adaptability_of_students_e_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e250626-8959-4866-a6a6-b26bbc5011b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/Parity-Boolean-Autocrats/02_Debuggers/main/datasets/students_adaptability_level_online_education.csv\", encoding=\"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a2af01c-0c37-4b47-bb1f-cf63e0de466f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Load-shedding</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "      <th>Class Duration</th>\n",
       "      <th>Self Lms</th>\n",
       "      <th>Device</th>\n",
       "      <th>Adaptivity Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boy</td>\n",
       "      <td>21-25</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
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       "      <td>3-6</td>\n",
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       "      <td>Moderate</td>\n",
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       "      <th>1</th>\n",
       "      <td>Girl</td>\n",
       "      <td>21-25</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Girl</td>\n",
       "      <td>16-20</td>\n",
       "      <td>College</td>\n",
       "      <td>Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Girl</td>\n",
       "      <td>11-15</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Girl</td>\n",
       "      <td>16-20</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Age Education Level Institution Type IT Student Location  \\\n",
       "0    Boy  21-25      University   Non Government         No      Yes   \n",
       "1   Girl  21-25      University   Non Government         No      Yes   \n",
       "2   Girl  16-20         College       Government         No      Yes   \n",
       "3   Girl  11-15          School   Non Government         No      Yes   \n",
       "4   Girl  16-20          School   Non Government         No      Yes   \n",
       "\n",
       "  Load-shedding Financial Condition Internet Type Network Type Class Duration  \\\n",
       "0           Low                 Mid          Wifi           4G            3-6   \n",
       "1          High                 Mid   Mobile Data           4G            1-3   \n",
       "2           Low                 Mid          Wifi           4G            1-3   \n",
       "3           Low                 Mid   Mobile Data           4G            1-3   \n",
       "4           Low                Poor   Mobile Data           3G              0   \n",
       "\n",
       "  Self Lms  Device Adaptivity Level  \n",
       "0       No     Tab         Moderate  \n",
       "1      Yes  Mobile         Moderate  \n",
       "2       No  Mobile         Moderate  \n",
       "3       No  Mobile         Moderate  \n",
       "4       No  Mobile              Low  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c29b8be3-e712-4e2f-9456-2b3d06e13211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1205 entries, 0 to 1204\n",
      "Data columns (total 14 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Gender               1205 non-null   object\n",
      " 1   Age                  1205 non-null   object\n",
      " 2   Education Level      1205 non-null   object\n",
      " 3   Institution Type     1205 non-null   object\n",
      " 4   IT Student           1205 non-null   object\n",
      " 5   Location             1205 non-null   object\n",
      " 6   Load-shedding        1205 non-null   object\n",
      " 7   Financial Condition  1205 non-null   object\n",
      " 8   Internet Type        1205 non-null   object\n",
      " 9   Network Type         1205 non-null   object\n",
      " 10  Class Duration       1205 non-null   object\n",
      " 11  Self Lms             1205 non-null   object\n",
      " 12  Device               1205 non-null   object\n",
      " 13  Adaptivity Level     1205 non-null   object\n",
      "dtypes: object(14)\n",
      "memory usage: 131.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5403991c-927b-4717-9e70-c704f4266769",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Location</th>\n",
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       "      <th>Adaptivity Level</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "      <td>1205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Boy</td>\n",
       "      <td>21-25</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mid</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>663</td>\n",
       "      <td>374</td>\n",
       "      <td>530</td>\n",
       "      <td>823</td>\n",
       "      <td>901</td>\n",
       "      <td>935</td>\n",
       "      <td>1004</td>\n",
       "      <td>878</td>\n",
       "      <td>695</td>\n",
       "      <td>775</td>\n",
       "      <td>840</td>\n",
       "      <td>995</td>\n",
       "      <td>1013</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender    Age Education Level Institution Type IT Student Location  \\\n",
       "count    1205   1205            1205             1205       1205     1205   \n",
       "unique      2      6               3                2          2        2   \n",
       "top       Boy  21-25          School   Non Government         No      Yes   \n",
       "freq      663    374             530              823        901      935   \n",
