Students Adaptability Level in Online Education.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12edd13b",
   "metadata": {
    "papermill": {
     "duration": 0.009839,
     "end_time": "2023-07-11T17:55:31.417323",
     "exception": false,
     "start_time": "2023-07-11T17:55:31.407484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Students Adaptability Level Prediction in Online Education Via Machine Learning & Deep Learning;\n",
    "\n",
    "->Online education has become increasingly popular in recent years, as it provides students with the flexibility to learn at their own pace and from anywhere in the world. However, not all students are equally adaptable to online learning. Some students may find it difficult to stay motivated and engaged in an online environment, while others may struggle with the technical aspects of online learning.\n",
    "\n",
    "This dataset can be used to explore a variety of research questions related to student adaptability in online education. For example, researchers could use the dataset to investigate the relationship between student characteristics and adaptability, or to identify the factors that contribute to student success in online learning. The dataset could also be used to develop predictive models that can be used to identify students who are at risk of struggling in online education.\n",
    "\n",
    "Here are some of the key questions that can be addressed using the dataset:\n",
    "\n",
    "1. What are the factors that influence student adaptability in online education?\n",
    "1. How can we identify students who are at risk of struggling in online education?\n",
    "1. How can we improve the quality of online learning experiences for students?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d2b9ab",
   "metadata": {
    "papermill": {
     "duration": 0.008658,
     "end_time": "2023-07-11T17:55:31.435041",
     "exception": false,
     "start_time": "2023-07-11T17:55:31.426383",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* **Required things And Import Librarries;**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d792472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:31.454312Z",
     "iopub.status.busy": "2023-07-11T17:55:31.453955Z",
     "iopub.status.idle": "2023-07-11T17:55:32.173108Z",
     "shell.execute_reply": "2023-07-11T17:55:32.172094Z"
    },
    "papermill": {
     "duration": 0.73157,
     "end_time": "2023-07-11T17:55:32.175525",
     "exception": false,
     "start_time": "2023-07-11T17:55:31.443955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a43b7db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:32.196459Z",
     "iopub.status.busy": "2023-07-11T17:55:32.194998Z",
     "iopub.status.idle": "2023-07-11T17:55:33.293904Z",
     "shell.execute_reply": "2023-07-11T17:55:33.292922Z"
    },
    "papermill": {
     "duration": 1.111351,
     "end_time": "2023-07-11T17:55:33.296316",
     "exception": false,
     "start_time": "2023-07-11T17:55:32.184965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")  #using style ggplot\n",
    "%matplotlib inline\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bcc96d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:33.316334Z",
     "iopub.status.busy": "2023-07-11T17:55:33.316028Z",
     "iopub.status.idle": "2023-07-11T17:55:33.990958Z",
     "shell.execute_reply": "2023-07-11T17:55:33.989974Z"
    },
    "papermill": {
     "duration": 0.687601,
     "end_time": "2023-07-11T17:55:33.993474",
     "exception": false,
     "start_time": "2023-07-11T17:55:33.305873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Importing the basic librarires for building model - classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import accuracy_score,r2_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import  MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.neighbors import  KNeighborsClassifier\n",
    "from sklearn.preprocessing import LabelEncoder,StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d733ed",
   "metadata": {
    "papermill": {
     "duration": 0.008835,
     "end_time": "2023-07-11T17:55:34.011695",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.002860",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Data Reads & Dataset Informations;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8602b87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.031431Z",
     "iopub.status.busy": "2023-07-11T17:55:34.030626Z",
     "iopub.status.idle": "2023-07-11T17:55:34.056084Z",
     "shell.execute_reply": "2023-07-11T17:55:34.055224Z"
    },
    "papermill": {
     "duration": 0.037564,
     "end_time": "2023-07-11T17:55:34.058274",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.020710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df =pd.read_csv(\"../input/students-adaptability-level-in-online-education/students_adaptability_level_online_education.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abc8dbca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.077591Z",
     "iopub.status.busy": "2023-07-11T17:55:34.077329Z",
     "iopub.status.idle": "2023-07-11T17:55:34.101155Z",
     "shell.execute_reply": "2023-07-11T17:55:34.099218Z"
    },
    "papermill": {
     "duration": 0.035759,
