Pcode27/Ghana-s-Indigenous-Intel-Challenge

This project aims to predict rainfall using Indigenous ecological indicators and traditional environmental knowledge. The approach integrates local signs, flora, fauna behaviors, and other ecological cues into data-driven models for improved precipitation forecasting. # Ghana Rainfall Prediction Using Indigenous Ecological Indicators (IEIs) This repository contains my submission for the Zindi Ghana Rainfall Prediction Challenge. The goal is to forecast rainfall in Ghana by integrating Indigenous Ecological Indicators (IEIs) with meteorological data and modern machine learning methods. ## Overview Indigenous communities in Ghana use ecological indicators—such as animal behavior, plant changes, and wind patterns—to predict rainfall. This project explores how these insights, combined with conventional meteorological data, can improve rainfall forecasting. ## Approach - **Data Collection:** Gather meteorological data and document IEIs relevant to rainfall in Ghana. - **Feature Engineering:** Encode IEIs as features for predictive modeling. - **Modeling:** Train and evaluate statistical and machine learning models using both IEIs and meteorological data. - **Analysis:** Compare predictions based on IEIs, meteorological data, and a hybrid approach. ## Repository Structure - `data/` — Datasets from Zindi and IEI records. - `notebooks/` — Jupyter/Colab notebooks for data exploration and modeling. - `src/` — Python scripts for data wrangling and modeling. - `results/` — Model predictions and evaluation outputs. - `docs/` — Additional documentation (if needed). ## Getting Started 1. Clone the repository. 2. Install dependencies (`pip install -r requirements.txt`). 3. Explore the notebooks and scripts. 4. Feel free to reach out if you have questions or feedback! ## References - Zindi Ghana Rainfall Prediction Challenge - Indigenous Ecological Knowledge - Rainfall Prediction Methods ## License This project is open-source under the MIT license. --- *Blending Indigenous wisdom and data science for better rainfall prediction in Ghana.*