model_building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b30dab79",
   "metadata": {},
   "source": [
    "## Import neccessary Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "51d14456",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import joblib\n",
    "\n",
    "from sklearn.utils import resample\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import scale, LabelEncoder, OneHotEncoder\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, ConfusionMatrixDisplay \n",
    "\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 50)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6660209e",
   "metadata": {},
   "source": [
    "## Dataset Description\n",
    "**Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:** \n",
    "\n",
    "1 school - student's school (binary: \"GP\" - Gabriel Pereira or \"MS\" - Mousinho da Silveira)\n",
    "\n",
    "2 sex - student's sex (binary: \"F\" - female or \"M\" - male)\n",
    "\n",
    "3 age - student's age (numeric: from 15 to 22)\n",
    "\n",
    "4 address - student's home address type (binary: \"U\" - urban or \"R\" - rural)\n",
    "\n",
    "5 famsize - family size (binary: \"LE3\" - less or equal to 3 or \"GT3\" - greater than 3)\n",
    "\n",
    "6 Pstatus - parent's cohabitation status (binary: \"T\" - living together or \"A\" - apart)\n",
    "\n",
    "7 Medu - mother's education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary \n",
    "education or 4 – higher education)\n",
    "\n",
    "8 Fedu - father's education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)\n",
    "\n",
    "9 Mjob - mother's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n",
    "\n",
    "10 Fjob - father's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n",
    "\n",
    "11 reason - reason to choose this school (nominal: close to \"home\", school \"reputation\", \"course\" preference or \"other\")\n",
    "\n",
    "12 guardian - student's guardian (nominal: \"mother\", \"father\" or \"other\")\n",
    "\n",
    "13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)\n",
    "\n",
    "14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n",
    "\n",
    "15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)\n",
    "\n",
    "16 schoolsup - extra educational support (binary: yes or no)\n",
    "\n",
    "17 famsup - family educational support (binary: yes or no)\n",
    "\n",
    "18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n",
    "\n",
    "19 activities - extra-curricular activities (binary: yes or no)\n",
    "\n",
    "20 nursery - attended nursery school (binary: yes or no)\n",
    "\n",
    "21 higher - wants to take higher education (binary: yes or no)\n",
    "\n",
    "22 internet - Internet access at home (binary: yes or no)\n",
    "\n",
    "23 romantic - with a romantic relationship (binary: yes or no)\n",
    "\n",
    "24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n",
    "\n",
    "25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)\n",
    "\n",
    "26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)\n",
    "\n",
    "27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n",
    "\n",
    "28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n",
    "\n",
    "29 health - current health status (numeric: from 1 - very bad to 5 - very good)\n",
    "\n",
    "30 absences - number of school absences (numeric: from 0 to 93)\n",
    "\n",
    "**These grades are related with the course subject, Math or Portuguese:**\n",
    "\n",
    "31 G1 - first period grade (numeric: from 0 to 20)\n",
    "\n",
    "32 G2 - second period grade (numeric: from 0 to 20)\n",
    "\n",
    "33 G3 - final grade (numeric: from 0 to 20, output target)\n",
    "\n",
    "Additional note: there are several (382) students that belong to both datasets . \n",
    "These students can be identified by searching for identical attributes\n",
    "that characterize each student, as shown in the annexed R file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f6b2dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty</th>\n",
       "      <th>sex</th>\n",
       "      <th>under_18</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>socialcohabitation</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>selfpayfees</th>\n",
       "      <th>activities</th>\n",
       "      <th>catch-up</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MI</td>\n",
       "      <td>F</td>\n",
       "      <td>GE18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Employee</td>\n",
       "      <td>course</td>\n",
       "      <td>guardian</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MI</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>course</td>\n",
       "      <td>alone</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IN</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>LE3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>other</td>\n",
       "      <td>guardian</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MI</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>employee</td>\n",
       "      <td>Civil_Servant</td>\n",
       "      <td>home</td>\n",
       "      <td>guardian</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>home</td>\n",
       "      <td>alone</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  specialty sex under_18 famsize        Mjob           Fjob  reason socialcohabitation  traveltime  studytime  failures schoolsup selfpayfees activities catch-up internet romantic  famrel  freetime  goout  health  absences  G3\n",
       "0        MI   F     GE18     GT3  Unemployed       Employee  course           guardian           2          2         0       yes          no         no      yes       no       no       4         3      4       3         6   6\n",
       "1        MI   F      U18     GT3  Unemployed     Contractor  course              alone           1          2         0        no          no         no      yes      yes       no       5         3      3       3         4   6\n",
       "2        IN   F      U18     LE3  Unemployed     Contractor   other           guardian           1          2         3       yes         yes         no      yes      yes       no       4         3      2       3        10  10\n",
       "3        MI   F      U18     GT3    employee  Civil_Servant    home           guardian           1          3         0        no         yes        yes      yes      yes      yes       3         2      2       5         2  15\n",
