Predictive Modeling: Best practices and lessons learnt the hard way
阿新 • • 發佈：2018-12-28
- The best way to is to prepare each variable in a separate script at the level of your data and then merge with the main data set at the same level.
- Whether you choose to remove or keep a certain variable depends on the specific use case. For e.g., a product like rice might not exhibit a lot of seasonal variation compared to a beverage. Know your use case and study your EDA results to decide whether to keep or drop a variable.
- Sometimes, modeling might just require talking about the variables in terms of their interaction. To give an example, suppose I want to my find how my discount depth and execution affect the sales of my product in conjunction with each other. In this case, I would have to create combined variables for both Execution + Discount Depth and then run a regression model. This might just show me that the effect of a 20% discount combined with ‘High’ execution is the same as a 40% discount combined with ‘Low’ execution. Hence, I can unlock growth without increasing my trade spend.