Stock Price Prediction

Abstract

1. Introduction

Predicting stock price using historical data of a company, using Neural networks (LSTM).

This project includes python programs to show Keras LSTM can be used to predict future stock prices for a company using it's historical stock price data. You can also find sample programs on how to fine tune Hyperprameters of LSTM (or any Neural Network for that matter).

Here is the final result : (although performance could be further improved to some extent by playing around with the parameters even more)

2. Objective

Predicting stock price using historical data of a company, using Neural networks (LSTM).

This project demonstrates how Python 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
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/nayash/Stock-Price-Prediction.git
cd Stock-Price-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 nayash and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.