ML-Traing/test.py at main · WCSW/ML-Traing · GitHub

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import tensorflow
import keras
import pandas as pd
import numpy as np
import sklearn
from matplotlib import pyplot
import pickle
from matplotlib import style
from sklearn import linear_model
from sklearn.utils import shuffle

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(100):
#
#     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])

style.use("ggplot")

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