Python Idle
A postal sorting facility is drowning in handwritten envelopes. They need a system that can read zip codes automatically. You've been handed a dataset of 1,797 handwritten digit images — each one an 8x8 pixel grayscale grid, flattened into 64 features. Your mission: build a classifier that can tell a "3" from an "8" and everything in between. This is your first step into computer vision.
~15 minscenario
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Goals: 4 tests
should create a model with a .predict method
should achieve accuracy above 0.90
should use the correct feature dimensions (64 features)
should split data into train and test sets
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