• Objective: Leveraging deep learning architecture to construct a prediction model for median house prices.

Dataset can be downloaded/loaded using the func:sklearn.datasets.fetch_california_housing function.

1. Data Standardization
2. Create simple linear regression neural network
3. Create dynamic non-linear regression neural network for hyperparameter tuning
4. Comparing the convergence performance of different loss functions
5. Hyperparameter tuning and cross-validation of hidden layers, number of neurons, activation function, and learning rate

• Best parameters: {'activation function': Tanh(), 'number of neurons': 16, 'hidden layers': 2, 'learning rate': 0.05}
• Best mean CV score (MAPE): 20.5663
• Test score (MAPE): 20.1813
• Test R2 Score: 0.792
• Average Accuracy: 79.82%