Predicted pCO₂ and fCO₂ using XGBoost (DMatrix API) on SOCAT datasets, analyzing CO₂ dynamics at various missing data rates ranging from 15% to 90% for the Arabian Sea & Bay of Bengal.
Built a comprehensive ML pipeline utilizing Random Forest (achieving 90% accuracy) for churn prediction using client and price datasets.
Technology used: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and Random Forest.
Port Operations Automation | CNN + MobileNetV2
Deep Learning | IIT Kanpur
01.2025 - 02.2025
Developed a CNN to classify nine types of boats, achieving 76.29% accuracy.
Optimized with MobileNetV2 for mobile deployment, improving accuracy to 97.12% with early stopping over 50 epochs.
Technologies used: NumPy, Matplotlib, TensorFlow, Keras, Max Pooling, Global Average Pooling, Early Stopping, Dropout, Batch Normalization, MobileNetV2.
Employee Turnover Analytics
Machine Learning | IIT Kanpur
11.2024 - 12.2024
Built three ML models using Logistic Regression, Gradient Boosting Machine, and Random Forest (achieved 99.2% accuracy) to classify left (or stay) employees using SMOTE-balanced HR data, and derived retention strategies from clustering.
Undergraduate Teaching Assistant at IIT Kharagpur, Hijli College and ITI HijliUndergraduate Teaching Assistant at IIT Kharagpur, Hijli College and ITI Hijli
MEDICAL INSURANCE PREMIUM PREDICTION ML MODEL at Python(NumPy, Pandas, Scikit-learn), Machine LearningMEDICAL INSURANCE PREMIUM PREDICTION ML MODEL at Python(NumPy, Pandas, Scikit-learn), Machine Learning
Advanced Bank Churn Prediction Project at Hong Kong University of Science and TechnologyAdvanced Bank Churn Prediction Project at Hong Kong University of Science and Technology