diff --git a/ML/aufgaben/randomforest/randomforest_digits.py b/ML/aufgaben/randomforest/randomforest_digits.py
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+"""
+Use the random forest classifier to classify the digits data set.
+
+- This is an example of a supervised ML algorithm
+    - it has labels on the training data
+    - you tell the model: this is class X during training
+"""
+
+import matplotlib.pyplot as plt
+
+from sklearn import datasets
+from sklearn import tree
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+digits = datasets.load_digits()
+print(digits.data.shape)
+
+# split into training and test data
+x_train, x_test, y_train, y_test = train_test_split(
+    digits.data, digits.target, test_size=0.2, random_state=0
+)
+
+# use a random forest classifier
+classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+# train on the split data
+classifier.fit(x_train, y_train)
+# test the model and print it's accurecy
+score = classifier.score(x_test, y_test)
+print(score)
+
+# get the first tree and turn it into an image
+fig, ax = plt.subplots(figsize=(20, 10))
+tree.plot_tree(
+    classifier.estimators_[0],
+    feature_names=digits.feature_names,
+    class_names=[str(n) for n in digits.target_names],
+    filled=True,
+    rounded=True,
+    ax=ax,
+)
+fig.savefig("randomforest_digits_tree_0.png", dpi=150, bbox_inches="tight")
+
+# for digits, plot feature importance as 8x8 heatmap
+importances = classifier.feature_importances_
+plt.figure()
+plt.imshow(importances.reshape(8, 8), cmap='hot')
+plt.title('Random Forest: Feature Importance')
+plt.colorbar()
+plt.savefig('randomforest_digits_feature_importance.png', dpi=150, bbox_inches='tight')
diff --git a/ML/aufgaben/randomforest/randomforest_digits_feature_importance.png b/ML/aufgaben/randomforest/randomforest_digits_feature_importance.png
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diff --git a/ML/aufgaben/randomforest/randomforest_digits_tree_0.png b/ML/aufgaben/randomforest/randomforest_digits_tree_0.png
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diff --git a/ML/aufgaben/randomforest/randomforest_iris.py b/ML/aufgaben/randomforest/randomforest_iris.py
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+"""
+Use the random forest classifier to classify the iris data set.
+
+- This is an example of a supervised ML algorithm
+    - it has labels on the training data
+    - you tell the model: this is class X during training
+"""
+
+import matplotlib.pyplot as plt
+
+from sklearn import datasets
+from sklearn import tree
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+iris = datasets.load_iris()
+print(iris.data.shape)
+
+# split into training and test data
+x_train, x_test, y_train, y_test = train_test_split(
+    iris.data, iris.target, test_size=0.2, random_state=0
+)
+
+# use a random forest classifier
+classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+# train on the split data
+classifier.fit(x_train, y_train)
+# test the model and print it's accurecy
+score = classifier.score(x_test, y_test)
+print(score)
+
+# get the first tree and turn it into an image
+fig, ax = plt.subplots(figsize=(20, 10))
+tree.plot_tree(
+    classifier.estimators_[0],
+    feature_names=iris.feature_names,
+    class_names=list(iris.target_names),
+    filled=True,
+    rounded=True,
+    ax=ax,
+)
+fig.savefig("randomforest_iris_tree_0.png", dpi=150, bbox_inches="tight")
+
+# plot a bar chart with the importance of all features
+plt.figure()
+plt.barh(iris.feature_names, classifier.feature_importances_)
+plt.xlabel('Importance')
+plt.title('Random Forest: Feature Importance (Iris)')
+plt.savefig('randomforest_iris_feature_importance.png', dpi=150, bbox_inches='tight')
diff --git a/ML/aufgaben/randomforest/randomforest_iris_feature_importance.png b/ML/aufgaben/randomforest/randomforest_iris_feature_importance.png
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diff --git a/ML/aufgaben/randomforest/randomforest_iris_tree_0.png b/ML/aufgaben/randomforest/randomforest_iris_tree_0.png
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