Project: Investigate a Dataset - No-show Appointment

Introduction

This repository contains the code and analysis for the "Investigate a Dataset - No-show Appointment" project. The aim of this project was to explore the characteristics of patients who fail to show up for their appointments in a clinic. By understanding the patterns and factors contributing to no-shows, the project sought to help the clinic's management devise strategies to reduce the number of missed appointments and improve their patient appointment system.

This project was completed as part of the ALX-Udacity Nano degree in Data Analytics.

Data Source

The data used for this project was manually downloaded from My Udacity workspace and contains information on various attributes related to patient appointments at the clinic. The dataset was assessed for tidiness and quality issues, and necessary cleaning operations were performed using Python libraries such as NumPy and Pandas.

Analysis and Visualization

The analysis of the dataset was carried out using Python, with the help of libraries such as Seaborn and Matplotlib for data visualization. The following insights were discovered during the analysis:

• The proportion of No-shows: The proportion of patients who failed to show up on their appointment date was found to be 20.19%.
• Demographic Factors: The majority of no-shows were among female patients within the age bracket of 18-36 years.
• Appointment Days: Most no-shows occurred for appointments scheduled between Mondays to Wednesdays.
• SMS Reminders: Patients who did not receive SMS reminders were more likely to miss their appointments.

Recommendations

Based on the analysis, the following recommendations are suggested to reduce the number of no-shows in the clinic:

1. Raise Awareness: The clinic should emphasize the importance of attending appointments and the potential consequences of missed appointments to patients.
2. Focus on Demographics: Particular attention should be given to female patients between 18-36 years old, as they have shown a higher likelihood of missing appointments.
3. Appointment Scheduling: Consideration should be given to optimizing the scheduling of appointments, possibly avoiding peak days when no-shows are more prevalent.
4. Timely SMS Reminders: Implement a system to send timely SMS reminders to patients, as this can significantly reduce the likelihood of no-shows.

Repository Structure

The repository is organized as follows:

• data/: Contains the dataset used for analysis.
• notebooks/: Includes Jupyter notebooks used for data cleaning, analysis, and visualization.
• README.md: The file you are currently reading, provides an overview of the project.

Dependencies

The analysis was performed using Python, and the following libraries were used:

• NumPy
• Pandas
• Seaborn
• Matplotlib

How to Use

To reproduce the analysis or explore the code further, follow these steps:

• Clone this repository to your local machine using git clone.
• Install the required dependencies by running pip install -r requirements.txt.
• Explore the Jupyter notebooks in the notebooks/ directory to understand the data cleaning, analysis, and visualization process.

Acknowledgments

We would like to express our gratitude to ALX and Udacity for providing the opportunity to work on this project as part of the Data Analytics Nano degree program.

Feel free to reach out to the project contributors for any questions or feedback.

Happy analyzing!