Customer Segmentation Analysis

This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Please do not republish the materials without authors' consents.

This project is made possible with the support from Professor Brandon Wales UCR @Brandon-Wales

This project is also available to view on Kaggle.com, pay a visit and upvote for us!

Introduction

For those who are new to this folder, the Project-Code.ipynb and Project-Code.html files are our main focus. This project is completed using Python 3.12.0. We are also attaching the documents which we will write for this project. The data is originally obtained from Kaggle.com, link will be attached below. Feel free to explore more options beyond this analysis report.

Project Idea

This project is centered around Customer Segmentation analysis. We aim to provide better insights into customer behaviors and preferences to help businesses tailor their strategies effectively.

Project Guideline

This project will follow the template.

1. Section 1: Introduction / Why This Topic / Pre-analysis
2. Section 2: Descriptive Analysis
3. Section 3: Model Selection
   • Part 1: Predictive Modeling
   • Part 2: Customer Segmentation Clustering
   • Part 3: Customer Segmentation Prediction (Classification)
   • Part 4: Feature Importance Analysis for Part 1 Predictive Modeling
4. Section 4: Business Insights / Suggestions
5. Section 5: Conclusion

Repository Contents

• Project-Code.ipynb: Jupyter notebook containing the code for our customer segmentation analysis.
• Project-Code.html: HTML export of the Jupyter notebook for easy viewing.
• Project-Proposals.pdf: Documents that report our initial ideas, studies, and finding before launching the analysis.
• Data Folder: Contains the datasets used for analysis. There are train.csv and test.csv. train.csv will be mainly used in descriptive analysis while test.csv will be mainly used in building models like predictive modeling. Disclaimer: The data is obtained from Kaggle.com Customer Segmentation Dataset published by Abishek Sudarshan. All data are used for educational purposes only. Do not republish original author Sudarshan's work without approval. License: Data files are copyrighted by the original authors.
• Final-Reports.pdf: Documentation and reports generated during the project.
• Final Presentations.pptx: Slides and other materials for presenting our findings.

Getting Started

To get started with the project, clone this repository to your local machine using the following command:

git clone https://github.com/Jen-uis/Customer-Segmentation-Analysis

Usage

1. Open the Project-Code.ipynb file in Jupyter Notebook.
2. Follow the instructions in the notebook to run the analyses.
3. Review the results and insights provided in the output cells.

License

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

Contributing

We welcome contributions ONLY from our Team 8. If you are a member of Team 8, please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

git checkout -b feature-branch

3. Make your changes and commit them:

git commit -m 'Add some feature'

4. Push to the branch:

git push origin feature-branch

5. Create a new Pull Request.

Note: If you have successfully push your changes to the branch, Nathaniel will review the final request and merge the changes to main if your file is approved.

Contact

If you have any questions or need further information, please contact our team at: connectnathaniel@gmail.com

Authors:

• Leader: Nathaniel Zhu @Jen-uis
• Youyi Fu (Chris)
• Xiaoya Wei (Shirley)
• Xujuan Liang (Teresa)