random_forest.py

# Code you have previously used to load data
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor


# Path of the file to read
iowa_file_path = './downloads/home-data-for-ml-course/train.csv'

home_data = pd.read_csv(iowa_file_path)
# Create target object and call it y
y = home_data.SalePrice
# Create X
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd', 'YearRemodAdd', 'TotalBsmtSF', 'HalfBath', 'YrSold', 'MSSubClass', 'OverallQual', 'OverallCond', 'Fireplaces', 'GarageArea', 'EnclosedPorch', 'WoodDeckSF' ]
X = home_data[features]

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

# Specify Model
iowa_model = DecisionTreeRegressor(random_state=1)
# Fit Model
iowa_model.fit(train_X, train_y)

# Make validation predictions and calculate mean absolute error
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE when not specifying max_leaf_nodes: {:,.0f}".format(val_mae))

# Using best value for max_leaf_nodes
iowa_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)
iowa_model.fit(train_X, train_y)
val_predictions = iowa_model.predict(val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE for best value of max_leaf_nodes: {:,.0f}".format(val_mae))

from sklearn.ensemble import RandomForestRegressor

rf_model = RandomForestRegressor(random_state=1)
rf_model.fit(train_X, train_y)

val_predictions = rf_model.predict(val_X)

rf_val_mae = mean_absolute_error(val_predictions, val_y)

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

# Exercise: Machine Learning Competitions
#########################################
# 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)

# fit rf_model_on_full_data on all data from the training data
rf_model_on_full_data.fit(X,y)

# path to file you will use for predictions
test_data_path = './downloads/home-data-for-ml-course/test.csv'

# read test data file using pandas
test_data = pd.read_csv(test_data_path)

# create test_X which comes from test_data but includes only the columns you used for prediction.
# The list of columns is stored in a variable called features
features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']
test_X = test_data[features]
# test_y = test_data.SalePrice

# # make predictions which we will submit. 
test_preds = rf_model_on_full_data.predict(test_X)
# val_mae = mean_absolute_error(test_preds, test_y)
# print("Validation MAE for best value of max_leaf_nodes: {:,.0f}".format(val_mae))