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HIMANK KALAL's Projects

book-dataset icon book-dataset

This dataset contains 207,572 books from the Amazon.com, Inc. marketplace.

book-recommendation-system-using-pyspark icon book-recommendation-system-using-pyspark

The book recommendation system is based on the Item based collaborative filtering technique. The script is written using pyspark on top of Spark's built in cluster manager. It is used to recommend similar books to each other based on the ratings and the strength of the ratings.

book_recommender-1 icon book_recommender-1

content-based book recommendation system using tf-idf/countvectorizer; rating prediction using deep learning

bookrecommendation icon bookrecommendation

A simple book recommendation system using Collaborative filtering and demographic information of user

books2rec icon books2rec

A recommender system built for book lovers.

collmetric icon collmetric

A Tensorflow implementation of Collaborative Metric Learning (CML)

earthengine-py-notebooks icon earthengine-py-notebooks

A collection of 300+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping

gutenberg icon gutenberg

A content-based recommender system for books using the Project Gutenberg text corpus

hybrid-weighted-embedding-recommender icon hybrid-weighted-embedding-recommender

A Hybrid Recommendation system which uses Content embeddings and augments them with collaborative features. Weighted Combination of embeddings enables solving cold start with fast training and serving

latentfactor-collaborativefiltering-re icon latentfactor-collaborativefiltering-re

This project contains Latent Factor based collaborative Filtering Recommendation Engine with optimized code which handles complexity problems with sparse matrices too.

machine-learning icon machine-learning

In this repository, I upload my Complete Machine Learning code which I have learned from different courses(Coursera, udemy, edx, udacity), different websites blogs, different tutorials from YouTube, books, and research papers. I have covered both Supervised and Unsupervised Machine Learning Algorithms with Practical Implementation.

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