Ashrita Singh@Ashritasingh

Designed and developed a high-performance LightGBM predictive analytics core to forecast residential energy consumption by engineering robust temporal, calendar, and weather-based feature pipelines. Evaluated and optimized model performance through systematic regression metrics (RMSE, MAE, and R²) to ensure maximum forecasting accuracy. To bridge the gap between complex machine learning and user action, integrated SHAP-based Explainable AI (XAI) with a custom rule-based recommendation engine, effectively translating feature-level insights into personalized, actionable energy-saving suggestions. Additionally, built advanced financial and operational security modules, including an electricity bill estimator that maps consumption forecasts to real multi-slab tariff structures, and an automated anomaly detection system to instantly flag abnormal consumption patterns and potential hardware inefficiencies.

🌎 Online Hackathon | 💻 Month-long challenge | Dates: Sep 1–30, 2026 | 🏆 $10,000 Prize Pool ($5k cash + $5k in AAI credits) A month-long online challenge run by lablab.ai together with AssemblyAI, the Voice AI infrastructure company for builders. Every participant builds on AssemblyAI. You can join at any point — registration stays open for the whole build window, so you can start on day one or pick the project up halfway through and still submit. 🌍 Fully online hackathon. Join and build from anywhere in the world.