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#import fireducks.pandas as pd | |
import pandas as pd | |
from sklearn.model_selection import train_test_split | |
from sklearn.preprocessing import LabelEncoder, StandardScaler | |
class DataProcessor: | |
def __init__(self, csv_file): | |
self.data = pd.read_csv(csv_file) | |
self.label_encoders = {} | |
self.scaler = StandardScaler() | |
def preprocess_data(self): | |
for column in ['IP', 'Hostnames', 'OS']: | |
self.label_encoders[column] = LabelEncoder() | |
self.data[column] = self.label_encoders[column].fit_transform(self.data[column].astype(str)) | |
self.data['Port'] = self.scaler.fit_transform(self.data[['Port']]) | |
self.data['Timestamp'] = pd.to_datetime(self.data['Timestamp']).astype(int) / 10**9 | |
return self.data | |
def get_features_and_labels(self, label_column='Anomaly'): | |
if label_column not in self.data.columns: | |
raise ValueError(f"Label column '{label_column}' not found in data.") | |
X = self.data.drop([label_column], axis=1) | |
y = self.data[label_column] | |
return X, y | |
def split_data(self, X, y, test_size=0.2, random_state=42): | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state) | |
return X_train, X_test, y_train, y_test | |
# # Example usage: | |
# processor = DataProcessor('shodan_scan_results.csv') | |
# processed_data = processor.preprocess_data() | |
# X, y = processor.get_features_and_labels() | |
# X_train, X_test, y_train, y_test = processor.split_data(X, y) | |