Binary Classification with Dense Neural Networks

Goal: Build, train, and evaluate a dense neural network for simple tasks such as binary classification.

Methodology

This project involved designing and optimizing a neural network for binary classification. The workflow included:

1. Data preprocessing (normalization and handling class imbalance using class weights)
2. Model architecture design and experimentation
3. Training and validation
4. Performance evaluation and hyperparameter tuning

Model Architecture

Multiple configurations were tested, with the most effective model consisting of:

• 4 Dense layers: [64, 32, 16, 1]
• Activation functions: ReLU for hidden layers, Sigmoid for the output
• Regularization techniques: Batch Normalization and Dropout

This architecture outperformed a simpler 3-layer model, highlighting the importance of carefully choosing the number of layers and neurons.

Key Techniques

• Data Preprocessing: Normalization improved model convergence. Class weights helped address imbalanced classes, significantly boosting classification accuracy.
• Regularization: Batch Normalization and Dropout improved model stability and generalization.
• Hyperparameter Tuning: Adjusting the learning rate and number of epochs showed measurable effects on performance.

Results

• The best configuration (4 layers + regularization) achieved a classification accuracy of ~0.78.
• Visualizations of loss and accuracy over epochs indicated better generalization with the use of regularization techniques.

Conclusion

Designing an effective neural network is not a one-size-fits-all process. It requires a systematic approach, continuous experimentation, and a solid understanding of both the data and the model's parameters.