In this session, we will dive into the fundamentals of predictive analysis, starting with the key differences between regression and classification models. We will discuss best practices for data preparation, including handling missing values, feature selection, and data transformation. Then, we will implement a classification machine learning algorithm, exploring key concepts such as training, testing, and optimization. Finally, we will evaluate model performance using various metrics and visualize actual vs. predicted values to assess model accuracy. By the end of this session, you will have a clear understanding of classification modeling and how to effectively apply it in real-world scenarios.