These are some of my Artificial Intelligence, Machine Learning and Data Science Projects. More of them can be seen on my GitHub account

December 9, 2024

Project-7: Quantitative-Trading-Platform

Aim: High-performance algorithmic trading platform with LSTM/Transformer models for real-time market prediction, automated execution, and comprehensive risk management across multiple asset classes.

Key Metrics: 87.3% directional accuracy, 2.1 Sharpe ratio, -3.2% max drawdown, 1M+ ticks/sec processing, <50ms latency, 68.5% win rate with +24.7% total return.

Tech Stack: PyTorch, LSTM/Transformer, Apache Kafka, Redis, PostgreSQL, Tableau, Docker. Real-time data pipeline with Monte Carlo backtesting and live analytics dashboards.

September 9, 2024

Project-8: Real-Time Payment Fraud Detection System

Aim: Production-ready fraud detection system processing 500K+ daily transactions with millisecond latency and enterprise-grade accuracy.

Key Metrics: 94.2% accuracy, < 3% false positives, < 50ms transaction scoring, $2.3M+ monthly fraud prevention across 15+ payment channels.

Tech Stack: Apache Kafka, Spark, Redis, Random Forest+XGBoost+LightGBM ensemble. Kubernetes deployment with real-time streaming architecture.

December 9, 2022

Project-9: E-commerce Shipping

Aim: Intelligent shipping prediction platform for international e-commerce with delivery optimization and supply chain insights.

Key Metrics: 89.7% delivery prediction accuracy, 250K+ monthly shipments, 35% reduced delivery variance, 92% on-time delivery across 45+ countries.

Tech Stack: Random Forest, Flask, AWS EC2, geospatial analysis. Integrated weather APIs and route optimization for global logistics.

October 9, 2023

Project-10: Customer Churn Prediction

Aim: Predictive analytics system for telecom customer churn with explainable AI insights and real-time risk scoring for proactive retention strategies.

Key Metrics: 92% churn prediction accuracy, 100K+ customer records processed, 95% precision for high-risk identification with SMOTE+ENN balancing.

Tech Stack: Random Forest, SMOTE+ENN, Flask, Pandas. Implemented RFM analysis and behavioral pattern detection for telecom domain.