     "end_time": "2023-07-11T17:55:34.103075",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.067316",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "      <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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Boy</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>Poor</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>3G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Boy</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>Wifi</td>\n",
       "      <td>4G</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Boy</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>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>8</th>\n",
       "      <td>Boy</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>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boy</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>3G</td>\n",
       "      <td>1-3</td>\n",
       "      <td>No</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Moderate</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",
       "5    Boy  11-15          School   Non Government         No      Yes   \n",
       "6    Boy  11-15          School   Non Government         No      Yes   \n",
       "7    Boy  11-15          School   Non Government         No      Yes   \n",
       "8    Boy  16-20         College       Government         No      Yes   \n",
       "9    Boy  11-15          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",
       "5           Low                Poor   Mobile Data           3G            1-3   \n",
       "6           Low                 Mid          Wifi           4G              0   \n",
       "7           Low                 Mid          Wifi           4G            1-3   \n",
       "8           Low                 Mid          Wifi           4G            1-3   \n",
       "9           Low                 Mid   Mobile Data           3G            1-3   \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  \n",
       "5       No  Mobile              Low  \n",
       "6       No  Mobile              Low  \n",
       "7       No  Mobile         Moderate  \n",
       "8       No  Mobile              Low  \n",
       "9       No  Mobile         Moderate  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6456f623",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.124670Z",
     "iopub.status.busy": "2023-07-11T17:55:34.124415Z",
     "iopub.status.idle": "2023-07-11T17:55:34.147573Z",
     "shell.execute_reply": "2023-07-11T17:55:34.146298Z"
    },
    "papermill": {
     "duration": 0.036214,
     "end_time": "2023-07-11T17:55:34.149677",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.113463",
     "status": "completed"
    },
    "tags": []
   },
   "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": [
    "#Checking the dtypes of all the columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9007e137",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.170239Z",
     "iopub.status.busy": "2023-07-11T17:55:34.169497Z",
     "iopub.status.idle": "2023-07-11T17:55:34.178438Z",
     "shell.execute_reply": "2023-07-11T17:55:34.177494Z"
    },
    "papermill": {
     "duration": 0.021318,
     "end_time": "2023-07-11T17:55:34.180434",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.159116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1205, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking the shape DataSet\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbbac3b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.201278Z",
     "iopub.status.busy": "2023-07-11T17:55:34.200570Z",
     "iopub.status.idle": "2023-07-11T17:55:34.212419Z",
     "shell.execute_reply": "2023-07-11T17:55:34.211463Z"
    },
    "papermill": {
     "duration": 0.024251,
     "end_time": "2023-07-11T17:55:34.214335",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.190084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                 0\n",
       "Age                    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 Level       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cheacking Missing values of the Dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a08adf5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.235343Z",
     "iopub.status.busy": "2023-07-11T17:55:34.234591Z",
     "iopub.status.idle": "2023-07-11T17:55:34.265390Z",
     "shell.execute_reply": "2023-07-11T17:55:34.264327Z"
    },
    "papermill": {
     "duration": 0.043451,
     "end_time": "2023-07-11T17:55:34.267464",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.224013",
     "status": "completed"
    },
    "tags": []
   },
   "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>Gender</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>Boy</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1205</td>\n",
       "      <td>6</td>\n",
       "      <td>21-25</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education Level</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>School</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Institution Type</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>Non Government</td>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT Student</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Load-shedding</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>Low</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Condition</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>Mid</td>\n",
