Data Intelligence, Business Analytics
By Ian Morton. Ian worked in credit risk for big banks for a number of years. He learnt about how to (and how not to) build “good” statistical models in the form of scorecards using the SAS Language.
Read original post and similar articles here. I thing Ian's list below is a good starting point. I would add a few steps such as deployment, maintenance at the end, and gathering requirements, understanding goal and success metrics at the top.
2. What is the outcome ? is it yes / no ? is it continuous ?
3. Decide upon the model required (logistic ! for yes / no outcome)
5. summary statistics to understand the distribution of the continuous variables
6. Ask questions about data quality:
7. Convert continuous variables into categorical variables
8. Check for multi-colinearity / correlation between variables (variance inflation factors), or correlation tests
9. Check for interactions
10. Choose type of logistic approach (e.g. forward, backward, stepwise)
11. Choose the baseline attribute for each categorical variable
12. Create a random variable – mustn’t step into the model - something is wrong if it does step into the model
13. Split the dataset into two parts (ratio 80%/20%)
15. Do diagnostic checks and plots of the fit (e.g. Somers D, residuals etc., etc.)
16. Put all variables from the test dataset (including interactions and the random variable) into a new model and run it
17. Back to the start, fine tune the grouping of the data, put variables in or take variables out.