This project focuses on building a predictive model to identify potential customer churn in the telecom industry using various supervised classification algorithms. Key steps included Exploratory Data Analysis (EDA), handling class imbalance with SMOTEENN, and applying models such as Random Forest, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, AdaBoost, XGBoost, LDA, QDA, and Gradient Boosting. Hyperparameter tuning was performed using GridSearchCV and RandomizedSearchCV to optimize model performance.