model.py

"""

The goal of this experiment is to develop a model that will predict (to a certain level of acceptable accuracy) whether a student will pass or fail a mathematics course over a year (two semesters).
The features will consist of all given features within the dataset excluding the GX factors that represent semester grades. G3 will be the target feature.

"""

""" Import helper libraries """
import numpy as np
import pandas as pd

""" Read data file as DataFrame """
df = pd.read_csv("./data/student-mat.csv", sep=";")

""" Import ML helpers """
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV, cross_val_score

from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.svm import LinearSVC # Support Vector Machine Classifier model

""" Split Data into Training and Testing Sets """
def split_data(X, Y):
    return train_test_split(X, Y, test_size=0.2, random_state=17)

""" Confusion Matrix """
def confuse(y_true, y_pred):
    cm = confusion_matrix(y_true=y_true, y_pred=y_pred)
    # print("\nConfusion Matrix: \n", cm)
    fpr(cm)
    ffr(cm)

""" False Pass Rate """
def fpr(confusion_matrix):
    fp = confusion_matrix[0][1]
    tf = confusion_matrix[0][0]
    rate = float(fp) / (fp + tf)
    print("False Pass Rate: ", rate)

""" False Fail Rate """
def ffr(confusion_matrix):
    ff = confusion_matrix[1][0]
    tp = confusion_matrix[1][1]
    rate = float(ff) / (ff + tp)
    print("False Fail Rate: ", rate)

    return rate

""" Train Model and Print Score """
def train_and_score(X, y):
    X_train, X_test, y_train, y_test = split_data(X, y)

    clf = Pipeline([
        ('reduce_dim', SelectKBest(chi2, k=2)),
        ('train', LinearSVC(C=100))
    ])

    scores = cross_val_score(clf, X_train, y_train, cv=5, n_jobs=2)
    print("Mean Model Accuracy:", np.array(scores).mean())

    clf.fit(X_train, y_train)

    confuse(y_test, clf.predict(X_test))
    print()

""" Main Program """
def main():
    print("\nStudent Performance Prediction")

    # For each feature, encode to categorical values
    class_le = LabelEncoder()
    for column in df[["school", "sex", "address", "famsize", "Pstatus", "Mjob", "Fjob", "reason", "guardian", "schoolsup", "famsup", "paid", "activities", "nursery", "higher", "internet", "romantic"]].columns:
        df[column] = class_le.fit_transform(df[column].values)

    # Encode G1, G2, G3 as pass or fail binary values
    for i, row in df.iterrows():
        if row["G1"] >= 10:
            df["G1"][i] = 1
        else:
            df["G1"][i] = 0

        if row["G2"] >= 10:
            df["G2"][i] = 1
        else:
            df["G2"][i] = 0

        if row["G3"] >= 10:
            df["G3"][i] = 1
        else:
            df["G3"][i] = 0

    # Target values are G3
    y = df.pop("G3")

    # Feature set is remaining features
    X = df

    print("\n\nModel Accuracy Knowing G1 & G2 Scores")
    print("=====================================")
    train_and_score(X, y)

    # Remove grade report 2
    X.drop(["G2"], axis = 1, inplace=True)
    print("\n\nModel Accuracy Knowing Only G1 Score")
    print("=====================================")
    train_and_score(X, y)

    # Remove grade report 1
    X.drop(["G1"], axis=1, inplace=True)
    print("\n\nModel Accuracy Without Knowing Scores")
    print("=====================================")
    train_and_score(X, y)



main()