Kaggle Link - https://www.kaggle.com/code/rasikasrimal/tumor-diagnosis

The Breast Cancer Diagnostic data is available on the UCI Machine Learning Repository. This database is also available through the UW CS ftp server.

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

• ID number
• Diagnosis (M = malignant, B = benign)
• Ten real-valued features are computed for each cell nucleus:
  1. radius (mean of distances from center to points on the perimeter)
  2. texture (standard deviation of gray-scale values)
  3. perimeter
  4. area
  5. smoothness (local variation in radius lengths)
  6. compactness (perimeter^2 / area - 1.0)
  7. concavity (severity of concave portions of the contour)
  8. concave points (number of concave portions of the contour)
  9. symmetry
  10. 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.

• All feature values are recorded with four significant digits.
• Missing attribute values: none
• Class distribution: 357 benign, 212 malignant

To run the notebook, you need to have the following dependencies installed:

• Python 3.x
• Jupyter Notebook
• pandas
• seaborn
• matplotlib

You can install the required packages using:

pip install pandas seaborn matplotlib