{"id":30360506,"url":"https://github.com/liuba9999/healthcare---analytics","last_synced_at":"2026-05-16T08:31:37.018Z","repository":{"id":309968799,"uuid":"1038210518","full_name":"Liuba9999/Healthcare---Analytics","owner":"Liuba9999","description":"Analysis of hospital admissions, patients demographics, and billing trends.","archived":false,"fork":false,"pushed_at":"2025-08-14T20:48:27.000Z","size":6732,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-14T22:27:54.737Z","etag":null,"topics":["healthcare","jupyter-notebook","matplotlib","panda","python","visualisation"],"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/Liuba9999.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,"zenodo":null}},"created_at":"2025-08-14T19:56:53.000Z","updated_at":"2025-08-14T21:00:47.000Z","dependencies_parsed_at":"2025-08-14T22:27:56.815Z","dependency_job_id":"6cf4fe60-4d44-441b-ab51-e9a83ca6848c","html_url":"https://github.com/Liuba9999/Healthcare---Analytics","commit_stats":null,"previous_names":["liuba9999/healthcare---analytics"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Liuba9999/Healthcare---Analytics","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liuba9999%2FHealthcare---Analytics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liuba9999%2FHealthcare---Analytics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liuba9999%2FHealthcare---Analytics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liuba9999%2FHealthcare---Analytics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Liuba9999","download_url":"https://codeload.github.com/Liuba9999/Healthcare---Analytics/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Liuba9999%2FHealthcare---Analytics/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271166866,"owners_count":24710585,"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":"2025-08-19T02:00:09.176Z","response_time":63,"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":["healthcare","jupyter-notebook","matplotlib","panda","python","visualisation"],"created_at":"2025-08-19T14:22:56.834Z","updated_at":"2026-05-16T08:31:36.990Z","avatar_url":"https://github.com/Liuba9999.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hospital-Dataset-Analysis\nAnalysis of hospital admissions, patient demographics, and billing trends to uncover actionable insights into healthcare utilization and costs.\n\n# Project Overview\nThis project explores hospital data to identify patterns in patient admissions, age groups, conditions, and billing amounts. \n\n# Dataset\nSource: [https://www.kaggle.com/datasets/soniyabablani/healthcare-dataset]\n\nContents: Patient demographics, hospital admissions, diagnoses, billing amounts.\n\nFormat: CSV\n\n# Objectives\nIdentify peak hospital admission periods.\n\nAnalyze patient demographics and age group utilization.\n\nExamine billing patterns conditions.\n\nHighlight trends to support hospital resource planning.\n\n# Tools \u0026 Technologies\nPython 3.11\n\nPandas, NumPy\n\nMatplotlib\n\nJupyter Notebook\n\n# Results\nAdults aged 25–64 make up the majority of admissions.\n\nCertain hospitals and conditions contribute to the highest billing amounts.\n\nPeak months and seasonal trends identified.\n\nClear visualizations make interpretation and decision-making easier.\n\n# Conclusion\nThis analysis provides a data-driven overview of hospital utilization, patient demographics, and cost distribution. Insights can guide administrators in improving healthcare service allocation and resource planning.\n\n# Usage\n1. Clone this repository.\n2. Open the Jupyter Notebook file.\n3. Run all cells to reproduce the analysis.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliuba9999%2Fhealthcare---analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fliuba9999%2Fhealthcare---analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fliuba9999%2Fhealthcare---analytics/lists"}