- Exploratory Analysis
First, “get to know” the data. This step should be quick, efficient, and decisive.
- Data Cleaning
Then, clean your data to avoid many common pitfalls. Better data beats fancier algorithms.
- Feature Engineering
Next, help your algorithms “focus” on what’s important by creating new features.
- Algorithm Selection
Choose the best, most appropriate algorithms without wasting your time.
- Model Training
Finally, train your models. This step is pretty formulaic once you’ve done the first 4.