Kaggle - Exercise 6 - Random Forest Summary

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# 1. Random forest in training dataset
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# Import helpful libraries
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split

# Load the data, and separate the target
iowa_file_path = '../input/train.csv'
home_data = pd.read_csv(iowa_file_path)

y = home_data.SalePrice
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
X = home_data[features]
X.head()

# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)

# Define a random forest model
rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)
rf_val_predictions = rf_model.predict(val_X)
rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)

print("Validation MAE for Random Forest Model: {:,.0f}".format(rf_val_mae))



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# 2. Random forest in ALL THE dataset - testing will be applied to a different dataset-table-file
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# To improve accuracy, create a new Random Forest model which you will train on all training data
rf_model_on_full_data = RandomForestRegressor(random_state=1)
rf_model_on_full_data.fit(X, y)

test_data_path = '../input/test.csv'
test_data = pd.read_csv(test_data_path)
test_X = test_data[features]

# make predictions which we will submit
test_preds = rf_model_on_full_data.predict(test_X)
print(test_preds)