       "      <td>878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Type</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>Mobile Data</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network Type</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>4G</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class Duration</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>1-3</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self Lms</th>\n",
       "      <td>1205</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Device</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adaptivity Level</th>\n",
       "      <td>1205</td>\n",
       "      <td>3</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count unique             top  freq\n",
       "Gender               1205      2             Boy   663\n",
       "Age                  1205      6           21-25   374\n",
       "Education Level      1205      3          School   530\n",
       "Institution Type     1205      2  Non Government   823\n",
       "IT Student           1205      2              No   901\n",
       "Location             1205      2             Yes   935\n",
       "Load-shedding        1205      2             Low  1004\n",
       "Financial Condition  1205      3             Mid   878\n",
       "Internet Type        1205      2     Mobile Data   695\n",
       "Network Type         1205      3              4G   775\n",
       "Class Duration       1205      3             1-3   840\n",
       "Self Lms             1205      2              No   995\n",
       "Device               1205      3          Mobile  1013\n",
       "Adaptivity Level     1205      3        Moderate   625"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look  describe data set\n",
    "\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48792f39",
   "metadata": {
    "papermill": {
     "duration": 0.01001,
     "end_time": "2023-07-11T17:55:34.287864",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.277854",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Data Visualizations and EDA Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2eb89be5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.309734Z",
     "iopub.status.busy": "2023-07-11T17:55:34.308896Z",
     "iopub.status.idle": "2023-07-11T17:55:34.600171Z",
     "shell.execute_reply": "2023-07-11T17:55:34.599167Z"
    },
    "papermill": {
     "duration": 0.304348,
     "end_time": "2023-07-11T17:55:34.602360",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.298012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Adaptivity Level'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the percentage Adaptivity Level in Dataset\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "df['Adaptivity Level'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ccc91e",
   "metadata": {
    "papermill": {
     "duration": 0.010818,
     "end_time": "2023-07-11T17:55:34.624165",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.613347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "->The percentage Adaptivity Level in Dataset\n",
    "\n",
    "Low = 39.8%, \n",
    "\n",
    "Moderate = 51.9%,\n",
    "\n",
    "High = 8.3 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a9f0349",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.647246Z",
     "iopub.status.busy": "2023-07-11T17:55:34.646473Z",
     "iopub.status.idle": "2023-07-11T17:55:34.851180Z",
     "shell.execute_reply": "2023-07-11T17:55:34.850301Z"
    },
    "papermill": {
     "duration": 0.219179,
     "end_time": "2023-07-11T17:55:34.854067",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.634888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Gender'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the percentage Gender in Dataset\n",
    "\n",
    "plt.figure(figsize=(13,9))\n",
    "\n",
    "df['Gender'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85fca2a3",
   "metadata": {
    "papermill": {
     "duration": 0.011576,
     "end_time": "2023-07-11T17:55:34.890042",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.878466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "->The percentage Gender in Dataset\n",
    "\n",
    "Boy= 55%,\n",
    "\n",
    "Girl = 45 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe8a0ee7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:34.913781Z",
     "iopub.status.busy": "2023-07-11T17:55:34.913499Z",
     "iopub.status.idle": "2023-07-11T17:55:35.172354Z",
     "shell.execute_reply": "2023-07-11T17:55:35.171080Z"
    },
    "papermill": {
     "duration": 0.276452,
     "end_time": "2023-07-11T17:55:35.177697",
     "exception": false,
     "start_time": "2023-07-11T17:55:34.901245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Device'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the Device using in Dataset\n",
    "\n",
    "plt.figure(figsize=(11,10))\n",
    "\n",
    "\n",
    "df['Device'].value_counts().plot.pie(autopct='%1.1f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af32000",
   "metadata": {
    "papermill": {
     "duration": 0.012246,
     "end_time": "2023-07-11T17:55:35.203904",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.191658",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "->The Device using in Dataset\n",
    "\n",
    "Mobile = 84.1%,\n",
    "\n",
    "Computer = 13.4%,\n",
    "\n",
    "Tab = 2.5%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0204d377",
   "metadata": {
