Added XGBClassifier Fixes: TheAlgorithms#6551

Author: Chaitanya134

machine_learning/xgboost_classifier.py

@@ -0,0 +1,70 @@
+"""
+Implementation of the XGBoost Classifier.
+Dataset - Iris Dataset
+Know more about XGBoost - https://en.wikipedia.org/wiki/XGBoost
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, confusion_matrix, plot_confusion_matrix
+from xgboost import XGBClassifier    # might have to do `!pip install xgboost`
+import warnings
+warnings.filterwarnings("ignore")
+
+def main():
+    # load the iris dataset
+    iris = load_iris()
+
+    print(type(iris))   # Currently the type is sklearn.utils.Bunch
+
+    # convert it to a dataframe
+    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+
+    # add the target column
+    df['target'] = iris.target
+
+    print(df['target'].unique())  # 0 -> sentosa, 1 -> versicolor, 2 -> virginica
+
+    # how the dataset looks
+    print(df.head())
+
+    X = df.drop('target', axis=1)
+    y = df['target']
+
+    # split dataset into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
+
+    # initialize the model
+    xgb = XGBClassifier(random_state=0)
+
+    # start the training on training set
+    xgb.fit(X_train, y_train)
+
+    # get the prediction on testing set
+    y_pred = xgb.predict(X_test)
+
+    # calculate the accuracy
+    acc = accuracy_score(y_test, y_pred)
+
+    # print the accuracy
+    print(f"Accuracy: {round(100 * acc, 2)}%")
+
+    # print the confusion matrix
+    print(confusion_matrix(y_test, y_pred))
+
+    # plot the confusion matrix
+    plot_confusion_matrix(
+        xgb,
+        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()