<jupyter_start><jupyter_text>Data Preparation  Import Libraries<jupyter_code>import numpy as np
import pandas as pd<jupyter_output><empty_output><jupyter_text>Import Data<jupyter_code>loan_data_backup = pd.read_csv('loan_data_2007_2014.csv')
loan_data = loan_data_backup.copy()<jupyter_output><empty_output><jupyter_text>Explore Data<jupyter_code>loan_data
pd.options.display.max_columns = None
loan_data
loan_data.head()
loan_data.tail()
loan_data.columns.values
loan_data.info()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10196078431372549, 'fraction_numerical': 0.01568627450980392, 'mean_word_length': 12.447368421052632, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '191247', 'license': 'MIT', 'n_tokens_mistral': 212, 'n_tokens_neox': 192, 'n_words': 23, 'path': 'notebooks/Credit Risk Modeling - Preparation - 3-1.ipynb', 'repo_name': 'akshayamrit/Udemy-Credit-Risk', 'size': '510'}