Create bar charts and histograms to visualize the distribution of categorical (gender) and continuous (age) variables in a dataset.
The Heart Disease dataset from the UCI Machine Learning Repository containing 303 patient records was taken. The data was loaded using Python and pandas, then created three main visualizations:
- Histograms showing age distribution of all patients and separated by gender
- Bar charts displaying gender distribution and its relationship with heart disease status
- Age group analysis categorizing patients into Young (29-40), Middle (41-50), Senior (51-60), and Elderly (60+) groups
matplotlib and seaborn libraries were used to create the visualizations and saved them as PNG files.
UCI Machine Learning Repository - Heart Disease Dataset
- URL: https://archive.ics.uci.edu/dataset/45/heart+disease
- Records: 303 patients
- Attributes: 13 features including age, sex, chest pain type, blood pressure, cholesterol levels, and heart disease diagnosis
- 68.3% male, 31.7% female patients in the dataset
- Age range: 29 to 77 years with an average age of 54.4 years
- 54.5% of patients have heart disease
- Middle-aged patients (50-60 years) form the largest group
- Males show higher heart disease incidence across all age groups
- Most patients fall in the middle age and senior categories, indicating heart disease primarily affects older populations
- Python 3.8+
- Pandas - Data manipulation
- Matplotlib - Data visualization
- Seaborn - Enhanced visualizations
- NumPy - Numerical operations
- Jupyter Notebook - Interactive analysis
| Metric | Value | Description |
|---|---|---|
| Total Patients | 303 | Complete dataset records |
| Male Patients | 207 (68.3%) | Majority male population |
| Female Patients | 96 (31.7%) | Female representation |
| Age Range | 29-77 years | Wide age distribution |
| Average Age | 54.4 years | Mean patient age |
| Heart Disease | 165 (54.5%) | Patients with diagnosis |