melbourne

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import pandas as pd

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from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

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melbourne_file_path = 'D:\MLlearn\melb_data1.csv'
melb_data = pd.read_csv(melbourne_file_path)

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melb_target = melb_data.Price
melb_predictors = melb_data.drop(['Price'], axis = 1)

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# For the sake of keeping the example simple, we'll use only numeric predictors. 
melb_numeric_predictors = melb_predictors.select_dtypes(exclude=['object'])

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X_train, X_test, y_train, y_test = train_test_split(melb_numeric_predictors, 
                                                    melb_target,
                                                    train_size=0.7, 
                                                    test_size=0.3, 
                                                    random_state=0)

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def score_dataset(X_train, X_test, y_train, y_test):
    model = RandomForestRegressor()
    model.fit(X_train, y_train)
    preds = model.predict(X_test)
    return mean_absolute_error(y_test, preds)

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cols_with_missing = [col for col in X_train.columns 
                                 if X_train[col].isnull().any()]
reduced_X_train = X_train.drop(cols_with_missing, axis=1)
reduced_X_test  = X_test.drop(cols_with_missing, axis=1)
print("Mean Absolute Error from dropping columns with Missing Values:")
print(score_dataset(reduced_X_train, reduced_X_test, y_train, y_test))

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from sklearn.preprocessing import Imputer
 
# It seems that sklearn.impute is not available now, replace it by sklearn.processing.imputer.    

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my_imputer = Imputer()
imputed_X_train = my_imputer.fit_transform(X_train)
imputed_X_test = my_imputer.transform(X_test)
print("Mean Absolute Error from Imputation:")
print(score_dataset(imputed_X_train, imputed_X_test, y_train, y_test))

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imputed_X_train_plus = X_train.copy()
imputed_X_test_plus = X_test.copy()

cols_with_missing = (col for col in X_train.columns 
                                 if X_train[col].isnull().any())
for col in cols_with_missing:
    imputed_X_train_plus[col + '_was_missing'] = imputed_X_train_plus[col].isnull()
    imputed_X_test_plus[col + '_was_missing'] = imputed_X_test_plus[col].isnull()

# Imputation
my_imputer = Imputer()
imputed_X_train_plus = my_imputer.fit_transform(imputed_X_train_plus)
imputed_X_test_plus = my_imputer.transform(imputed_X_test_plus)

print("Mean Absolute Error from Imputation while Track What Was Imputed:")
print(score_dataset(imputed_X_train_plus, imputed_X_test_plus, y_train, y_test))

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melb_data.head()