Comparing Different Supervised Learning Classification Algorithms

Performed data preprocessing (scaling, handling missing values, and encoding categorical features)

Trained supervised learning models (decision tree-along with bagging, and boosting, neural network, k-nearest neighbors-KNNs, and support vector machine-SVMs) on MNIST, HAR, and Credit Approval datasets [Python, Scikit-learn]

Handled overfitting (by pruning, cross validation, and regularization), and tuned hyperparameters (using grid search and randomized search)