This is a combined archive which houses all the submodules of project.
v3.0: Develop multi-compare and charts.
v3.1: Predict investability score (95% similarity with Zachs rank).
v2.1: Decided to use regression boosting (XGboost) for predicting company score.
v2.0: Decided to use GraphQl for backend services to limit endpoints.
v1.2: Develop web scraping tool for Extracting and transforming data.
v1.2: Use firebase NoSQL to store the model data.
v1.1: Decided on financial metrics to use. EBIDTA, ROI, Magic number, Customer Acquision Cost ...
v1.0: Build user interface on React and viteJs.
In a overview of the investability score prediction
Except for the ReChart and Tailwind CSS library for frontend most functionality is written in Vanilla-Js and React-Js. Python toolkit is created using beautiful soup and sklearn library.
Some icon images have been sourced from Font Awesome 5. The license is at this link. No changes (except scaling) were made to these images.
Links to original repositories are
Each submodule has its own defined technology specification as a README.md file.
First of all, thanks for your contribution! Every small bit of it counts! You can:
To setup the user interface repo, clone it, cd
into it, and then run npm install
. To start the server npm run dev
.
To setup the data-scraping repo. Create a firebase account. Clone the repo, cd
into it, and then run webscrape.py
.
Contact us - krishanu21saini@gmail.com - to discuss anything related to the above if you want to.
Out team: fluorspar20, akashg3627, pineapple45, neelanshu2001, gjain-7, Krish2208, COOLMudi, MihirK1212, Niyati