Student Performance MyCopy.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Student Performance<h1>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Project Stages<h3>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Formulate  the problem\n",
    "* Load the Data\n",
    "* Data Exploration\n",
    "* Clean The Data\n",
    "* Discover and Visualize the Data to Gain Insights\n",
    "* Looking for Correlations\n",
    "* Prepare the Data for Machine Learning Algorithms\n",
    "* Save Cleaned Data and Download it\n",
    "* creat the model\n",
    "* Visualize the Result"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Formulate  the problem</h2><br>\n",
    "we want to predict Math final grade based in students performance "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Load the Data </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>18.0</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>?</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</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>17.0</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>?</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>Female</td>\n",
       "      <td>15.0</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>10.0</td>\n",
       "      <td>7.0</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>15.0</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>2.0</td>\n",
       "      <td>15.0</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>16.0</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>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18.0</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>at_home</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>395 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    school     sex   age address famsize Pstatus  Medu  Fedu      Mjob  \\\n",
       "0       GP       F  18.0       U     GT3       A     4     4   at_home   \n",
       "1       GP       F  17.0       U     GT3       T     1     1   at_home   \n",
       "2       GP  Female  15.0       U     LE3       T     1     1   at_home   \n",
       "3       GP       F  15.0       U     GT3       T     4     2    health   \n",
       "4       GP       F  16.0       U     GT3       T     3     3     other   \n",
       "..     ...     ...   ...     ...     ...     ...   ...   ...       ...   \n",
       "390     MS       M  20.0       U     LE3       A     2     2  services   \n",
       "391     MS       M  17.0       U     LE3       T     3     1  services   \n",
       "392     MS       M  21.0       R     GT3       T     1     1     other   \n",
       "393     MS       M  18.0       R     LE3       T     3     2  services   \n",
       "394     MS       M  19.0       U     LE3       T     1     1     other   \n",
       "\n",
       "         Fjob  ... famrel freetime  goout  Dalc  Walc health absences    G1  \\\n",
       "0     teacher  ...      4        ?      4     1     1      3      NaN   5.0   \n",
       "1       other  ...      5        ?      3     1     1      3      NaN   5.0   \n",
       "2       other  ...      4        3      2     2     3      3     10.0   7.0   \n",
       "3    services  ...      3        2      2     1     1      5      2.0  15.0   \n",
       "4       other  ...      4        3      2     1     2      5      4.0   6.0   \n",
       "..        ...  ...    ...      ...    ...   ...   ...    ...      ...   ...   \n",
       "390  services  ...      5        5      4     4     5      4     11.0   9.0   \n",
       "391  services  ...      2        4      5     3     4      2      3.0  14.0   \n",
       "392     other  ...      5        5      3     3     3      3      3.0  10.0   \n",
       "393     other  ...      4        4      1     3     4      5      0.0  11.0   \n",
       "394   at_home  ...      3        2      3     3     3      5      5.0   8.0   \n",
       "\n",
       "     G2  G3  \n",
       "0     6   6  \n",
       "1     5   6  \n",
       "2     8  10  \n",
       "3    14  15  \n",
       "4    10  10  \n",
       "..   ..  ..  \n",
       "390   9   9  \n",
       "391  16  16  \n",
       "392   8   7  \n",
       "393  12  10  \n",
       "394   9   9  \n",
       "\n",
       "[395 rows x 33 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file_path = 'math-raw.csv'\n",
    "\n",
    "df = pd.read_csv(file_path)\n",
    "df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Data Exploration </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school         object\n",
       "sex            object\n",
       "age           float64\n",
       "address        object\n",
       "famsize        object\n",
       "Pstatus        object\n",
       "Medu            int64\n",
       "Fedu            int64\n",
       "Mjob           object\n",
       "Fjob           object\n",
       "reason         object\n",
       "guardian       object\n",
       "traveltime      int64\n",
       "studytime       int64\n",
       "failures      float64\n",
       "schoolsup      object\n",
       "famsup         object\n",
       "paid           object\n",
       "activities     object\n",
       "nursery        object\n",
       "higher         object\n",
