https://github.com/md-emon-hasan/ml-project-amazon-big-mart-sales-prediction
🛒 Big Mart store sales using a trained machine learning model. Web forms for user input and displays sales predictions based on historical data.
https://github.com/md-emon-hasan/ml-project-amazon-big-mart-sales-prediction
big-mart-sale data-preprocessing data-science machine-learning predictive-modeling retail-data sales-prediction
Last synced: 5 days ago
JSON representation
🛒 Big Mart store sales using a trained machine learning model. Web forms for user input and displays sales predictions based on historical data.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-amazon-big-mart-sales-prediction
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-08-11T12:04:04.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-11T13:05:13.000Z (about 1 year ago)
- Last Synced: 2025-03-02T21:42:56.168Z (8 months ago)
- Topics: big-mart-sale, data-preprocessing, data-science, machine-learning, predictive-modeling, retail-data, sales-prediction
- Language: Jupyter Notebook
- Homepage: https://amazon-big-mart-sales-prediction.onrender.com/
- Size: 2.11 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Amazon Big Mart Sales Prediction
Welcome to the **Amazon-Big-Mart-Sales-Prediction** repository! This project focuses on predicting sales for Big Mart stores using machine learning techniques. The application employs a machine learning model to forecast sales based on various features, providing valuable insights into retail performance.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository features a project aimed at predicting sales for Big Mart stores using a machine learning model. The project includes data preprocessing, model training, and deployment aspects. It's a practical example of leveraging machine learning for retail analytics and sales forecasting.
---
## 🔍 Topics Covered
- **Machine Learning Models:** Implementing models for sales prediction.
- **Data Preprocessing:** Techniques for preparing data for modeling.
- **Feature Engineering:** Creating and selecting features for better model performance.
- **Model Evaluation:** Assessing the performance of the prediction model.
- **Deployment:** Deploying the model using Flask for web-based interaction.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/Amazon-Big-Mart-Sales-Prediction.git
```
2. **Navigate to the project directory:**
```bash
cd Amazon-Big-Mart-Sales-Prediction
```
3. **Create a virtual environment and activate it:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application:**
```bash
python app.py
```
6. **Open your browser and visit:**
```
http://127.0.0.1:5000/
```
---
## 🎉 Live Demo
Check out the live version of the Big Mart Sales Prediction app [here](https://amazon-big-mart-sales-prediction.onrender.com/).
---
## 🌟 Best Practices
Recommendations for maintaining and improving this project:
- **Model Updating:** Regularly update the model with new data to keep predictions accurate.
- **Error Handling:** Implement robust error handling for both user input and system errors.
- **Security:** Secure the Flask application by implementing proper validation and HTTPS in production.
- **Documentation:** Keep the documentation up-to-date for better usability and future enhancements.
---
## ❓ FAQ
**Q: What is the purpose of this project?**
A: This project aims to predict sales for Big Mart stores using machine learning, providing insights into retail sales performance.
**Q: How can I contribute to this repository?**
A: Please refer to the [Contributing](#contributing) section for guidelines on contributing.
**Q: Where can I learn more about machine learning?**
A: Explore resources like [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html) and [Kaggle](https://www.kaggle.com/learn/overview) to expand your knowledge.
**Q: Can I deploy this app on cloud platforms?**
A: Yes, you can deploy the Flask app on platforms such as Heroku, Render, or AWS.
---
## 🛠️ Troubleshooting
Common issues and their solutions:
- **Issue: Flask App Not Starting**
*Solution:* Ensure that all dependencies are installed and the virtual environment is activated properly.
- **Issue: Model Not Loading**
*Solution:* Verify the path to the model file and ensure it is accessible and not corrupted.
- **Issue: Inaccurate Predictions**
*Solution:* Check if the input features are correctly formatted and the model is well-trained.
---
## 🤝 Contributing
Contributions 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:**
- Add new features, fix bugs, or enhance documentation.
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.**
---
## 📚 Additional Resources
Explore these resources for more insights into machine learning and Flask development:
- **Flask Official Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
- **Data Science Resources:** [Towards Data Science](https://towardsdatascience.com/)
---
## 💪 Challenges Faced
Some challenges during development:
- Handling large datasets and feature engineering.
- Ensuring accurate model predictions and proper evaluation.
- Deploying the application and managing dependencies.
---
## 📚 Lessons Learned
Key takeaways from this project:
- Effective use of machine learning for sales prediction.
- Importance of thorough data preprocessing and feature engineering.
- Deployment considerations and challenges for web applications.
---
## 🌟 Why I Created This Repository
This repository was created to showcase a practical application of machine learning for sales forecasting in a retail setting. It demonstrates how to build, train, and deploy a predictive model using Flask.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **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 adjust and expand this template according to your project’s specifics and requirements.