Data Science x Project Planning

The intended audience for this short blog post are data science practitioners who seek to implement predictive algorithms in a business-project-based setting, with special focus on presenting the work process flow. We will briefly introduce the k-Nearest Neighbors (k-NN) algorithm, and put more emphasis on the key phases, as opposed to walking through the technical theory behind the algorithm and its prediction performance. The example business project here is a typical sales forecasting problem where we want to accurately predict the quantity sold of a number of products in the future, in order to manage our inventory more wisely. The k-NN algorithm is probably better known for its classifier application, where we use a number of nearby points to determine the outcome of our target. The rationale is straight-forward; if we use height and age as our input, and gender as our target, then it makes sense to say that if a person is at age 25 and 6 feet tall, he is more likely to be male, because 5 other people who are at around the same age and with similar height happen to be male.