Crop Yield Prediction: Leveraging Machine Learning for Sustainable Agriculture

: Accurate crop yield prediction is essential for ensuring food security and optimizing agricultural practices. Traditional methods often rely on historical data and expert knowledge, which may not always be sufficient. This research explores the application of machine learning techniques to predict crop yield based on various factors such as soil properties, weather conditions, and crop management practices. By analyzing historical agricultural data, this research study aims to develop robust predictive models. The research utilizes Random Forest, a abstract- powerful machine learning algorithm to extract patterns from the data and make accurate yield predictions. The results of this study can provide valuable insights to farmers, enabling them to make informed decisions about crop selection, resource allocation, and risk management. By integrating machine learning, this research contributes to the development of sustainable agriculture practices and improves food security.