My Projects

Finding Fraudsters

Credit card fraud is a big problem in the world of e-commerce, and a difficult one to eliminate. This project uses the dataset obtained from IEEE-CIS Fraud Detection Kaggle competition. I used correlation heatmaps and feature engineering to create the model which got the best AUC score.

  • Developed a fraud detection model using boosted decision trees (BDT).
  • Dealt with a highly imbalanced dataset of 590,540 transactions, of which only 3.5% were fraudulent.
  • Achieved 93% ROC AUC score using ensemble model of XGBoost, LightGBM and CATBoost
Git Hub → GitHub
Project 1

Chicago Crimes

  • Analyzed crime data from the Chicago Data Portal which contained 8 million rows and 22 columns.
  • DUsed Seaborn and Folium for data visualization and building interactive maps.
  • Showed how different types of crimes are distributed among the different community areas

    • Git Hub → GitHub
    Project 1

    About Me

    Physics graduate with 3+ years of experience applying machine learning and quantitative methods to solve complex problems. Fast learner with exceptional attention to detail and a commitment to delivering high-quality results. Seeking opportunities in data science and quantitative analysis to tackle real-world challenges with data-driven solutions.

    My Resume