In this project, I performed churn classification using the XGBoost algorithm to predict customer attrition based on demographic and service usage data. After preprocessing the dataset by converting categorical variables to numeric, scaling features, and removing irrelevant columns, I split the data into training and testing sets. The model was optimized using GridSearchCV to find the best hyperparameters, achieving an accuracy of 80%, and I visualized the results with a confusion matrix. The dataset includes information on over 6000 users, covering attributes such as tenure, services used, and payment methods, with the target variable being churn status.