The Worker Data Segmentation Application is an interactive web application built using Streamlit. It aims to segment worker data into different clusters based on specific features such as age, annual income, and spending score. This segmentation helps in understanding different groups within the data, which can be useful for various analytical and strategic purposes.
Registrazione.2024-07-20.151306.mp4
- Upload CSV Data: Users can upload their worker data in CSV format.
- Data Segmentation: The application segments the uploaded data using a pre-trained clustering model.
- Visualization: Users can visualize the segmented clusters through interactive bar charts and box plots.
- Continuous Segmentation: The application can continuously update the segmentation results in real-time.
The primary purpose of this application is to provide a user-friendly platform for businesses and researchers to analyze worker data and gain insights through data segmentation. By clustering the data, users can identify patterns and group similar data points, which can inform decision-making processes in HR, marketing, and other business areas.
- Streamlit: Used for building the interactive web application.
- Python: The main programming language used in the project.
- Docker: For containerizing the application, ensuring consistency across different environments.
Users can upload a CSV file containing worker data. The CSV file should include the following columns:
- age: The age of the worker.
- annual_income: The annual income of the worker.
- spending_score: A score indicating the worker's spending behavior.
Once the CSV file is uploaded, the application segments the data using a pre-trained clustering model. The model is trained using features such as age, annual income, and spending score. The segmented data is then used to create visualizations.
The application provides interactive visualizations to help users understand the segmented data:
- Cluster Bar Chart: Displays the count of data points in each cluster.
- Feature Distribution: Shows the distribution of features (age, annual income, spending score) within each cluster using histograms.
- Compare All Clusters: Allows users to compare the distribution of features across all clusters using box plots.
This feature allows the application to continuously update the segmentation results in real-time. This can be useful for monitoring changes in the data and understanding dynamic patterns.
- Data Upload: Users upload their data in CSV format.
- Data Processing: The application reads the data and performs preprocessing.
- Segmentation: The pre-trained clustering model segments the data into clusters.
- Visualization: The application generates interactive visualizations to display the segmentation results.
- Continuous Monitoring (if enabled): The application continuously updates and displays the segmentation results.
- Streamlit: Chosen for its simplicity and ease of creating interactive web applications with Python.
- Docker: Ensures that the application runs consistently across different environments by containerizing the application and its dependencies.
- Enhanced Visualization: Add more types of visualizations to provide deeper insights.
- Model Improvement: Continuously improve the clustering model for better segmentation.
- User Authentication: Add user authentication to restrict access to the application.
- Custom Segmentation: Allow users to customize the segmentation criteria and algorithms.
This project is licensed under the MIT License - see the LICENSE file for details.
- Thanks to Streamlit for providing an easy-to-use framework for building interactive web applications.
- Inspired by various open-source data science projects and communities.
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