Skip to content

Latest commit

 

History

History
25 lines (20 loc) · 2.62 KB

10_yelp_votes.md

File metadata and controls

25 lines (20 loc) · 2.62 KB
Raw

Class 10 Homework: Yelp Votes

This assignment uses a small subset of the data from Kaggle's Yelp Business Rating Prediction competition.

Description of the data:

  • yelp.json is the original format of the file. yelp.csv contains the same data, in a more convenient format. Both of the files are in this repo, so there is no need to download the data from the Kaggle website.
  • Each observation in this dataset is a review of a particular business by a particular user.
  • The "stars" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review.
  • The "cool" column is the number of "cool" votes this review received from other Yelp users. All reviews start with 0 "cool" votes, and there is no limit to how many "cool" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business.
  • The "useful" and "funny" columns are similar to the "cool" column.

Homework tasks:

  1. Read yelp.csv into a DataFrame.
    • Bonus: Ignore the yelp.csv file, and construct this DataFrame yourself from yelp.json. This involves reading the data into Python, decoding the JSON, converting it to a DataFrame, and adding individual columns for each of the vote types.
  2. Explore the relationship between each of the vote types (cool/useful/funny) and the number of stars.
  3. Define cool/useful/funny as the features, and stars as the response.
  4. Fit a linear regression model and interpret the coefficients. Do the coefficients make intuitive sense to you? Explore the Yelp website to see if you detect similar trends.
  5. Evaluate the model by splitting it into training and testing sets and computing the RMSE. Does the RMSE make intuitive sense to you?
  6. Try removing some of the features and see if the RMSE improves.
  7. Bonus: Think of some new features you could create from the existing data that might be predictive of the response. Figure out how to create those features in Pandas, add them to your model, and see if the RMSE improves.
  8. Bonus: Compare your best RMSE on the testing set with the RMSE for the "null model", which is the model that ignores all features and simply predicts the mean response value in the testing set.
  9. Bonus: Instead of treating this as a regression problem, treat it as a classification problem and see what testing accuracy you can achieve with KNN.
  10. Bonus: Figure out how to use linear regression for classification, and compare its classification accuracy with KNN's accuracy.