Popular GitHub Repositories for Satisfaction

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark

基于在线民宿 UGC 数据的意见挖掘项目，包含数据挖掘和NLP 相关的处理，负责数据采集、主题抽取、情感分析等任务。目的是克服用户打分和评论不一致，实时对在线民宿的满意度评测，包含在线评论采集和情感可视化分析。搭建了百度地图POI查询入口，可以进行自动化的批量查询 POI 信息的功能；构建了基于在线民宿语料的 LDA 自动主题聚类模型，利用主题中心词能找出对应的主题属性字典；以用户打分作为标注，然后 litNlp 自带的字符级 TextCNN 进行情感分析，将情感分类概率分布作为情感趋势，最后通过 POI 热力图的方式对不同地域的民宿满意度进行展示。软件版本请见链接。

NuCS is a Python constraint programming library for solving Constraint Satisfaction and Optimization Problems over finite domains

Developed BERT, LSTM, TFIDF, and Word2Vec models to analyze social media data, extracting service aspects and sentiments from a custom dataset. Provided actionable insights to telecom operators for customer satisfaction and competitive analysis.

Develop a personalized recommendation system using a Knowledge Graph to model relationships between users, products, and interactions. Utilizing Python, Neo4j, Cypher, and Py2neo, this project aims to enhance user satisfaction through efficient data management and advanced recommendation algorithms.

Distributed constraint satisfaction with recursive message-passing agents

Software di gestione degli incroci semaforici sfruttando tecnologie di intelligenza artificiale per il corso di ingegneria della conoscenza

In the context of Morocco (Darija), sentiment analysis can be an invaluable asset for businesses and organizations looking to better understand the satisfaction of their customers. We will fine-tune a model for Darija

A comprehensive ETL pipeline for analyzing passenger satisfaction data. Features a modern data architecture with Apache Airflow for extraction, dbt/Snowflake for transformation, Python/Pandas for cleaning, and interactive dashboards for visualization with NextJS.

This repository provides a LangChain-powered chatbot for customer support, enabling businesses to deliver efficient and personalized support experiences to their customers. Leverage the power of natural language processing (NLP) to handle customer inquiries, resolve issues promptly, and enhance overall customer satisfaction.

A comprehensive ETL pipeline for analyzing passenger satisfaction data. Features a modern data architecture with Apache Airflow for extraction, dbt/Snowflake for transformation, Python/Pandas for cleaning, and interactive dashboards for visualization with NextJS.

This project provides a complete solution for retail inventory forecasting using a robust ML model and an interactive frontend. It's designed to help reduce costs and improve customer satisfaction by ensuring the right products are in stock at the right time.

This repository includes the projects of the CSMM101X - Artificial Intelligence course offered by Columbia University.

This project fine-tunes large language models (LLMs) for text-based recommendations, using a novel prompt mechanism to improve accuracy and user satisfaction. It demonstrates efficient model adaptation with diverse datasets, leveraging advanced libraries and techniques for optimal performance.

Our AI-Based Product Review System uses NLP to analyze product reviews, offering users sentiment insights and personalized recommendations to enhance their shopping experience. For businesses, it provides valuable feedback and sentiment analysis to improve product offerings and customer satisfaction.

Using machine learning algorithms like support vector machines, artificial neural networks and random forest to predict airline customers satisfaction

Search, Knowledge, Uncertainty, Optimization, Learning, Neural Networks and Language.

EXPERIMENT: Multi-agent AI system powered by AutoGen that provides contextual AWS support through specialized researchers and solution providers. Features expert validation, structured solutions, and satisfaction tracking.