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Releases: tieugem1997/CustomerSegmentation_Streamlit

v1.0.0

10 Sep 02:04
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Customer Segmentation App v1.0.0

We are thrilled to announce the inaugural release of the Customer Segmentation App. This version lays the foundation, providing a robust platform that leverages cutting-edge algorithms for customer data analysis and segmentation.

New Features

  • Business Understanding

    • Introduction to the concept and benefits of customer segmentation.
  • Data Understanding

    • Enables users to upload new datasets or utilize a sample file to understand data intricacies.
  • Data Preparation

    • Tools for data cleaning including removal of duplicate rows and handling missing values.
    • Insightful charts for data exploration built on various Python visualization libraries.
  • Modeling and Evaluation

    • RFM analysis for calculating Recency, Frequency, and Monetary values for each customer.
    • KMeans clustering algorithm aided by the Elbow method to find the optimal number of clusters.
  • Prediction

    • Facility for users to input new data and predict the corresponding customer segment.
    • Option to download the prediction results in CSV format.

Enhancements

  • Feedback Section

    • A dedicated section for users to share feedback and suggestions for future improvements.
  • User Guide

    • Detailed user guide to assist users in navigating through the application seamlessly.

Technical Details

  • Backend Development

    • Developed using the Streamlit framework, supported by various Python libraries for data handling and visualization.
  • Deployment

    • The application is hosted on Streamlit, offering a live platform for users to interact with the app in real-time.

Bug Fixes

  • Addressed minor bugs from the beta version to enhance the user experience.

Acknowledgements

A big thank you to everyone involved in this project. Your contributions have been invaluable in bringing this vision to life.

Conclusion

We invite you to explore the Customer Segmentation App v1.0.0. Your feedback will be instrumental in shaping the future versions. If you find this project beneficial, please support us by giving it a star ⭐ on GitHub.

Explore the Customer Segmentation App