main.py

import pandas as pd
import numpy as np
import sklearn
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"]]

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)
'''
best=0
for _ in range(30):
    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(acc)
    
    if acc>best:
        best=acc
        with open("studentmodel.pickle","wb") as f:
            pickle.dump(linear,f)'''

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

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

predictions=linear.predict(x_test)

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

p='G2'
style.use("ggplot")
pyplot.scatter(data[p],data["G3"])
pyplot.xlabel(p)
pyplot.ylabel("Final Grade")
pyplot.show()