Medical Data Visualizer is a Data Analysis with Python project from freeCodeCamp. Instructions and details can be found here: https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer
The file medical_examinations.csv contain data with the following features: age, weight, height, systolic blood pressure, diastolic blood pressure, glucose, and other medical examination information.
In this project, the tasks were to:
- Append an "overweight" column (boolean). From the height and weight information, the calculated BMI is used to determine if the person is overweight (1) or not (0).
- Cholesterol and Glucose information were normalized (converted to boolean) with 0 being good, and 1 being bad.
- Create side-by-side barplots detailing medical information for people with and without cardiovascular disease. Each barplot shows the value counts of different categorical features: glucose, cholesterol, smoking habit, alcohol habit, physical activity.
- Clean the blood pressure data for erroneous measurements (diastolic pressure higher than systolic pressure), and to exlude data where a person's height or weight are less than the 2.5th percentile or more than the 97.5 percentile.
- Create a correlation matrix for the data set. As a style requirement, the plot should only show the lower triangle.
Task #3 Categorical Plot showing value counts for different features. The data for people with (right) and without (left) cardiovascular disease are separated. The barplot is created using seaborn "catplot".
Task #5
Correlation matrix created using seaborn "heatmap".
Project Notes:
- for the categorical plot, the starter code requires the use of pandas "melt" method.