samyakrajbayar/Rhea-Soil-Nutrient-Prediction-Challenge

Soil health is the cornerstone of sustainable agriculture. In many parts of Africa, traditional laboratory soil testing is expensive, slow, and inaccessible to smallholder farmers. # Rhea Soil Nutrient Prediction Challenge This repository contains the solution for the Rhea Soil Nutrient Prediction Challenge hosted on Zindi. ## 📌 Project Overview Soil health is the cornerstone of sustainable agriculture. In many parts of Africa, traditional laboratory soil testing is expensive, slow, and inaccessible to smallholder farmers. The goal of this challenge is to develop a machine learning model that estimates the levels of **13 essential soil nutrients** using a combination of geospatial and existing soil data. By predicting these nutrients at locations where lab tests aren't available, we help **Rhea** provide tailored recommendations to farmers, ensuring better yields and long-term sustainability. ## 🎯 Objectives * **Predict 13 target nutrients:** Aluminum (Al), Boron (B), Calcium (Ca), Copper (Cu), Iron (Fe), Magnesium (Mg), Manganese (Mn), Phosphorus (P), Potassium (K), Sodium (Na), Sulfur (S), Zinc (Zn), and Nitrogen (N). * **Minimize RMSE:** The primary evaluation metric for this regression task is the Root Mean Squared Error (RMSE). ## 📊 Dataset Description The dataset consists of geospatial features and soil nutrient data: * **Training Data:** Locations with lab-tested results for the 13 target nutrients. * **Test Data:** Locations where nutrient levels need to be predicted. * **Features:** Environmental variables, geospatial coordinates, and potentially satellite-derived indicators (depending on the specific Zindi data provided). ## 🛠️ Tech Stack * **Language:** Python * **Libraries:** Pandas, NumPy, Scikit-learn, XGBoost/LightGBM/CatBoost, Matplotlib, Seaborn * **Environment:** Jupyter Notebook / Google Colab ## 🚀 Getting Started ### 1. Clone the repository ```bash git clone github.com cd Rhea-Soil-Nutrient-Prediction-Challenge ``` ### 2. Install Dependencies ```bash pip install -r requirements.txt ``` ### 3. Data Preparation Place the competition data fi …