Machine learning models are essential instruments in modern data analytics, propelling progress in a multitude of fields. However, the effectiveness of these approaches depends on careful model selection and data preprocessing. Consequently, it is imperative to devise an enhanced model builder that facilitate data preprocessing and improve the creation of machine learning models. Understanding the critical role that machine learning models play in contemporary data analytics is essential to this effort. On the other hand, careful data preprocessing and wise model selection are necessary for these models to function effectively. Our study presents a solution that automates data preparation activities by addressing data irregularities such as null values, incorrect inputs, and redundant entries. The program then creates a variety of models on its own, increasing the adaptability and effectiveness of machine learning applications in order to solve these issues. This work has produced software that is a substantial development in terms of both speedy preprocessing and data cleansing, as well as its ability to construct several machine learning models. The program employs a strict assessment methodology to choose and maintain the most effective model by looking at a wide range of performance measures. Our software framework's versatility and effectiveness are demonstrated through experimental validation, allaying worries about inflexible code. Rather, the program is a flexible framework that can easily learn from and adjust to a variety of datasets. In conclusion, by presenting a novel software solution designed for effective data preprocessing and model building, this work makes a substantial contribution to the development of machine learning approaches. Our software can help advance machine learning research and applications by automating important operations and making the process of selecting the best models easier.
