Jokes to recommend-Popularity based filtering

Introduction:

  • Each row may be a user (Row 1 = User #1)
  • Each column may be a joke (Column 1 = Joke #1)
  • Ratings are given as real values from -10.00 to +10.00
    99 corresponds to a null rating
  • As of May 2009, the jokes 7, 8, 13, 15, 16, 17, 18, 19 are the “gauge set”

Data Preprocessing

  • Add column headers
  • All other Joke rating columns would be renamed to 1–150
  • 0th column would be user_id
  • Some rows contain NaN values, replace them as 0
  • Many ratings are 99.0 such jokes were not rated by user, replace them as 0.

Recommend Popular Jokes

  • Find mean rating for all the jokes
  • Mean rating is an array that must be converted into Dataframe for sort into descending order
  • Recommend top n popular jokes
  1. https://www.analyticssteps.com/blogs/what-are-recommendation-systems-machine-learning
  2. https://medium.com/data-science-community-srm/recommendation-systems-in-machine-learning-2ec7909212a8#:~:text=Recommender%20systems%20are%20one%20of,algorithms%20in%20data%20science%20today.
  3. https://madasamy.medium.com/introduction-to-recommendation-systems-and-how-to-design-recommendation-system-that-resembling-the-9ac167e30e95
  4. https://www.kaggle.com/crawford/jester-online-joke-recommender

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