The following details and explains performing regression on the California housing dataset using a range of ML models:
Linear Regression, Support Vector Regression, Decision Trees, and Random Forest Regression.

Notebook

The dataset contains 20640 entries and 10 variables.

• Longitude
• Latitude
• Housing Median Age
• Total Rooms
• Total Bedrooms
• Population
• Households
• Median Income
• Median House Value
• Ocean Proximity

In the notebook, I perform:

• Data investigation
• Data cleaning
• Removing outliers
• Exploratory data analysis
• Feature engineering
• Dimensionality reduction
• Feature encoding
• Correlation and multicolinearity assessment
• Feature scaling
• Model training (including grid search)

The Random Forest Regression model emerged as the best performer among the trained models, with an average accuracy of $43,658.

• R^2 Score: 0.7933309926525507
• Mean Absolute Error: 29580.49344298964
• Mean Squared Error: 1906039202.1731477
• Root Mean Squared Error: 43658.208875000215
• Mean Absolute Percentage Error: 17.003087000720146%