{"id":20683365,"url":"https://github.com/muhammadadilnaeem/customer-segmentation-unsupervised-learning","last_synced_at":"2026-06-06T00:31:16.102Z","repository":{"id":247930573,"uuid":"827262457","full_name":"muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning","owner":"muhammadadilnaeem","description":"This project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.","archived":false,"fork":false,"pushed_at":"2024-07-11T10:41:49.000Z","size":218,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-12-14T05:15:09.513Z","etag":null,"topics":["customer-segmentation","dbscan-clustering-algorithm","hirarchical-clustering","kmeans-clustering","unsupervised-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/muhammadadilnaeem.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-11T09:59:47.000Z","updated_at":"2024-07-11T10:41:52.000Z","dependencies_parsed_at":"2024-07-11T11:42:25.377Z","dependency_job_id":"594d1ff8-2bbe-4430-a6a2-c526f70c5147","html_url":"https://github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning","commit_stats":null,"previous_names":["muhammadadilnaeem/customer-segmentation-unsupervised-learning"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadadilnaeem%2FCustomer-Segmentation-Unsupervised-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadadilnaeem%2FCustomer-Segmentation-Unsupervised-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadadilnaeem%2FCustomer-Segmentation-Unsupervised-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadadilnaeem%2FCustomer-Segmentation-Unsupervised-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/muhammadadilnaeem","download_url":"https://codeload.github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/muhammadadilnaeem%2FCustomer-Segmentation-Unsupervised-Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33965591,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-05T02:00:06.157Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["customer-segmentation","dbscan-clustering-algorithm","hirarchical-clustering","kmeans-clustering","unsupervised-machine-learning"],"created_at":"2024-11-16T22:16:23.698Z","updated_at":"2026-06-06T00:31:16.073Z","avatar_url":"https://github.com/muhammadadilnaeem.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n---\n\n# Customer Segmentation Using Clustering Techniques\n\nThis project explores customer segmentation using various clustering techniques on a dataset of mall customers. The goal is to identify distinct customer groups based on demographic and behavioral attributes, enabling businesses to tailor their marketing strategies more effectively.\n\n## **Project Overview**\n\nThe objective of this analysis is to segment customers to better understand their demographics and spending behavior, which can help businesses improve their marketing strategies and customer satisfaction.\n\n## **Dataset Description**\n\nThe dataset consists of the following attributes:\n- **CustomerID**: Unique identifier for each customer.\n- **Gender**: Gender of the customer.\n- **Age**: Age of the customer.\n- **Annual Income (k$)**: Annual income of the customer in thousands of dollars.\n- **Spending Score (1-100)**: Score assigned by the mall based on customer behavior and spending nature.\n\n## **Data Exploration and Cleaning**\n\n1. **Checked for missing values**: Ensured the dataset is complete with no missing values.\n2. **Summary statistics**: Provided an overview of the data distribution.\n3. **Feature Engineering**: Encoded the 'Gender' attribute and scaled the features to ensure they are on a comparable scale.\n\n## **Clustering Techniques Employed**\n\n### **K-Means Clustering**\nTried different numbers of clusters (k = 2 to 5) and selected the best one based on silhouette scores.\n\n### **Agglomerative Clustering**\nExperimented with various cluster counts and selected the best model based on silhouette scores.\n\n### **DBSCAN**\nExplored different epsilon values for density-based clustering and identified the best model based on silhouette scores.\n\n## **Key Findings and Insights**\n\n- Identified distinct customer groups based on age, income, and spending habits.\n- Uncovered patterns that can drive personalized marketing efforts and enhance customer experiences.\n\n## **Recommendations**\n\n- Further exploration with additional features could refine the segmentation.\n- Diving deeper into individual clusters for more targeted strategies.\n\n## **Project Structure**\n\n- `data/`: Contains the dataset used for the analysis.\n- `notebook/`: Jupyter notebooks with the data exploration, cleaning, and clustering models.\n\n\n## Usage\n\nTo reproduce the analysis, follow these steps:\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning.git\n   cd Customer-Segmentation-Unsupervised-Learning\n   ```\n\n2. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. Run the notebooks or scripts to perform the analysis:\n   ```bash\n   jupyter notebook notebook/data_exploration.ipynb\n   ```\n\n## Contributing\n\nIf you have suggestions for improvements or would like to contribute, feel free to open an issue or submit a pull request.\n\n## License\n\nThis project is licensed under the Apache License. See the [LICENSE]([LICENSE](https://github.com/muhammadadilnaeem/Customer-Segmentation-Unsupervised-Learning/blob/main/LICENSE)) file for details.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhammadadilnaeem%2Fcustomer-segmentation-unsupervised-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmuhammadadilnaeem%2Fcustomer-segmentation-unsupervised-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmuhammadadilnaeem%2Fcustomer-segmentation-unsupervised-learning/lists"}