Machine learning based multi-variate MBB-user growth prediction and worst-cell clustering in cellular network

Abstract

The thesis will analyze mean user number of cellular networks based on maximum user, data volume and cell availability from a KPI report generated from OSS server. Firstly, we will collect KPI data from OSS server of specific area where large number of cellular users taking service from network and then conduct data cleansing and data splitting. Finally, we will analyze the data with multiple variable regression and support vector machine (SVM) analysis to predict user number. We will also perform K-means clustering method to find the worst cells to improve the network performance and user experience. We will do the analysis with most popular and latest python which boasts the feature of pure object-oriented programming, platform independent, concise and very elegant language. So, we will call the corresponding library function to predict the user number and worst cells clustering which will help a telecom network operator to plan, design an effective and optimized network and also help to improve user experience as well. Moreover, this analysis will help to make cluster plan, and understand user behavior in a specific area.