Add application file

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import pickle
+
+# Load trained models
+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)
+
+# Load PCA
+with open('pca_transformer.pkl', 'rb') as file:
+    pca = pickle.load(file)
+
+# Define the column names as they were used in training
+columns = ['limit_balance', 'sex', 'education_level', 'marital_status', 'age',
+           'pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6',
+           'bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6',
+           'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
+
+
+# Define the columns that were scaled (continuous variables)
+transform_cols = ['limit_balance', 'age', 
+                  'bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6',
+                  'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
+
+# Threshold for deciding on log transformation
+skewness_threshold = 1  # Adjust this based on what you used during training
+
+# Function to predict default payment next month
+def predict_default(features):
+    # Create a DataFrame from the features
+    data = np.array([features])
+    # Initialize a DataFrame to hold the features
+    df = pd.DataFrame(data, columns=columns)  # Ensure 'columns' list matches training
+
+    # Apply log transformation and scaling to the appropriate columns
+    for col in transform_cols:
+        if abs(df[col].skew()) > skewness_threshold:
+            df[col] = np.log1p(df[col])  # Log transformation
+    # Replace any inf/-inf with NaN and fill NaNs with mean
+    df.replace([np.inf, -np.inf], np.nan, inplace=True)
+    df.fillna(df.mean(), inplace=True)
+    # Scale the data
+    scaled_data = scaler.transform(df[transform_cols])
+    # Replace the original columns with scaled ones
+    df[transform_cols] = scaled_data
+
+    # Apply PCA transformation (if you're using PCA in your pipeline)
+    pca_data = pca.transform(df)
+
+    # Predict using the model
+    prediction = model.predict(pca_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)
+# Repayment status
+pay_status = [st.selectbox(f'Payment Status in Month {i+1}', options=list(range(-2,9)), index=4) for i in range(6)]
+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"):
+    # Gather all feature inputs in the exact order and number as the model expects
+    features = [limit_balance, sex, education_level, marital_status, age] + pay_status + bill_amts + pay_amts
+    # Make prediction and display the result
+    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.")

logistic_regression_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2f56acf983a9371f6079168a6f9dbc7db3d1fe8d443de44c23cb00a3df8f5be4
+size 821

pca_transformer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a5dc5da85ec1a2d693ce04093dc6a74ad7527d866b2c2395b9d345c0c51e169d
+size 4425

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d039e6efe6f677ea61becebd0ba1a29e36ce17fa145866ad46fdd8c0352086fc
+size 1044