The exponential growth of Machine Learning as a domain has had a profound impact on all fields of society. Data analysis in the pre-machine-learning era relied on conventional and fundamental methods which were highly specialized domains, and often only restricted to subject-experts. Machine Learning, with its uncanny ability to figure out patterns and ‘learn’ intricate nuances of a dataset, has made it possible for everyone to observe, infer and perform meaningful operations on the dataset. From stock-prices forecasting to housing-price regression, the wings of Machine Learning have spread across various interdisciplinary fields. One such problem statement we will be analyzing in our project is Dry-Beans Classification, where given an extensive dataset of different types of Dry-Beans, our model would learn from the data given and then try to identify the class of dry-bean accurately from the given features.