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.