Bike Demand Prediction ML System
Production ML demand forecasting application developed at TS Bridge.
01 · The Problem & Hypothesis
Why this needed to be built
Urban mobility fleets experience extreme utilization volatility due to weather, seasonal shifts, and commuting patterns, requiring precise predictive demand modeling to prevent asset shortages.
Train, evaluate, and deploy an end-to-end regression pipeline on AWS with automated MLOps tracking and stakeholder analytics dashboards.
02 · Technical Implementation
How the system was architected
Developed during my Data Analytics & Machine Learning Internship at TS Bridge. Engineered an end-to-end ML application utilizing XGBoost and Scikit-learn, achieving 91% prediction accuracy with an MAE below 35. Integrated DVC and MLflow for lifecycle versioning, containerized via Docker on AWS EC2/S3, and deployed interactive Power BI dashboards utilized by 5+ business stakeholders.
03 · Key Takeaways Earned
“Model accuracy metrics must translate directly into operational stakeholder decisions.”
“Continuous data versioning and cloud containerization ensure seamless deployment parity.”