%matplotlib inline
import sys
sys.path.append('..')
from preamble import *
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=0)
svm = SVC(C=100)
svm.fit(X_train, y_train)
print("Test set Accuracy: {:.2f}".format(svm.score(X_test, y_test)))
Test set Accuracy: 0.63
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
svm.fit(X_train_scaled, y_train)
print("Scaled Test Accuracy: {:.2f}".format(svm.score(X_test_scaled, y_test)))
Scaled Test Accuracy: 0.97
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = sc