LinearRegression_StudentPerformance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Math Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mat = pd.read_csv(\"student-mat.csv\", sep=';')\n",
    "data_mat.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analyzing and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 395 entries, 0 to 394\n",
      "Data columns (total 33 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   school      395 non-null    object\n",
      " 1   sex         395 non-null    object\n",
      " 2   age         395 non-null    int64 \n",
      " 3   address     395 non-null    object\n",
      " 4   famsize     395 non-null    object\n",
      " 5   Pstatus     395 non-null    object\n",
      " 6   Medu        395 non-null    int64 \n",
      " 7   Fedu        395 non-null    int64 \n",
      " 8   Mjob        395 non-null    object\n",
      " 9   Fjob        395 non-null    object\n",
      " 10  reason      395 non-null    object\n",
      " 11  guardian    395 non-null    object\n",
      " 12  traveltime  395 non-null    int64 \n",
      " 13  studytime   395 non-null    int64 \n",
      " 14  failures    395 non-null    int64 \n",
      " 15  schoolsup   395 non-null    object\n",
      " 16  famsup      395 non-null    object\n",
      " 17  paid        395 non-null    object\n",
      " 18  activities  395 non-null    object\n",
      " 19  nursery     395 non-null    object\n",
      " 20  higher      395 non-null    object\n",
      " 21  internet    395 non-null    object\n",
      " 22  romantic    395 non-null    object\n",
      " 23  famrel      395 non-null    int64 \n",
      " 24  freetime    395 non-null    int64 \n",
      " 25  goout       395 non-null    int64 \n",
      " 26  Dalc        395 non-null    int64 \n",
      " 27  Walc        395 non-null    int64 \n",
      " 28  health      395 non-null    int64 \n",
      " 29  absences    395 non-null    int64 \n",
      " 30  G1          395 non-null    int64 \n",
      " 31  G2          395 non-null    int64 \n",
      " 32  G3          395 non-null    int64 \n",
      "dtypes: int64(16), object(17)\n",
      "memory usage: 102.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_mat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>Result</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  goout Dalc  Walc  health  absences  G1  G2  G3 Result Average  \n",
       "0     4    1     1       3         6   5   6   6      0    5.75  \n",
       "1     3    1     1       3         4   5   5   6      0    5.50  \n",
       "2     2    2     3       3        10   7   8  10      0    8.75  \n",
       "3     2    1     1       5         2  15  14  15      1   14.75  \n",
       "4     2    1     2       5         4   6  10  10      0    9.00  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's assume that students pass the course if %25(G1) + %25(G2) + %50(G3) >= 10\n",
    "\n",
    "data_mat['Result'] = 'nan'\n",
    "data_mat['Average'] = data_mat['G1']*25/100 + data_mat['G2']*25/100 + data_mat['G3']*50/100\n",
    "for i in range(len(data_mat['Average'])):\n",
    "    if data_mat['Average'].values[i] >= 10:\n",
    "        data_mat['Result'].values[i] = 1\n",
    "    else:\n",
    "        data_mat['Result'].values[i] = 0\n",
    "data_mat['Result'] = data_mat['Result'].astype(int)\n",
    "data_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, figsize=(10, 15))\n",
    "fig.suptitle('Analyzing the Student Performance on Math')\n",
    "\n",
    "sns.countplot(ax=axes[0, 0], data=data_mat, x='Result', hue = 'studytime')\n",
    "axes[0,0].set_title('Result-Study Time')\n",
    "\n",
    "sns.countplot(ax=axes[0, 1], data=data_mat, x='Result', hue = 'health')\n",
    "axes[0,1].set_title('Result-Health Status')\n",
    "\n",
    "sns.countplot(ax=axes[1, 0], data=data_mat, x='Result', hue = 'Mjob')\n",
    "axes[1,0].set_title('Result-Mother Job')\n",
    "\n",
    "sns.countplot(ax=axes[1, 1], data=data_mat, x='Result', hue = 'Fjob')\n",
    "axes[1,1].set_title('Result-Father Job')\n",
    "\n",
    "sns.countplot(ax=axes[2, 0], data=data_mat, x='Result', hue = 'school')\n",
    "axes[2,0].set_title('Result-School')\n",
    "\n",
