import pandas as pd
import numpy as np
import sklearn
from sklearn import linear_model
from sklearn.utils import shuffle
'''import matplotlib
from matplotlib import pyplot as py'''
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=0.7
for o in range(40):
    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('Coef: \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="G1"
style.use("ggplot")
py.scatter(data[p],data["G3"])
py.xlabel(p)
py.ylabel("G3")
py.show()'''
