# 1

# Load cancer data
# separate training and test data
# make svn, train it
# calculate zero_one_loss from sklearn metrics 
# make a for loop for that beautiful

# Plots :)
# plot.semilogx
# 

# More notes:
# Not important to standardise the functions


# 2

# GridSearch package

# create SVM
# Make GridSearchCV
# classifier.fit
# specify different parameters
# test, train predictions and errors
# Test with linear, poly, rbf
# Cross validation might be good to find correct parameters
# Note: Might e a best practice to randomise the data first before training and testing