Keras for Regression

Regression models predict continuous numeric values.

Examples:

• Predicting house prices
• Predicting temperature
• Forecasting sales
• Predicting age
• Predicting a score or rating

For this section, we will use the California Housing dataset, a real tabular dataset commonly used in ML.

We will cover:

1. Loading the dataset
2. Normalising the inputs
3. Building a regression network
4. Training it
5. Evaluating it (MAE/MSE/RMSE)
6. Predicting new values

Load the California Housing Dataset

The dataset comes from scikit-learn.

Check shapes:

There are 8 numerical features per house (median income, rooms, population, etc.).

Train/Test Split

We’ll split the dataset manually:

Normalise the Inputs

Neural networks must work with normalised features for stability.

Keras has a built-in preprocessing layer:

This layer:

• Computes mean and variance on training data
• Applies normalisation consistently during training and inference

Build a Regression Model

Simple MLP for regression:

Important details:

• No activation on final layer
• Regression networks predict raw values

Compile the Model

Regression uses:

• Loss: mean squared error (MSE)
• Metrics: mean absolute error (MAE)

MAE is the easiest to interpret (units are same as target).

Train the Model

This dataset trains very quickly.

Evaluate on Test Set

Typical results:

• MAE ≈ 0.40–0.55
• RMSE ≈ 0.55–0.70

This means your predictions are off by about:

• $0.40–$0.55 median house price units
• The dataset target is in $100,000s, so MAE ≈ 0.5 means ≈ $50,000 error

Making Predictions

Visualising Loss Curves

Improving Regression Models (Quick Tips)

Add more layers / units - Regression tasks often need deeper networks.

Add regularisation

Use Dropout

Use learning rate schedules

Train for more epochs - But watch for overfitting.

Try the Functional API - Useful for complex tabular models.

Full Working Script