Welcome to the retail-customer-segmentation repository. In today's competitive retail sector, it's paramount for businesses to grasp the diverse behaviors and needs of their customers. This project aims to help businesses with just that.
By harnessing the power of machine learning, particularly the K-Means clustering algorithm, we've developed a robust method to segment customers into distinct categories. These segments are based on variables like purchasing patterns and demographics.
With our Retail Customer Segmentation:
- Retailers can design targeted marketing campaigns for each customer segment, increasing ROI.
- They can provide tailored service offerings, enhancing customer satisfaction and loyalty.
- Businesses can make data-driven decisions to optimize sales and understand areas for growth.
We invite you to explore the repository, check out the datasets, the algorithms, and the insights we've derived. Any feedback, contributions, or insights of your own are most welcome!
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Ehtesham: Expertise in Database management, Python programming, and ML model creation. Responsible for the initial data preprocessing and setting up the database structure. Contact Ehtesham
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Sami: Specialized in ML models, Python coding, and Data Visualization. Played a pivotal role in model selection, tuning, and visual representation of data. Contact Sami
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Ayush: Keen expertise in Visualization techniques, Business understanding, and problem-solving. Provided valuable business insights to align the project with market needs. Contact Ayush