Spot checking Decision Tree Classifier for firewall logs. Original data (18 columns) trimmed to 10.

Spot Checking Classifier

# CART Classification
import pandas as pd
from sklearn import model_selection
from sklearn.tree import DecisionTreeClassifier
dataframe = pd.read_csv("data.csv", names=['ID', 'No.', 'Smth', 'Number', 'Count', 'Count2', 'UDP/TCP', 'RandomNo',
       'IP', 'AUDIT/ALLOW/BLOCK'])
array = dataframe.values
X = array[:,0:9]
Y = array[:,9]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = DecisionTreeClassifier()
results = model_selection.cross_val_score(model, X, Y, cv=kfold)
print(results.mean())

#0.731300116075