Analyze The Full Dataset

Lesson 13 of 17

So, to recap: we designed the data transformation pipeline specifically for a dataset of 10,000 trips, covering the early morning of December 1st, 2018. We made many assumptions based off the histograms, correlation matrix and scatterplot grid and baked those assumptions into the design of the machine learning pipeline.

Then we loaded the parquet dataset that covers the full month of December. The regression metrics of the full dataset are considerably worse than the ones for the limited set. This might be because the model was overfitting on the small dataset, or it could be because we now need to alter the data transformations to better match the new patters in the data.

We won’t know for sure until we recalculate the histograms, the correlation matrix and the scatterplots.

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