{"id":26262217,"url":"https://github.com/arush18/customer-segmentation","last_synced_at":"2026-04-27T15:31:53.569Z","repository":{"id":281121885,"uuid":"944275047","full_name":"arush18/Customer-Segmentation","owner":"arush18","description":"Machine learning-based customer segmentation using classification models, data preprocessing, and exploratory analysis.","archived":false,"fork":false,"pushed_at":"2025-03-07T04:29:46.000Z","size":943,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-01T14:38:29.182Z","etag":null,"topics":["marketing","sklearn","statsmodels","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":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arush18.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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":"2025-03-07T04:25:22.000Z","updated_at":"2025-07-17T14:15:53.000Z","dependencies_parsed_at":"2025-03-07T05:35:40.033Z","dependency_job_id":null,"html_url":"https://github.com/arush18/Customer-Segmentation","commit_stats":null,"previous_names":["arush18/customer-segmentation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/arush18/Customer-Segmentation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arush18%2FCustomer-Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arush18%2FCustomer-Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arush18%2FCustomer-Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arush18%2FCustomer-Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arush18","download_url":"https://codeload.github.com/arush18/Customer-Segmentation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arush18%2FCustomer-Segmentation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32343200,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T23:26:28.701Z","status":"online","status_checked_at":"2026-04-27T02:00:06.769Z","response_time":128,"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":["marketing","sklearn","statsmodels","unsupervised-machine-learning"],"created_at":"2025-03-14T00:18:03.809Z","updated_at":"2026-04-27T15:31:53.548Z","avatar_url":"https://github.com/arush18.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Segmentation\n\n## Overview\nThis project focuses on customer segmentation using machine learning techniques. It involves preprocessing customer data, encoding categorical variables, and building classification models to segment customers into different groups.\n\n## Dataset\nThe dataset is sourced from Kaggle: [Customer Segmentation Dataset](https://www.kaggle.com/datasets/abisheksudarshan/customer-segmentation/data). It contains both categorical and numerical features, providing valuable insights into customer behavior.\n\n## Steps Involved\n1. **Data Preprocessing**\n   - Handling missing values\n   - Encoding categorical variables\n   - Standardizing numerical features\n2. **Exploratory Data Analysis (EDA)**\n   - Visualizing distributions and relationships\n   - Checking for multicollinearity using Variance Inflation Factor (VIF)\n3. **Model Building**\n   - Logistic Regression\n   - K-Nearest Neighbors (KNN)\n   - Naïve Bayes\n   - Linear Discriminant Analysis (LDA)\n4. **Model Evaluation**\n   - Classification Report\n   - Confusion Matrix\n   - Precision, Recall, and F1-score\n   - ROC-AUC Score\n\n## Visualizations\nGraphs generated during data analysis and modeling are stored in the `visualizations/` directory.\n\n## How to Run\n1. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n2. Run the Jupyter Notebook:\n   ```bash\n   jupyter notebook main.ipynb\n   ```\n\n## Conclusion\nThis project demonstrates customer segmentation using machine learning models. The models' performance is evaluated using standard metrics, and insights are derived from the data to aid business decisions.\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farush18%2Fcustomer-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farush18%2Fcustomer-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farush18%2Fcustomer-segmentation/lists"}