Crop Recommnedation System

The project presented here focuses on developing a Crop Recommendation System using machine learning techniques. This system aims to assist farmers in making informed decisions about the most suitable crops to cultivate given specific soil and climate parameters. • This project utilizes a dataset comprising key agricultural features, including soil nutrients (Nitrogen, Phosphorus, Potassium), temperature, humidity, pH, and rainfall, alongside the target variable, which identifies the type of crop. The system leverages various machine learning algorithms to model the relationship between these features and the corresponding crops, ultimately providing recommendations tailored to specific environmental inputs

Technologies Used

Python: Programming language used for model development, data preprocessing, and web application development. Scikit-learn: Machine learning library used for model training, evaluation, and prediction. Pandas: Data manipulation library used for data preprocessing and analysis. NumPy: Library for numerical computing used for handling arrays and mathematical operations. Flask: Web framework used for building the user interface and handling HTTP requests. HTML/CSS: Markup and styling languages used for designing the web interface. JavaScript: Scripting language used for client-side interactions and enhancing the user interface.