Crop Recommendation System

In the Crop Recommendation System project, the objective was to provide farmers with personalized crop recommendations based on soil characteristics. The exploration involved experimenting with various machine learning models such as Decision Tree, Random Forest, Logistic Regression, and SVM, training them on a carefully curated crop dataset that underwent meticulous preprocessing. The integration of these models into a user-friendly web application was achieved through Flask for the backend and Jinja templating for the frontend. The focal point of the system is a well-designed user interface, allowing farmers to input their soil characteristics through an intuitively crafted form.

This form enhances the user experience and incorporates input validation mechanisms to ensure data accuracy. The backend processes these inputs using the trained models, offering farmers real-time predictions on suitable crops for their specific soil conditions. The project aimed to showcase proficiency in machine learning and web development while contributing to the agricultural community by empowering farmers with data-driven decision-making tools.