Predict how long a dog will be in the shelter before adoption time_in_shelter_days

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Problem: Predict how long a dog will be in the shelter before adoption (time_in_shelter_days).

Requirements: Complete the following steps, showing your approach in a slideshow (pptx).  You do not need to complete the steps separately or necessarily in this order… just do what makes sense.  Combining steps is totally fine… even encouraged.

1.       Begin with the ‘aac_intakes_outcomes_MODIFIED’ dataset available on HuskyCT. 

2.       Import the data into a permanent library in SAS 9.4. (You should have 74,660 observations and 19 variables) 

3.       Filter the dataset to include only dogs and only animals who were eventually adopted.  Also remove any outliers where ‘time_in_shelter_days’ is greater than 200.

a.       Adopted defined as an outcome type of either Adoption Return to Owner, or Rto-Adopt.(You should have 33,647 records) 

4.       Determine the baseline prediction. 

5.       Create some new variables to model on…

a.       Topbreed is any record with a breed that contains the words ‘LAB’, ‘BULLDOG’, ‘TERRIER’, ‘SHEPHERD’, or ‘BEAGLE’.  Set to 1 if yes, 0 if no.  Careful, case sensitive!

b.      OPTIMAL_COND_N is a numerical representation of OPTIMAL_COND that just has a 1 for ‘Y’ and 0 for ‘N’.

6.       Use Proc Reg to generate the following output:

(USE THIS TO ENSURE WE’RE ON THE SAME PAGE!)

Using only the variables that are eligible based on p-value (Pr > |t|) update the model as necessary before finalizing.  

7.       Use a Proc Score to apply the data to the dataset. 

(The mean of your prediction should be 13.40)

 8.       Calculate the MAD for model predictions and baseline.

 9.       Assess the performance of your model against baseline.

 10.   How would things have changed if we considered any time_in_shelter_days greater than 50 to be outliers, and filtered them out in step 3? Should we have done that?

Attachments:

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