47 131,-41 131,-41 131,-12 131,-12 131,-6 137,-0 143,-0 143,-0 232,-0 232,-0 238,-0 244,-6 244,-12 244,-12 244,-41 244,-41 244,-47 238,-53 232,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"158.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"148\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n",
       "<text text-anchor=\"start\" x=\"139\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>3&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M171.894,-88.9485C174.008,-80.6238 176.287,-71.6509 178.433,-63.2027\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"181.894,-63.7906 180.964,-53.2367 175.11,-62.0675 181.894,-63.7906\"/>\r\n",
       "</g>\r\n",
       "<!-- 7 -->\r\n",
       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M410,-53C410,-53 321,-53 321,-53 315,-53 309,-47 309,-41 309,-41 309,-12 309,-12 309,-6 315,-0 321,-0 321,-0 410,-0 410,-0 416,-0 422,-6 422,-12 422,-12 422,-41 422,-41 422,-47 416,-53 410,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"336.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"326\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"start\" x=\"317\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 2]</text>\r\n",
       "</g>\r\n",
       "<!-- 6&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>6&#45;&gt;7</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M381.106,-88.9485C378.992,-80.6238 376.713,-71.6509 374.567,-63.2027\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"377.89,-62.0675 372.036,-53.2367 371.106,-63.7906 377.89,-62.0675\"/>\r\n",
       "</g>\r\n",
       "<!-- 8 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M541,-53C541,-53 452,-53 452,-53 446,-53 440,-47 440,-41 440,-41 440,-12 440,-12 440,-6 446,-0 452,-0 452,-0 541,-0 541,-0 547,-0 553,-6 553,-12 553,-12 553,-41 553,-41 553,-47 547,-53 541,-53\"/>\r\n",
       "<text text-anchor=\"start\" x=\"467.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"457\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
       "<text text-anchor=\"start\" x=\"448\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 6&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>6&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M426.923,-88.9485C437.592,-79.526 449.201,-69.2731 459.825,-59.8906\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"462.181,-62.4798 467.359,-53.2367 457.547,-57.2331 462.181,-62.4798\"/>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x29e0587ea20>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names = list(X.columns)\n",
    "#class_names   = [\"C\",\"B\",\"A\"]\n",
    "\n",
    "#class_names = class_names,\n",
    "\n",
    "dot_data = tree.export_graphviz(clf_gini, out_file=None,                         \n",
    "                         feature_names=feature_names,  \n",
    "                         \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "\n",
    "\n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph \n",
    "\n",
    "#class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3FSEFYC8CU_x"
   },
   "outputs": [],
   "source": [
    "Y_pred = clf_gini.predict(X_test)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "1VrpuFAZCkdH",
    "outputId": "5d798da6-f185-4bc4-fef6-09ac7c205630"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 2 2]\n",
      " [1 3 1]\n",
      " [4 1 1]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.50      0.56      0.53         9\n",
      "          2       0.50      0.60      0.55         5\n",
      "          3       0.25      0.17      0.20         6\n",
      "\n",
      "avg / total       0.42      0.45      0.43        20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "print(confusion_matrix(Y_test, Y_pred))  \n",
    "print(classification_report(Y_test, Y_pred))  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "s7FHypR4DL1c",
    "outputId": "d78dbeec-9402-44fb-f4fb-bacdc8acecb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  45.0\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy is \", accuracy_score(Y_test,Y_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xd7KRUxgqbAW"
   },
   "outputs": [],
   "source": [
    "#cccc = pd.DataFrame({'Predicted':y_pred,'Actual':Y_test })  \n",
    "#cccc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ceH8FsnYvJ1-"
   },
   "source": [
    "# Regression Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "e8ALtQsevWjT",
    "outputId": "88ac1b3f-5b72-4b35-c28a-52391a8359e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lms_Count_File',\n",
       " 'lms_Count_Folder',\n",
       " 'lms_Count_Forum',\n",
       " 'lms_Count_Page',\n",
       " 'lms_Count_System',\n",
       " 'lms_Count_URL',\n",
       " 'lms_Assignment1_delay',\n",
       " 'lms_Assignment2_delay',\n",
       " 'lms_Assignment3_delay',\n",
       " 'lms_ActiveDaysCount',\n",
       " 'lms_LInactivityPeriod',\n",
       " 'lms_AccessDuration',\n",
       " 'lms_RescourceViewCount',\n",
       " 'lms_ActiveWeekCount']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X = pd.DataFrame(Final[[\t'CAgrade', 'ExamGrade', 'File', 'Folder',\t'Forum', 'Page', 'Quiz', 'System', 'URL', 'diff']])\n",
