02 - Linear Regression.py

import numpy as np
import pandas as pd
import sklearn
from sklearn import linear_model
import pickle

data = pd.read_csv("student-mat.csv", sep=";")
data = data[["G1", "G2", "G3", "studytime", "failures", "absences"]]

predict = "G3"

x = np.array(data.drop([predict], 1))
y = np.array(data[predict])

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)

linear = linear_model.LinearRegression()

linear.fit(x_train, y_train)

accuracy = linear.score(x_test,y_test)
print("Model Accuracy: ", accuracy)

print("Coefficient: ", linear.coef_) #We have a 5 dimensional space here so we have 5 Coefficients! (our line starting position depending on 5 axis )
print("Intercept: ", linear.intercept_) # y


with open("studentGradeModel.pickle", "wb") as modelFile:
    pickle.dump(linear, modelFile)