Masngo/Maize-Yield-Prediction-Using-Machine-Learning

This project focuses on developing a predictive model to estimate maize yield in Zimbabwe using machine learning techniques. Agriculture is a vital part of Zimbabwe’s economy, and maize is one of its staple crops. The main objective of this project is to leverage data-driven methods to provide more reliable yield predictions. # Maize-Yield-Prediction-Using-Machine-Learning This project focuses on developing a predictive model to estimate maize yield in Zimbabwe using machine learning techniques. Agriculture is a vital part of Zimbabwe’s economy, and maize is one of its staple crops. The main objective of this project is to leverage data-driven methods to provide more reliable yield predictions. Maize Yield Prediction Using Machine Learning 📌 Project Overview This project aims to build a predictive system for estimating maize crop yields in Zimbabwe using historical agricultural and climatic data. It uses supervised machine learning algorithms to analyze and predict maize yield based on various factors such as rainfall, temperature, soil quality, and fertilizer use. 🎯 Objectives - To enhance the accuracy of maize yield prediction by leveraging machine learning algorithms and techniques. - To evaluate model performance using appropriate metrics (R² Score, RMSE, MAE). - To help farmers and policymakers in Zimbabwe make informed agricultural decisions. 🧰 Technologies & Tools Used - *Programming Language*: Python - *Libraries*: - Pandas - NumPy - Scikit-learn - Matplotlib / Seaborn (for visualization) Algorithms Used: - Random Forest Regression - Lasso Regression - Linear Regression - Decision Tree Regression 📊 Dataset - Historical data collected from open sources, research papers, and local agricultural agencies. - Key features include: - Annual rainfall - Average temperature - Fertilizer usage - Soil pH level - Farming Districts in Zimbabwe - Pesticide - Maize yield (target variable) ⚙️ How to Use 1. *Clone the Repository* bash git clone github.com cd Maize-Yield-Prediction-Using-Machine-Learning 2. *Install Requirements* bash pip install -r requirements.txt 3. *Run the Script* bash python app.py 🔍 Project Structure maize-yield-prediction/ ├── data/ # CSV datasets ├── notebooks/ …