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07f0306 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | import pandas as pd
import gradio as gr
from sklearn.tree import DecisionTreeRegressor
from sklearn.preprocessing import LabelEncoder
# Sample BFSI dataset
data = {
'Age': [25, 45, 30, 50, 28, 42],
'Income': [300000, 1200000, 600000, 1500000, 350000, 1000000],
'CreditScore': [720, 800, 760, 820, 710, 790],
'RiskAppetite': ['High', 'Low', 'Medium', 'Low', 'High', 'Medium'],
'LoanAmount': [200000, 600000, 400000, 750000, 220000, 580000]
}
# Prepare data
df = pd.DataFrame(data)
le = LabelEncoder()
df['RiskAppetiteEncoded'] = le.fit_transform(df['RiskAppetite'])
X = df[['Age', 'Income', 'CreditScore', 'RiskAppetiteEncoded']]
y = df['LoanAmount']
# Train model
model = DecisionTreeRegressor(random_state=42)
model.fit(X, y)
# Prediction function
def predict_loan(age, income, credit_score, risk_appetite):
encoded_risk = le.transform([risk_appetite])[0]
input_df = pd.DataFrame([[age, income, credit_score, encoded_risk]],
columns=['Age', 'Income', 'CreditScore', 'RiskAppetiteEncoded'])
prediction = model.predict(input_df)[0]
return f"Predicted Loan Amount: ₹{prediction:,.0f}"
# Build Gradio interface
app = gr.Interface(
fn=predict_loan,
inputs=[
gr.Number(label="Age"),
gr.Number(label="Annual Income (₹)"),
gr.Number(label="Credit Score"),
gr.Dropdown(choices=['High', 'Medium', 'Low'], label="Risk Appetite")
],
outputs=gr.Textbox(label="Loan Prediction"),
title="Loan Amount Predictor (Tree-Based Regression)",
description="Enter customer details to predict the loan amount using a decision tree model."
)
# Launch the app
app.launch()
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