STOCK-PRICE-PREDICTION-USING-STACKED-LSTM

Forecast Smarter, Invest Confidently, Lead the Future

Built with the tools and technologies:Python,Jupyter Notebook, Keras, TensorFlow, Matplotlib, Seaborn, Numpy, Pandas

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📄 Table of Contents

• Overview
• Getting Started
  • Prerequisites
  • Installation
• Features
• Project Structure
  • Project Index
• Contributing
• License

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✨ Overview

Stock-Price-Prediction-Using-Stacked-LSTM is an advanced deep learning project designed to forecast Google stock prices by leveraging stacked LSTM neural networks. It provides a comprehensive pipeline for data preprocessing, model training, and evaluation, enabling accurate time-series predictions for financial analysis and investment insights.

Why Stock-Price-Prediction-Using-Stacked-LSTM?

This project aims to deliver precise stock price forecasts through deep learning. The core features include:

• 📊 Stacked LSTM Architecture: Captures complex temporal dependencies in stock data for improved prediction accuracy.
• 🧠 End-to-End Notebook: Facilitates seamless data preprocessing, model training, and evaluation within a single environment.
• 🔍 Focused Financial Analysis: Designed specifically for stock market data, supporting informed decision-making.
• ⚙️ Modular Design: Easy to customize and extend for different stocks or additional features.
• 🚀 Open-Source & License-Approved: Encourages collaboration and adaptation within the developer community.

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📌 Features

	Component	Details
⚙️	Architecture	Stacked LSTM model for time series forecasting Sequential data processing pipeline Data preprocessing, model training, evaluation modules
🔩	Code Quality	Clear modular structure with separate scripts/notebooks Consistent coding style, comments, and docstrings Use of standard Python libraries (e.g., NumPy, Pandas, TensorFlow/Keras)
📄	Documentation	README with project overview, setup instructions, and usage Jupyter notebooks demonstrating data analysis and model training
🔌	Integrations	TensorFlow/Keras for deep learning Matplotlib/Seaborn for visualization Jupyter Notebook for interactive development
🧩	Modularity	Separate scripts for data preprocessing, model definition, training, and evaluation Reusable functions for data scaling and sequence generation
🧪	Testing	Limited explicit testing; primarily relies on notebook outputs Potential for unit tests on data processing functions
⚡️	Performance	Uses GPU acceleration via TensorFlow if available Model training with batch processing and early stopping
🛡️	Security	No explicit security features; typical for ML notebooks Potential improvements: input validation, environment isolation
📦	Dependencies	Python packages: license, markdown, jupyternotebook Deep learning: TensorFlow/Keras (implied)

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📁 Project Structure

└── Stock-Price-Prediction-Using-Stacked-LSTM/
    ├── GOOG.csv
    ├── LICENSE
    ├── README.md
    └── Stock_Price_Prediction_Using_Stacked_LSTM.ipynb

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📑 Project Index

STOCK-PRICE-PREDICTION-USING-STACKED-LSTM/

__root__

⦿ __root__

File Name	Summary
README.md	- Provides an overview of the Stock-Price-Prediction-Using-Stacked-LSTM project, emphasizing its goal of forecasting Google stock prices through advanced deep learning techniques - It highlights the architectures focus on leveraging stacked LSTM models to capture temporal dependencies, enabling accurate and robust predictions within the broader financial data analysis framework.
Stock_Price_Prediction_Using_Stacked_LSTM.ipynb	- Stock Price Prediction Using Stacked LSTMThis Jupyter Notebook serves as the core component for a stock price prediction project, leveraging a stacked LSTM (Long Short-Term Memory) neural network - Its primary purpose is to analyze historical stock data and generate future price forecasts - Within the overall architecture, this notebook orchestrates data preprocessing, model training, and evaluation, enabling accurate time-series predictions that can inform investment decisions or further analytical insights - It acts as the predictive engine of the project, transforming raw stock data into meaningful forecasts through deep learning techniques.
LICENSE	- Provides the licensing terms for the project, establishing legal permissions and restrictions for software use, distribution, and modification within the overall architecture - Ensures clarity on rights granted to users and contributors, supporting open-source collaboration and safeguarding intellectual property across the codebase.

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🚀 Getting Started

📋 Prerequisites

This project requires the following dependencies:

• Programming Language: JupyterNotebook, Python
• Data Source: https://www.marketwatch.com/investing/stock/goog/download-data
• Libraries and Dependencies Used: Numpy, Pandas, Seaborn, Matplotlib, Scikit-Learn, Tensorflow, Keras
• Deep Learning Techniques Used: Stacked LSTM, Fine Tuning with Keras Tuner, Calculating Evalaution Metrics (R2-Score, RMSE, MAE, MSE)

⚙️ Installation

Build Stock-Price-Prediction-Using-Stacked-LSTM from the source and install dependencies:

1. Clone the repository:

   ❯ git clone https://github.com/muhammadhussain-2009/Stock-Price-Prediction-Using-Stacked-LSTM

2. Navigate to the project directory:

   ❯ cd Stock-Price-Prediction-Using-Stacked-LSTM

3. Install the dependencies:

echo 'INSERT-INSTALL-COMMAND-HERE'

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🤝 Contributing

• 💬 Join the Discussions: Share your insights, provide feedback, or ask questions.
• 🐛 Report Issues: Submit bugs found or log feature requests for the Stock-Price-Prediction-Using-Stacked-LSTM project.
• 💡 Submit Pull Requests: Review open PRs, and submit your own PRs.

Contributing Guidelines

1. Fork the Repository: Start by forking the project repository to your github account.
2. Clone Locally: Clone the forked repository to your local machine using a git client.

   git clone https://github.com/muhammadhussain-2009/Stock-Price-Prediction-Using-Stacked-LSTM

3. Create a New Branch: Always work on a new branch, giving it a descriptive name.

   git checkout -b new-feature-x

4. Make Your Changes: Develop and test your changes locally.
5. Commit Your Changes: Commit with a clear message describing your updates.

   git commit -m 'Implemented new feature x.'

6. Push to github: Push the changes to your forked repository.

   git push origin new-feature-x

7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.
8. Review: Once your PR is reviewed and approved, it will be merged into the main branch. Congratulations on your contribution!

Contributor Graph

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📜 License

Stock-price-prediction-using-stacked-lstm is protected under the LICENSE License. For more details, refer to the LICENSE file.

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⬆ Return

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