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Medium⏱ time2 hr$ cost0

House Price Predictor

Fit a model to housing data so it estimates a price from size, rooms and location.

Start building ↓
fig — working model Result first

The build

Step 01

Load data

Read the dataset of homes and prices.

Step 02

Pick features

Choose size, rooms, location, etc.

Step 03

Train regression

Fit a model to predict price.

Step 04

Predict

Enter a house and get an estimate.

Working Principle

The model finds the best-fit relationship between features and price, then plugs a new house's numbers into that formula.

The science behind it

A closer look

Linear regression learns a weight for each feature; the prediction is their weighted sum plus a baseline.

Take it further

Variables to test

  • 1 Drop a feature — how much does error rise?
  • 2 Which feature matters most?
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