Classification of books based on titles without prior knowledge of context or author

10 days 10 different practical applications of Deep Learning (primarily NLP) using Tensorflow and Keras

VIP Machine Learning Exercises and Practices

Parses apart a PDF file into separate documents and then uses Natural Language Processing, Machine Learning models, and statistics to rank the documents by similarity to a single document.

This repository is for Fake News Detection using Deep Learning models

( Scratch development ) Term Frequency Inverse Document Frequency is a vectorization technique used widely in Natural Language Processing. The vectorization effectively gives importance to rare words and important words.

This is a book analysis and recommendation system made in python and by using django framework, KNN, TF-IDF algorithm

Repository aimed at building a simple recommender system for a content based dataset. The textual information is analysed so as to utilised as a more concrete piece of information!

Movie recommender using movie reviews data from imdb

Compute the TF-IDF matrix from a collection of documents to measure the importance of words for text analysis and information retrieval tasks.

Recommender system from Yelp dataset

A model that predicts the sentiment for a given Amazon review. This is a Naive Bayes model that utilizes NLP for pre-processing.

Implementation of Machine Learning Algorithms with Different Dataset

Some NLP projects/assignments I've done in the past(Including beam search and tf-idf matrix from scratch)

This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.