diff --git a/classification/adaboost_classifier.py b/classification/adaboost_classifier.py
new file mode 100644
index 0000000..e0f23f0
--- /dev/null
+++ b/classification/adaboost_classifier.py
@@ -0,0 +1,39 @@
+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()
\ No newline at end of file
diff --git a/classification/gaussian_n_bayes.py b/classification/gaussian_n_bayes.py
new file mode 100644
index 0000000..8bbb146
--- /dev/null
+++ b/classification/gaussian_n_bayes.py
@@ -0,0 +1,32 @@
+# 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()
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