This project aims to analyze a dataset containing information about medical appointments and whether or not the patient attended as scheduled. The objective is to identify the factors that affect the patient's decision to attend or miss the appointment.
The dataset includes the patient's identification, appointment ID, gender, scheduled date, actual appointment date, age, neighborhood, enrollment in a government scholarship program, hypertension, diabetes, alcoholism, handicap, SMS received, and whether the patient attended the appointment or not.
- 'Python' libraries 'NumPy', 'pandas', and 'Matplotlib' are used.
- The project is divided into four sections: Introduction, Data Wrangling, Exploratory Data Analysis, and Conclusions.
- The Introduction section: the dataset is briefly described, and the question for analysis is presented.
- Data Wrangling section: the data is loaded, checked for cleanliness, and cleaned.
- The Exploratory Data Analysis section: includes data exploration using histograms, scatterplots, and count plots to identify the relationship between the variables.
- Conclusions: the findings are presented.
The findings indicate that the following factors affect the patient's attendance at scheduled appointments:
- Age: younger patients are more likely to miss appointments.
- Day of the week: appointments scheduled on Fridays have a higher no-show rate.
- SMS received: patients who receive an SMS reminder are more likely to attend their appointment.
This project provides insights into the factors that impact the patient's decision to attend or miss their scheduled appointments. The code used for analysis is presented along with the findings, making it easy for others to replicate the analysis or build on it.