https://github.com/chrnthnkmutt/mth234_lineartransformation

This repository is being used as the final project of the course MTH234: Linear Algebra, with having Asst.Prof. Songpon Sriwongsa
https://github.com/chrnthnkmutt/mth234_lineartransformation

Last synced: 6 days ago
JSON representation

This repository is being used as the final project of the course MTH234: Linear Algebra, with having Asst.Prof. Songpon Sriwongsa

• Host: GitHub
• URL: https://github.com/chrnthnkmutt/mth234_lineartransformation
• Owner: chrnthnkmutt
• License: mit
• Created: 2023-12-16T07:14:50.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2023-12-18T13:31:10.000Z (11 months ago)
• Last Synced: 2023-12-19T08:59:00.087Z (11 months ago)
• Language: Jupyter Notebook
• Size: 2.27 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# The Jupyter Repository for Linear Transformation Practices from Market Returns

![](banner.png)

## Introduction

This repository is being used for visualizing and calculating on Linear Regression with specifically determined on the part of using Eigendecomposition (both Eigenvalues and Eigenvectors) for calculating and visualizing the correlation, variances, covariances, and eigenvectors of correlation-distributed graph. In terms of making this repository, I needed to have some preliminaries or pre-requisited knowledge for contribute on this final project repository for MTH234: Linear Algebra course, which conducted by Assistant Professor Ph.D. Songpon Sriwongsa from Department of Mathematics, King Mongkut's University of Technology Thonburi (KMUTT).

## Requirements

This repository is used in Python 3.11.5 in Jupyter Notebook framework with some significant libraries or packages that we need to additionally install in the code editor/PATH environment, which are:

1. Pandas

2. Matplotlib

3. Sklearn (Scikit Learn)

4. Seaborn

5. Numpy

## What's inside the repository

In this repository, we use one sample dataset from UCI (UC Irvine Machine Learning Repository) to create the correlation/covariance graph and other types of graphs for showing the distribution of the stock market returns values between two main stocks, which is DAX and FTSE100, by using Jupyter Notebook, with Python 3.11.5 and installing some library packages as listed above.

## References

* Akbilgic, Oguz. (2013). ISTANBUL STOCK EXCHANGE. UCI Machine Learning Repository. [Click Here](https://archive-beta.ics.uci.edu/ml/datasets/istanbul+stock+exchange)

## Acknowledgement

I would like to send the gratitude to following advisors who take care or giving an advice for making repository, which are:

- Asst. Prof. Ph.D. Songpon Sriwongsa

- Ph.D. Sansiri Tarnpradab

- Asst. Prof. Ph.D. Stanislas Grare

- Podsawee Wannatham