<jupyter_start><jupyter_text><jupyter_code>from google.colab import drive
drive.mount("/content/gdrive")
import pandas as pd
import numpy as np
import seaborn as sns

from sklearn.preprocessing import StandardScaler
df_train = pd.read_csv('/content/gdrive/MyDrive/UNSW_2018_IoT_Botnet_Final_10_best_Training.csv')
df_train.head()
df_train.info()
df_train.drop(["pkSeqID","seq","subcategory"], axis=1, inplace=True)
df_train.dtypes[df_train.dtypes=='object']
df_train['proto'].value_counts()
df_train['saddr'].value_counts()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13562386980108498, 'fraction_numerical': 0.012658227848101266, 'mean_word_length': 9.862745098039216, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '536627', 'license': 'MIT', 'n_tokens_mistral': 212, 'n_tokens_neox': 203, 'n_words': 31, 'path': 'Iot_botnet_detection_models.ipynb', 'repo_name': 'adi-dev-vikram/IoT_botnet_inspect_detect', 'size': '553'}