𝐔𝐧𝐯𝐞𝐢𝐥𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡 𝐓𝐫𝐞𝐧𝐝𝐬 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧

Recently, I embarked on a fascinating project to analyze medical data and uncover hidden trends related to cardiac health. Using Python libraries like Pandas, Matplotlib, and Seaborn, I visualized data from a dataset containing patient information like body measurements, blood tests, and lifestyle choices.

One of the key tasks was to create a new column indicating whether a patient was overweight based on their Body Mass Index (BMI). I then normalized the data to ensure consistent interpretation of values.

To explore the relationships between different factors and cardiac disease, I transformed the data into a long format and created informative visualizations using catplots. These plots allowed me to compare the distribution of categorical features (like cholesterol and glucose levels) for patients with and without cardiac disease.

After cleaning the data to remove outliers and inconsistencies, I constructed a correlation matrix to identify the strongest relationships between various variables. This visual representation helped me understand how factors like body measurements, blood markers, and lifestyle choices might contribute to cardiac health.

I'm currently two (2) projects away from claiming my Data Analysis with Python certification from freeCodeCamp, but so far I must say this project was a valuable learning experience, demonstrating the power of data visualization in uncovering meaningful insights from complex medical data. By combining data analysis techniques with effective visualization, we can gain a deeper understanding of health trends and inform evidence-based medical practices.

𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐢𝐟 𝐲𝐨𝐮'𝐝 𝐥𝐢𝐤𝐞 𝐭𝐨 𝐫𝐞𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐜𝐨𝐝𝐞 𝐨𝐫 𝐝𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚: 𝐡𝐭𝐭𝐩𝐬://𝐟𝐫𝐞𝐞𝐜𝐨𝐝𝐞𝐜𝐚𝐦-𝐛𝐨𝐢𝐥𝐞𝐫𝐩𝐥𝐚𝐭𝐞-𝐮𝐱𝐞𝐳𝐝𝐫𝐰𝐛𝐤𝐜𝐜.𝐰𝐬-𝐞𝐮𝟏𝟏𝟔.𝐠𝐢𝐭𝐩𝐨𝐝.𝐢𝐨/