Neural Network Architecture Recommendations for Loan Prediction

Dataset Characteristics (Key Factors for Architecture Design)

Input Features: 9 carefully selected numerical features
Training Samples: 316,824 (large dataset)
Test Samples: 79,206
Problem Type: Binary classification
Class Distribution: 80.4% Fully Paid, 19.6% Charged Off (moderate imbalance)
Feature Correlations: Low to moderate (max 0.632)
Data Quality: Clean, standardized, no missing values

Recommended Architecture: Moderate Deep Network

Architecture Overview

Input Layer (9 neurons) 
    ↓
Hidden Layer 1 (64 neurons, ReLU)
    ↓
Dropout (0.3)
    ↓
Hidden Layer 2 (32 neurons, ReLU)
    ↓
Dropout (0.2)
    ↓
Hidden Layer 3 (16 neurons, ReLU)
    ↓
Dropout (0.1)
    ↓
Output Layer (1 neuron, Sigmoid)

Detailed Architecture Justification

1. Network Depth: 3 Hidden Layers

Why this choice:

Sufficient complexity: Financial relationships often involve non-linear interactions
Large dataset: 316k samples can support deeper networks without overfitting
Not too deep: Avoids vanishing gradient problems with tabular data
Sweet spot: Balances complexity with training stability

2. Layer Sizes: [64, 32, 16]

Rationale:

Funnel architecture: Progressively reduces dimensionality (9→64→32→16→1)
Power of 2 sizes: Computationally efficient, standard practice
64 first layer: 7x input size allows good feature expansion
Progressive reduction: Enables hierarchical feature learning
16 final layer: Sufficient bottleneck before final decision

3. Activation Functions

ReLU for Hidden Layers:

Computational efficiency: Faster than sigmoid/tanh
Avoids vanishing gradients: Critical for deeper networks
Sparsity: Creates sparse representations
Standard choice: Proven effective for tabular data

Sigmoid for Output:

Binary classification: Perfect for probability output [0,1]
Smooth gradients: Better than step function
Interpretable: Direct probability interpretation

4. Dropout Strategy: [0.3, 0.2, 0.1]

Progressive dropout rates:

Higher early dropout (0.3): Prevents early layer overfitting
Reducing rates: Allows final layers to learn refined patterns
Conservative final dropout: Preserves important final representations
Prevents overfitting: Critical with large dataset

5. Regularization Considerations

Additional techniques to consider:

L2 regularization: Weight decay of 1e-4 to 1e-5
Batch normalization: For training stability (optional)
Early stopping: Monitor validation loss

Alternative Architectures

Option 1: Lighter Network (Faster Training)

Input (9) → Dense(32, ReLU) → Dropout(0.2) → Dense(16, ReLU) → Dropout(0.1) → Output(1, Sigmoid)

When to use: If training time is critical or simpler patterns suffice

Option 2: Deeper Network (Maximum Performance)

Input (9) → Dense(128, ReLU) → Dropout(0.3) → Dense(64, ReLU) → Dropout(0.3) → 
Dense(32, ReLU) → Dropout(0.2) → Dense(16, ReLU) → Dropout(0.1) → Output(1, Sigmoid)

When to use: If computational resources are abundant and maximum accuracy is needed

Option 3: Wide Network (Feature Interactions)

Input (9) → Dense(128, ReLU) → Dropout(0.3) → Dense(128, ReLU) → Dropout(0.2) → 
Dense(64, ReLU) → Dropout(0.1) → Output(1, Sigmoid)

When to use: To capture more complex feature interactions

Training Hyperparameters

Learning Rate Strategy

Initial rate: 0.001 (Adam optimizer default)
Schedule: ReduceLROnPlateau (factor=0.5, patience=10)
Minimum rate: 1e-6

Batch Size

Recommended: 512 or 1024
Rationale: Large dataset allows bigger batches for stable gradients
Memory consideration: Adjust based on GPU/CPU capacity

Optimizer

Adam: Best for most scenarios
Alternative: AdamW with weight decay
Why Adam: Adaptive learning rates, momentum, proven with neural networks

Loss Function

Binary Cross-Entropy: Standard for binary classification
Class weights: Consider class_weight='balanced' due to 80/20 split
Alternative: Focal loss if class imbalance becomes problematic

Training Strategy

Epochs: Start with 100, use early stopping
Validation split: 20% of training data
Early stopping: Patience of 15-20 epochs
Metrics: Track accuracy, precision, recall, AUC-ROC

Why This Architecture is Optimal

1. Matches Data Complexity

9 features suggest moderate complexity needs
Network size proportional to feature count
Sufficient depth for non-linear patterns

2. Handles Class Imbalance

Dropout prevents majority class overfitting
Multiple layers allow nuanced decision boundaries
Sufficient capacity for minority class patterns

3. Computational Efficiency

Not overly complex for the problem
Reasonable training time
Good inference speed

4. Generalization Ability

Progressive dropout prevents overfitting
Balanced depth/width ratio
Suitable regularization

5. Financial Domain Appropriate

Conservative architecture (financial decisions need reliability)
Interpretable through feature importance analysis
Robust to noise in financial data

Expected Performance

Baseline Expectations

Accuracy: 82-85% (better than 80% baseline)
AUC-ROC: 0.65-0.75 (good discrimination)
Precision: 85-90% (low false positives important)
Recall: 75-85% (catch most defaults)

Performance Monitoring

Validation curves: Should show convergence without overfitting
Learning curves: Should indicate sufficient training data
Confusion matrix: Should show balanced performance across classes

Implementation Recommendations

1. Start Simple

Begin with recommended architecture
Establish baseline performance
Iteratively increase complexity if needed

2. Systematic Tuning

First optimize architecture (layers, neurons)
Then tune training hyperparameters
Finally adjust regularization

3. Cross-Validation

Use stratified k-fold (k=5) for robust evaluation
Ensures consistent performance across different data splits

4. Feature Importance

Analyze trained network feature importance
Validates feature selection from EDA
Identifies potential for further feature engineering

This architecture provides an excellent balance of complexity, performance, and reliability for your loan prediction problem.