Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Load trained model
+with open('logistic_regression_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Load scaler
+with open('scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+
+# Function to predict default payment next month
+def predict_default(data):
+    scaled_data = scaler.transform([data])
+    prediction = model.predict(scaled_data)
+    return prediction[0]
+
+# Creating a simple form
+st.title("Credit Default Prediction")
+st.write("Enter the details to predict default payment next month")
+
+# Input fields
+limit_balance = st.number_input('Limit Balance', min_value=0)
+sex = st.selectbox('Sex', options=[1, 2], format_func=lambda x: 'Male' if x == 1 else 'Female')
+education_level = st.selectbox('Education Level', options=[1, 2, 3, 4, 5, 6], format_func=lambda x: {1: 'graduate school', 2: 'university', 3: 'high school', 4: 'others', 5: 'unknown', 6: 'unknown'}.get(x, 'unknown'))
+marital_status = st.selectbox('Marital Status', options=[1, 2, 3], format_func=lambda x: {1: 'married', 2: 'single', 3: 'others'}.get(x, 'unknown'))
+age = st.number_input('Age', min_value=0)
+bill_amts = [st.number_input(f'Bill Amount {i+1}', min_value=0) for i in range(6)]
+pay_amts = [st.number_input(f'Previous Payment {i+1}', min_value=0) for i in range(6)]
+
+# Predict button
+if st.button("Predict"):
+    # On predict button click, predict and display the result
+    features = [limit_balance, sex, education_level, marital_status, age] + bill_amts + pay_amts
+    prediction = predict_default(features)
+    if prediction == 1:
+        st.write("The client is likely to default next month.")
+    else:
+        st.write("The client is unlikely to default next month.")