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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!")