Towards a Data Quality Score in open data (part 2)

<p>I find iterative product development is as key in data as other fields given many data science tasks, e.g. model tuning or feature engineering, can be an endless pursuit: that extra 1% in accuracy is not always worth the effort and defining product increments helps establish the &ldquo;good enough&rdquo; point.</p> <p>With that in mind, set as our delivery target a Minimum Viable Product (MVP) &mdash; a version of the product with just enough features to start learning from users with minimum effort.</p> <p>There were 5 goals for the MVP:</p> <p><a href="https://medium.com/open-data-toronto/towards-a-data-quality-score-in-open-data-part-2-3f193eb9e21d"><strong>Read More</strong></a></p>