https://github.com/ayushverma135/stockanalyzer-forecasting-and-visualization-of-stocks-using-python
This repository hosts a Dash web app for stock visualization and forecasting. It uses historical stock data from Yahoo Finance and an LSTM neural network for predicting future prices. Users input a stock symbol and date range, then select indicators for visualization.
https://github.com/ayushverma135/stockanalyzer-forecasting-and-visualization-of-stocks-using-python
dash flask lstm-model machine-learning python stock-price-prediction visualization
Last synced: 8 months ago
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This repository hosts a Dash web app for stock visualization and forecasting. It uses historical stock data from Yahoo Finance and an LSTM neural network for predicting future prices. Users input a stock symbol and date range, then select indicators for visualization.
- Host: GitHub
- URL: https://github.com/ayushverma135/stockanalyzer-forecasting-and-visualization-of-stocks-using-python
- Owner: Ayushverma135
- License: mpl-2.0
- Created: 2024-04-29T20:42:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-11T15:10:55.000Z (over 1 year ago)
- Last Synced: 2025-01-01T01:45:51.267Z (9 months ago)
- Topics: dash, flask, lstm-model, machine-learning, python, stock-price-prediction, visualization
- Language: Python
- Homepage:
- Size: 2.86 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# StockAnalyzer: Forecasting-and-Visualization-of-Stocks-using-Python
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.
## Project Structure
The project structure consists of the following files:
1. `app.py` - Main application file containing the Dash app instance and layout.
2. `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.
3. `assets/styles.css` - A file to style the webpage.
4. `requirements.txt` - A file containing all the dependencies required to run the application.
## Usage
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.
## Dependencies
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
```
## Conclusion
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.