       "internet       object\n",
       "romantic       object\n",
       "famrel          int64\n",
       "freetime       object\n",
       "goout           int64\n",
       "Dalc            int64\n",
       "Walc            int64\n",
       "health          int64\n",
       "absences      float64\n",
       "G1            float64\n",
       "G2              int64\n",
       "G3              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values of column  school :  ['GP' nan 'MS']\n",
      "Unique values of column  sex :  ['F' 'Female' 'Male' 'M' nan]\n",
      "Unique values of column  age :  [18. 17. 15. 16. nan 19. 22. 20. 21.]\n",
      "Unique values of column  address :  ['U' nan 'R']\n",
      "Unique values of column  famsize :  ['GT3' 'LE3']\n",
      "Unique values of column  Pstatus :  ['A' 'T']\n",
      "Unique values of column  Medu :  [4 1 3 2 0]\n",
      "Unique values of column  Fedu :  [4 1 2 3 0]\n",
      "Unique values of column  Mjob :  ['at_home' 'health' 'other' 'services' 'teacher']\n",
      "Unique values of column  Fjob :  ['teacher' 'other' 'services' 'health' 'at_home']\n",
      "Unique values of column  reason :  ['course' 'other' 'home' 'reputation']\n",
      "Unique values of column  guardian :  ['mother' 'father' 'other']\n",
      "Unique values of column  traveltime :  [2 1 3 4]\n",
      "Unique values of column  studytime :  [2 3 1 4]\n",
      "Unique values of column  failures :  [nan  3.  0.  2.  1.]\n",
      "Unique values of column  schoolsup :  ['yes' 'no']\n",
      "Unique values of column  famsup :  ['no' 'yes']\n",
      "Unique values of column  paid :  ['no' 'yes']\n",
      "Unique values of column  activities :  ['no' 'yes']\n",
      "Unique values of column  nursery :  ['yes' 'no']\n",
      "Unique values of column  higher :  ['yes' 'no']\n",
      "Unique values of column  internet :  ['no' 'yes']\n",
      "Unique values of column  romantic :  ['no' 'yes']\n",
      "Unique values of column  famrel :  [4 5 3 1 2]\n",
      "Unique values of column  freetime :  ['?' '3' '2' '4' '1' '5']\n",
      "Unique values of column  goout :  [4 3 2 1 5]\n",
      "Unique values of column  Dalc :  [1 2 5 3 4]\n",
      "Unique values of column  Walc :  [1 3 2 4 5]\n",
      "Unique values of column  health :  [3 5 1 2 4]\n",
      "Unique values of column  absences :  [nan 10.  2.  4.  0.  6. 16. 14.  7.  8. 25. 12. 54. 18. 26. 20. 56. 24.\n",
      " 28.  5. 13. 15. 22.  3. 21.  1. 75. 30. 19.  9. 11. 38. 40. 23. 17.]\n",
      "Unique values of column  G1 :  [ 5.  7. 15.  6. 12. 16. 14. 10. nan 13.  8. 11.  9. 17. 19. 18.  4.  3.]\n",
      "Unique values of column  G2 :  [ 6  5  8 14 10 15 12 18 16 13  9 11  7 19 17  4  0]\n",
      "Unique values of column  G3 :  [ 6 10 15 11 19  9 12 14 16  5  8 17 18 13 20  7  0  4]\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(\"Unique values of column \", col, \": \", df[col].unique())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "obtain a statistical summary of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</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>count</th>\n",
       "      <td>394.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.695431</td>\n",
       "      <td>2.749367</td>\n",
       "      <td>2.521519</td>\n",
       "      <td>1.448101</td>\n",
       "      <td>2.035443</td>\n",
       "      <td>0.335878</td>\n",
       "      <td>3.944304</td>\n",
       "      <td>3.108861</td>\n",
       "      <td>1.481013</td>\n",
       "      <td>2.291139</td>\n",
       "      <td>3.554430</td>\n",
       "      <td>5.712468</td>\n",
       "      <td>10.913486</td>\n",
       "      <td>10.713924</td>\n",
       "      <td>10.415190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.277573</td>\n",
       "      <td>1.094735</td>\n",
       "      <td>1.088201</td>\n",
       "      <td>0.697505</td>\n",
       "      <td>0.839240</td>\n",
       "      <td>0.745161</td>\n",
       "      <td>0.896659</td>\n",
       "      <td>1.113278</td>\n",
       "      <td>0.890741</td>\n",
       "      <td>1.287897</td>\n",
       "      <td>1.390303</td>\n",
       "      <td>8.023007</td>\n",
       "      <td>3.327015</td>\n",
       "      <td>3.761505</td>\n",
       "      <td>4.581443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
       "count  394.000000  395.000000  395.000000  395.000000  395.000000  393.000000   \n",
       "mean    16.695431    2.749367    2.521519    1.448101    2.035443    0.335878   \n",
       "std      1.277573    1.094735    1.088201    0.697505    0.839240    0.745161   \n",
       "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "25%     16.000000    2.000000    2.000000    1.000000    1.000000    0.000000   \n",
       "50%     17.000000    3.000000    2.000000    1.000000    2.000000    0.000000   \n",
       "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
       "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
       "\n",
       "           famrel       goout        Dalc        Walc      health    absences  \\\n",
       "count  395.000000  395.000000  395.000000  395.000000  395.000000  393.000000   \n",