    "sns.countplot(ax=axes[2, 1], data=data_mat, x='Result', hue = 'sex')\n",
    "axes[2,1].set_title('Result-Sex')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "school :  ['GP' 'MS']\n",
      "sex :  ['F' 'M']\n",
      "address :  ['U' 'R']\n",
      "famsize :  ['GT3' 'LE3']\n",
      "Pstatus :  ['A' 'T']\n",
      "Mjob :  ['at_home' 'health' 'other' 'services' 'teacher']\n",
      "Fjob :  ['teacher' 'other' 'services' 'health' 'at_home']\n",
      "reason :  ['course' 'other' 'home' 'reputation']\n",
      "guardian :  ['mother' 'father' 'other']\n",
      "schoolsup :  ['yes' 'no']\n",
      "famsup :  ['no' 'yes']\n",
      "paid :  ['no' 'yes']\n",
      "activities :  ['no' 'yes']\n",
      "nursery :  ['yes' 'no']\n",
      "higher :  ['yes' 'no']\n",
      "internet :  ['no' 'yes']\n",
      "romantic :  ['no' 'yes']\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(data_mat.columns)):\n",
    "    if data_mat.dtypes[i] == 'O':\n",
    "        print(data_mat.columns[i] , \": \", data_mat[data_mat.columns[i]].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mat['school'].replace({\n",
    "    'GP':0,\n",
    "    'MS':1\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['sex'].replace({\n",
    "    'M':0,\n",
    "    'F':1\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['address'].replace({\n",
    "    'U':0,\n",
    "    'R':1\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['famsize'].replace({\n",
    "    'GT3':0,\n",
    "    'LE3':1\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['Pstatus'].replace({\n",
    "    'A':0,\n",
    "    'T':1\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['Mjob'].replace({\n",
    "    'at_home': 0,\n",
    "    'health': 1, \n",
    "    'other': 2, \n",
    "    'services': 3, \n",
    "    'teacher': 4\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['Fjob'].replace({\n",
    "    'at_home': 0,\n",
    "    'health': 1, \n",
    "    'other': 2, \n",
    "    'services': 3, \n",
    "    'teacher': 4\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['reason'].replace({\n",
    "    'course': 0,\n",
    "    'home': 1, \n",
    "    'other': 2, \n",
    "    'reputation': 3\n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['guardian'].replace({\n",
    "    'other': 0,\n",
    "    'mother': 1, \n",
    "    'father': 2 \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['schoolsup'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['famsup'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['paid'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['activities'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['nursery'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['higher'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['internet'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)\n",
    "\n",
    "data_mat['romantic'].replace({\n",
    "    'no': 0,\n",
    "    'yes': 1, \n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>Result</th>\n",
       "      <th>Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>14.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0       0    1   18        0        0        0     4     4     0     4  ...   \n",
       "1       0    1   17        0        0        1     1     1     0     2  ...   \n",
       "2       0    1   15        0        1        1     1     1     0     2  ...   \n",
       "3       0    1   15        0        0        1     4     2     1     3  ...   \n",
       "4       0    1   16        0        0        1     3     3     2     2  ...   \n",
       "\n",
       "   goout  Dalc  Walc  health  absences  G1  G2  G3  Result  Average  \n",
       "0      4     1     1       3         6   5   6   6       0     5.75  \n",
       "1      3     1     1       3         4   5   5   6       0     5.50  \n",
       "2      2     2     3       3        10   7   8  10       0     8.75  \n",
       "3      2     1     1       5         2  15  14  15       1    14.75  \n",
       "4      2     1     2       5         4   6  10  10       0     9.00  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        0\n",
       "sex           0\n",
       "age           0\n",
       "address       0\n",
