Stock Exchange Prediction using Machine Learning

Video

Below is the video which describes our project's idea

Go through the wiki page containing project details and user manual.

Wiki

Test Plan

1. Go to TestPlan spreadsheet.
2. Assign keys to each user.
3. Record the data generated from the participant. Each participant will perform following activities.
   1. Analyse the project based on Ease of use, Accuracy, Cost, Range of choice.
   2. Compare current project with other tools available online (Wallet Investor, AIStockFinder)
   3. Report bugs(if found)
   4. Review the project on scale 1(Low) to 5(High).
4. Utilize the data and analyse the feedback received from each participant.
5. Generate aggregated rating for the entire project based on Ease of use, Accuracy, Cost, Range of choice.

Web App Screenshots

Technologies and Tools

Language: Python 3, HTML, CSS

Web Application Framework: Docker, Flask

Test Framework: pytest

Database: MySQL

Tools: Visual Studio Code

Syntax Checker & Style Checker: flake8, pylint

Code Formatter: black, prettier

Version Control: git

Installation Guide

Running the application using Docker (Recommended for testers)

$ cd projDir
$ docker build -t csc510/p2:latest .
$ docker run -p 5000:5000 csc510/p2:latest

You can now find the app at http://localhost:5000/auth/login.

Running the application using Flask (for development)

Insure that you have flask installed, then initialize the default flask db and run the app.

$ cd projDir
$ set FLASK_APP=flaskr
$ flask init-db
$ flask run

Application Overview

Database Schema