Machine Learning Project: Simple Stock Price Prediction App

Welcome to the Simple Stock Price Prediction App machine learning project repository! This project focuses on predicting stock prices using machine learning techniques and providing a simple web-based application for users to interact with.

📋 Contents

• Introduction
• Why This Project
• Dataset
• Features
• Setup and Installation
• Demo
• Contributing
• Challenges Faced
• Lessons Learned
• License
• Contact

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📖 Introduction

This repository contains a machine learning project focused on predicting stock prices using historical data and providing a user-friendly web application for users to obtain predictions and insights.

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🎯 Why This Project

The primary motivation behind creating this project is to assist investors and traders in making informed decisions by predicting future stock prices based on historical trends and patterns.

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📊 Dataset

The dataset used for this project contains historical stock prices, volume, and other relevant financial indicators. It is crucial for training and evaluating the prediction models.

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🌟 Features

• Data Preprocessing: Cleaning and transforming financial data for model compatibility.
• Deployment: Developing a simple web-based application for users to input stock symbols and obtain predictions.

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🚀 Setup and Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Md-Emon-Hasan/1-Simple-Stock-Price-ML-App.git

2. Navigate to the project directory:

   cd 1-Simple-Stock-Price-ML-App

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the web application:

   python app.py

5. Open your web browser and go to http://localhost:5000 to interact with the app.

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🌐 Demo

Explore the live demo of the project here

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🤝 Contributing

Contributions to enhance or expand the project are welcome! Here's how you can contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/new-feature

3. Make your changes:

   • Implement new features, improve model performance, or enhance user interface.

4. Commit your changes:

   git commit -am 'Add a new feature or update'

5. Push to the branch:

   git push origin feature/new-feature

6. Submit a pull request.

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🛠️ Challenges Faced

During the development of this project, the following challenges were encountered:

• Handling financial data preprocessing and feature engineering.
• Developing an intuitive and responsive web application interface.

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📚 Lessons Learned

Key lessons learned from this project include:

• Importance of feature selection and engineering in financial prediction tasks.
• Evaluation and comparison of various regression models for stock price forecasting.
• Deployment and usability considerations for interactive web applications.

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📄 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

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📬 Contact

• Email: iconicemon01@gmail.com
• WhatsApp: +8801834363533
• GitHub: Md-Emon-Hasan
• LinkedIn: Md Emon Hasan
• Facebook: Md Emon Hasan

Feel free to reach out for any questions or feedback regarding the project!

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