Chapter 3 – Malware Detection with API Calls and PE Headers 

1. Load the dataset using the pandas python library, and this time, add the low_memory=False parameter. Search for what that parameter does.

df = pd.read_csv(file_name, low_memory=False)

2. Prepare the data that will be used for training.

original_headers = list(df.columns.values)
total_data = df[original_headers[:-1]]
total_data = total_data.as_matrix()
target_strings = df[original_headers[-1]]

3. Split the data with the test_size=0.33 parameter.

train, test, target_train, target_test = train_test_split(total_data, target_strings, test_size=0.33, random_state=int(time.time()))

4. Create a set of classifiers that contains DecisionTreeClassifier(), RandomForestClassifier(n_estimators=100), and AdaBoostClassifier():

classifiers = [
RandomForestClassifier(n_estimators=100),
DecisionTreeClassifier(),
AdaBoostClassifier()] 

5. What is an AdaBoostClassifier()?

An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset.

6. Train the model using the three classifiers and print out the metrics of every classifier.

Check the Chapter3-Practice folder for the solution: https://github.com/PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing/tree/master/Chapter%203/Chapter3-Practice.