# -*- coding: utf-8 -*-
"""
Created on Fri Jul 21 08:09:19 2017

@author: Geanderson
"""

import pandas as pd
import numpy as np
from sklearn import linear_model

from sklearn import datasets ## imports datasets from scikit-learn
data = datasets.load_boston() ## loads Boston dataset from datasets library

# define the data/predictors as the pre-set feature names
df = pd.DataFrame(data.data, columns=data.feature_names)

# Put the target (housing value -- MEDV) in another DataFrame
target = pd.DataFrame(data.target, columns=["MEDV"])

# statistical resume of data
df.describe()

# variable definition
X = df
y = target["MEDV"]

# fit a model linear regression
lm = linear_model.LinearRegression()
model = lm.fit(X,y)

ridge = linear_model.Ridge(alpha = 0.5)
model2 = ridge.fit(X, y)

lasso = linear_model.Lasso(alpha = 0.1)
model3 = lasso.fit(X, y)

elasticnet = linear_model.ElasticNet()
model4 = elasticnet.fit(X, y)

# predictions
predictions = lm.predict(X)
predictions2= ridge.predict(X)
predictions3 = lasso.predict(X)
predictions4 = elasticnet.predict(X)


# score of regression
score = lm.score(X,y)
score2 = ridge.score(X,y)
score3 = lasso.score(X,y)
score4 = elasticnet.score(X,y)

# variable coefficients
coefficients = lm.coef_
coefficients2= ridge.coef_
coefficients3 = lasso.coef_


# intercept
intercept = lm.intercept_
intercept2 = ridge.intercept_
intercept3 = lasso.intercept_