<jupyter_start><jupyter_text>This notebook generates two html files, showing a summary of training data<jupyter_code>!pip install sweetviz
import pandas as  pd
import sweetviz as sv

# import raw training set
df = pd.read_csv('https://raw.githubusercontent.com/KushajveerSingh/ds_cup/main/data_orig/train.csv')

#analyzing the dataset
advert_report = sv.analyze(df)
#display the report
advert_report.show_html('EDA.html')<jupyter_output><empty_output><jupyter_text>Returning the summary between two classes of target variable<jupyter_code># separate two classes to do EDA on each 
df1 = df[df['Default_ind'] == 1]
df0 = df[df['Default_ind'] == 0]

# delete problematic column
del df1['auto_open_36_month_num']
del df0['auto_open_36_month_num']

dfc = sv.compare(df1, df0)
dfc.show_html('Compare.html')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10709987966305656, 'fraction_numerical': 0.01444043321299639, 'mean_word_length': 7.1568627450980395, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '454436', 'license': 'Apache-2.0', 'n_tokens_mistral': 304, 'n_tokens_neox': 296, 'n_words': 70, 'path': 'Codes/EDA.ipynb', 'repo_name': 'KushajveerSingh/ds_cup', 'size': '831'}