Initial commit of Streamlit loan prediction app

app.py

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+# app.py
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load the trained model and encoders
+loaded_model = joblib.load('loan_prediction_model.joblib')
+label_encoder_education = joblib.load('label_encoder_education.joblib')
+
+
+# Title of the Streamlit app
+st.title("IntuiPy Loan Default Prediction App")
+
+st.write("Enter the details below to predict whether a loan will default or not.")
+
+# Function to make predictions based on user input
+def predict_loan_default(user_input):
+    # Encode categorical features
+    user_input['education'] = label_encoder_education.transform([user_input['education']])[0]
+
+    # Create DataFrame for prediction
+    user_input_df = pd.DataFrame([user_input])
+
+    # Make prediction
+    prediction = loaded_model.predict(user_input_df)
+    return prediction[0]  # Return the predicted class
+
+# Input fields for the user to provide data
+age = st.number_input("Age", min_value=18, max_value=100, value=30)
+education = st.selectbox("Education Level", options=label_encoder_education.classes_)
+loan_amount = st.number_input("Loan Amount", min_value=0, value=5000)
+asset_cost = st.number_input("Asset Cost", min_value=0, value=6000)
+no_of_loans = st.number_input("Number of Loans", min_value=0, value=2)
+no_of_curr_loans = st.number_input("Number of Current Loans", min_value=0, value=1)
+last_delinq_none = st.selectbox("Previously failed to make required payments on time ?(1 for True, 0 for False)", options=[1, 0])
+
+# Prepare the input for prediction
+user_input = {
+    'age': age,
+    'education': education,
+    #'proof_submitted': proof_submitted,
+    'loan_amount': loan_amount,
+    'asset_cost': asset_cost,
+    'no_of_loans': no_of_loans,
+    'no_of_curr_loans': no_of_curr_loans,
+    'last_delinq_none': last_delinq_none
+}
+
+# Predict button
+if st.button("Predict Loan Default"):
+    prediction = predict_loan_default(user_input)
+    result = "There is a likelihood of default. We regret to inform you that we cannot grant your loan" if prediction == 1 else "We are pleased to inform you that you will not default. We will be sending #" +str(loan_amount) +""
+    st.write(f" {result}")

label_encoder_education.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:edcf0413eed88edf524b04fb818e191a4705443722a2d6a6383a925ff4df284c
+size 343

loan_prediction_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:70a421df02baea04e746fc6901c1f8a4a2e92c2927d88e756c68e661bea6c4fc
+size 1355

requirements.txt

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+streamlit
+pandas
+joblib