       "mean     3.944304    3.108861    1.481013    2.291139    3.554430    5.712468   \n",
       "std      0.896659    1.113278    0.890741    1.287897    1.390303    8.023007   \n",
       "min      1.000000    1.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "25%      4.000000    2.000000    1.000000    1.000000    3.000000    0.000000   \n",
       "50%      4.000000    3.000000    1.000000    2.000000    4.000000    4.000000   \n",
       "75%      5.000000    4.000000    2.000000    3.000000    5.000000    8.000000   \n",
       "max      5.000000    5.000000    5.000000    5.000000    5.000000   75.000000   \n",
       "\n",
       "               G1          G2          G3  \n",
       "count  393.000000  395.000000  395.000000  \n",
       "mean    10.913486   10.713924   10.415190  \n",
       "std      3.327015    3.761505    4.581443  \n",
       "min      3.000000    0.000000    0.000000  \n",
       "25%      8.000000    9.000000    8.000000  \n",
       "50%     11.000000   11.000000   11.000000  \n",
       "75%     13.000000   13.000000   14.000000  \n",
       "max     19.000000   19.000000   20.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Clean The Data </h2>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "find missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        22\n",
       "sex            1\n",
       "age            1\n",
       "address        1\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       2\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       2\n",
       "G1             2\n",
       "G2             0\n",
       "G3             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), cmap='viridis')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Convert \"?\" to NaN</h4>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>18.0</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>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</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>17.0</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>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>Female</td>\n",
       "      <td>15.0</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>10.0</td>\n",
       "      <td>7.0</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>15.0</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>2.0</td>\n",
       "      <td>15.0</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>16.0</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>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GP</td>\n",
       "      <td>Male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>15.0</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>15.0</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school     sex   age address famsize Pstatus  Medu  Fedu      Mjob  \\\n",
       "0     GP       F  18.0       U     GT3       A     4     4   at_home   \n",
       "1     GP       F  17.0       U     GT3       T     1     1   at_home   \n",
       "2     GP  Female  15.0       U     LE3       T     1     1   at_home   \n",
       "3     GP       F  15.0       U     GT3       T     4     2    health   \n",
       "4     GP       F  16.0       U     GT3       T     3     3     other   \n",
       "5     GP    Male  16.0       U     LE3       T     4     3  services   \n",
       "6     GP       M  16.0       U     LE3       T     2     2     other   \n",
       "7    NaN     NaN   NaN     NaN     GT3       A     4     4     other   \n",
       "8    NaN       M  15.0       U     LE3       A     3     2  services   \n",
       "9    NaN       M  15.0       U     GT3       T     3     4     other   \n",
       "\n",
       "       Fjob  ... famrel freetime  goout  Dalc  Walc health absences    G1  G2  \\\n",
       "0   teacher  ...      4      NaN      4     1     1      3      NaN   5.0   6   \n",
       "1     other  ...      5      NaN      3     1     1      3      NaN   5.0   5   \n",
       "2     other  ...      4        3      2     2     3      3     10.0   7.0   8   \n",
       "3  services  ...      3        2      2     1     1      5      2.0  15.0  14   \n",
       "4     other  ...      4        3      2     1     2      5      4.0   6.0  10   \n",
       "5     other  ...      5        4      2     1     2      5     10.0  15.0  15   \n",
       "6     other  ...      4        4      4     1     1      3      0.0  12.0  12   \n",
       "7   teacher  ...      4        1      4     1     1      1      6.0   6.0   5   \n",
       "8     other  ...      4        2      2     1     1      1      0.0  16.0  18   \n",
       "9     other  ...      5        5      1     1     1      5      0.0  14.0  15   \n",
       "\n",
       "   G3  \n",
       "0   6  \n",
       "1   6  \n",
       "2  10  \n",
       "3  15  \n",
       "4  10  \n",
       "5  15  \n",
       "6  11  \n",
       "7   6  \n",
       "8  19  \n",
       "9  15  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(\"?\", np.nan, inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deal with missing data in column (shcool) by droping missing column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['school'] , axis=0 , inplace=True)\n",