    "papermill": {
     "duration": 0.011865,
     "end_time": "2023-07-11T17:55:35.227762",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.215897",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Data Pre-Processing And Feature Extractions:\n",
    "\n",
    "We need change all data type using LabelEncoding Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d6729e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.254057Z",
     "iopub.status.busy": "2023-07-11T17:55:35.253470Z",
     "iopub.status.idle": "2023-07-11T17:55:35.268954Z",
     "shell.execute_reply": "2023-07-11T17:55:35.268096Z"
    },
    "papermill": {
     "duration": 0.031234,
     "end_time": "2023-07-11T17:55:35.270920",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.239686",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data=df\n",
    "\n",
    "label_encoders = {}\n",
    "categorical_columns = data.columns  # I would recommend using columns names here if you're using pandas. If you're using numpy then stick with range(n) instead\n",
    "\n",
    "for column in categorical_columns:\n",
    "    label_encoders[column] = LabelEncoder()\n",
    "    data[column] = label_encoders[column].fit_transform(data[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9c45a1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.296701Z",
     "iopub.status.busy": "2023-07-11T17:55:35.295929Z",
     "iopub.status.idle": "2023-07-11T17:55:35.309919Z",
     "shell.execute_reply": "2023-07-11T17:55:35.309004Z"
    },
    "papermill": {
     "duration": 0.029214,
     "end_time": "2023-07-11T17:55:35.312017",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.282803",
     "status": "completed"
    },
    "tags": []
   },
   "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>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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Education Level  Institution Type  IT Student  Location  \\\n",
       "0       0    3                2                 1           0         1   \n",
       "1       1    3                2                 1           0         1   \n",
       "2       1    2                0                 0           0         1   \n",
       "3       1    1                1                 1           0         1   \n",
       "4       1    2                1                 1           0         1   \n",
       "5       0    1                1                 1           0         1   \n",
       "6       0    1                1                 1           0         1   \n",
       "7       0    1                1                 1           0         1   \n",
       "8       0    2                0                 0           0         1   \n",
       "9       0    1                1                 1           0         1   \n",
       "\n",
       "   Load-shedding  Financial Condition  Internet Type  Network Type  \\\n",
       "0              1                    0              1             2   \n",
       "1              0                    0              0             2   \n",
       "2              1                    0              1             2   \n",
       "3              1                    0              0             2   \n",
       "4              1                    1              0             1   \n",
       "5              1                    1              0             1   \n",
       "6              1                    0              1             2   \n",
       "7              1                    0              1             2   \n",
       "8              1                    0              1             2   \n",
       "9              1                    0              0             1   \n",
       "\n",
       "   Class Duration  Self Lms  Device  Adaptivity Level  \n",
       "0               2         0       2                 2  \n",
       "1               1         1       1                 2  \n",
       "2               1         0       1                 2  \n",
       "3               1         0       1                 2  \n",
       "4               0         0       1                 1  \n",
       "5               1         0       1                 1  \n",
       "6               0         0       1                 1  \n",
       "7               1         0       1                 2  \n",
       "8               1         0       1                 1  \n",
       "9               1         0       1                 2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31097f5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.338465Z",
     "iopub.status.busy": "2023-07-11T17:55:35.338184Z",
     "iopub.status.idle": "2023-07-11T17:55:35.345088Z",
     "shell.execute_reply": "2023-07-11T17:55:35.344222Z"
    },
    "papermill": {
     "duration": 0.023086,
     "end_time": "2023-07-11T17:55:35.347454",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.324368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Defined X value and y value , and split the data train\n",
    "X = data.drop(columns=\"Adaptivity Level\")           \n",
    "y = data[\"Adaptivity Level\"]    # y = quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ed250eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.373152Z",
     "iopub.status.busy": "2023-07-11T17:55:35.372900Z",
     "iopub.status.idle": "2023-07-11T17:55:35.379525Z",
     "shell.execute_reply": "2023-07-11T17:55:35.378707Z"