       "\n",
       "       Load-shedding Financial Condition Internet Type Network Type  \\\n",
       "count           1205                1205          1205         1205   \n",
       "unique             2                   3             2            3   \n",
       "top              Low                 Mid   Mobile Data           4G   \n",
       "freq            1004                 878           695          775   \n",
       "\n",
       "       Class Duration Self Lms  Device Adaptivity Level  \n",
       "count            1205     1205    1205             1205  \n",
       "unique              3        2       3                3  \n",
       "top               1-3       No  Mobile         Moderate  \n",
       "freq              840      995    1013              625  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03a31bf-b5f6-475d-b35a-c669e4410e5b",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "292e3c9c-85a9-4b2c-a411-44457dab144b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'Adaptivity Level':'Adaptivity'},inplace = True)\n",
    "df.rename(columns = {'Age': 'Age Range'},inplace = True)\n",
    "\n",
    "df.replace(\"Boy\",\"Male\",inplace=True)\n",
    "df.replace(\"Girl\",\"Female\",inplace=True)\n",
    "df.replace(\"Mid\",\"Middle Class\",inplace=True)\n",
    "df.replace(\"Poor\",\"Lower Class\",inplace=True)\n",
    "df.replace(\"Rich\",\"Upper Class\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "771b84cf-9afb-4628-9682-2aaa163657e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Range</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Load-shedding</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "      <th>Class Duration</th>\n",
       "      <th>Self Lms</th>\n",
       "      <th>Device</th>\n",
       "      <th>Adaptivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>21-25</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>3-6</td>\n",
       "      <td>No</td>\n",
       "      <td>Tab</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21-25</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>16-20</td>\n",
       "      <td>College</td>\n",
       "      <td>Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>11-15</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>16-20</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Lower Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Age Range Education Level Institution Type IT Student Location  \\\n",
       "0    Male     21-25      University   Non Government         No      Yes   \n",
       "1  Female     21-25      University   Non Government         No      Yes   \n",
       "2  Female     16-20         College       Government         No      Yes   \n",
       "3  Female     11-15          School   Non Government         No      Yes   \n",
       "4  Female     16-20          School   Non Government         No      Yes   \n",
       "\n",
       "  Load-shedding Financial Condition Internet Type Network Type Class Duration  \\\n",
       "0           Low        Middle Class          Wifi           4G            3-6   \n",
       "1          High        Middle Class   Mobile Data           4G            1-3   \n",
       "2           Low        Middle Class          Wifi           4G            1-3   \n",
       "3           Low        Middle Class   Mobile Data           4G            1-3   \n",
       "4           Low         Lower Class   Mobile Data           3G              0   \n",
       "\n",
       "  Self Lms  Device Adaptivity  \n",
       "0       No     Tab   Moderate  \n",
       "1      Yes  Mobile   Moderate  \n",
       "2       No  Mobile   Moderate  \n",
       "3       No  Mobile   Moderate  \n",
       "4       No  Mobile        Low  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9dcaaa5-b817-49ef-a1d2-e59f4f567095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                 0\n",
       "Age Range              0\n",
       "Education Level        0\n",
       "Institution Type       0\n",
       "IT Student             0\n",
       "Location               0\n",
       "Load-shedding          0\n",
       "Financial Condition    0\n",
       "Internet Type          0\n",
       "Network Type           0\n",
       "Class Duration         0\n",
       "Self Lms               0\n",
       "Device                 0\n",
       "Adaptivity             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Null Values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5809d284-abaa-465e-b0fa-c96b08b43aea",
   "metadata": {},
   "source": [
    "## Seeing how students are adapting to the online system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3798ab15-11a1-423f-9bc4-a0ec2ce8bff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cmap = plt.get_cmap(\"tab20c\")\n",
    "outer_colors = cmap(np.arange(3)*4)\n",
    "inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))\n",
    "\n",
    "Adapt= df.groupby([  \n",
    "        'Adaptivity'])['Adaptivity'].count().to_frame()\n",
    "\n",
    "plt.pie(Adapt.Adaptivity,colors= inner_colors,\n",
    "        labels = Adapt.index,startangle = 90, autopct = \"%1.0f%%\", \n",
    "        explode = None, shadow = True)\n",
