This project aims to provide a simple yet effective tool for stock market investors to visualize company stock data and make predictions based on machine learning models. The web application is built using the Dash framework, a Python library for building web applications with interactive user interfaces.
The project structure consists of the following files:
app.py- Main application file containing the Dash app instance and layout.model.py- A helper file containing functions to fetch data from the yfinance library and to train and predict stock prices using a machine learning algorithm.assets/styles.css- A file to style the webpage.requirements.txt- A file containing all the dependencies required to run the application.
To use the application, simply run the app.py file in your Python environment and open a web browser to the specified address. The user will be presented with a single page interface, where they can enter a company stock code and a date range for which they would like to see the stock data plotted.
Once the user enters the stock code and date range, the application will fetch the relevant stock data using the yfinance library and plot the stock price data. Additionally, the user can select an option to view predicted stock prices based on a machine learning algorithm trained on the historical data.
The following dependencies are required to run the application:
- Dash
- yfinance
- pandas
- scikit-learn
- plotly
The dependencies can be installed by running the following command in your Python environment:
pip install -r requirements.txt
This project provides a simple yet powerful tool for investors to visualize and analyze stock data of a specific company using machine learning models. It is a great project for beginners to learn about web application development, data visualization, and machine learning in Python.