Book Recommendation System Project Machine Learning

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A recommendation system is one of the top applications of data science. Every consumer Internet company requires a recommendation system like Netflix, Youtube, a news feed, etc. What you want to show out of a huge range of items is a recommendation system.  recommendation engine is a class of machine learning which offers relevant suggestions to the customer.  Before the recommendation system, the major tendency to buy was to take a suggestion from friends. But Now Google knows what news you will read, Youtube knows what type of videos you will watch based on your search history, watch history, or purchase history.

Book Recommendation System Development Steps:

  1. Collect the data by scraping the web using beautifulsoup
  2. Encode the data using tensorflow-hub
  3. Build a nearest neighbor model using scikit-learn
  4. Make a flask web app to see recommendations
  5. Make a REST API using flask to get recommendations

Book Recommendation Methods:

  1. Euclidean distance.
  2. cosine similarity.


All the code is written in python 3.7 and the following packages are used:

  1. tensorflow-hub
  2. tensorflow-text
  3. scikit-learn
  4. numpy
  5. flask
  6. beautifulsoup4
  7. requests
  8. gunicorn

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