{"id":20812468,"url":"https://github.com/chandkund/customer-segmentation","last_synced_at":"2026-04-15T19:41:25.331Z","repository":{"id":254915564,"uuid":"847951561","full_name":"chandkund/Customer-Segmentation","owner":"chandkund","description":"Customer segmentation divides customers into distinct groups based on characteristics and behaviors. 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This project leverages K-Means clustering, a popular unsupervised machine learning algorithm, to segment customers and uncover actionable insights that can help in crafting targeted marketing strategies.\n\n## Table of Contents\n- [Project Overview](#overview)\n- [Dataset](#dataset)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [License](#license)\n\n## Dataset\nThe dataset contains various features that describe customer preferences, behaviors, and demographics, including:\n\n- **yummy**: Indicates if the product is perceived as yummy (Yes/No).\n- **convenient**: Indicates if the product is perceived as convenient (Yes/No).\n- **spicy**: Indicates if the product is perceived as spicy (Yes/No).\n- **fattening**: Indicates if the product is perceived as fattening (Yes/No).\n- **greasy**: Indicates if the product is perceived as greasy (Yes/No).\n- **fast**: Indicates if the product is perceived as fast (Yes/No).\n- **cheap**: Indicates if the product is perceived as cheap (Yes/No).\n- **tasty**: Indicates if the product is perceived as tasty (Yes/No).\n- **expensive**: Indicates if the product is perceived as expensive (Yes/No).\n- **healthy**: Indicates if the product is perceived as healthy (Yes/No).\n- **disgusting**: Indicates if the product is perceived as disgusting (Yes/No).\n- **Like**: A numeric rating of the product on a scale from negative to positive.\n- **Age**: The age of the customer.\n- **VisitFrequency**: The frequency with which the customer visits or uses the product/service.\n- **Gender**: The gender of the customer (Male/Female).\n\n## Installation\nTo get started with this project, clone the repository and install the necessary dependencies:\n\n```bash\ngit clone https://github.com/chandkund/customer-segmentation.git\ncd customer-segmentation\npip install -r requirements.txt\n```\n\nEnsure you have Python 3.x and all the required libraries installed.\n\n## Usage\nYou can run the segmentation analysis by executing the main Python script:\n\n```bash\npython customer_segmentation.py\n```\n\nThis will load the dataset, perform data preprocessing, apply K-Means clustering, and output the results.\n\n### Key Features\n- **Data Preprocessing**: Cleans and prepares the data for analysis.\n- **K-Means Clustering**: Segments the customer base into distinct groups.\n- **Visualization**: Provides visual representations of the clusters.\n\n## Results\nThe project outputs various insights, including:\n- Identification of key customer segments.\n- Visualization of clusters based on different customer characteristics.\n- Recommendations for targeted marketing strategies based on segment analysis.\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Fcustomer-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchandkund%2Fcustomer-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchandkund%2Fcustomer-segmentation/lists"}