    "plt.title(\"Adaptivity Levels Among Students\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d134b8a-a8be-4d9c-8acf-1f509e62a76c",
   "metadata": {},
   "source": [
    "> Conclusion: Only 8% of the students are adaptive to online education"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ba36ae-b372-47f0-9fca-484aed916742",
   "metadata": {},
   "source": [
    "## Relationship between Apaptavity and other Independent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "865fc2dd-ff68-4994-8dbf-acc9dad138b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_groups(df: pd.DataFrame, var1: str, var2: str)->pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Helper function to create groups of different combinations\n",
    "    \"\"\"\n",
    "    category = df.groupby([var1,var2])[var2].count().to_frame()\n",
    "    \n",
    "    print('Adaptivity' + ' by ' + var1)\n",
    "    \n",
    "    return category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3979f85-91a4-44e6-b18c-1488ff2ccf1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaptivity by Education Level\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></th>\n",
       "      <th>Adaptivity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education Level</th>\n",
       "      <th>Adaptivity</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">College</th>\n",
       "      <th>High</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">School</th>\n",
       "      <th>High</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">University</th>\n",
       "      <th>High</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moderate</th>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Adaptivity\n",
       "Education Level Adaptivity            \n",
       "College         High                 3\n",
       "                Low                120\n",
       "                Moderate            96\n",
       "School          High                47\n",
       "                Low                182\n",
       "                Moderate           301\n",
       "University      High                50\n",
       "                Low                178\n",
       "                Moderate           228"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adaptivity with Education Level\n",
    "create_groups(df, 'Education Level', 'Adaptivity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eb4ad82-bdf3-40e7-9568-ecf2d0d23fad",
   "metadata": {},
   "source": [
    "## Creating Visualizations for Different Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f36fd706-9142-41cf-93db-9ea1657d5983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x3600 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plt.figure(figsize = (30,50))\n",
    "for feature in [col for col in df.columns if col!='Adaptivity' ]:\n",
    "    plt.subplot(6,3,i),  \n",
    "    sns.countplot(x = feature , hue='Adaptivity', data = df,)\n",
    " \n",
    "    i +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994bbb3b-78f6-476d-8760-d56cc5b8807f",
   "metadata": {},
   "source": [
    "> **Conclusions**\n",
    "\n",
    "- Ages 11-15 and 21-25 show higher adaptability \n",
    "- Adaptability was better for students in urban areas as compared to those in rural areas\n",
    "- Students with limited power supply tend to adapt less as compared to the others\n",
    "- Having better Wi-Fi connectivity naturally helped students get better access to the internet and have relatively lesser hurdles in managing online classes\n",
    "- Usage of LMS (Learning Management System) showed decent growth in rates of adaptivity in online education"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb3d3d3-9511-40b3-979a-36a72726e1fb",
   "metadata": {},
   "source": [
    "## Ordinal Encoding to sort age ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3237c240-d49e-49fe-830d-4880b7a5f9c3",
   "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>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Institution Type</th>\n",
       "      <th>IT Student</th>\n",
       "      <th>Location</th>\n",
       "      <th>Load-shedding</th>\n",
       "      <th>Financial Condition</th>\n",
       "      <th>Internet Type</th>\n",
       "      <th>Network Type</th>\n",
       "      <th>Class Duration</th>\n",
       "      <th>Self Lms</th>\n",
       "      <th>Device</th>\n",
       "      <th>Adaptivity</th>\n",
       "      <th>Lower limit Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>3-6</td>\n",
       "      <td>No</td>\n",
       "      <td>Tab</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>University</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>College</td>\n",
       "      <td>Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Middle Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>4G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>School</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>Lower Class</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Low</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender Education Level Institution Type IT Student Location Load-shedding  \\\n",
       "0    Male      University   Non Government         No      Yes           Low   \n",
       "1  Female      University   Non Government         No      Yes          High   \n",