    "df['school'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deal with missing data in column (failures) replace by frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['failures']=df['failures'].fillna(df['failures'].value_counts().idxmax())\n",
    "df['failures'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deal with missing data in column ( absences ) replace by frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['absences']=df['absences'].fillna(df['absences'].value_counts().idxmax())\n",
    "\n",
    "df['absences'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deal with missing data in column ( freetime ) replace by mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "avg_free=df[\"freetime\"].astype(\"float\").mean(axis=0)\n",
    "df[\"freetime\"].replace(np.nan ,avg_free , inplace=True )\n",
    "\n",
    "df['freetime'].isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Correct data format</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age'] = df['age'].astype('int')\n",
    "df['failures'] = df['failures'].astype('int')\n",
    "df['G2'] = df['G2'].astype('float')\n",
    "df['G3'] = df['G3'].astype('float')\n",
    "df['absences'] = df['absences'].astype('int')\n",
    "df['freetime'] = df['freetime'].astype('int')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> remove duplicates </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Data Standardization</h3>\n",
    "Standardization is the process of transforming data into a common format which allows the researcher to make the meaningful comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(\"F\", 'Female', inplace = True)\n",
    "df.replace(\"M\", 'Male', inplace = True)\n",
    "df['sex'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Identify Outliers </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,10))\n",
    "sns.boxplot(df['absences'].values, orient='v',)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Remove outliers</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['absences'] = df['absences'].clip(0, 20)\n",
    "plt.figure(figsize=(10,15))\n",
    "sns.boxplot(df['absences'].values, orient='v')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Discover and Visualize the Data to Gain Insights</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='school', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(x='school',data=df, palette='tab10')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='health', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='health',data=df, palette='tab10')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Relationships Between Variables</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "sns.stripplot(x=\"studytime\", y=\"G3\", data=df);\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.stripplot(x=\"G1\", y=\"G3\", data=df);\n",
    "\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.stripplot(x=\"health\", y=\"G3\", data=df);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Weak and Strong Relationships<h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='G2', ylabel='G3'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22,10))\n",
    "\n",
    "plt.subplot(231)#subplot to add a subplot to the current figure\n",
    "sns.regplot(x='G1', y='absences', data=df,  line_kws={'color': 'red'}) \n",
    "\n",
    "plt.subplot(232)\n",
    "sns.regplot(x='G1', y='health', data=df , line_kws={'color': 'red'})\n",
    "\n",
    "plt.subplot(233)\n",
    "sns.regplot(x='G1', y='studytime', data=df , line_kws={'color': 'red'})\n",
    "\n",
    "plt.subplot(234)\n",
    "sns.regplot(x='G2', y='Medu', data=df,  line_kws={'color': 'red'})\n",
    "\n",
    "plt.subplot(235)\n",
    "sns.regplot(x='G1', y='G3', data=df , line_kws={'color': 'red'})\n",
    "                    \n",
    "plt.subplot(236)\n",
    "sns.regplot(x='G2', y='G3', data=df,  line_kws={'color': 'red'})\n",
    "\n",
    "\n",
    "                    \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "G1 , G2 , G3 , Medu and studytime have strong relationship,while absences and health have weak relationship\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Looking for Correlations</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "numeric_cols = df.select_dtypes(include=['float64', 'int64'])\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(numeric_cols.corr())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Feature  Engineering</h4>\n",
    "select Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>studytime</th>\n",
       "      <th>Medu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>373 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       G1    G2    G3  studytime  Medu\n",
       "0     5.0   6.0   6.0          2     4\n",
       "1     5.0   5.0   6.0          2     1\n",
       "2     7.0   8.0  10.0          2     1\n",
       "3    15.0  14.0  15.0          3     4\n",
       "4     6.0  10.0  10.0          2     3\n",
       "..    ...   ...   ...        ...   ...\n",
       "390   9.0   9.0   9.0          2     2\n",
       "391  14.0  16.0  16.0          1     3\n",
       "392  10.0   8.0   7.0          1     1\n",
       "393  11.0  12.0  10.0          1     3\n",
       "394   8.0   9.0   9.0          1     1\n",
