ML_script_3

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import pandas as pd

from sklearn.model_selection import train_test_split

import mglearn


'''diabetes = datasets.load_diabetes()

df = pd.DataFrame(data= np.c_[diabetes['data'], diabetes['target']], columns=diabetes['feature_names']+['target'])




df_features= df.drop(labels=["age","sex","s3","target"],axis=1)'''

X, y = mglearn.datasets.load_extended_boston()
X_trn, X_tst, y_trn, y_tst = train_test_split(X,y, test_size=0.33, random_state= 0)

lm = linear_model.LinearRegression()


# In[42]:


lm.fit(X_trn, y_trn)


# In[43]:


print(lm.score(X_trn,y_trn))


# In[44]:


print(lm.score(X_tst,y_tst))