Smart Crop and Price Predictor Regions

The objective of this project is to develop a predictive system that suggests suitable crops and estimates crop prices based on soil and environmental conditions. The project will evaluate the performance of Decision Tree, Random Forest, XGBoost, and AdaBoost for classification and regression tasks. A user-friendly interface will be created for easy data input and prediction output, along with a registration and login system for secure access. The system will focus on providing clear, interpretable results for users without technical expertise, ensuring flexibility for future enhancements or updates, and supporting agricultural decision-making with reliable predictive models.