Waikato Environment for Knowledge Analysis (WEKA) is a cutting-edge software tool that has been developed for data mining and ML. We applied it for the analysis of metabolite data, where it enables efficient processing and analysis of large datasets. WEKA was utilized to perform classification tasks on a dataset, using different classification algorithms. These metabolites were initially labeled as "selected" or "random" based on their source. The InfoGainAttributeEval application in WEKA was then used to evaluate the relevance of each attribute to the class label. This process effectively eliminated any unnecessary data, resulting in the selection of only significant attributes. Furthermore, WEKA's preprocessing algorithms proved invaluable in filtering out unwanted instances through its attribute filter function. This ensured that the final dataset used for classification was optimized and devoid of any redundant information.