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Portfolio-2022

Financial Portfolio Competition Application

MERN Stack Application (MongoDB, Express.js, React and Node.js) under Agile / Scrum Project Management

Link to the code repository

Features:

  • Users can log in as an existing user or register as a new user
  • Users can create a financial porfolio by:
    • buying shares of a specific company with real-time stock value, using Yahoo Finance API
    • selling shares of current holdings with the updated real-time stock values
  • Users can earn points based on the performance of their financial portfolios, calucated by the Return on Investment values from purchasing/selling stocks
  • Users can see their ranks with respect to other existing users' on the leaderboard

Calendar Web Application

JavaScript & LAMP Stack Project (Linux, Apache HTTP Server, MySQL, PHP)

Link to the code repository

Features:

  • Users can log in and add/delete/modify events on the calendar
  • Simple to-do feature is embedded to the application
  • The project adheres to FIEO principle (Filter Input, Escape Output)
  • Passwords are hashed and salted before being stored in the database
  • The application is safe from SQL injection attacks

Movie Trailer Browser

MERN Stack Project (MongoDB, Express.js, React.js, Node.js)

Link to the code repository

Features:

  • The application is powered by The Movie Database (TMDB) external APIs
  • User is able to save and unsave movies as favorites
  • Upon clicking on the movie poster, the application displays the summary of the movie, runtime, and movie trailer ready to be played on embedded YouTube player
  • Log in feature using JWT token to be added soon

Instruction

  1. Clone this repository
  2. Open terminal window and change the directory to the project root folder

cd Portfolio-2022/Movie-Trailer-Browser(MERN)

  1. Run npm install to install dependencies

npm install

  1. Run npm start to start the React development server

npm start

  1. Open a new terminal window and change directory to backend/ subfolder of the project

cd Portfolio-2022/Movie-Trailer-Browser(MERN)/backend

  1. Run npm install to install dependencies

npm install

  1. Run node app.js to start the web server

node app.js

  1. Open http://localhost:3000 in the browser to use the application

Housemate Chores Application

Swift (iOS) Project Code Sample

Link to the code repository

This is a code snippet from an iOS application project. The code achieves the following:

  • Import iOS Calendar library made by WenchaoD (https://github.com/WenchaoD/FSCalendar)
  • Clicking on a specific cell of the calendar allows the users to create and delete events
  • Connect with Firebase Realtime Database for data persistence
  • Display all events created by a fellow group of housemates (and ignore events made by users that are not housemates)

Chat Server

Node.js and Socket.IO Project

Link to the code repository

Features:

  • Users can create chat rooms with an arbitrary room name
  • Users can join an arbitrary chat room
  • The chat room displays all users currently in the room
  • A password-protected private room can be created
  • Creators of chat rooms can temporarily kick others out of the room
  • Creators of chat rooms can permanently ban users from joining that particular room
  • Users can send private messages to another user in the same room
  • A user is able to block another user from sending a message

Instruction:

  1. Clone this repository
  2. Open terminal window and change the directory to the project root folder

cd Portfolio-2022/Chat-Server(Node.js)

  1. Run npm install to install dependencies

npm install

  1. Run node chat-server.js to start the web server

node chat-server.js

  1. Open http://localhost:3456 in the browser to use the application

Text Analysis of Jane Austen and Louisa May Alcott novels

Python Data Analysis Project

Link to the code repository

  • The code analyzes five novels by Jane Austen and five novels by Louisa May Alcott and run k-means algorithm on two different feature sets: "positive-sentiment" words and stopwords. Using these two feature sets resulted in classifying the corpus to the two authors with high accuracy. Refer to the the Jupyter Notebook file (FINAL_CODE.ipynb) for the code and detailed analysis.

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