I fielded an interesting request recently from our PR team, who wanted to generate a creative representation of our data based on the direction and distance of trips booked on our platform. Distance a key attribute of interest for a travel business, so it is naturally easy to retrieve this data. However the direction of a trip is something that had not been previously analyzed, and so it was not available off-the-shelf in our data warehouse. Read more

What are hurdle models? Google explains best, The hurdle model is a two-part model that specifies one process for zero counts and another process for positive counts. The idea is that positive counts occur once a threshold is crossed, or put another way, a hurdle is cleared. — Getting started with hurdle models [University of Virginia Library] What are hurdle models useful for? Many statistical learning models—particularly linear models—assume some level of normality in the response variable being predicted. Read more

Problem You initially built a data pipeline for a project you were working on, but eventually other members of your team started using it as well. You move the logic into Airflow, so that the pipeline is updated automatically on some regular basis. You’d like to set schedule_interval to daily so that the data is always fresh, but you’d also like the ability to execute relatively quick backfills. With a daily schedule, backfilling data from 5 years ago will take days to complete. Read more

My coefficients are bigger than your coefficients I was attempting to fit a simple linear regression model the other day with sklearn.linear_model.LinearRegression but the model was making terribly inaccurate predictions on the test dataset. Upon inspecting the estimated coefficients, I noticed that they were of a crazy magnitude, on the order of billions. For reference, I was predicting a response which was approximately normally distributed with a mean value of 100. Read more

I was looking at my Anki deck stats the other day and realized that I have been using it for just over three years now. During that time I have added 20k cards and reviewed 140k. On average I spent 17 minutes each day to review 130 cards. Since this amounts to over 300 hours of my life at this point, I figured it would be worth reflecting on this habit and deciding whether it is a worthwhile investment of time going forward. Read more