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https://github.com/farha-dahman/investigate_dataset

Investigate [No-show appointments] Dataset
https://github.com/farha-dahman/investigate_dataset

data-analyst jupyter-notebook matplotlib numpy pandas python

Last synced: 28 days ago
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Investigate [No-show appointments] Dataset

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README 

        
# No-show Appointments Data Analysis



## Project Overview

This 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.



## Dataset Description

The 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.



## Key Variables

- **PatientID:** Unique identifier for each patient.

- **AppointmentID:** Unique identifier for each appointment.

- **Gender:** Gender of the patient.

- **ScheduledDay:** The day the patient scheduled the appointment.

- **AppointmentDay:** The actual day of the appointment.

- **Age:** Age of the patient.

- **Neighbourhood:** The location of the appointment.

- **Scholarship:** Indicates if the patient is enrolled in the Bolsa Família program (a social welfare program).

- **Hipertension:** Indicates if the patient has hypertension.

- **Diabetes:** Indicates if the patient has diabetes.

- **Alcoholism:** Indicates if the patient has a history of alcoholism.

- **Handcap:** Indicates if the patient has any disability.

- **SMS_received:** Indicates if the patient received an SMS reminder.

- **No-show:** Indicates if the patient did not show up for the appointment. ("Yes" means they did not show up; "No" means they did.)



## Research Questions

The primary research questions explored in this project include:



- Does receiving an SMS reminder influence no-show rates?

- What factors are associated with higher no-show rates?

- How do no-show rates vary by neighborhood?

- Is there a relationship between age and no-show rates?

- Does having a scholarship affect showing up to an appointment?

- Is there a relationship between gender and no-show rates?



## Project Structure

- ***Data Wrangling:*** Loaded and cleaned the data, including handling missing values, correcting data types, and transforming columns where necessary.



- ***Exploratory Data Analysis (EDA):*** Visualized trends and distributions related to no-show rates, analyzed relationships between variables, and conducted basic statistical tests.



- ***Conclusions:*** Summarized key findings from the EDA and outlined potential factors influencing no-show rates. Also noted limitations and provided suggestions for further investigation.



### Libraries Used

- Pandas: For data manipulation and cleaning.

- NumPy: For numerical operations.

- Matplotlib and Seaborn: For data visualization.



### Limitations and Further Research

- Data Limitation: The dataset only includes appointments from Brazil, which may limit the generalizability of the findings.

- Further Research: Additional data, such as transportation availability or weather conditions, could help provide a more comprehensive view of factors affecting no-show rates.



### Conclusion

This 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.