Supervised machine learning model builder for regression and classification analysis featured as a web application

A web-based tool called the machine learning model builder is created to create different machine learning models. For the most part, these models use regression or classification to address practical issues. Systems that have the same code hardcoded across all of them tend to be static. Dynamic file acceptance and understanding of the necessary visualizations and results are used to resolve this issue. The paper discusses a machine learning model builder that can quickly generate code and do repetitive tasks. This tool aids in the creation of a rigorous model. Various extensions have been added to the system to process rich datasets for the development of intelligent models. The text also addresses regression and other classification models as the mathematical aspect becomes more involved. Support Vector Machine, K-Nearest Neighbor, along with various Classification algorithms including Decision Tree Classifier and Random Forest Classifier; Ensemble learning approaches such as the XG-Boost classifier are all explored in detail below. Users can create training and validation sets as well as test the models they have created. The Python-based framework Django and the foundations of machine learning were used in the architecture’s creation. With little effort, backend systems can create, install, monitor, and manage the infrastructure. To help consumers better understand machine learning and its capabilities as a web application, this paper takes a user-centered approach.