There are more cool time series libraries for Python than you can shake a stick at. You might have heard of some of them: sktime tslearn tsfresh prophet pyts Each of these libraries has different methods for dealing with the various time series learning tasks — regression, classification and forecasting…

How many dates should you go on before you settle down? The classic “secretary” problem has many names and guises but it’s essentially a mathematical decision theory problem that goes like this: imagine you are interviewing a pool of candidates, and you want to find the best one, but you…

ROCKET vs. Time Series Forest vs. Temporal Convolutional Networks vs. XGBoost — If you dabble in stock trading, as I do, you might wonder how you can tell how the stock is going to do by the time of the closing bell — is it going to close above where it started, or not? There are intraday patterns, surely — people always…

You’ve probably heard of the Poisson distribution, a probability distribution often used for modeling counts, that is, positive integer values. Imagine you’re modeling “events”, like the number of customers that walk into a store, or birds that land in a tree in a given hour. That’s what the…

Forecasting Hourly Bike Rentals in Seoul with pycaret, keras, and Tensorflow Probability — Data: The Seoul Bike Sharing Data Set from UCI Machine Learning Repository. The target is Rented Bike Count. That means this is count data, which is positive integer values. The predictors/features are mostly weather-related, e.g. sunshine, rain, wind, visibility, as well as temporal features such as the hour of the…

Using pycaret and keras to find good models — The SELFBACK dataset contains wearable data of 9 activity classes; 6 ambulatory activities and 3 sedentary activities, performed by 33 participants. Data are recorded with two tri-axial accelerometers sampling at 100Hz, mounted on the dominant side wrist and the thigh of the participant. Experimental Structure First, we create windows of data for…

(for Heart Failure Survival Prediction) — The original paper is as follows: Chicco, D., Jurman, G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20, 16 (2020). https://doi.org/10.1186/s12911-020-1023-5 This data set has 12 features and you can download it from the UCI…

Well, it wasn’t a complete waste, you live and learn. But I spent a lot of time on the project and wanted to write it up as an academic paper — which means you have to consider what added value your paper is offering to the world, and I realized…

This is the second in my Machine Learning Mini Project series, where I take some public data sets, such as those found in the UCI Machine Learning Repository (which are often used in peer reviewed papers, so have some level of vetting), and see…