Python Idle
A local real estate agency has cleaned up their historical sales data and wants to move beyond gut-feel pricing. They've handed you a CSV of 510 past sales and asked a simple question: "Given what we know about a house, can you predict what it will sell for?" Your mission: select the right features, split the data, train a linear regression model, and prove it works with an R-squared score.
~15 minscenario510 rows
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Goals: 3 tests
should create a model with a .predict method
should achieve R-squared > 0.5
predictions should match the test set size
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