Gaussian naive Bayes algorithm

classification/adaboost_classifier.py

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+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import plot_confusion_matrix
+from matplotlib import pyplot as plt
+
+
+"""Adaboost classifier example"""
+
+
+def adaboost():
+    cancer_df = load_breast_cancer()
+    print(cancer_df.keys())
+    X, y = cancer_df.data, cancer_df.target
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+
+    abc = AdaBoostClassifier(base_estimator=None,
+                             n_estimators=300, learning_rate=1, random_state=0)
+    abc.fit(X_train, y_train)
+    y_pred = abc.predict(X_test)
+    print(y_pred[:20])
+    # Display Confusion Matrix of Classifier
+    plot_confusion_matrix(
+        abc,
+        X_test,
+        y_test,
+        display_labels=cancer_df["target_names"],
+        cmap="Blues",
+        normalize="true",
+    )
+    plt.title("Normalized Confusion Matrix - Cancer Dataset")
+    plt.show()
+
+    # to see the accuracy of the model
+    print("Accuracy of adaboost is:", abc.score(X_test, y_test))
+
+
+if __name__ == "__main__":
+    adaboost()

classification/gaussian_n_bayes.py

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+# importing libraries
+from sklearn.naive_bayes import GaussianNB
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+
+
+"""To implement Gaussian naves bayes for flowers clssification"""
+
+
+def main():
+
+    iris = load_iris()
+    print(iris.keys())
+    iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
+    iris_df['target'] = iris.target
+    print(iris_df.head())
+    X, y = iris_df.drop('target', 1), iris_df.target
+    print(X.shape, y.shape)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
+    model = GaussianNB()
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    print(y_pred[:10])
+    accuracy = accuracy_score(y_test, y_pred)
+    print("The accuracy of Gaussian naves is {}".format(accuracy))
+    # classification report
+    print(classification_report(y_test, y_pred))
+
+
+main()