G5 Data Challenge
This coding exercise should be performed in R or python. You are free to use the internet and any libraries or packages. Please save your work in an RMarkdown file or Jupyter notebook. When finished please email it to us for review at
After our review please be prepared to present your findings in a zoom interview and discuss your methods and results.
This should take no more than 2-3 hours.
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In this fictitious scenario, G5 has noticed that employees with good cats have been happier and produce better work. The company has tasked you to find out what attributes an optimal cat has
and if To do 1.
2. 3. 4. 5. 6. 7.
we can predict if a cat will be a good cat or not.
this, please follow these general guidelines and feel free to expand on the steps below:
Read the file and display columns.
Examine the data and state your observations.
Select columns that might be important to predict y.
If you removed columns, explain why you removed those.
Do any feature engineering that you feel is needed and explain why.
Build a machine learning model to predict good_cat.
Describe hyper-parameters in your model and how you would change them to improve the performance of the model.
How would you interpret your model? Can you give an example on a single row?
Any questions/clarifications can be sent to
Data Dictionary
You have collected the following data based on a survey to people with cats at G5. Note: This is a synthetic dataset.
Column Name
Description
Target variable. 1 = good cat
0 = not good cat
Age of the cat
Sex of the cat
Whether or not the cat is declawed
litter_box
Whether the cat is litter box trained or not
playfullness_index
Rating of how playful the cat is from most playful to least:
1. Super Ultra Playful
2. Super Playful
3. Pretty Playful
4. Playful
5. Not Playful
hiss_ferocity
Level of hissiness the cat displays
fur_length
Length of the cats coat
night_time_singing_ability
How well the cat sings at night
fluffiness
How fluffy the cat’s coat is
How soft the cat¡¯s fur is
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