Built and evaluated classification models using structured datasets to understand model performance, preprocessing techniques, and evaluation methods. This project focused on applying supervised learning to real-world-style data and interpreting results.

This project explores classification tasks using datasets such as IMDB and Reuters. The focus was on building a complete machine learning workflow, including data preprocessing, feature preparation, model training, and evaluation. I implemented classification models using scikit-learn and evaluated their performance using metrics such as accuracy and loss. The project also involved experimenting with different approaches to data representation and observing how these changes impacted model performance. Through this work, I developed an understanding of how preprocessing, feature engineering, and validation techniques such as train/test splits influence the effectiveness of machine learning models.