Update app.py

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+# Load the trained model
+with open('loandefaulter.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Initialize the scaler
+scaler = StandardScaler()
+
+# Define numerical features for scaling
+num_features = [
+    'loan_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'revol_bal', 'revol_util', 'total_acc', 'mort_acc', 'loan_amnt_by_income'
+]
+
+# Create the Streamlit app
+st.title('Loan Default Prediction')
+st.write('Enter the loan details to get a prediction.')
+
+# Input fields for user data
+loan_amnt = st.number_input('Loan Amount', min_value=0.0)
+int_rate = st.number_input('Interest Rate', min_value=0.0)
+installment = st.number_input('Installment', min_value=0.0)
+annual_inc = st.number_input('Annual Income', min_value=0.0)
+dti = st.number_input('Debt-to-Income Ratio', min_value=0.0)
+revol_bal = st.number_input('Revolving Balance', min_value=0.0)
+revol_util = st.number_input('Revolving Utilization', min_value=0.0)
+total_acc = st.number_input('Total Accounts', min_value=0)
+mort_acc = st.number_input('Mortgage Accounts', min_value=0)
+loan_amnt_by_income = loan_amnt / (annual_inc + 1)
+
+# Create a DataFrame for the input
+input_data = pd.DataFrame({
+    'loan_amnt': [loan_amnt],
+    'int_rate': [int_rate],
+    'installment': [installment],
+    'annual_inc': [annual_inc],
+    'dti': [dti],
+    'revol_bal': [revol_bal],
+    'revol_util': [revol_util],
+    'total_acc': [total_acc],
+    'mort_acc': [mort_acc],
+    'loan_amnt_by_income': [loan_amnt_by_income]
+})
+
+# Scale the input data
+input_data[num_features] = scaler.fit_transform(input_data[num_features])
+
+# Predict using the model
+if st.button('Predict'):
+    prediction = model.predict(input_data)
+    st.write(f'Prediction: {"Charged Off" if prediction[0] == 1 else "Not Charged Off"}')