End-To-End Machine Learning Workflow on Chronic Kidney Disease Dataset

The contemporary landscape of technology is abuzz with the terms “Artificial Intelligence” and “Machine Learning.“ However, for many, the process of developing and deploying end-to-end machine learning applications can be quite daunting. In this paper, our objective is to elucidate the step-by-step procedure for creating a comprehensive machine learning workflow. To achieve this, we have chosen to work with the chronic kidney disease dataset, a publicly available open-source dataset. Our intention is to provide a clear framework that can serve as a valuable resource for students, academics, and practitioners, allowing them to grasp the essence of a machine learning project's workflow. Our approach encompasses a meticulous and detailed procedure, aiming to demystify the complex steps involved in building machine learning applications. Within the realm of machine learning, there is an abundance of algorithms, each designed for specific applications. Selecting the most suitable algorithm for your specific purpose is a challenge. In our paper, we apply widely used algorithms that can be adapted to a broad range of use cases. This approach is designed to provide insight into the potential enhancements achievable through parameter tuning and fine-tuning, tailored to meet specific needs.