       "famsize       0\n",
       "Pstatus       0\n",
       "Medu          0\n",
       "Fedu          0\n",
       "Mjob          0\n",
       "Fjob          0\n",
       "reason        0\n",
       "guardian      0\n",
       "traveltime    0\n",
       "studytime     0\n",
       "failures      0\n",
       "schoolsup     0\n",
       "famsup        0\n",
       "paid          0\n",
       "activities    0\n",
       "nursery       0\n",
       "higher        0\n",
       "internet      0\n",
       "romantic      0\n",
       "famrel        0\n",
       "freetime      0\n",
       "goout         0\n",
       "Dalc          0\n",
       "Walc          0\n",
       "health        0\n",
       "absences      0\n",
       "G1            0\n",
       "G2            0\n",
       "G3            0\n",
       "Result        0\n",
       "Average       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mat.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mat.to_csv('student-mat-preprocessed.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.heatmap(data_mat.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Average       1.000000\n",
       "G3            0.979771\n",
       "G2            0.957525\n",
       "G1            0.892353\n",
       "Result        0.789458\n",
       "Medu          0.224048\n",
       "higher        0.189007\n",
       "Fedu          0.170434\n",
       "studytime     0.124864\n",
       "reason        0.121681\n",
       "internet      0.102269\n",
       "Mjob          0.101201\n",
       "paid          0.093944\n",
       "famsize       0.082914\n",
       "guardian      0.071424\n",
       "nursery       0.061690\n",
       "Fjob          0.053464\n",
       "activities    0.033903\n",
       "famrel        0.030593\n",
       "freetime      0.006026\n",
       "absences      0.005879\n",
       "school       -0.044159\n",
       "Pstatus      -0.047820\n",
       "famsup       -0.055469\n",
       "Dalc         -0.067856\n",
       "health       -0.075441\n",
       "Walc         -0.078127\n",
       "sex          -0.102660\n",
       "address      -0.107745\n",
       "romantic     -0.111625\n",
       "schoolsup    -0.122630\n",
       "traveltime   -0.126016\n",
       "age          -0.143677\n",
       "goout        -0.149407\n",
       "failures     -0.374394\n",
       "Name: Average, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mat.corr()['Average'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_mat[['Medu', 'failures', 'higher', 'G1', 'G2']]\n",
    "y = data_mat[['G3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xtrain Shape: (276, 5) \n",
      "Xtest Shape: (119, 5) \n",
      "Ytrain Shape: (276, 1) \n",
      "Ytest Shape: (119, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3) \n",
    "print('Xtrain Shape:', x_train.shape, '\\nXtest Shape:', x_test.shape, '\\nYtrain Shape:' ,y_train.shape, '\\nYtest Shape:', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coefficient: [[ 0.14167795 -0.04575331  0.53969811  0.14822771  0.97799144]]\n",
      "\n",
      "Intercept: [-2.57995044]\n",
      "\n",
      "Mean Squared Error: 3.09 \n",
      "\n",
      "R-Squared: 0.83 \n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "print(\"\\nCoefficient:\", model.coef_)\n",
    "print(\"\\nIntercept:\", model.intercept_)\n",
    "print(\"\\nMean Squared Error: %.2f \" % mean_squared_error(y_test, y_pred))\n",
    "print(\"\\nR-Squared: %.2f \" % r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  [8.52075382] \tReal Value:  [10]\n",
      "Prediction:  [6.1443151] \tReal Value:  [9]\n",
      "Prediction:  [8.36597635] \tReal Value:  [8]\n",
      "Prediction:  [8.08917022] \tReal Value:  [10]\n",
      "Prediction:  [12.43926933] \tReal Value:  [12]\n",
      "Prediction:  [11.17883166] \tReal Value:  [12]\n",
      "Prediction:  [9.65352273] \tReal Value:  [10]\n",
      "Prediction:  [7.63868702] \tReal Value:  [8]\n",
      "Prediction:  [12.18931691] \tReal Value:  [12]\n",
      "Prediction:  [13.86849366] \tReal Value:  [13]\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(\"Prediction: \", y_pred[i], \"\\tReal Value: \", y_test.values[i])"
   ]
  }
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