Stock Price Prediction Using LSTM

Abstract

1. Introduction

In this project, we leverage historical stock market data to train an LSTM model. The model learns from past price patterns and trends, enabling it to predict future stock prices. The LSTM network is specifically designed to capture long-term dependencies and has proven to be effective in time series forecasting tasks.

Welcome to the Stock Market Prediction using LSTM project! This repository contains the code and resources for predicting stock market trends using Long Short-Term Memory (LSTM) neural networks. With the power of deep learning, we aim to forecast stock prices and make informed investment decisions.

We use a publicly available dataset containing historical stock prices of various companies. The dataset includes features like opening price, closing price, volume, etc. We preprocess the data, splitting it into training and testing sets, and perform any necessary data transformations.

2. Objective

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

This project demonstrates how Jupyter Notebook, Machine Learning, Matplotlib 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/034adarsh/Stock-Price-Prediction-Using-LSTM.git
cd Stock-Price-Prediction-Using-LSTM

1. Clone this repository: git clone https://github.com/034adarsh/Stock-Price-Prediction-Using-LSTM
2. No need to download any dataset, this project uses Yahoo finance library to directly fetch data, just write the correct company code.
3. Run the training code script in the notebook to train the LSTM model.
4. Run the predict code script in the notebook to make predictions on new data.

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 034adarsh 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.