Investigate-a-Dataset---No-Show-Appointments

Introductory project to Exploratory Data Analysis.

Introduction and Data

In this project, I will investigate the factors causing patients to not show up for their medical appointments in Brazil, using the 'Medical Appointment No Shows' dataset from Kaggle. For convenience purposes, I saved the CSV file as 'no_show.csv'.

I will be studying the influence of age and gender on appointment attendance. I will also show whether SMS notifications increase likelihood of attendance.

Research Questions

1. How do age and gender affect appointment attendance?
2. Are patients more likely to show up if they receive SMS notifications?

Findings

1. The median age of patients who show up for appointments is slightly more than that of patients who do not show up (38 > 33).
2. There is a gender disparity is appointment attendance and more females (57,246) than males (30,962) show up for appointments.
3. More patients who do not receive SMS messages (62,510) show up than patients who receive SMS messages (25,698).

Author

Gorata Malose
Linkedin: Gorata Malose

License

This project is licensed under the MIT License and it was submitted by Gorata Malose as part of Udacity's Data Analyst Nanodegree programme.