Summary
What are the do's and don'ts of a predictive modelling project? This chapter dealt with these pressing questions and listed a number of best practices to make a predictive modelling project successful. Following are the important points:
Codes should be well-commented, modular, version-controlled, generalized, and not have hard-coded values.
Data should be observed carefully after every import and manipulation in order to check for any errors that might creep in while performing these operations.
The choice of the algorithm is guided by the nature of the predictor and outcome variable. The ultimate selection of the algorithm depends upon whether the user prioritizes accuracy or the understandability of the algorithm.
While reporting the results of a predictive model, the most optimum value of the important statistics and their relevance should be clearly stated.
Main business questions should be clearly answered. Major finding should be reported clearly. Some actionable recommendations...
