Python Bytes - Machine Learning Mean Squared Error
#Coded by Andrew C
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
wine_dataset = datasets.load_wine()
wine = pd.DataFrame(wine_dataset.data, columns=wine_dataset.feature_names)
X = wine[['ash']]
Y = wine['alcohol']
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state=1)
model = LinearRegression()
model.fit(X_train, Y_train)
y_test_predicted = model.predict(X_test)
print("Mean Squared Error: " + str(mean_squared_error(Y_test, y_test_predicted)))
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