aaronjalapon/crop-price-prediction

Crop Price Prediction (Senegal) is an end-to-end machine learning application designed to forecast food prices in Senegal. # 🌾 Crop Price Prediction (Senegal) An end-to-end machine learning project to forecast food prices in Senegal. This repository contains the data analysis, model training pipeline, and a production-ready Streamlit web application. ## 📋 Overview Food price volatility is a critical issue in many developing regions. This project aims to provide transparent and accessible price forecasting for crops in Senegal markets. The solution consists of: 1. **Data Analysis**: Exploratory Data Analysis (EDA) on WFP market data. 2. **Machine Learning**: A Random Forest Regressor trained to predict crop prices based on location, crop type, and temporal features. 3. **Web Application**: An interactive Streamlit dashboard for real-time predictions. ## ✨ Features - **Interactive Dashboard**: User-friendly interface to select crops, markets, and dates. - **Real-time Predictions**: Instant price forecasting in West African CFA Franc (XOF). - **Uncertainty Quantification**: Displays 95% confidence intervals for every prediction. - **Model Confidence Score**: Provides a reliability metric based on ensemble variance. - **Comprehensive Metadata**: Supports various crops (Rice, Maize, Millet, etc.) and regions across Senegal. ## 🚀 Quick Start ### Prerequisites - Python 3.8+ - pip ### Installation 1. **Clone the repository** ```bash git clone github.com cd crop-price-prediction ``` 2. **Install dependencies** ```bash pip install -r requirements.txt ``` 3. **Run the application** ```bash streamlit run app.py ``` 4. **Access the app** Open your browser to `localhost` ## 🐳 Docker Usage Run the application in a containerized environment: ```bash # Build the image docker build -t crop-price-app . # Run the container docker run -p 8501:8501 crop-price-app ``` ## 📂 Project Structure ``` crop-price-prediction/ ├── app.py # Main Streamlit application ├── Price_Prediction.ipynb …