prediction_helper.py

import joblib
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler


MODEL_PATH = 'artifacts/credit_risk_model.joblib'

model_data = joblib.load(MODEL_PATH)
model = model_data['model']
scaler = model_data['scaler']
features = list(model_data['features'])
cols_to_scale = model_data['cols_to_scale']

# input data from X_train_encoded.columns
# additional fields from cols_to_scale(Assign Dummy values)

def prepare_df(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency, delinquency_ratio,
                                            credit_utilization_ratio,number_of_open_accounts,residence_type,loan_purpose,loan_type):

    input_data = {
        'age' : age,
        'loan_tenure_months' : loan_tenure_months,
        'number_of_open_accounts' : number_of_open_accounts,
        'credit_utilization_ratio' : credit_utilization_ratio,
        'loan_to_income' : loan_amount / income if income > 0 else 0,
        'delinquency_ratio' : delinquency_ratio,
        'avg_dpd_per_delinquency' : avg_dpd_per_delinquency,
        'residence_type_Owned' : 1 if residence_type == 'Owned' else 0,
        'residence_type_Rented' : 1 if residence_type == 'Rented' else 0,
        'loan_purpose_Education' : 1 if loan_purpose == 'Education' else 0,
        'loan_purpose_Home': 1 if loan_purpose == 'Home' else 0,
        'loan_purpose_Personal': 1 if loan_purpose == 'Personal' else 0,
        'loan_type_Unsecured': 1 if loan_type == 'Unsecured' else 0,
        'number_of_dependants' : 1,
        'years_at_current_address': 1,
        'sanction_amount' : 1,
        'processing_fee' : 1,
        'gst' : 1,
        'net_disbursement' : 1,
        'principal_outstanding' : 1,
        'bank_balance_at_application' : 1,
        'number_of_closed_accounts' : 1,
        'enquiry_count' : 1
    }

    df = pd.DataFrame([input_data])
    df[cols_to_scale] = scaler.transform(df[cols_to_scale])
    df = df[features]
    return df

def calculate_credit_score(input_df, base_score = 300, scale_length = 600):
    x = np.dot(input_df.values, model.coef_.T) + model.intercept_
    default_probability = 1 / (1 + np.exp(-x))
    non_default_probability = 1 - default_probability
    credit_score = base_score + non_default_probability.flatten() * scale_length

    def get_rating(score):
        if 300 <= score < 500:
            return 'Poor'
        elif 500 <= score < 650:
            return 'Average'
        elif 650 <= score < 750:
            return 'Good'
        elif 750 <= score < 900:
            return 'Excellent'
        else:
            return 'Undefined'

    rating = get_rating(credit_score[0])

    return default_probability.flatten()[0],int(credit_score), rating

def predict(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency, delinquency_ratio,
                credit_utilization_ratio, number_of_open_accounts, residence_type, loan_purpose, loan_type):

    input_df = prepare_df(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency, delinquency_ratio,
                          credit_utilization_ratio, number_of_open_accounts, residence_type, loan_purpose, loan_type)

    probability, credit_score, rating = calculate_credit_score(input_df)

    return probability, credit_score, rating