From f52b97f2c56aadfd30a7384cff62d8354157b9cb Mon Sep 17 00:00:00 2001
From: Miggelito <a.bidakowski@gmail.com>
Date: Fri, 7 Feb 2020 19:37:14 +0100
Subject: [PATCH] Added Random Forest Regressor and tested with flake8 (#1733)

* Added Random Forest Regressor

* Updated file to standard
---
 machine_learning/random_forest_regressor.py | 42 +++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 machine_learning/random_forest_regressor.py

diff --git a/machine_learning/random_forest_regressor.py b/machine_learning/random_forest_regressor.py
new file mode 100644
index 000000000..f6c470f09
--- /dev/null
+++ b/machine_learning/random_forest_regressor.py
@@ -0,0 +1,42 @@
+# Random Forest Regressor Example
+
+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import mean_squared_error
+
+
+def main():
+
+    """
+    Random Tree Regressor Example using sklearn function.
+    Boston house price dataset is used to demonstrate algorithm.
+    """
+
+    # Load Boston house price dataset
+    boston = load_boston()
+    print(boston.keys())
+
+    # Split dataset into train and test data
+    X = boston["data"]  # features
+    Y = boston["target"]
+    x_train, x_test, y_train, y_test = train_test_split(
+        X, Y, test_size=0.3, random_state=1
+    )
+
+    # Random Forest Regressor
+    rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
+    rand_for.fit(x_train, y_train)
+
+    # Predict target for test data
+    predictions = rand_for.predict(x_test)
+    predictions = predictions.reshape(len(predictions), 1)
+
+    # Error printing
+    print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
+    print(f"Mean Square Error  :\t {mean_squared_error(y_test, predictions)}")
+
+
+if __name__ == "__main__":
+    main()