Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.
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Updated
Jan 19, 2024 - HTML
Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.
This repository contains code for the Recommendation system to find restaurants. An End to End Project developed using Flask and python. The website is hosted on Heroku.
Search engine for the Lex Fridman Podcast 🎤
Book Recommendation System- A Web app made using flask framework to recommend your favorite book using content based filtering and cosine similarity metrices.
An Application to check plagiarism in Hindi
Determining similarity between two sentences in terms of semantic using pre trained Universal Sentence Encoder from TensorFlow.js
Design and compare the performances of Information Retrieval Models of TF-IDF, Cosine Similarity, BM25. Implemented query expansion using psuedo relevance feedback to display better results.
Recommend movies to users using Sentiment Analysis on Text data
A lightweight demo app which recommends similar jobs. Combines unsupervised ML + IR.
using feature maximisation for summarizing scientifc documents
A web application which recommends movies to the user.
📝 Exploratory analysis on Linkin Park lyrics
Recommendation of similar images to the given image using ResNet50, K-Means and cosine similarity.
This tool analyzes input text and suggests improvements based on semantic similarity to a list of standard phrases. It provides both a command-line interface (CLI) and a simple web-based user interface (UI).
Portfolio Project.ipynb and Recommendation.py are the finalized Jupiter notebook scripts for this project. Other files are a work in progress to migrate into a web app.
Popularity based Recommendation System, Content Based Recommendation System, Cosine Similarity
Welcome to the "Book Recommender System" project! This collaborative-based filtering model uses cosine similarity to recommend books. It's not just a recommendation system; it's your personalized book guide.
The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.
EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.
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