The study will utilize machine learning algorithms such as Random Forest, Gradient Boosting, and Support Vector Regression (SVR) on data collected from the districts of Nalgonda, Yadadri Bhuvanagiri, and Suryapet over the past two years. The project seeks to meet the growing demand for crop yield prediction models that enhance agricultural productivity and enable informed decision-making by farmers. Model accuracy will be evaluated using metrics like mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). The dataset provided includes features such as soil nutrient levels, climate data, and crop yield, which will be preprocessed and subjected to feature selection. The study results are expected to contribute to the development of precise and efficient crop yield prediction models, supporting sustainable agricultural practices and empowering farmers with informed decisions regarding crop management, planting, harvesting, and overall farm management. The project's focus is to determine the best-performing algorithm and pave the way for enhanced agricultural productivity and decision-making in crop management.

Keywords - Machine Learning, Data Visualization, Predictive Modelling

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