Pre-Examination of Breast Cancer Dataset Using Exploratory Data Analysis (EDA) Approach

The breast cancer dataset is thoroughly and insightfully pre-examined using the exploratory data analysis (EDA) method, shedding light on the relationships and traits essential to breast cancer diagnosis. This dataset contains a wide range of features that depict various cell nuclei characteristics extracted from breast mass tissue digital images. The EDA showed that benign and malignant cases were distributed relatively evenly, with the latter having slightly more occurrences. Critical parameters such as mean radius, texture, perimeter, area, smoothness, and compactness exhibited greater values for malignant cases than benign ones. The correlation matrix and heat map highlighted strong relationships between specific traits, indicating their potential value in identifying cancerous or benign cells. Further analysis utilizing scatter plots and Principal Component Analysis (PCA) revealed a definite positive association between the "mean" and "worst" values of each feature, especially for cases of malignancy. Although these results provide a solid basis for constructing prediction models, their usefulness requires model training and evaluation to be confirmed. The EDA offers a compelling starting point for additional, more profound investigation of the data, offering doors for looking at the distributions of the "se" and "worst" features or using different dimensionality reduction methods. The individual objectives and analysis setting are always considered when choosing an exploratory approach.