Predict the enzyme class of a given FASTA sequence using deep learning methods including CNNs, LSTM, BiLSTM, GRU, and attention models along with a host of other ML methods.

Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.

In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.

Scripts and figures as a part of an ongoing research initiative for Advancing Hepatocellular Carcinoma Staging and Prognosis

thesis for Mathematics in Machine Learning course at @Politecnico di Torino.

Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

Develop Machine Learning Models to Predict the UCI Bank Telemarketing Dataset