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Mike Benas

NHS: Diagnostic Analysis using Python

Working with real-world data to address a problem faced by the National Health Service (NHS). The analysis utilised Python to explore the available data, create visualisations to identify trends, and extract meaningful insights to inform decision-making.

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Key Skills & Resources Used

Pandas & Numpy
Jupyter Notebook
GIT
Seaborn & Matplotlib
Web Scrapping
Moving Averages

Project Overview

Project has been engaged by the National Health Services (NHS) in England to address the issue of missed general practitioner (GP) appointments, which incur significant costs for the healthcare system. The objective is to gain a better understanding of the reasons behind missed appointments and develop data-informed strategies to reduce or eliminate them.

Working towards those goals, i analyzed the monthly and seasonal trends, observing patterns in appointment numbers across different service settings, context types, and national categories. Additionally, have explored the top trending hashtags (#) on Twitter related to healthcare in the UK. By answering a set of agreed (by stakeholders) questions and examining the data, i provided valuable insights and recommendations for the NHS.

This project enabled me to contribute to the development of effective strategies to optimize staff and resource allocation, ultimately reducing missed appointments and improving the overall efficiency of the healthcare system.

Phases of the project include: Data Exploration, Data Cleaning & Validation, Data Visualizations to identify trends, Time-Series Analysis, Web Scrapping