I recently completed a machine learning project where I built models to classify emails into spam and ham. The workflow included text preprocessing and data cleaning, checking data distribution, and handling class imbalance using SMOTE and resampling techniques. For feature extraction, I applied TF-IDF vectorization to convert raw emails into numerical features. I then trained and tested two models—Naive Bayes and Logistic Regression—achieving accuracies of 96% and 95% respectively. This project not only strengthened my skills in text preprocessing, feature engineering, and model evaluation but also gave me a deeper understanding of techniques that form the foundation for Deep Learning and Generative AI.