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Add breast cancer dataset

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Breast Cancer Wisconsin/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level** Intermediate <br/>
**Recommended Use:** Classification/Anomaly Detection<br/>
**Domain:** Health Sciences <br/>
## Breast Cancer Wisconsin Data Set
---
![](cancer.jpg)
---
The features of this dataset 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.
The target feature records the prognosis (malignant or benign).
Feel free to do **exploratory data analysis** and **data visualization** on the data set. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
**Column Position** | **Attribute Name** | **Description** | **Attribute Type**
--------------------| ------------------------------| --------------------------------------|------------------
#1 | Id number | Sample code number | quantitative
#2 | Clump Thickness | Integer value from 1 to 10 | qualitative
#3 | Uniformity of Cell Size | Integer value from 1 to 10 | qualitative
#4 | Uniformity of Cell Shape | Integer value from 1 to 10 | quantitative
#5 | Marginal Adhesion | Integer value from 1 to 10 | quantitative
#6 | Single Epithelial Cell Size | Integer value from 1 to 10 | qualitative
#7 | Bare Nuclei | Integer value from 1 to 10 | qualitative
#8 | Bland Chromatin | Integer value from 1 to 10 | quantitative
#9 | Normal Nucleoli | Integer value from 1 to 10 | qualitative
#10 | Mitoses | Integer value from 1 to 10 | quantitative
#11 | Class | 2 for benign, 4 for malignant | qualitative
---
### Acknowledgement
[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original))
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  • Muhammad @bilostacey commented

    Nice work on updating the dataset—these versions and commit logs really help track changes clearly. For anyone interested in exploring tools that reveal deeper patterns in one’s journey, check out https://destinymatrixfree.com/

  • Solo @solosuccess15 commented

    Great work on maintaining and updating this dataset — clear commit histories like this make it much easier for others to follow changes and use the data effectively. For anyone interested in checking PC performance and identifying potential slowdowns, you can visit https://bottlenackcalculator.com/ — it’s a simple and practical tool to analyze and optimize system performance.

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