This project combines clustering and classification techniques to explore patterns in data using K-Means, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). K-Means is applied to uncover hidden groupings within the dataset, while KNN and SVM are used to classify data points based on labeled examples. Hyperparameter tuning is performed to optimize each model’s performance, resulting in more accurate and reliable predictions. The project demonstrates how integrating unsupervised and supervised learning can enhance data understanding and model effectiveness.