Mainabryan/Maize_Yield_prediction

Machine Learning model to predict maize yield in Kenya using Ridge Regression, plus Tableau dashboards # Maize_Yield_prediction Machine Learning model to predict maize yield in Kenya using Ridge Regression, plus Tableau dashboards # 🌾 Kenya Maize Yield Prediction using Ridge Regression This project explores how Machine Learning can help predict **maize crop yield** based on key agricultural factors like rainfall, soil quality, fertilizer usage, and pest attacks. The goal is to support farmers, NGOs, and policymakers in making data-informed decisions for food security and sustainable agriculture. --- ## 🔍 Problem Statement Inconsistent yields across different Kenyan regions impact farmer income and national food stability. This project uses a realistic agricultural dataset to predict **current maize yield (in tonnes per hectare)** and identify **key factors that influence it**. --- ## 🧠 Tools & Technologies | Category | Tools Used | |----------------|-----------------------------------------------| | Programming | Python, Pandas, Scikit-learn | | ML Model | Ridge Regression (with Regularization) | | Visualization | Seaborn, Matplotlib, **Tableau** | | Data Cleaning | Numpy, Label Encoding, One-Hot Encoding | | Deployment (later) | Streamlit (planned) | | Reporting | Jupyter Notebook, GitHub | --- ## 📊 Features Used in the Model - **Region, County** - **Soil Quality** - **Average Rainfall (mm)** - **Fertilizer Type & Usage** - **Seed Type** - **Farming Method (Manual vs Mechanized)** - **Pest Attack Level** - **Previous Season Yield** - **Altitude & Temperature** - **Planting Season** Target Variable: - `Current_Yield_tonnes_per_hectare` --- ## 📈 Tableau Visualizations (In Progress) The Tableau dashboard will help visualize insights like: - 📍 Yield distribution across **Kenyan counties/regions** - 🌦️ Correlation between **rainfall/fertilizer and yield** - 🚜 Manual vs Mechanized farming yield comparison - 🐛 …