Applied machine learning toolkit implementing Double Machine Learning for Energy Analytics.

Multi-AI Agent Energy Management System with HILS simulation, Hybrid AI (ML+LLM), and MCP Runtime - Real-time visualization demo for smart grid optimization

Global EV charging & EV models (2025) EDA tutorial: infrastructure density vs adoption, manufacturer/model dynamics, and regional gap insights.

Repository of the paper "Detecting and interpreting faults in vulnerable power grids with machine learning".

Complete data architecture and machine learning project for industrial cost analysis. Includes a modern ETL pipeline (Bronze-Silver-Gold), validations with Great Expectations, Prefect orchestration, in-depth EDA, predictive models (Random Forest and Gradient Boosting), and deployment via Streamlit.

AI system for energy demand forecasting and grid optimization. Uses time series analysis and weather data to predict energy consumption, optimize renewable energy integration, and prevent grid failures.

Data-driven analysis of Nigeria’s electricity system (2000–2024) using Python, Sql, Power BI.

🔋⚡ Professional LOAD OUTPUT monitoring for Victron MPPT solar chargers - Real-time kWh tracking of load consumption with ESP82XX + ioBroker + MQTT

Data-driven analysis of solar farm data from Benin, Sierra Leone, and Togo, using statistical analysis and EDA to identify high-potential regions for solar installations. Delivers actionable insights to enhance operational efficiency and support sustainable energy investments.

Smart Grid ETL + Real-Time Energy Anomaly Analytics Dashboard

SPARK is a extensible energy analytics platform designed for processing large-scale renewable energy datasets using the Hadoop MapReduce framework. It offers data analytics, machine learning-based forecasting, and energy trend insights in a fully modular setup utilizing Hadoop, MapReduce, Apache Spark complementing the Streamlit UI.

AI-powered Energy Forecasting System using XGBoost, MLP & LSTM. Includes Flask API, responsive frontend, feature engineering pipeline, notebooks, datasets, and end-to-end machine learning workflow. Perfect for energy analytics, predictive modeling, and ML engineering portfolios.

Scenario-based analysis of global oil market supply–demand balances, OPEC+ policy sensitivity and geopolitical risk drivers, using OPEC, EIA and OEUK data.

Machine learning project for predicting residential electricity consumption using building and occupancy features. Implements seasonal ML models (monsoon & summer) with CatBoost, regression analysis, cross-validation, and model interpretability for smart city and energy efficiency applications.

Learn how to visualize power consumption trends using Syncfusion WPF Surface Charts. This guide covers creating interactive 3D charts, binding energy data, customizing chart appearance, and optimizing performance to display annual power usage patterns across countries.

End-to-end time series forecasting project to predict power consumption using SARIMA, including data analysis, modeling, and an interactive user interface.

SPARK is a extensible energy analytics platform designed for processing large-scale renewable energy datasets using the Hadoop MapReduce framework. It offers data analytics, machine learning-based forecasting, and energy trend insights in a fully modular setup utilising Hadoop, MapReduce, Apache Spark complementing the Streamlit UI.

An end-to-end Data Science pipeline analyzing the impact of extreme heat on India's energy grid. Features automated NASA weather data processing, state-wise energy demand correlation, and a live Streamlit dashboard.

This repository contains my structured notes from DataCamp courses, exercises, and projects. It’s meant to serve as a quick reference for me and a showcase of my learning journey.