Steps followed are:

1. Introduction to SVM

Used SVM to build and train a model using human cell records, and classify cells to whether the samples are benign (mild state) or malignant (evil state).

SVM works by mapping data to a highdimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable (This gets done by kernel function of SVM classifier). A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane.


2. Necessary imports

3. About the Cancer data

Original Author UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepositor...]

Public Source https://s3api.usgeo.objectstorage.s...

4. Load Data From CSV File
The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign.

The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4).


5. Distribution of the classes

6. Selection of unwanted columns

7. Remove unwanted columns

8. Divide the data as Train/Test dataset

9. Modeling (SVM with Scikitlearn)

10. Evaluation (Results)