import gradio as gr import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings("ignore") # Mock data generation for demo (replace with your actual data loading) def generate_mock_data(): np.random.seed(42) n_samples = 4269 # Generate synthetic data similar to your dataset data = { 'no_of_dependents': np.random.randint(0, 6, n_samples), 'education': np.random.choice([' Graduate', ' Not Graduate'], n_samples), 'self_employed': np.random.choice([' Yes', ' No'], n_samples), 'income_annum': np.random.normal(5000000, 2000000, n_samples), 'loan_amount': np.random.normal(15000000, 8000000, n_samples), 'loan_term': np.random.choice(range(2, 21), n_samples), 'cibil_score': np.random.normal(600, 100, n_samples), 'residential_assets_value': np.random.exponential(5000000, n_samples), 'commercial_assets_value': np.random.exponential(3000000, n_samples), 'luxury_assets_value': np.random.exponential(2000000, n_samples), 'bank_asset_value': np.random.exponential(4000000, n_samples), } # Create loan_status based on cibil_score (main predictor from your analysis) loan_status = [] for score in data['cibil_score']: if score > 550: loan_status.append(' Approved' if np.random.random() > 0.15 else ' Rejected') else: loan_status.append(' Rejected' if np.random.random() > 0.15 else ' Approved') data['loan_status'] = loan_status return pd.DataFrame(data) # Load and prepare data def prepare_model(): # Generate mock data (replace with your actual data loading) df = generate_mock_data() # Create dummy variables loan_dummies = pd.get_dummies(df) loan_dummies.rename(columns={ 'education_ Graduate': 'education', 'self_employed_ Yes': 'self_employed', 'loan_status_ Approved': 'loan_status' }, inplace=True) # Drop redundant columns cols_to_drop = ['education_ Not Graduate', 'self_employed_ No', 'loan_status_ Rejected'] loan_dummies = loan_dummies.drop([col for col in cols_to_drop if col in loan_dummies.columns], axis=1) # Separate features and target y = loan_dummies['loan_status'] X = loan_dummies.drop(['loan_status'], axis=1) # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train Random Forest model rf_model = RandomForestClassifier( n_estimators=150, max_depth=None, min_samples_leaf=1, min_samples_split=5, random_state=42 ) rf_model.fit(X_train, y_train) return rf_model, X.columns.tolist() # Initialize model model, feature_names = prepare_model() def predict_loan_approval( no_of_dependents, education, self_employed, income_annum, loan_amount, loan_term, cibil_score, residential_assets_value, commercial_assets_value, luxury_assets_value, bank_asset_value ): # Prepare input data input_data = { 'no_of_dependents': no_of_dependents, 'income_annum': income_annum, 'loan_amount': loan_amount, 'loan_term': loan_term, 'cibil_score': cibil_score, 'residential_assets_value': residential_assets_value, 'commercial_assets_value': commercial_assets_value, 'luxury_assets_value': luxury_assets_value, 'bank_asset_value': bank_asset_value, 'education': 1 if education == "Graduate" else 0, 'self_employed': 1 if self_employed == "Yes" else 0 } # Create DataFrame with correct column order input_df = pd.DataFrame([input_data]) input_df = input_df.reindex(columns=feature_names, fill_value=0) # Make prediction prediction = model.predict(input_df)[0] probability = model.predict_proba(input_df)[0] # Get feature importance for this prediction feature_importance = dict(zip(feature_names, model.feature_importances_)) top_features = sorted(feature_importance.items(), key=lambda x: x[1], reverse=True)[:5] # Format result result = "✅ **APPROVED**" if prediction == 1 else "❌ **REJECTED**" confidence = f"Confidence: {max(probability):.2%}" # Format top features feature_text = "\n**Top 5 Important Features:**\n" for feature, importance in top_features: feature_text += f"• {feature}: {importance:.3f}\n" # Add interpretation based on your analysis interpretation = "\n**Key Insights:**\n" if cibil_score > 550: interpretation += "• Credit score is above the critical threshold (550) ✓\n" else: interpretation += "• Credit score is below the critical threshold (550) ⚠️\n" if loan_term <= 4: interpretation += "• Short loan term increases approval chances ✓\n" elif loan_term > 10: interpretation += "• Long loan term may reduce approval chances ⚠️\n" if income_annum > 5000000: interpretation += "• Above median annual income ✓\n" return f"{result}\n{confidence}\n{feature_text}{interpretation}" # Create Gradio interface with gr.Blocks(title="Loan Prediction System", theme=gr.themes.Soft()) as demo: gr.Markdown(""" # 🏦 Loan Approval Prediction System This application predicts loan approval based on various financial and personal factors. The model achieves **97%+ accuracy** using Random Forest algorithm. ## Key Findings from Analysis: - **Credit Score (CIBIL)** is the most important factor - Scores above 550 significantly increase approval chances - Short-term loans (2-4 years) have higher approval rates - Higher annual income correlates with loan approval """) with gr.Row(): with gr.Column(): gr.Markdown("### 👤 Personal Information") no_of_dependents = gr.Slider( minimum=0, maximum=5, step=1, value=2, label="Number of Dependents" ) education = gr.Radio( choices=["Graduate", "Not Graduate"], value="Graduate", label="Education Level" ) self_employed = gr.Radio( choices=["Yes", "No"], value="No", label="Self Employed" ) gr.Markdown("### 💰 Financial Information") income_annum = gr.Number( value=5000000, label="Annual Income (₹)", info="Enter your annual income in rupees" ) loan_amount = gr.Number( value=15000000, label="Loan Amount (₹)", info="Enter requested loan amount in rupees" ) loan_term = gr.Slider( minimum=2, maximum=20, step=1, value=4, label="Loan Term (Years)" ) cibil_score = gr.Slider( minimum=300, maximum=850, step=1, value=650, label="CIBIL Score", info="Credit score (300-850)" ) with gr.Column(): gr.Markdown("### 🏠 Asset Information") residential_assets_value = gr.Number( value=5000000, label="Residential Assets Value (₹)", info="Value of residential properties" ) commercial_assets_value = gr.Number( value=3000000, label="Commercial Assets Value (₹)", info="Value of commercial properties" ) luxury_assets_value = gr.Number( value=2000000, label="Luxury Assets Value (₹)", info="Value of luxury items" ) bank_asset_value = gr.Number( value=4000000, label="Bank Assets Value (₹)", info="Value of bank deposits/investments" ) gr.Markdown("### 🔮 Prediction") predict_btn = gr.Button("Predict Loan Approval", variant="primary", size="lg") result_output = gr.Markdown(label="Prediction Result") # Examples gr.Markdown("### 📝 Try These Examples:") examples = gr.Examples( examples=[ [2, "Graduate", "No", 6000000, 20000000, 4, 700, 8000000, 5000000, 3000000, 6000000], [1, "Graduate", "Yes", 8000000, 25000000, 2, 750, 10000000, 8000000, 5000000, 8000000], [3, "Not Graduate", "No", 3000000, 10000000, 10, 500, 2000000, 1000000, 500000, 2000000], [0, "Graduate", "No", 10000000, 30000000, 5, 800, 15000000, 12000000, 8000000, 10000000], ], inputs=[ no_of_dependents, education, self_employed, income_annum, loan_amount, loan_term, cibil_score, residential_assets_value, commercial_assets_value, luxury_assets_value, bank_asset_value ] ) # Connect button to prediction function predict_btn.click( fn=predict_loan_approval, inputs=[ no_of_dependents, education, self_employed, income_annum, loan_amount, loan_term, cibil_score, residential_assets_value, commercial_assets_value, luxury_assets_value, bank_asset_value ], outputs=result_output ) gr.Markdown(""" ### 📊 Model Performance - **Accuracy**: 97.3% - **Precision**: 97.8% - **Recall**: 97.9% - **F1 Score**: 97.9% ### 🔍 About the Model This Random Forest model was trained on loan application data and uses the following key insights: - Credit score is the most important predictor - Loan term and annual income are significant factors - Asset values provide additional context - Demographic factors have minimal impact """) if __name__ == "__main__": demo.launch(share=True)