Student Classification Based on Academic and Training Satisfaction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description\n",
    "\n",
    "This data was collected from students using digital designed questionnaires (Kobocollect) from multiple mobile devices.\n",
    "systematic sampling method was applied in data collection.\n",
    "\n",
    "**DEFINITION OF ATTRIBUTES: The attributes represent questions/statements.**\n",
    "\n",
    "**dependent variable** = SATISFIED (Are you generally satisfied with training (YES=1, NO=0))\n",
    "\n",
    "\n",
    "**independent variables:** the below statements were to be responded to in  5 point linear lickert scale \n",
    "(strongly disagree=1, disagree=2, undecided=3, agree=4, strongly agree=5 )\n",
    "\n",
    ">**L_explain:** The trainer explains the application of concept taught in class to real life experience.  \n",
    "**L_avail:** The availability and accessibility of the trainer is satisfactory.  \n",
    "**C_ref:** There are enough reference and resaearch materials in the institution.  \n",
    "**L_exam:** exams are related to content covered   \n",
    "**C_dept:** The department are accessibile and responds to students swiftly.  \n",
    "**C_incorporated:** The course is incorporated with career expo activites  \n",
    "**T_net:** The institution is equiped with reliable internet connection   \n",
    "**I_interclass:** The interraction between you and the trainer during training is adequate   \n",
    "**I_inquire:** The trainer makes it comfortable for inquiries.  \n",
    "**I_consult:** you are comfortable consulting after class  \n",
    "**L_covers:** The trainer covers the whole syllabus before exams   \n",
    "**T_comp:** the institution is equiped with relable computers for research and IT work.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.set(rc={'figure.figsize':(9,9)})\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "association of gender and satisfaction.png\n",
      "Classifying satisfied students with training using Logistic regression.R\n",
      "course vs satisfaction.png\n",
      "gender_vs_satisfaction.png\n",
      "hist_lec.png\n",
      "Logistic regression Student satisfaction with triaining.ipynb\n",
      "ML_BREAST CANCER PREDICTION USING SVM.ipynb\n",
      "projo.csv\n",
      "projo_README.txt\n",
      "Student Classification Based on Academic and Training Satisfaction.ipynb\n",
      "wbcd.csv\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>course</th>\n",
       "      <th>L_explain</th>\n",
       "      <th>L_avail</th>\n",
       "      <th>C_ref</th>\n",
       "      <th>L_exam</th>\n",
       "      <th>C_dept</th>\n",
       "      <th>C_incorporated</th>\n",
       "      <th>T_net</th>\n",
       "      <th>I_interclass</th>\n",
       "      <th>I_inquire</th>\n",
       "      <th>I_consult</th>\n",
       "      <th>L_covers</th>\n",
       "      <th>T_comp</th>\n",
       "      <th>SATISFIED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>STASTISTICS</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>22.0</td>\n",
       "      <td>MATHS_COMP</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>BBIT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>MINING</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>BCOM</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender   age       course  L_explain  L_avail  C_ref  L_exam  C_dept  \\\n",
       "0      M  24.0  STASTISTICS          4        4      2       3       1   \n",
       "1      M  22.0   MATHS_COMP          4        5      2       5       4   \n",
       "2      M  19.0         BBIT          4        4      2       4       2   \n",
       "3      F  18.0       MINING          4        4      5       5       3   \n",
       "4      M  19.0         BCOM          3        4      4       4       2   \n",
       "\n",
       "   C_incorporated  T_net  I_interclass  I_inquire  I_consult  L_covers  \\\n",
       "0               2      4             3          4          2         4   \n",
       "1               1      1             5          5          5         5   \n",
       "2               2      2             4          4          4         4   \n",
       "3               1      2             4          1          4         5   \n",
       "4               1      1             4          4          4         3   \n",
       "\n",
       "   T_comp SATISFIED  \n",
       "0       1       YES  \n",
       "1       2       YES  \n",
       "2       3       YES  \n",
       "3       5       YES  \n",
       "4       2        NO  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_df = pd.read_csv('projo.csv')\n",
    "student_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1622de30>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Check for unique values of course\n",
    "student_df.course.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'age', 'course', 'L_explain', 'L_avail', 'C_ref', 'L_exam',\n",
       "       'C_dept', 'C_incorporated', 'T_net', 'I_interclass', 'I_inquire',\n",
       "       'I_consult', 'L_covers', 'T_comp', 'SATISFIED'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17d8fab0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "sns.countplot(x='age', data=student_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(student_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(student_df.iloc[:, 2:].columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\botieno\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\numpy\\lib\\histograms.py:754: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "c:\\users\\botieno\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\numpy\\lib\\histograms.py:755: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1d37d4b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(student_df.iloc[:, 1:5])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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