XGBoost Heart Disease Prediction Function

This function uses the XGBoost library to predict heart disease. First, it splits the dataset into training and testing sets, then creates an XGBoost classifier, fits the model, and makes predictions on the test set. Finally, it calculates and returns the accuracy of the model.

Technology Stack : XGBoost, NumPy, Pandas, Scikit-learn

Code Type : Function

Code Difficulty : Intermediate

                
                    
import xgboost as xgb
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

def predict_heart_disease(X, y):
    # Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Create an XGBoost classifier
    xgb_clf = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
    
    # Fit the model
    xgb_clf.fit(X_train, y_train)
    
    # Predict the labels of the test set
    y_pred = xgb_clf.predict(X_test)
    
    # Calculate the accuracy of the model
    accuracy = xgb_clf.score(X_test, y_test)
    
    return accuracy