def predict(self):
'''
Output a predicted price, and compare to the initial listed of
user-guessed price
'''
testing_df = create_testing_df(self.df)
y = int(testing_df.pop('price').values)
X = testing_df.values
prediction = int(self.rfr.predict(X)[0])
predict_statement = 'Your model-recommended price is: $%s' %prediction
compare_statement = 'Your initial stated price was: $%s' %y
return predict_statement, compare_statement
gs = GridSearchCV(Ridge(), pars, cv=cv)
gs.fit(X, y)
ridge = gs.best_estimator_
print gs.best_params_
print gs.best_score_
cPickle.dump(ridge, open('models/ridge.pkl', 'wb'))
pars = {'max_depth': [5, 8, 10, 20, 50, 100],
'min_samples_split': [2, 3, 5, 10, 20]}
gs = GridSearchCV(RFR(n_estimators=10),
pars, cv=cv)
gs.fit(X, y)
rfr = gs.best_estimator_
print gs.best_params_
print gs.best_score_
cPickle.dump(rfr, open('models/rfr.pkl', 'wb'))
return ridge, rfr
if __name__ == '__main__':
df = pd.read_csv('data/complete_df.csv', header=False)
testing_df = create_testing_df(df)
y = testing_df.pop('price').values
X = testing_df.values
ridge, rfr = grid_search(X, y)
grid_search(X, y)
