Stock Price Prediction Web App

Overview

This project aims to predict stock prices using machine learning techniques and provide visualizations for technical indicators. This web application is built with Streamlit and powered by a pretrained LSTM model to forecast stock prices for the next 30 days.

Technologies Used

• Python
• Streamlit
• TensorFlow
• Pandas
• NumPy
• Matplotlib

Features

• Historical stock price data visualization.
• LSTM model-based forecasting for the next 30 days.
• User-friendly interface.

Getting Started

Prerequisites

Make sure you have the following prerequisites installed:

• Python 3.9
• Required Python libraries (provided in requirements.txt)

Installation

1. Clone this repository to your local machine.

   git clone https://github.com/lanre-akinbo/stock-price-forecast-app.git
   cd stock-price-forecast-app

2. Install the required Python libraries.

   pip install -r requirements.txt

3. Run the Streamlit app.

   streamlit run app.py

Usage

• Open the app in your browser.
• Enter a valid stock ticker symbol.
• Choose a start date.
• Explore historical TSLA stock prices.
• View technical indicators.
• Check the forecasted prices for the next 30 days.
• Gain insights into potential price trends.

Demo

A live demo of this project is available here.

Project Structure

• app.py: The main Streamlit application.
• LSTM Model.ipynb: The training of the machine learning model.
• model.h5: The trained model.
• requirements.txt: List of Python libraries used in the project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Data provided by Yahoo Finance.
• Inspired by various deep learning tutorials and resources.