Stock Prediction

Abstract

1. Introduction

Stock price prediction with RNN. The data we used is from the Chinese stock.

I personally recommend you to use Anaconda to build your virtual environment. And the program probably cost a significant time if you are not using the GPU version Tensorflow.

2. Objective

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Kulbear/stock-prediction.git
cd stock-prediction

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Kulbear and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.