Skip to content

Instantly share code, notes, and snippets.

Created May 20, 2021 13:33
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
Star You must be signed in to star a gist
What would you like to do?
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("")
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(), ytrain)
y_pred = linear_regression.predict(xtest)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment