Preacher-Y/rwanda-potato-yield-insight

# _**Welcome to the Rwanda Potato Yield Insight**_ ## **Intriduction** The Rwanda Potato Yield Prediction Project aims to enhance agricultural productivity in Rwanda by leveraging machine learning to predict potato yields. This report provides a comprehensive overview of the project, including its background, objectives, data collection, model development, web application design, user interaction, optimization tips, results, challenges, future work, and conclusions. ## _Overview_ The project utilizes historical agricultural data from 2000 to 2017 to train a Random Forest machine learning model. The model predicts potato yields based on various input parameters such as climate conditions, soil type, fertilizer usage, irrigation, pests/diseases, season, and potato variety. The predictions are delivered through an interactive web application built using Streamlit, providing farmers with actionable insights to optimize their farming practices. ## _Importance_ Potato farming is a crucial agricultural activity in Rwanda, contributing significantly to food security and economic stability. Accurate yield predictions can help farmers make informed decisions, optimize resource allocation, and enhance productivity. This project aims to bridge the gap between traditional farming practices and modern technological advancements. ## _Agriculture challeges in Rwanda_ Rwandan farmers face several challenges, including unpredictable weather patterns, soil degradation, limited access to quality fertilizers, and pest infestations. These challenges necessitate the use of advanced technologies to provide accurate yield predictions and actionable recommendations. ## _Objectives_ The primary objectives of the Rwanda Potato Yield Prediction Project are: 1. Develop a Predictive Model: Create a machine learning model that accurately predicts potato yields based on historical data. 2. Build a User-Friendly Web Application: Design an interactive web application that allows farmers to …