    },
    "papermill": {
     "duration": 0.021667,
     "end_time": "2023-07-11T17:55:35.381388",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.359721",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# split the data train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d154f87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.406964Z",
     "iopub.status.busy": "2023-07-11T17:55:35.406706Z",
     "iopub.status.idle": "2023-07-11T17:55:35.412239Z",
     "shell.execute_reply": "2023-07-11T17:55:35.411310Z"
    },
    "papermill": {
     "duration": 0.020716,
     "end_time": "2023-07-11T17:55:35.414314",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.393598",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(964, 13)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e676cadc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.440095Z",
     "iopub.status.busy": "2023-07-11T17:55:35.439847Z",
     "iopub.status.idle": "2023-07-11T17:55:35.445026Z",
     "shell.execute_reply": "2023-07-11T17:55:35.444179Z"
    },
    "papermill": {
     "duration": 0.020275,
     "end_time": "2023-07-11T17:55:35.446911",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.426636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c348a5bc",
   "metadata": {
    "papermill": {
     "duration": 0.012624,
     "end_time": "2023-07-11T17:55:35.472008",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.459384",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Machine Learning Algorithms Build & Performance Comparisions;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c1992c89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.498702Z",
     "iopub.status.busy": "2023-07-11T17:55:35.497976Z",
     "iopub.status.idle": "2023-07-11T17:55:35.503163Z",
     "shell.execute_reply": "2023-07-11T17:55:35.502300Z"
    },
    "papermill": {
     "duration": 0.020683,
     "end_time": "2023-07-11T17:55:35.505219",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.484536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Defined object from library classification  without Fine tuning Methods\n",
    "\n",
    "LR = LogisticRegression()\n",
    "DTR = DecisionTreeClassifier()\n",
    "\n",
    "RFR = RandomForestClassifier()\n",
    "KNR = KNeighborsClassifier()\n",
    "\n",
    "MLP = MLPClassifier()\n",
    "XGB = XGBClassifier()\n",
    "SVC=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a1a24b68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:35.531311Z",
     "iopub.status.busy": "2023-07-11T17:55:35.531031Z",
     "iopub.status.idle": "2023-07-11T17:55:37.021302Z",
     "shell.execute_reply": "2023-07-11T17:55:37.019964Z"
    },
    "papermill": {
     "duration": 1.505691,
     "end_time": "2023-07-11T17:55:37.023385",
     "exception": false,
     "start_time": "2023-07-11T17:55:35.517694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression() : 66.39004149377593\n",
      "DecisionTreeClassifier() : 90.87136929460581\n",
      "RandomForestClassifier() : 91.70124481327801\n",
      "KNeighborsClassifier() : 79.25311203319502\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier() : 83.81742738589212\n",
      "KNeighborsClassifier() : 79.25311203319502\n",
      "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              objective='multi:softprob', predictor=None, ...) : 91.70124481327801\n",
      "SVC() : 73.44398340248964\n"
     ]
    }
   ],
   "source": [
    "# make for loop for classification \n",
    "\n",
    "li = [LR,DTR,RFR,KNR,MLP,KNR,XGB,SVC]\n",
    "d = {}\n",
    "for i in li:\n",
    "    i.fit(X_train,y_train)\n",
    "    ypred = i.predict(X_test)\n",
    "    print(i,\":\",accuracy_score(y_test,ypred)*100)\n",
    "    d.update({str(i):i.score(X_test,y_test)*100})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a927f15",
   "metadata": {
    "papermill": {
     "duration": 0.014959,
     "end_time": "2023-07-11T17:55:37.051865",
     "exception": false,
     "start_time": "2023-07-11T17:55:37.036906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The best ML Model is RandomForestClassifier-> 92.12% Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "826613db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:37.079179Z",
     "iopub.status.busy": "2023-07-11T17:55:37.078885Z",
     "iopub.status.idle": "2023-07-11T17:55:37.553980Z",
     "shell.execute_reply": "2023-07-11T17:55:37.553058Z"
    },
    "papermill": {
     "duration": 0.492505,
     "end_time": "2023-07-11T17:55:37.557421",
     "exception": false,
     "start_time": "2023-07-11T17:55:37.064916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make graph about Accuracy\n",
    "\n",
    "plt.figure(figsize=(24, 10))\n",
    "plt.title(\"Algorithm vs Accuracy\")\n",
    "plt.xlabel(\"Algorithm\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.plot(d.keys(),d.values(),marker='o',color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c80b44fd",
   "metadata": {
    "papermill": {
     "duration": 0.014812,
     "end_time": "2023-07-11T17:55:37.587416",
     "exception": false,
     "start_time": "2023-07-11T17:55:37.572604",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "-> Deep Neural Networks Model Performance Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a7410930",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:37.618282Z",
     "iopub.status.busy": "2023-07-11T17:55:37.617964Z",
