Create gaussian_naive_bayes.py

machine_learning/gaussian_naive_bayes.py

@@ -0,0 +1,45 @@
+# Gaussian Naive Bayes Example
+
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+
+
+def main():
+
+    """
+    Gaussian Naive Bayes Example using sklearn function.
+    Iris type dataset is used to demonstrate algorithm.
+    """
+
+    # Load Iris 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
+    )
+
+    # Gaussian Naive Bayes
+    NB_model = GaussianNB()
+    NB_model.fit(x_train, y_train)
+
+    # Display Confusion Matrix
+    plot_confusion_matrix(
+        NB_model,
+        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()

requirements.txt

@@ -15,3 +15,4 @@ scikit-fuzzy
 sklearn
 sympy
 tensorflow; python_version < '3.8'
+xgboost