A utility library for comparing strings via Cosine Similarity
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Updated
May 31, 2024 - C#
A utility library for comparing strings via Cosine Similarity
This repository houses a comprehensive Machine Learning project aimed at classifying Yelp reviews using Multinomial Naive Bayes and Natural Language Processing (NLP) techniques.
An internet search engine written mostly in python. Currently TF-IDF based.
Apply ensemble technique of model stacking to predict patient's readmission
Slides, exercises, and exams for my course "Natural Language Processing" (École Pour l'Informatique et les Techniques Avancées, 2024)
This project involves developing a machine learning model to predict user preferences in chatbot conversations, using a dataset of head-to-head responses from various large language models. The goal is to enhance chatbot-human interactions by aligning chatbot responses more closely with human preferences.
A small, fast, local-first, searchable index for client side apps written in Typescript. Supports required, negated, and phrase queries.
An ML-based project designed to accurately classify email messages as either spam or ham (non-spam)
Simple chatbot (NLP ONLY without machine learning) using Levenshtein Distance + TF-IDF + Cosine Similiarity :D
Movie Recommender
Search anything, instantly
A custom search engine built with Rust. It parses HTML files and utilizes TF-IDF scoring to rank document relevance based on search queries. The project includes a Rust-based backend server and vanilla HTML/CSS for the web frontend.
Fast and fuzzy website search (TF-IDF). For HTML, and native-text assets.
AI Resume Screening is a tool that uses artificial intelligence to automate the process of resume screening and shortlisting. The tool uses natural language processing and machine learning algorithms to analyze resumes and classify them to the job roles based on the words in their resume.
This project aims to simplify and summarize scientific data , convert it to a audio format as a podcast , and create a power point presentation from the paper. This helps researchers, academics and students altogether.
Fuzzy string matching, grouping, and evaluation.
A structured collection of notes (mostly, on machine learning) and a Flask app for reading and searching them.
Explore NLP model evaluation on answer scores and tweet sentiment. Features preprocessing, BoW, TF-IDF, Word2Vec, and models like Linear Regression, Decision Tree, SVM, Logistic Regression, and Random Forest.
Fake news detection using TF-IDF vectorization and LinearSVC
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