     "iopub.status.idle": "2023-07-11T17:55:45.625167Z",
     "shell.execute_reply": "2023-07-11T17:55:45.624082Z"
    },
    "papermill": {
     "duration": 8.025635,
     "end_time": "2023-07-11T17:55:45.627802",
     "exception": false,
     "start_time": "2023-07-11T17:55:37.602167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "203c73fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:45.660415Z",
     "iopub.status.busy": "2023-07-11T17:55:45.659551Z",
     "iopub.status.idle": "2023-07-11T17:55:48.728524Z",
     "shell.execute_reply": "2023-07-11T17:55:48.727510Z"
    },
    "papermill": {
     "duration": 3.087529,
     "end_time": "2023-07-11T17:55:48.730883",
     "exception": false,
     "start_time": "2023-07-11T17:55:45.643354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# building model and we made split the data - train and test\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.InputLayer(input_shape = X_train.shape[1:]),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Dense(512 , activation = 'selu',kernel_initializer = 'lecun_normal'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Dense(256 , activation = 'selu' , kernel_initializer = 'lecun_normal'),\n",
    "    tf.keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Dense(3 , activation = 'softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae7e5b16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:48.764456Z",
     "iopub.status.busy": "2023-07-11T17:55:48.764110Z",
     "iopub.status.idle": "2023-07-11T17:55:48.780233Z",
     "shell.execute_reply": "2023-07-11T17:55:48.779372Z"
    },
    "papermill": {
     "duration": 0.034443,
     "end_time": "2023-07-11T17:55:48.782362",
     "exception": false,
     "start_time": "2023-07-11T17:55:48.747919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Model Compiple\n",
    "model.compile(loss = tf.keras.losses.sparse_categorical_crossentropy ,\n",
    "              optimizer = tf.keras.optimizers.Nadam() ,\n",
    "              metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "97eea7cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:48.813599Z",
     "iopub.status.busy": "2023-07-11T17:55:48.813330Z",
     "iopub.status.idle": "2023-07-11T17:55:58.038108Z",
     "shell.execute_reply": "2023-07-11T17:55:58.037180Z"
    },
    "papermill": {
     "duration": 9.242673,
     "end_time": "2023-07-11T17:55:58.040171",
     "exception": false,
     "start_time": "2023-07-11T17:55:48.797498",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "31/31 [==============================] - 7s 6ms/step - loss: 1.0011 - accuracy: 0.5913\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.7000 - accuracy: 0.7313\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.6273 - accuracy: 0.7583\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.5630 - accuracy: 0.7707\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 0s 6ms/step - loss: 0.5200 - accuracy: 0.7894\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.5240 - accuracy: 0.7853\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4915 - accuracy: 0.7873\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4295 - accuracy: 0.8143\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 0s 6ms/step - loss: 0.4583 - accuracy: 0.8039\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4598 - accuracy: 0.8039\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4354 - accuracy: 0.8216\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4346 - accuracy: 0.8102\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 0s 5ms/step - loss: 0.4418 - accuracy: 0.8174\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7bed28368f70>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model Training\n",
    "model.fit(X_train , y_train,\n",
    "          epochs = 100,\n",
    "          callbacks = [tf.keras.callbacks.EarlyStopping(monitor='loss' ,mode = 'min', patience = 5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4a505b56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:55:58.084434Z",
     "iopub.status.busy": "2023-07-11T17:55:58.083632Z",
     "iopub.status.idle": "2023-07-11T17:56:01.183971Z",
     "shell.execute_reply": "2023-07-11T17:56:01.182525Z"
    },
    "papermill": {
     "duration": 3.124218,
     "end_time": "2023-07-11T17:56:01.186023",
     "exception": false,
     "start_time": "2023-07-11T17:55:58.061805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 0s 3ms/step - loss: 0.4380 - accuracy: 0.8340\n",
      "The Score TF model : 0.8340249061584473\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the Model Training \n",
    "a , score = model.evaluate(X_test , y_test)\n",
    "\n",
    "print(\"The Score TF model :\",score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9afe294",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-11T17:56:01.229932Z",
     "iopub.status.busy": "2023-07-11T17:56:01.229048Z",
     "iopub.status.idle": "2023-07-11T17:56:01.233585Z",
     "shell.execute_reply": "2023-07-11T17:56:01.232644Z"
    },
    "papermill": {
     "duration": 0.028534,
     "end_time": "2023-07-11T17:56:01.235713",
     "exception": false,
     "start_time": "2023-07-11T17:56:01.207179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 44.048117,
   "end_time": "2023-07-11T17:56:05.126251",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-07-11T17:55:21.078134",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}