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https://github.com/md-emon-hasan/1-simple-stock-price-ml-app
A simple mahcine learning application for stock prices, demonstrating data preprocessing, model training, and deployment using scikit-learn.
https://github.com/md-emon-hasan/1-simple-stock-price-ml-app
data-analysis data-science eda ml-app streamlit-webapp time-series time-series-analysis webapp
Last synced: about 2 months ago
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A simple mahcine learning application for stock prices, demonstrating data preprocessing, model training, and deployment using scikit-learn.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/1-simple-stock-price-ml-app
- Owner: Md-Emon-Hasan
- License: apache-2.0
- Created: 2024-01-22T16:45:09.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2024-08-03T11:25:21.000Z (5 months ago)
- Last Synced: 2024-08-03T12:35:56.997Z (5 months ago)
- Topics: data-analysis, data-science, eda, ml-app, streamlit-webapp, time-series, time-series-analysis, webapp
- Language: Python
- Homepage: https://one-simple-stock-price-web-apps.onrender.com
- Size: 13.7 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](#introduction)
- [Why This Project](#why-this-project)
- [Dataset](#dataset)
- [Features](#features)
- [Setup and Installation](#setup-and-installation)
- [Demo](#demo)
- [Contributing](#contributing)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [License](#license)
- [Contact](#contact)
---
## 📖 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.
---
## 🎯 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.
---
## 📊 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.
---
## 🌟 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.
---
## 🚀 Setup and Installation
To run this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/Md-Emon-Hasan/1-Simple-Stock-Price-ML-App.git
```
2. Navigate to the project directory:
```bash
cd 1-Simple-Stock-Price-ML-App
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the web application:
```bash
python app.py
```
5. Open your web browser and go to `http://localhost:5000` to interact with the app.
---
## 🌐 Demo
Explore the live demo of the project [here](https://one-simple-stock-price-web-apps.onrender.com)
---
## 🤝 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:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Implement new features, improve model performance, or enhance user interface.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 🛠️ 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.
---
## 📚 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.
---
## 📄 License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
Feel free to reach out for any questions or feedback regarding the project!
---
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