Cancer Cell Classification

Breast Cancer Wisconsin (Diagnostic) Data Set

Predict whether the cancer is benign or malignant

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)
2. 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

Notes on Model Building

1. The Libraries used are:

• Numpy, Pandas, Matplotlib, Seaborn for data manipulation and exploratory data analysis

2. Classification Techniques used:

• SVM
• Logistic Regression

To Do

Analysis in

1. KNN
2. Naive Bayes
3. Decision Tree
4. Random Forest