Optimizing Crop Production for Commercial Markets: A Machine Learning Approach

This study aims to analyze the impact of various environmental and agricultural factors on crop yields using multiple regression and support vector machine (SVM) techniques. The research focuses on key variables such as rainfall, temperature, humidity, soil type, and area under cultivation. By examining the interplay between these factors, we seek to develop predictive models that can aid in optimizing crop management practices. Data was collected from various agricultural regions in the state of Karnataka, and statistical analysis was performed to identify significant correlations among the variables. The findings suggest that specific combinations of these variables significantly influence crop yields, providing valuable insights for farmers and policymakers. This study highlights the importance of integrating environmental data with advanced analytical methods to enhance agricultural productivity and sustainability. The dataset underwent preprocessing through onehotencoder and standardization of numerical features, which guarantees the robustness and reliability of the developed models. Overall, the research contributes to a deeper understanding of the factors affecting crop yields and provides forward-looking predictions for future agricultural practices.