Health Data Analysis: Visualizing Vital Signs with Python & Seaborn

Health Data Analysis & Visualization Project

This project demonstrates a complete Exploratory Data Analysis (EDA) workflow for health monitoring data. Executed in a Google Colab environment using Python, the task focused on loading, processing, and visually exploring key vital signs to uncover underlying distributions and patterns.

Key Tasks Performed:

· Loaded and processed the healthmonitoring.csv dataset using Pandas.

· Performed initial data cleaning and preparation to ensure analysis readiness.

· Generated a comprehensive set of histograms for critical health metrics: Age, Heart Rate, Body Temperature, and Oxygen Saturation.

· Utilized Matplotlib and Seaborn libraries to create professional, clear, and informative visualizations.

Technical Stack:

· Programming Language: Python

· Environment: Google Colab

· Libraries: Pandas, NumPy, Matplotlib, Seaborn

· Core Skills: Data Cleaning, Data Analysis, Data Visualization, Exploratory Data Analysis (EDA)

Outcome: The project successfully transformed raw health data into an insightful visual dashboard. The visualizations provide immediate, at-a-glance understanding of the distribution for each vital sign, which is a crucial first step for any further statistical analysis or machine learning modeling in the healthcare domain.