File size: 2,132 Bytes
cda703b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | 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 = [ #reducing noise
'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',
'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)
#creating bad_loan column for statistical analysis
bad_statuses = ['Charged Off',
'Late (31-120 days)',
'In Grace Period',
'Late (16-30 days)',
'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()
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