👉 The Project Inputs phase in machine learning involves defining and preparing the data and models necessary for a project. This includes gathering relevant datasets, preprocessing them to clean and format the data, selecting appropriate algorithms or models, and configuring the environment for training and evaluation. It also encompasses setting up infrastructure for model deployment and monitoring performance. The goal is to ensure that the project has all the necessary inputs to effectively train and validate models, ultimately leading to accurate predictions or insights.
