main.py

import numpy as np
import pandas as pd
import sklearn as sk
from sklearn import linear_model
from sklearn.utils import shuffle
import matplotlib.pyplot as pyplot
import pickle
from matplotlib import style

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

predict = "G3"

x = np.array(data.drop([predict], 1))
y = np.array(data[predict])
x_train, x_test, y_train, y_test = sk.model_selection.train_test_split(x, y, test_size=0.1)

best_score = 0
''''
for _ in range(30):
    x_train, x_test, y_train, y_test = sk.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(accuracy)

    if accuracy > best_score:
        best_score = accuracy
    with open("studentmodel.pickle", "wb") as f:
        pickle.dump(linear, f)'''

pickle_in = open("studentmodel.pickle", "rb")
linear = pickle.load(pickle_in)

predictions = linear.predict(x_test)

for i in range(len(predictions)):
    print(predictions[i], x_test[i], y_test[i])

p =  "G1"
style.use("ggplot")
pyplot.scatter(data[p], data["G3"])
pyplot.xlabel(p)
pyplot.ylabel("(Final Grade)")
pyplot.title("Student Grade Predictor")
pyplot.show()