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https://github.com/md-emon-hasan/5-eda-sp500-stock-ml-apps
A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.
https://github.com/md-emon-hasan/5-eda-sp500-stock-ml-apps
finance financial-analysis machine-learning sp500 sp500-data-analysis stock-price-prediction time-series-analysis
Last synced: about 2 months ago
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A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/5-eda-sp500-stock-ml-apps
- Owner: Md-Emon-Hasan
- License: apache-2.0
- Created: 2024-01-23T07:36:23.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2024-08-03T11:42:13.000Z (5 months ago)
- Last Synced: 2024-08-03T12:46:57.804Z (5 months ago)
- Topics: finance, financial-analysis, machine-learning, sp500, sp500-data-analysis, stock-price-prediction, time-series-analysis
- Language: Python
- Homepage: https://five-eda-sp500-stock-ml-app.onrender.com/
- Size: 16.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Machine Learning Project: EDA S&P 500 Stock ML Apps
Welcome to the **EDA S&P 500 Stock ML Apps** machine learning project repository! This project focuses on performing exploratory data analysis (EDA) on S&P 500 stocks and building machine learning applications for stock price prediction.
## 📋 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 performing exploratory data analysis (EDA) on S&P 500 stocks and predicting stock prices using various machine learning techniques.
---
## 🎯 Why This Project
The primary motivation behind creating this project is to gain insights into the S&P 500 stock data through EDA and assist investors 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 of S&P 500 stocks. It is crucial for training and evaluating the prediction models.
---
## 🌟 Features
- **Data Preprocessing:** Cleaning and transforming financial data for model compatibility.
- **Exploratory Data Analysis (EDA):** Detailed analysis of S&P 500 stock data to uncover trends and patterns.
- **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/5-Eda-sp500-Stock-ML-Apps.git
```
2. Navigate to the project directory:
```bash
cd 5-Eda-sp500-Stock-ML-Apps
```
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://five-eda-sp500-stock-ml-app.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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