Customer churn analysis using XGBoosted decision trees

Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.

Guided Exploration

Many monitoring tools that help developers in analyzing the run-time behavior of their applications share a common shortcoming: they require their users to have a fair amount of experience in monitoring applications to understand the used terminology and the available analysis features. Consequently, novice users who lack this knowledge often struggle to use these tools efficiently. In this paper, we introduce the guided exploration (GE) method that aims to make interactive monitoring tools easier to use and learn. In general, tools that implement GE should provide four support operations on each analysis step: they should automatically (1) detect and (2) highlight the most important information on the screen, (3) explain why it is important, and (4) suggest which next steps are appropriate. This way, tools guide users through their analysis processes, helping them to explore the root cause of a problem. At the same time, users learn the capabilities of the tool and how to use them efficiently. We show how GE can be implemented in new monitoring tools as well as how it can be integrated into existing ones. To demonstrate GE's feasibility and usefulness, we present how we extended the memory monitoring tool AntTracks to provided guided exploration support during memory leak analysis and memory churn analysis. We use these guidances in two user scenarios to inspect and improve the memory behavior of the monitored applications. We hope that our contribution will help usability researchers and developers in making monitoring tools more novice-friendly by improving their usability and learnability.