- I have a dataset from survey results of Genetic Testing that looks like below:
For more details sample csv file here:
I am trying to create a model to predict the column “actionspost” (the extreme right column of the above file). The column currently holds comma separated values like “2, 4, 6”. Each numerical value is a coded action that a patient would take after knowing the results of the genetic test as per the survey filled by the patient. For example, 2 indicates: Change eating habits, 4 indicates: Getting members of his/her family tested and so on. Based on the features in the csv file above, I want to create a model that would help me predict the “actionspost” values.
One way I thought, I could do this was break down comma separated values in “actionspost” to create duplicate rows for same customer Ids holding only one “actionspost” value at a time (For example: There would be 4 rows for customer ID: C00003 with first row having “actionspost” value of 2, second row with “actionspost” value 8, third with 9 and fourth with 10. Then create subset of the data set for each unique value of “actionspost”. This time, the dependent variable will not be actionspost but something like “isactionpost2?” (Yes =1 and No = 0). And train models for each of this subset for different values within the “actionspost” column.
I personally don’t like the above method as it too cumbersome and in my opinion, not the optimal one too. I was wondering if there could be a better way to address such scenario? My end goal is to try to train different models like Decision Trees, Naïve Bayes, and Neural Nets and check which one leads to better predictability.