In any ML endeavor, the quality of data is the key to the success of the modeling. Data collection involves sourcing, gathering, and recording from various origins, such as observational studies, controlled experiments, publications, and databases. The collected data should be pertinent to the problem, accurate, and suitable for developing ML models. Along with data collection, one needs to document the sources, methods, and potential biases associated with the data to ensure transparency and reproducibility.