Your disaster management assistant
A FastAPI-based backend system for the GreenFund platform, designed to help farmers track and manage their environmental impact, get climate-smart agriculture recommendations, and participate in a community-driven knowledge sharing platform.
GreenFund is an AI-powered web application that empowers farmers to make data-driven, climate-smart agricultural decisions. The platform focuses on analysis of soil health then additionally tracks farm activities, measures carbon emissions, and provides AI-driven crop recommendations to promote sustainable and climate-resilient farming.
Machine learning solution for predicting CO2 emissions using economic and social indicators. Leverages World Bank data to identify key factors influencing emissions. Helps policymakers make data-driven decisions for climate action and sustainable development.
This project aims to classify tweets related to different types of disasters using Natural Language Processing (NLP) techniques.
Visualises emissions from the transport sector and access to public transport.
Visualises the complex layers of ice that make up Antarctica.
A student project advocating for a sachet-free Philippines through education and awareness, featuring webpages using HTML, CSS, and JavaScript.
Visualises how ready and how vulnerable economies are to projected climate impacts.
Real-time weather forecasting platform for farmers and fishers | Reliance Foundation Internship (Nov–Dec 2023)
InnerSage is a responsive climate action awareness website aligned with UN SDG 13 (Climate Action). Built using HTML, CSS, and JavaScript, the platform delivers educational content on climate change and sustainability while ensuring cross-device compatibility. Automated validation and deployment are handled באמצעות GitHub Actions, including W3C val
Home of CarbonSasa. Main goal is carbon sequestration on SDG 13: Climate Action, which focuses on taking immediate action to combat climate change and its impacts. Carbon sequestration contributes to SDG 13 by storing carbon, reducing emissions, and creating carbon sinks through various methods, including both technological and natural approaches.
Exploratory data analysis of historical U.S. EPA automotive trends (1975–2024) examining vehicle weight, horsepower, fuel efficiency, and CO2 emissions using Python and Tableau.
Urban land-use efficiency analysis using GIS, Google Earth Engine, GHSL spatial data, and dynamic-panel GMM. Introduces the stable BpCR metric.
Preliminary Period ; Lead coder of these using Tableau, Tableau Prep Builder, and Python using Jupyter Notebook environment Having EDA, building charts and visualizations using US EPA dataset spanning from 1975 to preliminary 2024 ; Contributors on Analysis : Valles James, Rodelas John
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