{"id":21513826,"url":"https://github.com/farha-dahman/investigate_dataset","last_synced_at":"2026-04-15T14:31:41.972Z","repository":{"id":262168268,"uuid":"886415449","full_name":"Farha-Dahman/Investigate_Dataset","owner":"Farha-Dahman","description":"Investigate [No-show appointments] Dataset","archived":false,"fork":false,"pushed_at":"2024-11-18T15:03:48.000Z","size":2741,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-17T15:57:51.484Z","etag":null,"topics":["data-analyst","jupyter-notebook","matplotlib","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/Farha-Dahman.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":"2024-11-10T23:25:35.000Z","updated_at":"2024-11-18T15:04:16.000Z","dependencies_parsed_at":null,"dependency_job_id":"9c9b7f23-d273-49df-b859-ae6be49e8b0d","html_url":"https://github.com/Farha-Dahman/Investigate_Dataset","commit_stats":null,"previous_names":["farha-dahman/investigate_dataset"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Farha-Dahman/Investigate_Dataset","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Farha-Dahman%2FInvestigate_Dataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Farha-Dahman%2FInvestigate_Dataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Farha-Dahman%2FInvestigate_Dataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Farha-Dahman%2FInvestigate_Dataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Farha-Dahman","download_url":"https://codeload.github.com/Farha-Dahman/Investigate_Dataset/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Farha-Dahman%2FInvestigate_Dataset/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273377502,"owners_count":25094593,"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":"2025-09-03T02:00:09.631Z","response_time":76,"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":["data-analyst","jupyter-notebook","matplotlib","numpy","pandas","python"],"created_at":"2024-11-23T23:18:12.532Z","updated_at":"2026-04-15T14:31:41.885Z","avatar_url":"https://github.com/Farha-Dahman.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# No-show Appointments Data Analysis\n\n## Project Overview\nThis project investigates factors influencing patient no-shows for medical appointments using the [Medical Appointment No Shows](https://www.kaggle.com/datasets/joniarroba/noshowappointments) dataset. The analysis focuses on identifying trends and associations related to no-show rates, which can provide insights for healthcare providers to improve appointment attendance.\n\n## Dataset Description\nThe dataset used in this analysis contains information about 110,527 medical appointments in Brazil. Each record in the dataset represents a single appointment, including details about the patient and the appointment itself. The goal is to determine what factors are associated with whether or not a patient shows up for their appointment.\n\n## Key Variables\n- **PatientID:** Unique identifier for each patient.\n- **AppointmentID:** Unique identifier for each appointment.\n- **Gender:** Gender of the patient.\n- **ScheduledDay:** The day the patient scheduled the appointment.\n- **AppointmentDay:** The actual day of the appointment.\n- **Age:** Age of the patient.\n- **Neighbourhood:** The location of the appointment.\n- **Scholarship:** Indicates if the patient is enrolled in the Bolsa Família program (a social welfare program).\n- **Hipertension:** Indicates if the patient has hypertension.\n- **Diabetes:** Indicates if the patient has diabetes.\n- **Alcoholism:** Indicates if the patient has a history of alcoholism.\n- **Handcap:** Indicates if the patient has any disability.\n- **SMS_received:** Indicates if the patient received an SMS reminder.\n- **No-show:** Indicates if the patient did not show up for the appointment. (\"Yes\" means they did not show up; \"No\" means they did.)\n\n\n## Research Questions\nThe primary research questions explored in this project include:\n\n- Does receiving an SMS reminder influence no-show rates?\n- What factors are associated with higher no-show rates?\n- How do no-show rates vary by neighborhood?\n- Is there a relationship between age and no-show rates?\n- Does having a scholarship affect showing up to an appointment?\n- Is there a relationship between gender and no-show rates?\n\n\n## Project Structure\n- ***Data Wrangling:*** Loaded and cleaned the data, including handling missing values, correcting data types, and transforming columns where necessary.\n\n- ***Exploratory Data Analysis (EDA):*** Visualized trends and distributions related to no-show rates, analyzed relationships between variables, and conducted basic statistical tests.\n\n- ***Conclusions:*** Summarized key findings from the EDA and outlined potential factors influencing no-show rates. Also noted limitations and provided suggestions for further investigation.\n\n### Libraries Used\n- Pandas: For data manipulation and cleaning.\n- NumPy: For numerical operations.\n- Matplotlib and Seaborn: For data visualization.\n\n### Limitations and Further Research\n- Data Limitation: The dataset only includes appointments from Brazil, which may limit the generalizability of the findings.\n- Further Research: Additional data, such as transportation availability or weather conditions, could help provide a more comprehensive view of factors affecting no-show rates.\n\n### Conclusion\nThis project provided insights into factors affecting no-show rates in medical appointments. Understanding these factors can help healthcare providers take actionable steps to improve attendance rates, potentially by targeting specific patient groups with reminders or other interventions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarha-dahman%2Finvestigate_dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarha-dahman%2Finvestigate_dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarha-dahman%2Finvestigate_dataset/lists"}