A predictive modeling machine learning project can be broken down into 6 top-level tasks

  1. Define Problem : Investigate and characterize the problem in order to better understand the goals of the project.
  2. Analyze Data : Use descriptive statistics and visualization to better understand the data you have available.
  3. Prepare Data : Use data transforms in order to better expose the structure of the prediction problem to modeling algorithms.
  4. Evaluate Algorithms : Design a test harness to evaluate a number of standard algorithms on the data and select the top few to investigate further.
  5. Improve Results : Use algorithm tuning and ensemble methods to get the most out of
    well-performing algorithms on your data.
  6. Present Results: Finalize the model, make predictions and present results.
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