Breast Cancer Classification model

Predicting breast cancer using a machine learning model

This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not somebody has breast cancer.

1. Dataset

Wisconsin Diagnostic Breast Cancer Dataset: This dataset has 569 samples with 32 attributes.

2. Features

Attribute information:

• ID number
• 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) smootness (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)

3. Evaluation

The project will be considered successful if we reach accuracy score higher than 95.8% at predicting whether the patient has a malignant or benign tumor.