add files

Author: dhavalsays

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+.idea
 
 # C extensions
 *.so

artifacts/model_data.joblib

@@ -0,0 +1,62 @@
+import streamlit as st
+from prediction_helper import predict  # Ensure this is correctly linked to your prediction_helper.py
+
+# Set the page configuration and title
+st.set_page_config(page_title="Lauki Finance: Credit Risk Modelling", page_icon="📊")
+st.title("Lauki Finance: Credit Risk Modelling")
+
+# Create rows of three columns each
+row1 = st.columns(3)
+row2 = st.columns(3)
+row3 = st.columns(3)
+row4 = st.columns(3)
+
+# Assign inputs to the first row with default values
+with row1[0]:
+    age = st.number_input('Age', min_value=18, step=1, max_value=100, value=28)
+with row1[1]:
+    income = st.number_input('Income', min_value=0, value=1200000)
+with row1[2]:
+    loan_amount = st.number_input('Loan Amount', min_value=0, value=2560000)
+
+# Calculate Loan to Income Ratio and display it
+loan_to_income_ratio = loan_amount / income if income > 0 else 0
+with row2[0]:
+    st.text("Loan to Income Ratio:")
+    st.text(f"{loan_to_income_ratio:.2f}")  # Display as a text field
+
+# Assign inputs to the remaining controls
+with row2[1]:
+    loan_tenure_months = st.number_input('Loan Tenure (months)', min_value=0, step=1, value=36)
+with row2[2]:
+    avg_dpd_per_delinquency = st.number_input('Avg DPD', min_value=0, value=20)
+
+with row3[0]:
+    delinquency_ratio = st.number_input('Delinquency Ratio', min_value=0, max_value=100, step=1, value=30)
+with row3[1]:
+    credit_utilization_ratio = st.number_input('Credit Utilization Ratio', min_value=0, max_value=100, step=1, value=30)
+with row3[2]:
+    num_open_accounts = st.number_input('Open Loan Accounts', min_value=1, max_value=4, step=1, value=2)
+
+
+with row4[0]:
+    residence_type = st.selectbox('Residence Type', ['Owned', 'Rented', 'Mortgage'])
+with row4[1]:
+    loan_purpose = st.selectbox('Loan Purpose', ['Education', 'Home', 'Auto', 'Personal'])
+with row4[2]:
+    loan_type = st.selectbox('Loan Type', ['Unsecured', 'Secured'])
+
+
+# Button to calculate risk
+if st.button('Calculate Risk'):
+    probability, credit_score, rating = predict(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency,
+                                                delinquency_ratio, credit_utilization_ratio, num_open_accounts,
+                                                residence_type, loan_purpose, loan_type)
+
+    # Display the results
+    st.write(f"Deafult Probability: {probability:.2%}")
+    st.write(f"Credit Score: {credit_score}")
+    st.write(f"Rating: {rating}")
+
+# Footer
+# st.markdown('_Project From Codebasics ML Course_')

main.py

@@ -0,0 +1,92 @@
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+# Path to the saved model and its components
+MODEL_PATH = 'artifacts/model_data.joblib'
+
+# Load the model and its components
+model_data = joblib.load(MODEL_PATH)
+model = model_data['model']
+scaler = model_data['scaler']
+features = model_data['features']
+cols_to_scale = model_data['cols_to_scale']
+
+def prepare_df(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency,
+            delinquency_ratio, credit_utilization_ratio, num_open_accounts,
+            residence_type, loan_purpose, loan_type):
+
+    input_data = {
+        'age': age,
+        'loan_tenure_months': loan_tenure_months,
+        'number_of_open_accounts': num_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,
+        # add addtional fields
+        'number_of_dependants': 1,  # Dummy value
+        'years_at_current_address': 1,  # Dummy value
+        'zipcode': 1,  # Dummy value
+        'sanction_amount': 1,  # Dummy value
+        'processing_fee': 1,  # Dummy value
+        'gst': 1,  # Dummy value
+        'net_disbursement': 1,  # Computed dummy value
+        'principal_outstanding': 1,  # Dummy value
+        'bank_balance_at_application': 1,  # Dummy value
+        'number_of_closed_accounts': 1,  # Dummy value
+        'enquiry_count': 1  # Dummy value
+    }
+
+    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_lenth=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_lenth
+    # Determine the rating category based on the credit score
+    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'  # in case of any unexpected score
+
+    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, num_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, num_open_accounts,
+                residence_type, loan_purpose, loan_type)
+
+    probability, credit_score, rating = calculate_credit_score(input_df)
+
+    return probability, credit_score, rating