Early-Stage-Breast-Cancer-Classification

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

STEP #1: PROBLEM STATEMENT

Predicting if the cancer diagnosis is benign or malignant based on several observations/features

30 features are used, examples:

  - radius (mean of distances from center to points on the perimeter)
  - texture (standard deviation of gray-scale values)
  - perimeter
  - area
  - smoothness (local variation in radius lengths)
  - compactness (perimeter^2 / area - 1.0)
  - concavity (severity of concave portions of the contour)
  - concave points (number of concave portions of the contour)
  - symmetry 
  - fractal dimension ("coastline approximation" - 1)

Datasets are linearly separable using all 30 input features
Number of Instances: 569
Class Distribution: 212 Malignant, 357 Benign
Target class:

   - Malignant
   - Benign

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

STEP #2: IMPORTING DATA

#import libraries import pandas as pd # Import Pandas for data manipulation using dataframes import numpy as np # Import Numpy for data statistical analysis import matplotlib.pyplot as plt # Import matplotlib for data visualisation import seaborn as sns # Statistical data visualization #%matplotlib inline #Import Cancer data drom the Sklearn library from sklearn.datasets import load_breast_cancer cancer = load_breast_cancer()

STEP #3: VISUALIZING THE DATA

STEP #4: MODEL TRAINING (FINDING A PROBLEM SOLUTION)

STEP #5: EVALUATING THE MODEL

STEP #6: IMPROVING THE MODEL

Refer Code Notebook

IMPROVING THE MODEL - PART 2

param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']} from sklearn.model_selection import GridSearchCV grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=4) grid.fit(X_train_scaled,y_train)