Student at Institute of Engineering and Technology
Computer Science and Engineering student with experience in machine learning projects and competitive programming.
Bachelor of Technology - BTech, Artificial Intelligence
2023
Intermediate
Apr 2013 - Mar 2023
B.Tech., Computer Science and Engineering
Oct 2023 - Jun 2027
Current CGPA: 8.66/10
Built and benchmarked an end-to-end fraud detection pipeline in Python using Scikit-learn, XGBoost, and imbalanced-learn on a severely imbalanced dataset of more than 284,000 records, with 0.17% fraudulent transactions. Achieved a best test ROC-AUC of 0.98 with 92% sensitivity using XGBoost on undersampled data and compared four machine learning algorithms.
Built and compared a Vanilla RNN and a 2-layer bidirectional LSTM with 0.3 dropout for binary sentiment classification on the 50,000-review IMDB dataset using Python, PyTorch, NumPy, and Matplotlib. Achieved peak validation accuracy of 76.02% with the LSTM versus 52.32% with the Vanilla RNN.