ASD early screening in children using ML & ANN — 9 classifiers (MLP-ANN, Random Forest, SVM, KNN, QDA, Naïve Bayes, LR), SMOTE, 7-metric evaluation, overfitting analysis. Streamlit web app deployment. MSc ISE thesis — Purbanchal University, Nepal.

FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.

This project implements the classical LeNet-5 CNN for MNIST digit classification using PyTorch. It covers a complete pipeline from data preprocessing to deployment. The model achieves ~98.8% test accuracy, showing the strong effectiveness of early CNN architectures for image classification.