bias and variance1.py

from mlxtend.evaluate import bias_variance_decomp
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error

data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/student-mat.csv")
data = data[["G1", "G2", "G3", "studytime", "failures", "absences"]]

predict = "G3"
x = np.array(data.drop([predict], 1))
y = np.array(data[predict])

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2)

linear_regression = LinearRegression()
linear_regression.fit(xtrain, ytrain)
y_pred = linear_regression.predict(xtest)