Welcome to the Customer-Segmentation application. This tool uses clustering methods to help you understand your customers better. It identifies distinct groups based on their spending habits using K-Means and DBSCAN algorithms.
- Clustering Methods: Utilizes K-Means and DBSCAN for effective customer segmentation.
- User-Friendly: Simple interface designed for everyone, regardless of technical knowledge.
- Data-Driven Insights: Makes sense of mall customer data to enhance marketing strategies.
- Easy Visualization: Displays results clearly, making it simpler to interpret customer segments.
- Operating System: Windows 10 or later, macOS, or Linux
- Memory: At least 4 GB of RAM
- Storage: 500 MB of available disk space
- Python: Version 3.6 or higher (included in the package)
- Libraries: Scikit-learn, Matplotlib, Pandas (all included)
To get started with Customer-Segmentation, follow these simple steps:
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Visit our Releases Page: Click the button below to go to the download page. Download Customer-Segmentation
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Choose the Latest Version: On the Releases page, find the most recent release labeled as “Latest Release.”
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Download the File:
- Locate the asset with the format suitable for your operating system.
- Click on it to begin the download.
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Run the Application:
- After the download finishes, locate the file in your Downloads folder.
- Double-click the file to launch the application.
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Prepare Your Data:
- Ensure your customer data is in a CSV format. This application accepts a structured CSV file containing relevant customer information.
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Input Your Data:
- Follow the prompt to upload your CSV file.
- Click 'Submit' to start the segmentation process.
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View the Results:
- The application will display segmented customer groups.
- You can visualize these segments and download the results for further analysis.
The Customer-Segmentation application uses advanced clustering algorithms to analyze patterns in your data. The K-Means algorithm groups customers based on similarities in spending, while DBSCAN detects clusters in noisy data. This dual approach provides you with reliable insights.
- Data Preparation: Data is cleaned and prepared for analysis.
- Clustering: The application applies K-Means and DBSCAN to identify customer segments.
- Visualization: Results are shown using clear graphs and charts, making it easy to understand.
To use the application effectively, your input CSV file should have the following columns:
- CustomerID: Unique identifier for each customer
- Age: Age of the customer
- Annual Income: Yearly income of the customer
- Spending Score: Score assigned by the mall based on customer spending behavior
Sample CSV:
CustomerID,Age,Annual Income (k$),Spending Score
1,23,40,52
2,45,60,78
3,34,80,86
If you encounter any issues during installation or while using the application, consider the following:
- Error Messages: Take note of any error messages. They often point to what needs fixing.
- Check Requirements: Ensure your system meets the requirements listed above.
- Data Format: Verify your CSV follows the sample structure mentioned.
For further assistance, visit the Issues section on our GitHub repository.
Thank you for choosing Customer-Segmentation. We hope this tool enhances your ability to understand customer behavior and make informed marketing decisions!