- Linear and logistic regression implementation.
- Decision trees, random forests, SVMs, K-NN, and ensemble methods.
- Unsupervised learning with K-Means and PCA.
- Feature engineering, scaling, and encoding techniques.
- Evaluate models with metrics like accuracy, ROC AUC, MAE, and MSE.
