Stock Price Prediction

Abstract

1. Introduction

Investment firms, hedge funds and even individuals have been using financial models to understand market behaviour better and make profitable investments and trades. A wealth of information is available in the form of historical stock prices and company performance data, suitable for machine learning algorithms to process.

Can we predict stock prices with machine learning? Investors make educated guesses by analyzing data. They will read the news, study the company history, industry trends, and other data points that go into making a prediction. The prevailing theories are that stock prices are totally random and unpredictable, raising the question of why top firms like Morgan Stanley and Citigroup hire quantitative analysts to build predictive models. We have this idea of the trading floor being filled with adrenaline infuse men with loose ties running around yelling something into a phone. However, these days we are more likely to see rows of machine learning experts quietly sitting in front of computer screens. About 70% of all orders on Wall Street are now placed by software. We are now living in the age of algorithms.

This project utilizes the ARIMA model for base predictions and then built a Deep Learning model to improve it further. Stock prices are predicted for Tech Giants like Apple, Google, Tesla, Microsoft and Amazon.

2. Objective

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.

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/amn-jain/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 amn-jain 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.