Objectives: Investigated factors contributing to patient no-shows for medical appointments.
Tools and Technologies: Used Python, Pandas, and Matplotlib for data analysis and visualization.
Strategies and Techniques: Performed data cleaning, exploratory data analysis, and statistical modeling to identify key trends. Then design dashboard.
Results: Identified significant predictors of no-shows, providing insights to improve appointment attendance rates. The dashboard to monitor the change in factors that affect the no-show appointment rate.
Conclusions:
From the information above, it becomes clear that '(Gender, Diabetes, Alcoholism, Handicap) has no apparent impact on attendance.
From the information above, it becomes clear that (Hypertension, Scholarship, SMS received, Age, Neighbourhood of hospital) has impact on attendance.
It is clear from the above information that the average age of patients is (37), and that there are anomalous values for age (115), And that there is a difference between the average age of those who attended (38) and the average age of those who did not attend (33).
It is evident from the above information that the hospital in the neighborhood JARDIM CAMBURI has the highest percentage of the market (7%).
Submitting your Project:
Lack of sufficient information about the hospital, such as reputation, advertising budget, quality of service, and other factors that could affect patients ’decision to continue dealing with this hospital.
The sample size of the data is good, but it is not proportional to the size of the community and insufficient to extract high quality information.
Not mentioning the patient’s social level, although it is a factor that affects frequency to the doctor.