identifies Product Vector

GitHub Repo https://github.com/gh-tri/Multimodal-RAG-VertexAI-AstraDB

gh-tri/Multimodal-RAG-VertexAI-AstraDB

Identify coffee machine parts from an image and surface similar replacement products using multimodal embeddings, vector search, and Gemini.

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/Jayhepat/Implement-Vector-Quantized-Variational-Autoencoder-VQ-VAE-for-defect-detection-in-product-images

Jayhepat/Implement-Vector-Quantized-Variational-Autoencoder-VQ-VAE-for-defect-detection-in-product-images

This project uses a VQ-VAE model to detect defects in product images by learning to reconstruct only defect-free images. Anomalies are identified through high reconstruction errors, enabling unsupervised defect detection.

GitHub Repo https://github.com/mehatabnabi/Product-Optimization

mehatabnabi/Product-Optimization

Identifying the best price-feature-vector

GitHub Repo https://github.com/AmirhosseinHonardoust/Fake-Review-Detector

AmirhosseinHonardoust/Fake-Review-Detector

An AI-powered Fake Review Detector built with Python, Streamlit, and Scikit-learn. Uses TF-IDF vectorization, Logistic Regression, and behavioral text analytics (sentiment, exclamations, clichés) to identify synthetic or spammy product reviews. Includes training scripts and a full interactive dashboard.

GitHub Repo https://github.com/Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

Thaneshwar-sahu/Price-Feature_Vector_Analysis_for_Beverage_Mug_Product_Optimization

This project aims to optimize the price-feature vector for a beverage mug line, identifying the best combination of price and features to enhance market competitiveness. The goal is to determine which features and price points attract consumers, helping to create a product that balances consumer preferences with market trends.

GitHub Repo https://github.com/nadyanvl/sephora-products-recommender-system

nadyanvl/sephora-products-recommender-system

Personalized recommender system for Sephora's cosmetics e-commerce platform. Using content-based filtering, with TF-IDF Vectorizer to extract product features and cosine similarity to recommend similar items based on user preferences. And collaborative filtering with SVD for identifying user patterns and recommending highly-rated products.

GitHub Repo https://github.com/Vishwakarma10036/Implement-Vector-Quantized-Variational-Autoencoder-VQ-VAE-for-defect-detection-in-product-images.

Vishwakarma10036/Implement-Vector-Quantized-Variational-Autoencoder-VQ-VAE-for-defect-detection-in-product-images.

This project implements a Vector Quantized Variational Autoencoder (VQ-VAE) model for automated defect detection in product images, leveraging the power of unsupervised learning to identify anomalies in industrial and manufacturing workflows.

GitHub Repo https://github.com/rajnandiniis/Derma-Decision-Your-Personalized-Skincare-Guide

rajnandiniis/Derma-Decision-Your-Personalized-Skincare-Guide

Derma Decision is an AI-powered skincare recommendation system designed to analyze user data and provide personalized product suggestions. By leveraging cosine similarity and vectorization techniques, the system identifies the most suitable skincare products tailored to individual needs.

GitHub Repo https://github.com/byukan/Marketing-Data-Science

byukan/Marketing-Data-Science

Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).