Showing results for web Product Vector
GitHub Repo
https://github.com/usamahklair/ZyntraChain-Web3-UI-Library
usamahklair/ZyntraChain-Web3-UI-Library
An open-source Web3 library of vector-based UI components for blockchain and Web3 applications, designed to help developers and designers build consistent, user-friendly decentralized products for the community.
GitHub Repo
https://github.com/binoydutt/Resume-Job-Description-Matching
binoydutt/Resume-Job-Description-Matching
The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score
GitHub Repo
https://github.com/plastic-plant/vectorshop
plastic-plant/vectorshop
Generate a test web shop with household product items, images, descriptions and user reviews. Example with vector embeddings in Typesense for semantic search.
GitHub Repo
https://github.com/sanchezmarcelo/Vector-Operations-Tutorial-Website
sanchezmarcelo/Vector-Operations-Tutorial-Website
CSCI 346: Computer Graphics Vector Operations Tutorials inclusive of: Cross Product, Dot Product, Point - Point Subtraction, Vector Normalization, Vector Addition, Vector Subtraction, and Scalar - Vector Multiplication.
GitHub Repo
https://github.com/aditya-jha-kumar/Vector-Ecommerce-website
aditya-jha-kumar/Vector-Ecommerce-website
Vector is an E-commerce website with a user-friendly interface for customers to browse products, add items to their cart, and save items for later. Developed responsive front end using JavaScript, HTML, and CSS
GitHub Repo
https://github.com/dylsub/vectorscalc
dylsub/vectorscalc
A vector web calculator web application that can solve dot product, cross product, and magnitude
GitHub Repo
https://github.com/abhijithllai/ReviewSleuth-Fake-Product-Review-Detector
abhijithllai/ReviewSleuth-Fake-Product-Review-Detector
ReviewSleuth is a machine learning-powered web app designed to detect whether a product review is genuine or fake. Built with a Logistic Regression model and TF-IDF vectorizer, the app provides a simple and clean UI for users to paste a review and instantly check its authenticity.
GitHub Repo
https://github.com/valkey-io/valkey-bundle-demo
valkey-io/valkey-bundle-demo
Demo of a modern, AI-powered e-commerce web app running locally. It showcases Valkey-bundle's Vector Search as a high-performance, multi-modal backend, allowing users to receive personalized product recommendations through a hybrid search combining keyword filtering and vector similarity.
GitHub Repo
https://github.com/sahidesu25/Sentiment-Analysis-on-Amazon-Product-Reviews
sahidesu25/Sentiment-Analysis-on-Amazon-Product-Reviews
With the explosion of social networking sites, blogs and review sites a lot of information is available on the web. This information contains emotions and opinions about various product features and the makers of these products. This form of opinion and feedback is important to the companies developing these products as well as the companies that want to develop better rival products. Sentiment Analysis is the task of analyzing all this data, retrieving opinions about these products and services and classifying them as positive or negative, in other words good or bad. The key parts of any review of any product are the numeric rating and the textual description provided along with this product. In our project we will take into consideration both these vectors for product reviews to conclusively decide on a classifier that is best suited to analysis of product reviews. We have gathered reviews and based on the features that best describe the sentiment for each review, we have created a feature set of 1000 features, and with this limited set we will determine which classifier gives the best result on review type data. To determine the best classifier we perform evaluations on it, by running various data set generators, calculating the resubstitution and generalization errors for each classifier. We then use the mean of these results to compute the paired Student’s t-test to relatively compare the performance of the classifiers. Based on the results of this evaluation, we can state which is the best classifier.
GitHub Repo
https://github.com/harsh2k1/Web-Scraping-and-Emotion-Mining-on-Amazon-product-review