       "4        IN   F      U18     GT3  unemployed     Contractor    home              alone           1          2         0        no         yes         no      yes       no       no       4         3      2       5         4  10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_data_new = pd.read_csv('student_data/student_mat_new.csv', sep=',')\n",
    "math_data_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc96fc0",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff6545e",
   "metadata": {},
   "source": [
    "- Let's a copy of our original dataset for manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "04a3dc68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of the original dataframe\n",
    "math_data_new_copy = math_data_new.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1421188e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>specialty</th>\n",
       "      <th>sex</th>\n",
       "      <th>under_18</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>socialcohabitation</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
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       "      <th>catch-up</th>\n",
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       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>MI</td>\n",
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       "      <td>course</td>\n",
       "      <td>guardian</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MI</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>course</td>\n",
       "      <td>alone</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>IN</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>LE3</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>other</td>\n",
       "      <td>guardian</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MI</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>employee</td>\n",
       "      <td>Civil_Servant</td>\n",
       "      <td>home</td>\n",
       "      <td>guardian</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN</td>\n",
       "      <td>F</td>\n",
       "      <td>U18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>home</td>\n",
       "      <td>alone</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  specialty sex under_18 famsize        Mjob           Fjob  reason socialcohabitation  traveltime  studytime  failures schoolsup selfpayfees activities catch-up internet romantic  famrel  freetime  goout  health  absences  G3\n",
       "0        MI   F     GE18     GT3  Unemployed       Employee  course           guardian           2          2         0       yes          no         no      yes       no       no       4         3      4       3         6   6\n",
       "1        MI   F      U18     GT3  Unemployed     Contractor  course              alone           1          2         0        no          no         no      yes      yes       no       5         3      3       3         4   6\n",
       "2        IN   F      U18     LE3  Unemployed     Contractor   other           guardian           1          2         3       yes         yes         no      yes      yes       no       4         3      2       3        10  10\n",
       "3        MI   F      U18     GT3    employee  Civil_Servant    home           guardian           1          3         0        no         yes        yes      yes      yes      yes       3         2      2       5         2  15\n",
       "4        IN   F      U18     GT3  unemployed     Contractor    home              alone           1          2         0        no         yes         no      yes       no       no       4         3      2       5         4  10"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_data_new_copy.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bf20c53f",
   "metadata": {},
   "source": [
    "- **Encoding categorical data**: We will be using **OneHotEncoder** to convert/encode nominal variables and **Label encoder** for ordinal variable that are still in text form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73fb0838",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select ordinal and binary variables to encode\n",
    "ordinal_cols = [\"under_18\", \"famsize\", \"schoolsup\", \"selfpayfees\", \"activities\", \"catch-up\", \"internet\", \"romantic\"]\n",
    "\n",
    "# Select nominal variables to encode\n",
    "nominal_cols = [\"specialty\", \"sex\", \"Mjob\", \"Fjob\", \"reason\", \"socialcohabitation\"]\n",
    "# Initialize OneHotEncoder\n",
    "# ohe = OneHotEncoder()\n",
    "# Encode nominal variables\n",
    "# math_data_new_copy[nominal_cols] = math_data_new_copy[nominal_cols].apply(lambda col: pd.get_dummies(col , prefix= col.name))\n",
    "\n",
    "# Select categorical variables\n",
    "categorical_cols = ordinal_cols + nominal_cols\n",
    "\n",
    "# Initialize LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Encode categorical variables\n",
    "math_data_new_copy[categorical_cols] = math_data_new_copy[categorical_cols].apply(lambda col: le.fit_transform(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2de69b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>specialty</th>\n",
       "      <th>sex</th>\n",
       "      <th>under_18</th>\n",
       "      <th>famsize</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   specialty  sex  under_18  famsize  Mjob  Fjob  reason  socialcohabitation  traveltime  studytime  failures  schoolsup  selfpayfees  activities  catch-up  internet  romantic  famrel  freetime  goout  health  absences  G3\n",
       "0          2    0         0        0     0     2       0                   1           2          2         0          1            0           0         1         0         0       4         3      4       3         6   6\n",
       "1          2    0         1        0     0     1       0                   0           1          2         0          0            0           0         1         1         0       5         3      3       3         4   6\n",
       "2          0    0         1        1     0     1       2                   1           1          2         3          1            1           0         1         1         0       4         3      2       3        10  10\n",
       "3          2    0         1        0     2     0       1                   1           1          3         0          0            1           1         1         1         1       3         2      2       5         2  15\n",
