{"id":14067660,"url":"https://github.com/NiranjanStack/Customer-Behavior-Analysis-in-R-Shiny","last_synced_at":"2025-07-30T02:30:57.856Z","repository":{"id":223338625,"uuid":"52042346","full_name":"NiranjanStack/Customer-Behavior-Analysis-in-R-Shiny","owner":"NiranjanStack","description":"Analyzing transactions of a retailer to predict promotional items.","archived":false,"fork":false,"pushed_at":"2017-01-10T00:06:39.000Z","size":884,"stargazers_count":4,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-12-04T08:36:14.315Z","etag":null,"topics":["analysis","analyzing-transactions","data-science","machinelearning-r","predictive-analytics","retailer","shiny","shiny-apps","svm-model"],"latest_commit_sha":null,"homepage":null,"language":"R","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/NiranjanStack.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":"2016-02-18T22:08:03.000Z","updated_at":"2024-05-22T21:55:23.000Z","dependencies_parsed_at":null,"dependency_job_id":"4c0ee0ba-d1ba-4cfb-9cfe-1b8b1d8f2b42","html_url":"https://github.com/NiranjanStack/Customer-Behavior-Analysis-in-R-Shiny","commit_stats":null,"previous_names":["niranjanstack/customer-behavior-analysis-in-r-shiny"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/NiranjanStack/Customer-Behavior-Analysis-in-R-Shiny","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NiranjanStack","download_url":"https://codeload.github.com/NiranjanStack/Customer-Behavior-Analysis-in-R-Shiny/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267798625,"owners_count":24145727,"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":"2025-07-30T02:00:09.044Z","response_time":70,"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":["analysis","analyzing-transactions","data-science","machinelearning-r","predictive-analytics","retailer","shiny","shiny-apps","svm-model"],"created_at":"2024-08-13T07:05:42.790Z","updated_at":"2025-07-30T02:30:57.530Z","avatar_url":"https://github.com/NiranjanStack.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"# Customer-Behavior-Analysis-in-R-Shiny\nAnalyzing transactions of a retailer to predict promotional items.\n\nThe dataset has previous five years purchase transactions of customers. Predictive analysis is done by applying machine learning\nalgorithms to find the most frequent item sets purchased.\n\nAprioir Algorithms : To generate associatin rules and find the most frequent item sets.\nSupport Vector Machines : To predict the month of the year in which the sales of a particular item is maximum.\n\nShiny (Library in R) is used to display the results.\nShiny Dashboard: https://niranjanrshiny.shinyapps.io/Prediction_App/\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNiranjanStack%2FCustomer-Behavior-Analysis-in-R-Shiny/lists"}