Showing results for Feature Vector Vector Vector Vector Product Vector
GitHub Repo
https://github.com/bassem-elsodany/distance_metrics_in_vector_search
bassem-elsodany/distance_metrics_in_vector_search
Distance Metrics Detective Story – An interactive Jupyter notebook that explores when to use Cosine, Euclidean, Manhattan, Dot Product, and Hamming distances in vector search. Featuring hands‑on financial contracts dataset, visual comparisons, and a practical decision framework to help engineers select the right similarity measure
GitHub Repo
https://github.com/shahed-dev01/house-feature-vector-analysis
shahed-dev01/house-feature-vector-analysis
Demonstrates foundational linear algebra concepts (vectors, dot products, normalization) and their direct application to data preprocessing and feature analysis in Machine Learning.
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/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/Pratyush-IITBHU/Music-Recomendation-System
Pratyush-IITBHU/Music-Recomendation-System
Music recommendation system using Spotify datasets and feature vector dot product method.
GitHub Repo
https://github.com/mehatabnabi/Product-Optimization
mehatabnabi/Product-Optimization
Identifying the best price-feature-vector
GitHub Repo
https://github.com/templateprotection/BioHashing
templateprotection/BioHashing
Another biometric template protection scheme based on BioHashing, a feature-transformation technique involving projection of the biometric vector onto several random vectors via dot product and quantization.
GitHub Repo
https://github.com/ShahrukhS/latent-factor-model-recommender-system
ShahrukhS/latent-factor-model-recommender-system
Recommender system with factorized user ratings of products in user and item feature vectors
GitHub Repo
https://github.com/harshhrawte/Text-to-Product-Matching-via-Embeddings
harshhrawte/Text-to-Product-Matching-via-Embeddings
An semantic search system leveraging transformer-based sentence embeddings for fashion product retrieval. Implements a hybrid ranking mechanism combining dense vector similarity with lexical feature overlap for robust query-product alignment.
GitHub Repo
https://github.com/sumedh25k/Product_text_similarity