main.py

# Import Libraries
import numpy as np
import pandas as pd
from sklearn import linear_model
import sklearn
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
from matplotlib import style
import pickle

"""Pickle in Python is primarily used in serializing and deserializing a Python object structure.
 In other words, it's the process of converting a Python object into a byte stream to store it in a file/database,
 maintain program state across sessions, or transport data over the network."""

style.use("ggplot")

data = pd.read_csv("student-mat.csv", sep=";")

predict = "G3"

data = data[["G1", "G2", "absences", "failures", "studytime", "G3"]]
data = shuffle(data)  # Optional - shuffle the data

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)

# TRAIN MODEL MULTIPLE TIMES FOR BEST SCORE
best = 0
for _ in range(20):
    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)
    acc = linear.score(x_test, y_test)
    print("Accuracy: " + str(acc))

    if acc > best:
        best = acc
        with open("studentgrades.pickle", "wb") as f:
            pickle.dump(linear, f)

# LOAD MODEL
pickle_in = open("studentgrades.pickle", "rb")
linear = pickle.load(pickle_in)

print("-------------------------")
print('Coefficient: \n', linear.coef_)
print('Intercept: \n', linear.intercept_)
print("-------------------------")

predicted = linear.predict(x_test)
for x in range(len(predicted)):
    print(predicted[x], x_test[x], y_test[x])

# Drawing and plotting model
plot = "failures"
plt.scatter(data[plot], data["G3"])
plt.xlabel(plot)
plt.ylabel("Final Grade")
plt.show()