House Price Prediction

Explore our California House Price Prediction example, which uses real data from over 20,000 California households. This regression model has been trained to predict continuous values, such as house prices, based on various features like median income, house age, and location. By using this tool, you can gain insights into:

• Identifying key drivers of outcomes: Understand how different factors, such as demographic or product-related data, influence predictions.
• Applying regression models to business problems: Learn how regression analysis can predict continuous outcomes, whether for pricing strategies, customer behavior, or demand forecasting.
• Supporting data-driven decision-making: Use regression analysis to identify trends and relationships that help optimize business strategies and resource allocation.

This example demonstrates how regression models can be applied to a variety of business contexts, turning complex datasets into actionable insights for better decision-making and strategic planning.

Make a selection to predict a house price

Median Income in Block Group Less than $20,000 $20,000 - $40,000 $40,000 - $60,000 $60,000 - $80,000 More than $80,000

Median House Age in Block Group (1 - 52 years)

26

years

Average Number of Rooms (1 - 15)

7

rooms

Average Number of Bedrooms (1 - 5)

3

bedrooms

Population in Block Group Less than 500 500 - 1,000 1,000 - 2,000 2,000 - 3,000 3,000 - 4,000 More than 4,000

Average Occupancy per Household (1 - 5)

3

persons

Region Northern California Central California Southern California Bay Area

Predict