SVM example and parameter optimization through grid search

Here, we are taking a breast cancer dataset wherein we have classified according to whether the cancer is benign/malignant.

The following is for importing all the required libraries:

import pandas as pd
import numpy as np
from sklearn import svm, datasets
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.utils import shuffle
%matplotlib inline

Now, let's load the breast cancer dataset:

BC_Data = datasets.load_breast_cancer()

The following allows us to check the details of the dataset:

print(BC_Data.DESCR)

This if for splitting the dataset into train and test:

X_train, X_test, y_train, y_test = train_test_split(BC_Data.data, BC_Data.target, random_state...