The objective is to predict the Median House value of houses in different types of localities in California.
The model is trained on Kaggle dataset - https://www.kaggle.com/datasets/camnugent/california-housing-prices.
Experiment tracking is done using MLFlow on DagsHub remote tracking server.

The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.

• longitude
• latitude
• housing_median_age
• total_rooms
• total_bedrooms
• population
• households
• median_income
• median_house_value
• ocean_proximity

Notebook preview url - https://k14anb.github.io/California-Housing-Prices/Notebooks/California-Housing-Prices.html

MLFlow Experiments Overview - https://dagshub.com/K14aNB/California-Housing-Prices.mlflow/#/experiments/0?viewStateShareKey=d537fdc0c959fa49a2e0b67727b54010fcd4ffd194d195a2598ac3fdf5c6e596