Implementation of Data Mining for Churn Prediction in Music Streaming Company Using 2020 Dataset

Customer is an important asset in a company as it is the lifeline of a company. For a company to get a new customer, it will cost a lot of money for campaigns. On the other hand, maintaining old customer tend to be cheaper than acquiring a new one. Because of that, it is important to be able to prevent the loss of customers from the products we have. Therefore, customer churn prediction is important in retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural Network, and Logistic Regression to compare the performance generated using data from a company that develops a song streaming application. The company suffers from the churn rate of the customer. Uninstall rate of the customers reaching 90% compared to the customer’s installs. The data will come from Google Analytics, a service from Google that will track the customer’s activity in the music streaming application. After finding out the method that will give the highest accuracy on the churn prediction, the attribute of data that most influence on the churn prediction will be determined

Secure Sharing of Geospatial Wildlife Data

Modern tracking technologies enables new ways for data mining in the wild. It allows wildlife monitoring centers to permanently collect geospatial data in a non-intrusive manner in real-time and at low cost. Unfortunately, wildlife data is exposed to crime and there is already a first reported case of ‘cyber-poaching’. Based on stolen geospatial data, poachers can easily track and kill animals. As a result, cautious monitoring centers limited data access for research and public use. This means that the data cannot fully exploit its potential. We propose a novel solution to overcome the security problem. It allows monitoring centers to securely answer questions from the research community and to provide aggregated data to the public while the raw data is protected against unauthorized third parties. This data service can also be monetized. Several new applications are conceivable, such as a mobile app for preventing conflicts between human and wildlife or for engaging people in wildlife donation. Besides presenting the solution and potential use cases, the intention of present article is to start a discussion about the need for data protection and privacy in the animal world