    "#XX = pd.DataFrame(Final[[\t 'File', 'Folder',\t'Forum', 'Page', 'Quiz', 'System', 'URL', 'diff1','AF']])\n",
    "\n",
    "filter_col = [col for col in Final if col.startswith('lms_') and not col.startswith('lms_di')]\n",
    "X = pd.DataFrame(Final[filter_col])\n",
    "\n",
    "Y = pd.DataFrame(Final[FGradeCol])\n",
    "\n",
    "X.fillna('-20', inplace=True)\n",
    "filter_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rmcn5nR4e88i"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.6, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "0cTvXl-fvKIC",
    "outputId": "b2bb0e98-fb51-4b92-89ec-964fd873b067"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor  \n",
    "\n",
    "regressor = DecisionTreeRegressor()  \n",
    "regressor.fit(X_train, Y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "3Zi4D5m4vp8q",
    "outputId": "674de469-d08b-4b1d-de1c-8ad677af1157"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50. , 84.5,  0. , 77.3, 87. , 50. , 74.9, 32.1, 87. , 32.1, 50. ,\n",
       "       87. , 50. , 40.1,  0. , 50. ,  0. , 77.3, 60.2,  0. ])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = regressor.predict(X_test)  \n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "Ipcuw-A1vv4G",
    "outputId": "5f46a9dc-6cc5-4982-bb3c-d67289dbdf8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 23.309999999999995\n",
      "Mean Squared Error: 1031.8600000000001\n",
      "Root Mean Squared Error: 32.12257772969038\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(Y_test, Y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zbq3y-TbdA2U"
   },
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XBTmxsRihVgE"
   },
   "outputs": [],
   "source": [
    "#XX = pd.DataFrame(Final[[\t'CAgrade', 'ExamGrade', 'File', 'Folder',\t'Forum', 'Page', 'Quiz', 'System', 'URL', 'diff1']])\n",
    "\n",
    "filter_col = [col for col in Final if col.startswith('lms_') and not col.startswith('lms_dif') ]\n",
    "X = pd.DataFrame(Final[filter_col])\n",
    "\n",
    "\n",
    "Y = pd.DataFrame(Final[FGradeCol])\n",
    "\n",
    "# X.fillna('-20', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 921
    },
    "colab_type": "code",
    "id": "4WYLz2Aic9P3",
    "outputId": "8de5497c-a813-44e0-9cf7-4c528318e28f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     Course total (Real)   R-squared:                       0.944\n",
      "Model:                             OLS   Adj. R-squared:                  0.901\n",
      "Method:                  Least Squares   F-statistic:                     21.79\n",
      "Date:                 Thu, 21 Mar 2019   Prob (F-statistic):           1.90e-08\n",
      "Time:                         10:12:58   Log-Likelihood:                -132.11\n",
      "No. Observations:                   32   AIC:                             292.2\n",
      "Df Residuals:                       18   BIC:                             312.7\n",
      "Df Model:                           14                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "lms_Count_File            -0.3403      0.153     -2.225      0.039      -0.662      -0.019\n",
      "lms_Count_Folder          -0.2735      0.473     -0.578      0.570      -1.268       0.721\n",
      "lms_Count_Forum            7.3090      8.793      0.831      0.417     -11.165      25.783\n",
      "lms_Count_Page            -0.1055      0.278     -0.379      0.709      -0.690       0.479\n",
      "lms_Count_System           0.2881      0.186      1.551      0.138      -0.102       0.678\n",
      "lms_Count_URL             -0.7826      0.634     -1.234      0.233      -2.115       0.550\n",
      "lms_Assignment1_delay      0.0667      0.572      0.116      0.909      -1.136       1.269\n",
      "lms_Assignment2_delay      0.1308      0.762      0.172      0.866      -1.469       1.731\n",
      "lms_Assignment3_delay     -1.4844      1.243     -1.194      0.248      -4.096       1.127\n",
      "lms_ActiveDaysCount       -0.2113      0.815     -0.259      0.798      -1.923       1.501\n",
      "lms_LInactivityPeriod     -0.0004      0.002     -0.237      0.815      -0.004       0.003\n",
      "lms_AccessDuration         0.9437      1.091      0.865      0.398      -1.348       3.235\n",
      "lms_RescourceViewCount     0.9463      0.270      3.502      0.003       0.379       1.514\n",
      "lms_ActiveWeekCount       -4.8576      6.617     -0.734      0.472     -18.759       9.044\n",
      "==============================================================================\n",
      "Omnibus:                        2.545   Durbin-Watson:                   1.891\n",
      "Prob(Omnibus):                  0.280   Jarque-Bera (JB):                1.413\n",
      "Skew:                           0.184   Prob(JB):                        0.493\n",
      "Kurtosis:                       2.039   Cond. No.                     4.03e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.03e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "#X = sm.add_constant(X) \n",
    "\n",
    "model = sm.OLS(Y, X.astype(float)).fit()\n",
    "\n",
    "print (model.summary())"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "AnalyseData.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}