Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science by Thomas W. Miller

Author:Thomas W. Miller [Thomas W. Miller]
Language: eng
Format: epub
Tags: Big Data
Publisher: PH Professional Business
Published: 2014-09-30T21:00:00+00:00

# employ training-and-test regimen for model validation

np.random.seed(4444)

houses_selected['runiform'] = uniform.rvs(loc = 0, scale = 1,\

size = len(houses_selected))

houses_selected_train = houses_selected[houses_selected['runiform'] >= 0.33]

houses_selected_test = houses_selected[houses_selected['runiform'] < 0.33]

# check training data frame

print('\nhouses_selected_train data frame (rows, columns): ',\

houses_selected_train.shape)

print(houses_selected_train.head())

# check test data frame

print('\nhouses_selected_test data frame (rows, columns): ',\

houses_selected_test.shape)

print(houses_selected_test.head())

# examine the correlations across the variables before we begin modeling

houses_train_df_vars = houses_selected_train.loc[ : ,['log_value', 'income',\

'log_pc_rooms', 'log_pc_bedrooms', 'rooms', 'bedrooms', 'hh', \

'age', 'pop', 'log_pop_hh']]

print(houses_train_df_vars.corr())

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