| import pandas as pd
|
| import os
|
| import streamlit as st
|
|
|
|
|
| @st.cache_resource(show_spinner=False)
|
|
|
| def load_data():
|
| """
|
| Loads the optimized parquet file from the data directory.
|
| Includes graceful error handling for missing files.
|
| """
|
| file_path = os.path.join("data","loan_cleaned.parquet")
|
| drop_cols = [
|
| 'id', 'member_id', 'url', 'desc', 'emp_title', 'title', 'zip_code',
|
| 'policy_code', 'pymnt_plan', 'application_type', 'initial_list_status',
|
| 'funded_amnt', 'funded_amnt_inv', 'out_prncp', 'out_prncp_inv',
|
| 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int',
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| 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',
|
| 'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d'
|
| ]
|
|
|
| try:
|
| df = pd.read_parquet(file_path,engine='pyarrow')
|
| """
|
| Calculates the global baseline default rate of the portfolio.
|
| This acts as the 'Visual Anchor' for all our risk assessments.
|
| """
|
| df.drop(columns=drop_cols, errors='ignore', inplace=True)
|
|
|
|
|
| bad_statuses = ['Charged Off',
|
| 'Late (31-120 days)',
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| 'In Grace Period',
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| 'Late (16-30 days)',
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| 'Does not meet the credit policy. Status:Charged Off',
|
| 'Default']
|
| df['bad_loan'] = df['loan_status'].apply(lambda x:1 if x in bad_statuses else 0)
|
|
|
| for col in df.select_dtypes(include=['object', 'string']).columns:
|
| df[col] = df[col].astype('category')
|
|
|
|
|
| float_cols = df.select_dtypes(include=['float64']).columns
|
| df[float_cols] = df[float_cols].astype('float32')
|
|
|
|
|
| int_cols = df.select_dtypes(include=['int64']).columns
|
| df[int_cols] = df[int_cols].astype('int32')
|
|
|
|
|
| return df
|
| except FileNotFoundError:
|
| st.error(f"Critical Error: The Dataset was not found at {file_path}. Please check your pipeline")
|
| st.stop()
|
|
|
|
|
