diff --git a/machine_learning/random_forest_classifier.py b/machine_learning/random_forest_classifier.py
new file mode 100644
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+++ b/machine_learning/random_forest_classifier.py
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+# Random Forest Classifier Example
+
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import plot_confusion_matrix
+import matplotlib.pyplot as plt
+
+
+def main():
+
+    """
+    Random Tree Classifier Example using sklearn function.
+    Iris type dataset is used to demonstrate algorithm.
+    """
+
+    # Load Iris house price dataset
+    iris = load_iris()
+
+    # Split dataset into train and test data
+    X = iris["data"]  # features
+    Y = iris["target"]
+    x_train, x_test, y_train, y_test = train_test_split(
+        X, Y, test_size=0.3, random_state=1
+    )
+
+    # Random Forest Classifier
+    rand_for = RandomForestClassifier(random_state=42, n_estimators=100)
+    rand_for.fit(x_train, y_train)
+
+    # Display Confusion Matrix of Classifier
+    plot_confusion_matrix(
+        rand_for,
+        x_test,
+        y_test,
+        display_labels=iris["target_names"],
+        cmap="Blues",
+        normalize="true",
+    )
+    plt.title("Normalized Confusion Matrix - IRIS Dataset")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/machine_learning/random_forest_regressor.py b/machine_learning/random_forest_regressor.py
new file mode 100644
index 000000000000..f6c470f0975a
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+++ b/machine_learning/random_forest_regressor.py
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+# 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()