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