* use book-crossing dataset .
* data cleaning & preprocesssing done. (.ipynb & .py file). Do more analysis & preprocessing, if needed.
* develop a content based recommendation system verified with different similarity measures.
* this model should recommend similar books (input: a math book; output: some other similar math books).
* submit .ipynb file & other files(if needed, ex: pickle etc.) that is running properly.
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