Implementation of the paper "Adapting Deep Network Features to Capture Psychological Representations"

文本相似度计算

Personalized Music Recommender using Word2vec and Gower Similarity.

Collaborative and Content based Recommendation System ( POC with additional business logic )

Similarity between bank customers and nearest neighbors

Recommendation systems: simple theory of collaborative filtering

Machine learning / Deep learning / Data analysis notebooks, tools and scripts for learning purpose.

An R script that calculates a similarity matrix for a list of protein sequences with the aid of Bleakley-Yamanishi Normalized Smith-Waterman Similarity Score.

Contains the implementation of the Apriori Algorithm on French Retail Store dataset and the conclusion and suggestions to increase the profits from analysis.

Natural language processing project to predict growth from emerging technologies from SEC 10K and YouTube data.

Using digital form of the actual scripts of the 'Star Trek' science fiction series to perform interesting NLP tasks and answering some questions on Topic Modelling, Character properties and the plot as a whole.

This JavaScript implementation detects the areas where two DNA/RNA/protein sequences are similar to each other. All symbols from UTF-8 are accepted by this algorithm.

A machine learning database that allows users to search for films of their choosing. Users have the ability to filter results by female-directed films, low-budget films, and foreign films.

Large scale sparse similarity calculation with tools for parallelisation and incorporation of hierarchical domain knowledge

Project for helping brother in finding duplicates in his photos directory.

Movie Recommender System leverages a content-based approach, suggesting films to users based on the attributes of movies they have previously enjoyed. By analyzing movie metadata such as genre, cast, director, keywords, etc., this project offers personalized recommendations aligned with users' cinematic tastes.

Movie Select - Discover Your Movie Mojo is a Streamlit app offering personalized movie recommendations based on user preferences. It uses a detailed dataset from IMDB and TMDB, allowing users to filter by recommendations, IMDB rating, release category, and revenue, all within an intuitive and visually appealing interface.

Data visualization project using graphs and similarity matrix