**A playbook for data science projects.** This playbook is an evolution of the original checklist found in [Hands-on Machine Learning by Aurelien Géron](https://github.com/ageron/handson-ml3), possibly the best Machine Learning book out there. When working through the playbook, check for [antipatterns and challenges](AAn%20Overview.md) in your ML setup. Important anti-patterns and pitfalls will be linked or mentioned in the playbook. # Data Science Playbook - [[1. Validate the objective]]. - [[2. Frame the problem]] by looking at the big picture. - [[3. Plan the analysis]]. - [[4. Get the data]]. - [[5. Explore the data]] to gain insights. - [[6. Prepare the data]] to better expose the underlying data patterns to machine learning algorithms. - [[7. Explore different models]] and shortlist the best ones. - [[8. Tune your models]] and combine them into a great solution. - [[9. Present your solution]]. - [10. Reflect on the results](10.%20Reflect%20on%20the%20results.md) to learn and improve. - [[11. Launch, monitor, and maintain]] your system.