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I am trying to perform a comparison between 5 algorithms against the KDD Cup 99 dataset and the NSL-KDD datasets using Python and I am having an issue when trying to build and evaluate the models against the KDDCup99 dataset and the NSL-KDD dataset.

Whenever I try to run the algorithms on the datasets I get the following error 'could not convert string to float: S0'

This error is produced during the during the evaluation of the 5 models; Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Classification and Regression Trees, Gaussian Naive Bayes and Support Vector Machines.

Here is the code that I am using to evaluate the datasets:

#Load KDD dataset

dataset = pandas.read_csv('Datasets/KDDCUP 99/kddcup.csv', names = ['duration','protocol_type','service','src_bytes','dst_bytes','flag','land','wrong_fragment','urgent',
'hot','num_failed_logins','logged_in','num_compromised','root_shell','su_attempted','num_root','num_file_creations',
'num_shells','num_access_files','num_outbound_cmds','is_host_login','is_guest_login','count','serror_rate',
'rerror_rate','same_srv_rate','diff_srv_rate','srv_count','srv_serror_rate','srv_rerror_rate','srv_diff_host_rate',
'dst_host_count','dst_host_srv_count','dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate',
'dst_host_srv_diff_host_rate','dst_host_serror_rate','dst_host_srv_serror_rate','dst_host_rerror_rate','dst_host_srv_rerror_rate','class'])


# split data into X and y
array = dataset.values
X = array[:,0:41]
Y = array[:,41]

# Split-out validation dataset
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = cross_validation.train_test_split(X, Y, test_size=validation_size, random_state=seed)

# Test options and evaluation metric
num_folds = 7
num_instances = len(X_train)
seed = 7
scoring = 'accuracy'

# Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))

# evaluate each model in turn
results = []
names = []
for name, model in models:
 kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, 

random_state=seed)

 #Here is where the error is spit out

 cv_results = cross_validation.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) # Could not convert string to float happens here. Scoring uses string. 
 results.append(cv_results)
 names.append(name)
 msg = "%s: %f (%f)" % (name, cv_results.mean()*100, cv_results.std()*100)#multiplying by 100 to show percentage
 print(msg)


# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(Y)
plt.show()

Here is a 3 line sample from the KDDcup99 datatset:

0 tcp http SF 215 45076 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 normal.
0 tcp http SF 162 4528 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 0 normal.
0 tcp http SF 236 1228 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 2 2 1 0 0.5 0 0 0 0 0 normal.

I have tried using label encoding and it still spits out the same error and when I was looking through the sklearn websites, I noticed that the scoring value was for the string type, is this the cause of the issue? and if not, is there a problem with the way I have loaded the dataset?

EDIT I tried removing scoring value from the code and still got the same error.

I notice you mentioned that you used Label encoding but I did it myself and the code runs just fine. I used the 10 percent version of the dataset . Just put this piece of code after you load the dataset:

for column in dataset.columns:
 if dataset[column].dtype == type(object):
 le = LabelEncoder()
 dataset[column] = le.fit_transform(dataset[column])

After label encoding you should use a One Hot Encoder to improve the performance of some algorithms. You should also avoid using cross_validation module as it is deprecated, it will be removed in version 0.20.