       "2  Female         College       Government         No      Yes           Low   \n",
       "3  Female          School   Non Government         No      Yes           Low   \n",
       "4  Female          School   Non Government         No      Yes           Low   \n",
       "\n",
       "  Financial Condition Internet Type Network Type Class Duration Self Lms  \\\n",
       "0        Middle Class          Wifi           4G            3-6       No   \n",
       "1        Middle Class   Mobile Data           4G            1-3      Yes   \n",
       "2        Middle Class          Wifi           4G            1-3       No   \n",
       "3        Middle Class   Mobile Data           4G            1-3       No   \n",
       "4         Lower Class   Mobile Data           3G              0       No   \n",
       "\n",
       "   Device Adaptivity Lower limit Age  \n",
       "0     Tab   Moderate              21  \n",
       "1  Mobile   Moderate              21  \n",
       "2  Mobile   Moderate              16  \n",
       "3  Mobile   Moderate              11  \n",
       "4  Mobile        Low              16  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "col1 = df[\"Age Range\"].apply(lambda x: x.split(\"-\")[0])\n",
    "df1 = df.join(col1.to_frame(name=\"Lower limit Age\"))\n",
    "df1.drop(['Age Range'], axis = 1, inplace = True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b7b75d7-ad95-4c0f-af31-47d3418fb32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change Lower Limit to INT\n",
    "\n",
    "df1[\"Lower limit Age\"] = df1[\"Lower limit Age\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "53a5ac8a-caf3-4965-aa5f-01d7fd683f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education Level</th>\n",
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      ],
      "text/plain": [
       "   Gender  Education Level  Institution Type  IT Student  Location  \\\n",
       "0     1.0              2.0               1.0         0.0       1.0   \n",
       "1     0.0              2.0               1.0         0.0       1.0   \n",
       "2     0.0              0.0               0.0         0.0       1.0   \n",
       "3     0.0              1.0               1.0         0.0       1.0   \n",
       "4     0.0              1.0               1.0         0.0       1.0   \n",
       "5     1.0              1.0               1.0         0.0       1.0   \n",
       "6     1.0              1.0               1.0         0.0       1.0   \n",
       "7     1.0              1.0               1.0         0.0       1.0   \n",
       "8     1.0              0.0               0.0         0.0       1.0   \n",
       "9     1.0              1.0               1.0         0.0       1.0   \n",
       "\n",
       "   Load-shedding  Financial Condition  Internet Type  Network Type  \\\n",
       "0            1.0                  1.0            1.0           2.0   \n",
       "1            0.0                  1.0            0.0           2.0   \n",
       "2            1.0                  1.0            1.0           2.0   \n",
       "3            1.0                  1.0            0.0           2.0   \n",
       "4            1.0                  0.0            0.0           1.0   \n",
       "5            1.0                  0.0            0.0           1.0   \n",
       "6            1.0                  1.0            1.0           2.0   \n",
       "7            1.0                  1.0            1.0           2.0   \n",
       "8            1.0                  1.0            1.0           2.0   \n",
       "9            1.0                  1.0            0.0           1.0   \n",
       "\n",
       "   Class Duration  Self Lms  Device  Adaptivity  Lower limit Age  \n",
       "0             2.0       0.0     2.0         2.0              4.0  \n",
       "1             1.0       1.0     1.0         2.0              4.0  \n",
       "2             1.0       0.0     1.0         2.0              3.0  \n",
       "3             1.0       0.0     1.0         2.0              2.0  \n",
       "4             0.0       0.0     1.0         1.0              3.0  \n",
       "5             1.0       0.0     1.0         1.0              2.0  \n",
       "6             0.0       0.0     1.0         1.0              2.0  \n",
       "7             1.0       0.0     1.0         2.0              2.0  \n",
       "8             1.0       0.0     1.0         1.0              3.0  \n",
       "9             1.0       0.0     1.0         2.0              2.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = OrdinalEncoder()\n",
    "names = df1.columns\n",
    "d = scaler.fit_transform(df1)\n",
    "\n",
    "scaled_df = pd.DataFrame(d, columns=names)\n",
    "scaled_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca81318-4289-46ca-8a6b-0fe46c3bb464",
   "metadata": {},
   "source": [
    "# Data Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ecfd5c0-5c39-4669-9088-d7b6547becfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "58ad91f2-b890-4449-93f9-12eaf0b0eef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = scaled_df.drop(['Adaptivity','Gender',],axis = 1)\n",
