Linear Regression Boston Dataset

# coding: utf-8

# In[1]:

import numpy as np
import pandas as pd


# In[2]:

from sklearn.datasets import load_boston
boston_ds = load_boston()


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print boston_ds['DESCR']


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print boston_ds['feature_names']


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df_boston = pd.DataFrame(boston_ds.data)


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df_boston.columns = boston_ds.feature_names


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df_boston.head()


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print boston_ds.data.shape


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print boston_ds.target.shape


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print boston_ds['target']


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X_features = boston_ds.data
Y_target = boston_ds.target


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from sklearn.linear_model import LinearRegression
linearRegr = LinearRegression()


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linearRegr.fit(X_features,Y_target)


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linearRegr.intercept_


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print 'the estimated intercept %.2f '% linearRegr.intercept_


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linearRegr.coef_


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print 'the coefficient is %d'% len(linearRegr.coef_)


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from sklearn import cross_validation
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X_features, Y_target)


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print boston_ds.data.shape


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print X_train.shape, X_test.shape, Y_train.shape, Y_test.shape


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linearRegr.fit(X_train,Y_train)


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print 'Mean Squared Estimator value is %.2f '% np.mean((linearRegr.predict(X_test) - Y_test) ** 2)


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print 'Variance Score is %.2f '% linearRegr.score(X_test,Y_test)


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