Abstract
Machine learning and Artificial Intelligence are two fast-growing fields these days. Every day a new model is being invented, and we do not need to know all the models. Simultaneously, the programming skills required to create, train, and test a machine learning model are challenging. Moreover, time and human effort are needed when we don't know whether a particular model is accurate for an exact dataset. Hence, we understand all the models related to that dataset and program them and get the precise result. To address this, we created a generalized interactive web application where one can upload the dataset, drag-and-drop various models through UI, tweak the parameters as required, and train the machine learning model. The user has all the control over the training and testing process. The user can change iterations, manipulate the input dataset's shape, even Auto clean them, and select optimizers, loss functions, test size, etc. Classification/Regression auto-detect will reduce the overhead of generating classes. In the end, the graphical report of the complete train-evaluation-test and the generated Python code displayed on the result page. Moreover, the user will see the results of iterations, accuracy, loss, precision, recall, and F1 score. After looking at the result, the user can decide whether they want to use a particular model or not. If they do not get the expected result, they can use a different model and check the impact on the same dataset.