An AI-powered sustainability analytics and energy forecasting platform built using Machine Learning, Data Science, and Interactive Visualization technologies.
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Updated
Jun 8, 2026 - Jupyter Notebook
An AI-powered sustainability analytics and energy forecasting platform built using Machine Learning, Data Science, and Interactive Visualization technologies.
An advanced, real-time deep learning dashboard designed to forecast and analyze streaming seismic telemetry. Built specifically to process and predict amplitude responses from the F3 block in the Dutch sector of the North Sea.
An enterprise-grade, real-time telemetry and AI forecasting dashboard designed for Smart Grids. This system integrates machine learning (XGBoost) for load forecasting with a high-performance backend (FastAPI) and an interactive, state-managed frontend (Streamlit).
An enterprise-grade, high-performance telemetry dashboard designed to simulate real-time electrical grid monitoring and AI-driven load forecasting. Built with a robust Machine Learning pipeline and a cutting-edge React-based Python frontend.
An enterprise-grade, high-performance telemetry dashboard designed to simulate real-time electrical grid monitoring and AI-driven load forecasting. Built with a robust Machine Learning pipeline and a cutting-edge React-based Python frontend.
An advanced, real-time deep learning dashboard designed to forecast and analyze streaming seismic telemetry. Built specifically to process and predict amplitude responses from the F3 block in the Dutch sector of the North Sea.
A data analysis and forecasting project examining casualty trends from 2000-2021. Features time-series decomposition, correlation analysis, and predictive modeling using ARIMA and LSTM (Deep Learning) to forecast future trends.
Optimizing and Forecasting Supply Chain Performance
I developed this pipeline for researchers who want a dynamic and accurate dataset of Sri Lanka vegetable and Fruit price history. the dataset is update automatically everyday. the dataset start from 2023-01-05.
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