https://github.com/sn2606/breast-cancer-wisconsin-diagnostic

Data Visualization of the Breast Cancer Wisconsin diagnostic dataset
https://github.com/sn2606/breast-cancer-wisconsin-diagnostic

breast-cancer-wisconsin data-visualization matplotlib plotting python-3 seaborn-plots

Last synced: 26 days ago
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Data Visualization of the Breast Cancer Wisconsin diagnostic dataset

• Host: GitHub
• URL: https://github.com/sn2606/breast-cancer-wisconsin-diagnostic
• Owner: sn2606
• Created: 2021-02-18T18:25:32.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-05-03T05:08:28.000Z (almost 4 years ago)
• Last Synced: 2024-11-26T08:08:36.950Z (3 months ago)
• Topics: breast-cancer-wisconsin, data-visualization, matplotlib, plotting, python-3, seaborn-plots
• Language: Python
• Homepage: https://www.kaggle.com/swaranjananayak/bcwd-data-viz
• Size: 9.14 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Breast-Cancer-Wisconsin-Diagnostic

Data Visualization of the Breast Cancer Wisconsin diagnostic dataset.

#

## About this Dataset

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image.

n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

```

ftp ftp.cs.wisc.edu

cd math-prog/cpo-dataset/machine-learn/WDBC/

```

Also can be found on [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)

Attribute Information:

1) ID number

2) Diagnosis (M = malignant, B = benign)

3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)

b) texture (standard deviation of gray-scale values)

c) perimeter

d) area

e) smoothness (local variation in radius lengths)

f) compactness (perimeter^2 / area - 1.0)

g) concavity (severity of concave portions of the contour)

h) concave points (number of concave portions of the contour)

i) symmetry

j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three

largest values) of these features were computed for each image,

resulting in 30 features. For instance, field 3 is Mean Radius, field

13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits.

Missing attribute values: none

Class distribution: 357 benign, 212 malignant

#

[Kaggle Link for the dataset](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)

[Kaggle Notebook](https://www.kaggle.com/swaranjananayak/bcwd-data-viz)