    "test = scaled_df['Adaptivity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a90445ea-1cf2-44f8-a3c7-afca0fc159e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(train,test ,test_size=0.33,random_state=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9102cbe-3f38-4af4-bdf6-e321da07067a",
   "metadata": {},
   "source": [
    "## Applying Random Forest and Gradient Boosting Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d23877e7-ddba-4d2e-bb68-dbf05ab7dd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Random Forest Classifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.66      0.59      0.62        32\n",
      "         1.0       0.81      0.90      0.86       155\n",
      "         2.0       0.89      0.83      0.86       211\n",
      "\n",
      "    accuracy                           0.84       398\n",
      "   macro avg       0.79      0.78      0.78       398\n",
      "weighted avg       0.84      0.84      0.84       398\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "Model: Gradient Boosting Classifier\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.54      0.44      0.48        32\n",
      "         1.0       0.85      0.82      0.83       155\n",
      "         2.0       0.82      0.86      0.84       211\n",
      "\n",
      "    accuracy                           0.81       398\n",
      "   macro avg       0.73      0.70      0.72       398\n",
      "weighted avg       0.81      0.81      0.81       398\n",
      "\n",
      "------------------------------ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf = RandomForestClassifier(n_estimators=20, max_depth=25, random_state=3)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "    \n",
    "print('Model: Random Forest Classifier')\n",
    "print(classification_report(y_test,y_pred_rf, zero_division=1))\n",
    "print('-'*30, '\\n')\n",
    "\n",
    "## Gradient Boosting\n",
    "\n",
    "gb = GradientBoostingClassifier(learning_rate=0.5, n_estimators=25, random_state=5)\n",
    "gb.fit(X_train, y_train)\n",
    "y_pred_gb = gb.predict(X_test)\n",
    "print('Model: Gradient Boosting Classifier')\n",
    "print(classification_report(y_test,y_pred_gb, zero_division=1))\n",
    "print('-'*30, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154ff168-9f2c-4c2b-866e-07e7353557b7",
   "metadata": {},
   "source": [
    "> We see that RF classifier works better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd5500d1-7257-4203-83b5-cf74247eaea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.65      0.69      0.67        32\n",
      "         1.0       0.84      0.90      0.87       155\n",
      "         2.0       0.89      0.83      0.86       211\n",
      "\n",
      "    accuracy                           0.85       398\n",
      "   macro avg       0.79      0.81      0.80       398\n",
      "weighted avg       0.85      0.85      0.85       398\n",
      "\n"
     ]
    }
   ],
   "source": [
    "FinalM= RandomForestClassifier(n_estimators=20, max_depth=25, random_state=7)\n",
    "FinalM.fit(X_train, y_train)\n",
    "y_pred = FinalM.predict(X_test)\n",
    "print(classification_report(y_test,y_pred, zero_division=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00608979-8eec-4b8f-b378-49d11423f772",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a7562a1-8a64-437e-ae3e-5fd8add801db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(FinalM.predict(X_test),y_test)\n",
    "disp = ConfusionMatrixDisplay(cm, display_labels=[\"High\",\"Low\",\"Moderate\"])\n",
    "disp.plot()\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4103dd8-2a2a-42ab-9c10-007a85bb8863",
   "metadata": {},
   "source": [
    "## Understanding Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f172cc6-b9c0-4cfe-99f4-3e55c25e9079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '5 most important features')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances=FinalM.feature_importances_\n",
    "feature_importances_df=pd.DataFrame({'Variable':list(X_train), 'Variable importance':feature_importances})\n",
    "\n",
    "feature_importances_df.sort_values('Variable importance',ascending=False)\n",
    "\n",
    "feat_importances = pd.Series(FinalM.feature_importances_, index=train.columns)\n",
    "\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.title(\"5 most important features\")"
   ]
  },
  {
   "cell_type": "markdown",
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    "> Conclusions: We get clarity about the factors that impact the credibility of systems and operations put in place for online learning. What we can infer from this analysis is that:\n",
    "- The class duration has a significant impact on adaptability, meaning it has to be in suitable times, managed well with breaks, interactive sessions, practical use cases, etc. \n",
    "- Financial Conditions highly impact the students' adaptivity. The quality of audio/video, the overall \"snappiness\" of devices in use ultimately providing a better user experience naturally result in better adaptivity.\n",
    "- Age appropriate methods of teaching should still be employed even while teaching online.\n",
    "- Much like devices, networks and internet quality also impacts in the adaptability of students. Having a better network results in a smoother flow during online classes. "
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