main.py

import pandas as pd
import numpy as np
import sklearn
from sklearn import linear_model
import matplotlib.pyplot as pyplot
from sklearn.utils import shuffle
import pickle
import os
from matplotlib import style
# read the data using pandas library
data =pd.read_csv("student-mat.csv", sep=";")
# categorize data
data = data[["G1","G2","G3","studytime","failures","absences"]]
print(data.head(5))
# our main target is to predict grade 3 of students
predict="G3"

# set up the attributes(features) in array /
# using the drop function to  remove column of grade 3 from the DataFrame which are specify in first parameter labels
x= np.array(data.drop([predict], 1))
# set up the labels in an array
y=np.array(data[predict])
 # let´s try to find best result with good accuracy
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):
    # dividing the data into test and train data to test our accuracy of our model
    x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.1)
    # implementing our training  model ( algorithm)
    linear = linear_model.LinearRegression()
    # find the best fit to the data using X train data and y train data
    linear.fit(x_train, y_train)
    # test our model : how well is working
    acc = linear.score(x_test, y_test)
    print(acc)
    if acc > best:
        best=acc
# save the model to avoid training the model every time
    with open(" studentmodel.pickle","wb") as f:
         pickle.dump(linear,f)"""""
    #read the pickle  file
filename = " studentmodel.pickle"
amine= os.stat(filename).st_size
if amine>0:
    pickle_in= open(filename,"rb")
    linear=pickle.load(pickle_in)
# print out the coefficiant and the intercept
# we will get 5 coefficiant since we have 5 attributes ( 5 dimentions )
print("coeff: \n",linear.coef_)
print("intercept: \n",linear.intercept_)
prediction =linear.predict(x_test)
#print("prediction:\n",prediction)
for x in range (len(prediction)):
    print((prediction[x],x_test[x],y_test[x]))

    d= "G1"
    style.use("ggplot")
    pyplot.scatter(data[d],data["G3"])
    pyplot.xlabel(d)
    pyplot.ylabel("G3")
    pyplot.show()