Projects
End-to-end ML systems — from data preprocessing and model training to FastAPI deployment and Docker containerization.
Real-Time Fraud Detection
Real-Time Fraud Detection
End-to-end fraud detection system built from raw transaction data through production deployment. Includes feature engineering, XGBoost model training with Optuna hyperparameter optimization, 5-fold cross-validation, threshold tuning, and FastAPI-based inference services deployed with Docker.
- Data preprocessing & feature engineering on transaction signals
- XGBoost training with cross-validation and ROC-AUC evaluation
- FastAPI batch inference service (POST /predict_batch)
- Docker containerization for reproducible deployment
0.997
ROC-AUC
0.861
F1 Score
FastAPI + Docker
Deployment
Live Demo
Upload a CSV of transaction data to the external FastAPI inference service. Results include fraud probabilities, risk levels, summary statistics, and downloadable predictions.
Launch Fraud Detection DemoSystem Architecture
Data Preprocessing
Clean transaction records, handle missing values, process timestamps, and prepare categorical features for modeling.
Feature Engineering
Transaction velocity, customer spending behavior, amount ratios, time-based features, and geospatial distance calculations.
Model Training
XGBoost with Optuna hyperparameter optimization, class imbalance handling, and 5-fold cross-validation.
FastAPI Serving
Batch inference via POST /predict_batch, containerized with Docker
Model Evaluation
ROC-AUC, precision-recall curves, and business-aligned fraud metrics
Exoplanet Host Star Classification
Exoplanet Host Star Classification
Binary classification system for identifying stars that are similar to known exoplanet-host stars using Gaia DR3 and NASA Exoplanet Archive data. Includes astrophysical feature engineering, XGBoost model training with cross-validation, model evaluation using ROC-AUC, F1 score, precision, and recall, and deployment through a FastAPI inference service for interactive predictions.
- Gaia DR3 and NASA Exoplanet Archive data preprocessing
- Astrophysical feature engineering from stellar parameters
- XGBoost model training with cross-validation
- Evaluation with ROC-AUC, F1 score, precision, recall, and confusion matrix
- FastAPI inference API for interactive host-likeness predictions
0.991
Accuracy
0.912
F1 Score
13
Features Used
Live Prediction Form
Interactive demo for estimating how similar a star is to known exoplanet-host stars using stellar properties such as metallicity, mass, radius, temperature, luminosity, age, and surface gravity. The form sends inputs to FastAPI inference service and returns a host-likeness score.
View Exoplanet Demo