Smart Crop Recommendation System
Developed a machine learning-based crop recommendation system that predicts the most suitable crop based on soil nutrients and environmental conditions. The project uses a dataset containing Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH, and rainfall values. Multiple machine learning models were trained and evaluated, including Logistic Regression, Decision Tree, and Random Forest. A web application was built using Streamlit, allowing users to enter soil and weather information and receive real-time crop recommendations.
