Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis

Stock Market Prediction Web App Using Machine Learning and Sentiment Analysis

The Stock Market Prediction Web App is an innovative application designed to forecast future stock prices using a combination of machine learning and sentiment analysis. Built by leveraging technologies such as Flask and WordPress for the frontend, this app is capable of predicting stock trends on exchanges like NASDAQ and NSE.

System Functionality

This application boasts a comprehensive set of features for both typical users and admins:

User Capabilities:

• Registration and Login: Users can create an account and log into the system.
• Real-time Stock Prices: Access to current stock valuations ensures that users can always be aware of the latest market conditions.
• News Updates: Users can read the latest news about various stocks, aiding in informed decision-making.
• Currency Converter: Directly convert currency values within the app.
• Profile Management: Options to edit or delete their own profiles.
• Stock Education: Tools and resources to improve users' understanding of the stock market.
• Stock Prediction: Predict stock prices for the next seven days for any stock on NASDAQ and NSE.
• List of Stock Tickers: Users can download a comprehensive list of stock tickers.

Admin Capabilities:

• User Management: Admins have enhanced permissions, allowing them to create, retrieve, update, and delete user accounts.
• Email Management: Admins can manually send emails to users.
• The admin can also access all functionalities available to regular users.

How It Works

The app uses three primary algorithms for making stock predictions:

• ARIMA (AutoRegressive Integrated Moving Average)
• LSTM (Long Short-Term Memory networks)
• Linear Regression

These algorithms, combined with sentiment analysis of tweets, enable the app to recommend whether stock prices are likely to rise or fall.

Technologies Used

The project employs a rich set of technologies for development:

• Python and Django for server-side functionality.
• JavaScript, Node.js, and React for client-side interactions.
• HTML, CSS, and Bootstrap for layout and design.
• jQuery for enhanced browser interactions.
• WordPress as part of the web app framework.
• Machine learning libraries like Keras, NumPy, and Pandas to enable complex computations and data analysis.

Installation Guide

To get started with this app, follow these steps:

1. Install XAMPP server to manage databases locally.
2. Download the WordPress zip folder linked in the installation section.
3. Extract the folder into the htdocs directory of XAMPP.
4. Configure the wp-config.php with your PHPMyAdmin credentials.
5. Create a new database named wordpress and import the SQL file to set up the database structure.
6. Clone the GitHub repository and install required Python libraries using pip install -r requirements.txt.
7. Start the application server with python main.py.
8. Access the application through localhost/wordpress.

Author

This project was developed by Kaushik Jadhav. You can find more about his work through various platforms:

• GitHub: Kaushik Jadhav
• Medium: Kaushik Jadhav

Community and Contribution

Future contributors can find the project's source code and propose new features or identify bugs using the issue tracker available on its GitHub repository. The community engagement is vital, given the ever-evolving nature of stock trading apps.

By merging state-of-the-art technology with financial prediction, the Stock Market Prediction Web App provides a powerful tool for traders and everyday users looking to gain insights into the stock market's future behavior.