I will be performing a exploratory data analysis to understand the characteristics that a patient who shows up for his/her scheduled appointment in a healthcare dataset from Kaggle.
The project includes the following contents:
- Introduction
- Data Wrangling
- Exploratory Data analysis
- Conclusions
- Proporsal for the next step
- References
The project is using Python3. The packages in this project include numpy, pandas, matplotlib.pyplot, seaborn and scipy.stats.
| Variable Name | Metadata |
|---|---|
| Patientid | Identification of a patient |
| AppointmentID | Identification of each appointment |
| Gender | M=Male, F=Female |
| ScheduledDay | The dates of patient set up their appointments |
| AppointmentDay | The dates of the appointments |
| Age | The age of the patients (Year) |
| Neighbourhood | The location of the hospitals |
| Scholarship | Indicates whether or not the patients were enrolled in Brasilian welfare program |
| Hipertension | 0: non-hypertension, 1: hypertension |
| Diabetes | 0: non-diabetes, 1: diabetes |
| Alcoholism | 0: non-alcoholic, 1: alcoholic |
| Handcap | 0: False, 1: True |
| SMS_received | 0: did not send any messages to the patients, 1: sent one or more messages to the patients |
| No-show | Yes: the patients did not show up to their appointments, No: the patients showed up |
- Descriptive statistics (Risk Ratio for binomial variables)