Customer Data Segmentation with KNN and K-Means in Python

Understanding your customers is the key to business growth, but manually analyzing thousands of profiles is impossible. By leveraging machine learning, you can automatically group similar customers and predict their behavior to optimize marketing and product strategies. This text-based course guides you from foundational data concepts to building and evaluating your own classification and clustering models using Python. You will learn how to prepare customer data, apply algorithms, and interpret the results to make data-driven decisions. What you'll learn: 1. Understand the core concepts of supervised classification and unsupervised clustering. 2. Prepare and scale customer data, including income and spending scores, for machine learning models. 3. Implement the K-Nearest Neighbors (KNN) algorithm to classify customer segments. 4. Apply K-Means clustering to discover hidden patterns and group profiles. 5. Evaluate model performance using metrics like accuracy, silhouette scores, and elbow method analysis. 6. Write clean, modern Python code using scikit-learn pipelines and type hints. You will start with key terminology and foundational data science definitions before moving into practical implementation. Through structured written explanations and clear code snippets, you will progress from raw customer records to fully evaluated machine learning models. This course is designed for beginners, aspiring data analysts, and marketers who want to learn machine learning basics. No prior data science experience is required. Start reading today to unlock the power of customer data segmentation.