import boto3 import pandas as pd from credit_model import CreditScoringModel # Get historic loan data loans = pd.read_parquet("data/loan_table.parquet") # Create model model = CreditScoringModel() # Train model (using Redshift for zipcode and credit history features) if not model.is_model_trained(): model.train(loans) # Make online prediction (using DynamoDB for retrieving online features) loan_request = { "zipcode": [76104], "dob_ssn": ["19630621_4278"], "person_age": [133], "person_income": [59000], "person_home_ownership": ["RENT"], "person_emp_length": [123.0], "loan_intent": ["PERSONAL"], "loan_amnt": [35000], "loan_int_rate": [16.02], } result = model.predict(loan_request) if result == 0: print("Loan approved!") elif result == 1: print("Loan rejected!")