diya-thabet/agriBORA-Commodity-Price-Forecasting-Challenge

This is a winning solution for the Zindi "Maize Price Prediction Challenge," designed to forecast weekly maize prices in Kenya. # **Zindi AgriBORA Maize Price Prediction** This is a winning solution for the Zindi "Maize Price Prediction Challenge," designed to forecast weekly maize prices in Kenya. This script achieved the \#1 rank on the public leaderboard (as of Nov 15, 2025\) by correcting a data processing error in the original starter notebook, which allowed it to train on two extra months of recent data. ## **Competition Goal** The goal is to predict the average weekly price of dry white maize for five counties in Kenya: Kiambu, Kirinyaga, Mombasa, Nairobi, and Uasin-Gishu. The competition features a rolling leaderboard, where new data is released weekly. ## **How it Works** The model (ElasticNet) is trained on two datasets. The key to this script's success is in process\_data(): 1. It merges agribora\_maize\_prices.csv (which has recent data up to Sept 2025\) and kamis\_maize\_prices.csv (which stops in July 2025). 2. It uses an how="outer" merge to **keep the "missing" 2 months of data** from the Agribora file that other solutions might miss. 3. It forward-fills the feature columns to create a complete, recent training set. 4. It trains a stable ElasticNet model. 5. It recursively forecasts prices week-by-week to bridge the 7-week gap between the last known data (Sept 29\) and the first prediction target (Nov 24). ## **How to Run** 1. Place the following files in the same directory: * v1\_explained.py (or your script name) * agribora\_maize\_prices.csv * kamis\_maize\_prices.csv * SampleSubmission.csv 2. Run the script: python v1\_explained.py 3. The script will generate a submission.csv file, which is ready to be uploaded to Zindi.