This project aims to predict precipitation, flood, and drought events in Togo using machine learning methods. Unfortunately, weather data from Togo's weather stations is not readily available. As a result, the team had to scrape flood and drought data from social media sources. A LSTM neural network is employed for forecasting rainfall and extreme weather events. The model is deployed through a Streamlit web application hosted on the AWS cloud.

This project aims to develop and implement a chatbot feature on the Omdena website to provide efficient and interactive support. RASA, Open AI LLM, Streamlit, Hugging Face are used in this project.

Selected as one of the 120 collaborators to work on the project that utilized data analysis and machine learning methods to enhance student success in the education system and promote data-driven education policies.