predicting student grades

I created a neural network using linear regression that would be able to predict the grades that students would get after how many hours they spent studying. I then used the matplotlib library to plot all of the data on a graph to see what it looks like.

Code:

import pandas as pd

import numpy as np

import sklearn

from sklearn import linear_model

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" #predicting the final grade

X = np.array(data.drop([predict], 1)) #return new data frame wo g3

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) #spliting up attributes into arrays

pickle_in = open("studentmodel.pickel", "rb")

linear = pickle.load(pickle_in)

#y = mx + b

print("Co: \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 = 'studytime'

style.use("ggplot")

pyplot.scatter(data[p], data['G3'])

pyplot.xlabel(p)

pyplot.ylabel("Final Grade")

pyplot.show()

Model: