2 posts tagged with "Scikit-Learn"

Predicting customer churn from a telecom provider

I’ve always believed that to truly learn data science you need to practice data science and I wanted to do this project to practice working with imbalanced classes in classification problems. This was also a perfect opportunity to start working with mlflow to help track my machine learning experiments: it allows me to track the different models I have used, the parameters I’ve trained with, and the metrics I’ve recorded.

This project was aimed at predicting customer churn using the telecommunications data found on Kaggle (which is a publicly available synthetic dataset). That is, we want to be able to predict if a given customer is going the leave the telecom provider based on the information we have on that customer. Now, why is this useful? Well, if we can predict which customers we think are going to leave before they leave then we can try to do something about it! For example, we could target them with specific offers, and maybe we could even use the model to provide us insight into what to offer them because we will know, or at least have an idea, as to why they are leaving.

An end-to-end data science project, from data collection to model deployment, aimed at predicting user interaction on Strava activities based on the given activity’s attributes.

Strava is a service for tracking human exercise which incorporates social network type features. It is mostly used for cycling and running, with an emphasis on using GPS data. A typical Strava post from myself is shown below and we can see that it contains a lot of information: distance, moving time, pace, elevation, weather, GPS route, who I ran with, etc., etc.