Breast Cancer Prediction Dataset

Dataset Overview

This dataset is used for breast cancer prediction and is based on the Wisconsin Breast Cancer Diagnostic dataset. It contains features computed from fine‑needle aspiration (FNA) images of breast lumps, describing nuclear characteristics.

Data Files

The dataset file is data.csv and includes the following columns:

• id: Identifier
• diagnosis: Diagnosis result (M = malignant, B = benign)
• radius_mean: Mean distance from center to peripheral points
• texture_mean: Standard deviation of gray‑level values
• perimeter_mean: Mean perimeter of the tumor core
• area_mean: Mean area of the tumor core
• smoothness_mean: Mean local variation in radius length
• compactness_mean: Mean of (perimeter² / area – 1.0)
• concavity_mean: Mean severity of concave regions of the contour
• concave points_mean: Mean number of concave points on the contour
• symmetry_mean: Mean symmetry
• fractal_dimension_mean: Mean "coastline approximation" – 1
• radius_se: Standard error of the mean radius
• texture_se: Standard error of texture
• perimeter_se: Standard error of perimeter mean
• area_se: Standard error of area mean
• smoothness_se: Standard error of smoothness mean
• compactness_se: Standard error of compactness mean
• concavity_se: Standard error of concavity mean
• concave points_se: Standard error of concave points mean
• symmetry_se: Standard error of symmetry mean
• fractal_dimension_se: Standard error of fractal dimension mean
• radius_worst: "Worst" measurement (not listed in the original excerpt)