1.py

# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
from sklearn.cross_validation import train_test_split
from sklearn.metrics import r2_score
from sklearn.metrics import make_scorer
from sklearn.grid_search import GridSearchCV
from sklearn.tree import DecisionTreeRegressor

# Lendo conjunto de dados de imóveis
data = pd.read_csv('housing.csv')
prices = data['MEDV']
features = data.drop('MEDV', axis = 1)

print "\n\nO conjunto de dados de imóveis tem {} pontos " \
	  "com {} variáveis em cada.\n\n".format(*data.shape)