Exploratory Data Analysis EDA on the Haberman cancer survival dataset: this article explains how to approach a complete exploratory analysis to understand patterns, identify relevant variables, and prepare data for predictive models. The Haberman dataset contains records of patients who underwent breast cancer surgery, with typical variables such as age, year of operation, number of positive axillary nodes, and 5-year survival status. A rigorous EDA helps uncover relationships between age and survival, identify outliers, and verify data quality.
Essential steps: data cleaning and verification to detect missing values or inconsistencies; descriptive statistics such as mean, median, standard deviation; univariate visualizations with histograms and boxplots for age and number of nodes; bivariate analysis with scatterplots and contingency tables between survival status and other variables; correlation analysis and exploration of interactions. For survival problems, it is advisable to complement with Kaplan-Meier curves and log-rank tests when the time and censoring variables are present.
Best practices: normalize and scale variables if distance-based models will be used; transform skewed variables; create derived variables that capture relevant clinical information; use stratified sampling if classes are imbalanced; document EDA assumptions and results to ensure reproducibility.
Recommended tools: Python with pandas, seaborn, and matplotlib for visualization; scikit-learn for preprocessing and models; lifelines for survival analysis. To see an example notebook with code and replicable steps, visit the following link
https://github.com/ash322ash422/tut_ml/blob/e4b9c7abf1df9a80c1a6a7ed2ccf03e4f34b1302/tut_misc_EDA/tut_EDA-haberman-cancer-survival.ipynb
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