jialin1210 Goto Github PK

6156-contact-microservice

cmu-multimodalsdk

CMU MultimodalSDK is a machine learning platform for development of advanced multimodal models as well as easily accessing and processing multimodal datasets.

digital-twins

Azure Digital Twins building scenario sample code

f22-simple-web-ui

Simple started Angular application for my Columbia Classes, Fall 2022.

f22-starter-db

Simple starter database project for F22 Columbia classes.

f22-starter-microservice

Simple Flask microservice for F22 Columbia classes.

investment-portfolio-optimization

find strategies that satisfy clients who want some level of returns and the minimum amount of risk and also clients who want the least amount of risk and the highest amount of returns.

neurobrain

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

nlp-yelp-reviews-sentiment-analysis

The general restaurant ratings in Yelp only provide people with an overall evaluation. It does not tell any detailed stories about actual customer experience for business owners as well as consumers. If a restaurant has a rating of four stars, consumers are likely to check it out online, but they cannot determine whether it is worthwhile to visit until they skim through the review section. According to the 2018 Online Reviews Survey, Yelp has the second highest percentage of consumers that check Yelp reviews before visiting a restaurant (ReviewTrackers, 2018). For business owners, online reviews not only help build business brands and reputation but also engage with potential and existing customers, which are critical to boost their businesses. Our goal is to provide insightful suggestions for users by applying sentiment analysis to mainly four perspectives of restaurants -- food quality, service, ambience, and price. Ultimately, our models should help people better understand why customers like or dislike a restaurant and what the difference of one restaurant is from the other in terms of these categories.

restaurant-management-system

Project 1 for W4111 Introduction to Databases at Columbia University in Spring 2022

rottenpotatoes-rails-intro

text-complexity-classification

The goal of our project is to build a classifier that can identify text complexity with three readability levels for people who feel hard to understand to read texts in English to some extent, especially language learners. We also tested the hypothesis that clustering texts based on their topics can improve the performance on the text complexity classification because of the pertinence.

text-to-art-generation

warehouse-layout-visualization

AB InBev