{"id":26880468,"url":"https://github.com/pranavv34/customer-segmentation","last_synced_at":"2026-04-02T04:46:26.857Z","repository":{"id":250172771,"uuid":"822290845","full_name":"pranavv34/customer-segmentation","owner":"pranavv34","description":"Predictive modeling and customer segmentation project using neural networks to forecast sales and categorize customers for targeted marketing in the online retail sector.","archived":false,"fork":false,"pushed_at":"2024-07-25T14:53:03.000Z","size":172,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-16T02:13:19.985Z","etag":null,"topics":["keras","matplotlib","numpy","pandas","python","seaborn","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pranavv34.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-06-30T20:31:13.000Z","updated_at":"2024-07-25T14:55:08.000Z","dependencies_parsed_at":"2024-07-25T17:18:04.395Z","dependency_job_id":null,"html_url":"https://github.com/pranavv34/customer-segmentation","commit_stats":null,"previous_names":["pranavv34/customer-segmentation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pranavv34/customer-segmentation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pranavv34%2Fcustomer-segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pranavv34%2Fcustomer-segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pranavv34%2Fcustomer-segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pranavv34%2Fcustomer-segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pranavv34","download_url":"https://codeload.github.com/pranavv34/customer-segmentation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pranavv34%2Fcustomer-segmentation/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268240356,"owners_count":24218356,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-01T02:00:08.611Z","response_time":67,"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":["keras","matplotlib","numpy","pandas","python","seaborn","sklearn","tensorflow"],"created_at":"2025-03-31T14:36:15.099Z","updated_at":"2025-12-30T20:06:41.381Z","avatar_url":"https://github.com/pranavv34.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# CUSTOMER SEGMENTATION\n\n\nWelcome to the Customer Segmentation project! This repository contains the code and resources for a predictive modeling and customer segmentation project developed as part of the B.E. program at Chaitanya Bharathi Institute of Technology.\n\n## Project Overview\nThis project aims to develop and assess predictive models to forecast customer behaviors in the online retail sector. The focus is on predicting total sales and maximum cart value using historical transactional data. Additionally, advanced segmentation techniques are employed to categorize customers into distinct groups based on their purchasing patterns.\n\n## Key Features\n- **Data Integration and Preprocessing**: Combining diverse datasets to create a standardized dataset.\n- **Predictive Modeling**: Custom neural network models for predicting total sales and maximum cart value using TensorFlow and Keras.\n- **Model Evaluation**: Utilizing Mean Squared Error (MSE) to evaluate model performance.\n- **Customer Segmentation**: Using clustering techniques to categorize customers and facilitate targeted marketing initiatives.\n- **Visualization and Analysis**: Detailed visualizations and comprehensive analysis to deliver actionable insights.\n\n## Technologies Used\n- **Programming Language**: Python\n- **Frameworks**: TensorFlow, Keras\n- **Libraries**: Numpy, Pandas, Matplotlib, Seaborn, Sklearn\n\n## Authors\n- Vishnu K (160121737200)\n- Pranav V (160121737203)\n- K. Saketh (160121737316)\n\n## Supervisor\n- Dr. Jayaram Dharmana, Assistant Professor, Department of IT, CBIT, Hyderabad\n\nFeel free to explore the repository and contribute to the project. For any queries, you can reach out to us through our GitHub profiles or via email.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpranavv34%2Fcustomer-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpranavv34%2Fcustomer-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpranavv34%2Fcustomer-segmentation/lists"}