       "\n",
       "[373 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features = ['G1', 'G2', 'G3','studytime', 'Medu',]\n",
    "df_final = df[selected_features]\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_csv('Student_cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Student_cleaned_data.csv'\n",
    "\n",
    "df = pd.read_csv(file_path)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Create the model</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['G1', 'G2','studytime', 'Medu']\n",
    "labels = df_final['G3'].values\n",
    "features = df_final[list(columns)].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "# Bulid a new Model\n",
    "regression = linear_model.LinearRegression()\n",
    "\n",
    "# Train the Model\n",
    "regression.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict for training set\n",
    "y_train_predict = regression.predict(X_train)\n",
    "\n",
    "# Predict for training set\n",
    "y_test_predict = regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "RMSE is 2.0013920238935605\n",
      "MAE is 1.2078120988811782\n",
      "---------------------------------------\n",
      "The model performance for testing set\n",
      "RMSE is 1.8959009677752106\n",
      "MAE is 1.165254676580012\n",
      "---------------------------------------\n",
      "The score of model for testing set\n",
      "Test score 0.8\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error , mean_absolute_error\n",
    "\n",
    "\n",
    "# # model evaluation for training set\n",
    "mse_train = mean_squared_error(y_train, y_train_predict)\n",
    "mae_train = mean_absolute_error(y_train, y_train_predict)\n",
    "print(\"The model performance for training set\")\n",
    "print('RMSE is {}'.format(np.sqrt(mse_train)))\n",
    "print('MAE is {}'.format(mae_train))\n",
    "print(\"---------------------------------------\")\n",
    "\n",
    "# model evaluation for testing set\n",
    "mse_test = mean_squared_error(y_test, y_test_predict) # Return the loss it is non-negative . The best value is 0.0\n",
    "mae_test = mean_absolute_error(y_test, y_test_predict) # Return the loss it is non-negative . The best value is 0.0\n",
    "print(\"The model performance for testing set\")\n",
    "print('RMSE is {}'.format(np.sqrt(mse_test)))\n",
    "print('MAE is {}'.format(mae_test))\n",
    "\n",
    "print(\"---------------------------------------\")\n",
    "test_score= regression.score(X_test, y_test)# Return R2 score . The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse) \n",
    "print(\"The score of model for testing set\")\n",
    "print('Test score {}'.format(test_score.round(2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Actual</th>\n",
       "      <th>Predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.218568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.616065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.542549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.109303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.0</td>\n",
       "      <td>14.839063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.707733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.884619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.859184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>13.0</td>\n",
       "      <td>14.284100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.297833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual    Predict\n",
       "0       6.0   6.218568\n",
       "1      10.0   9.616065\n",
       "2      11.0  11.542549\n",
       "3       8.0   6.109303\n",
       "4      15.0  14.839063\n",
       "..      ...        ...\n",
       "107    11.0  11.707733\n",
       "108    12.0  12.884619\n",
       "109    10.0  10.859184\n",
       "110    13.0  14.284100\n",
       "111    15.0  15.297833\n",
       "\n",
       "[112 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare={\"Actual\": y_test, \"Predict\": y_test_predict}\n",
    "pd.DataFrame(compare)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Visualize the Result</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx= np.random.choice(X_test.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "plt.scatter(y_test, y_test_predict,  color='black')\n",
    "plt.scatter(y_test[idx], y_test_predict[idx])\n",
    "plt.xlabel('(Actual G3')\n",
    "plt.ylabel('Predicted G3)')\n",
    "plt.plot([df_final['G3'].min(), df_final['G3'].max()], [df_final['G3'].min(), df_final['G3'].max()], color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(22,5))\n",
    "plt.scatter(y_test, y_test_predict,  color='blue')\n",
    "plt.scatter(y_train, y_train_predict,  color='Orange')\n",
    "plt.plot([df_final['G3'].min(), df_final['G3'].max()], [df_final['G3'].min(), df_final['G3'].max()], color='red')\n",
    "plt.xlabel('Actual G3')\n",
    "plt.ylabel('Predicted G3')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downlodel the model\n",
    "from jupyter import files\n",
    "\n",
    "files.download('model.pkl')"
   ]
  }
 ],
 "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",
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