diff --git a/Supervised/XGBoost-Classifier-breast-cancer.py b/Supervised/XGBoost-Classifier-breast-cancer.py
new file mode 100644
index 0000000..55c8d26
--- /dev/null
+++ b/Supervised/XGBoost-Classifier-breast-cancer.py
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+from xgboost import XGBClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_breast_cancer
+from sklearn.tree import export_graphviz
+import matplotlib.pyplot as plt
+
+cancer = load_breast_cancer()
+X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=0)
+
+
+learning_rates =[x * 0.004 for x in range(1, 61)]
+training_accuracy = []
+test_accuracy = []
+best_train_score = 0
+best_test_score = 0
+
+for r in learning_rates:
+	#build the model
+	model = XGBClassifier(learning_rate = r)
+
+	model.fit(X_train, y_train)
+	training_accuracy.append(model.score(X_train, y_train))
+	best_train_score = max(best_train_score,model.score(X_train, y_train))
+	#record generalization accuracy
+	test_accuracy.append(model.score(X_test, y_test))
+	best_test_score = max(best_test_score,model.score(X_test, y_test))
+
+
+plt.plot(learning_rates, training_accuracy, label="training accuracy")
+plt.plot(learning_rates, test_accuracy, label="test accuracy")
+plt.ylabel("Accuracy")
+plt.xlabel("Learning Rate")
+plt.legend()
+plt.show()
+
+print("Accuracy on training set: {:.3f}".format(best_train_score))
+print("Accuracy on test set: {:.3f}".format(best_test_score))