This project is part of the Data Analyst Nanodegree program by Udacity. It focuses on performing exploratory data analysis (EDA) on a dataset of ~110,000 medical appointment records from Brazil, with the goal of identifying factors associated with patients missing their scheduled appointments.
- Does age influence whether patients miss their appointments?
- Does waiting time between scheduling and the appointment date affect the likelihood of a no-show?
- Do SMS reminders reduce the probability of patients missing their appointments?
- Are there differences in no-show rates across neighborhoods?
- Source: No-show Appointments Dataset by Joni Hoppen and Aquarela Analytics
- Size: ~110,000 medical appointment records
- Location: Brazil, 2016
- Age: Teens had the highest no-show rate, while seniors had the lowest. On average, patients who showed up were slightly older than those who did not.
- Waiting Time: Patients who missed their appointments waited slightly longer (~16 days on average) compared to those who showed up (~14 days), suggesting a modest association.
- SMS Reminders: Patients who received SMS reminders had a slightly lower no-show rate (~27.6%) compared to those who did not (~29.4%). However, the difference is small and does not strongly indicate causation.
- Neighborhood: No-show rates varied across neighborhoods (approximately 22.5% to 38%), suggesting that location-related factors may play a role.
- Python
- Pandas
- NumPy
- Matplotlib
Investigate_a_Dataset.ipynb— main analysis notebookInvestigate_a_Dataset.html— exported HTML version of the notebook