5 core steps to building a successful model

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