Python Idle
Your real-estate analytics team has a housing dataset with dozens of numeric features — square footage, number of rooms, tax rates, crime indices, and more. Visualizing this many dimensions is impossible, and models are overfitting. Your manager asks: "Can you compress the data down to just two dimensions so we can actually see what's going on?" Enter Principal Component Analysis.
~15 minscenario510 rows
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Goals: 4 tests
should produce a PCA-transformed array with 2 columns
should compute explained variance ratios
total explained variance should be greater than 0.4
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