Kaggle - Exercise 1 - DecisionTreeRegressor

import pandas as pd

# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
home_data = pd.read_csv(iowa_file_path)

print(home_data.describe())
print(home_data.head())
print(home_data.columns)

y = home_data.SalePrice
# Create the list of features below
feature_names = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
# Select data corresponding to features in feature_names
X = home_data[feature_names]

print(X.describe())
print(X.head())


from sklearn.tree import DecisionTreeRegressor

iowa_model = DecisionTreeRegressor(random_state=1)
iowa_model.fit(X, y)
predictions = iowa_model.predict(X)
print(predictions)

print(home_data.SalePrice.head())