       "4          0    0         1        0     3     1       1                   0           1          2         0          0            1           0         1         0         0       4         3      2       5         4  10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if encoding was successful\n",
    "math_data_new_copy.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c079e446",
   "metadata": {},
   "source": [
    "- Encode grade(G3) to reflect fail or pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e81638b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def assign_fail_pass(numeric_grade):\n",
    "#     '''This function checks the numeric grade is greater than or equals 10 and assigns pass(1). \n",
    "#     If the score is less than 10, then it gets a fail(0) '''\n",
    "#     if numeric_grade >= 10:\n",
    "#         return 1\n",
    "#     else: \n",
    "#         return 0\n",
    "\n",
    "# Assign 1 for pass and 0 for fail\n",
    "math_data_new_copy[\"G3\"] = np.where(math_data_new_copy[\"G3\"] >= 10, 1, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "55aac4b3",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   specialty  sex  under_18  famsize  Mjob  Fjob  reason  socialcohabitation  traveltime  studytime  failures  schoolsup  selfpayfees  activities  catch-up  internet  romantic  famrel  freetime  goout  health  absences  G3\n",
       "0          2    0         0        0     0     2       0                   1           2          2         0          1            0           0         1         0         0       4         3      4       3         6   0\n",
       "1          2    0         1        0     0     1       0                   0           1          2         0          0            0           0         1         1         0       5         3      3       3         4   0\n",
       "2          0    0         1        1     0     1       2                   1           1          2         3          1            1           0         1         1         0       4         3      2       3        10   1\n",
       "3          2    0         1        0     2     0       1                   1           1          3         0          0            1           1         1         1         1       3         2      2       5         2   1\n",
       "4          0    0         1        0     3     1       1                   0           1          2         0          0            1           0         1         0         0       4         3      2       5         4   1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_data_new_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055c5c22",
   "metadata": {},
   "source": [
    "- Seperate the data into Features (X) and Target(y) : Here our **target** is **G3** and rest of the columns are **features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "817e1dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = math_data_new_copy[\"G3\"]\n",
    "X = math_data_new_copy.drop([\"G3\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a7f1f4",
   "metadata": {},
   "source": [
    "- Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "054c9f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7659aa",
   "metadata": {},
   "source": [
    "- **Scale and Centre the Data**: Since we will be using the Radial Basis Function (RBF) kernel with our Support Vector Machine, we need to scale the data. Thatis each column should have a mean of 0 and a standard deviation of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a72e73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e6ba3a",
   "metadata": {},
   "source": [
    "## Machine Learning Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc85e3a7",
   "metadata": {},
   "source": [
    "- Define the support vector machine classifier model and train on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9584b76b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm = SVC(kernel='rbf', C=1.0, random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0174e064",
   "metadata": {},
   "source": [
    "- Test the model on the test data and visualize the results as a Confusion Matrix\n",
    "    - **Confusion Matrix**: A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm. Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa). The name stems from the fact that it makes it easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "30e2fa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_estimator(clf_svm, X_test_scaled, y_test, cmap=plt.cm.Blues, values_format='d');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3c761c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.19      0.29        27\n",
      "           1       0.69      0.96      0.81        52\n",
      "\n",
      "    accuracy                           0.70        79\n",
      "   macro avg       0.70      0.57      0.55        79\n",
      "weighted avg       0.70      0.70      0.63        79\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf_svm.predict(X_test_scaled)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e895a250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Percentage: 69.6%\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy Percentage: {acc*100:.1f}%\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ad31c3ee",
   "metadata": {},
   "source": [
    "## Results (TO BE UPDATED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a802dd51",
   "metadata": {},
   "source": [
    "Recall\n",
    "- Out of all the students who passed, the model predicted 88% correctly \n",
    "- Out of all the students who failed, the model predicted 93% correctly\n",
    "\n",
    "Precision\n",
    "- Out of all the students the model predicted to pass, 96% actually passed\n",
    "- Out of all the students the model predicted to fail, 81% actually failed "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "14fc1845",
   "metadata": {},
   "source": [
    "### SAVE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d9cad6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['student_model.pkl']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(clf_svm, \"student_model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4bec3f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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