This study applies data analytics to LTE network performance data to identify the key drivers of customer experience degradation during peak traffic periods. Using a structured approach combining exploratory data analysis, visualisation, hypothesis testing, correlation, and regression modelling, the analysis demonstrates that network congestion—driven primarily by high resource utilisation and traffic demand—is the dominant factor reducing throughput. A Capacity Stress Framework was developed to identify high-risk cells, enabling targeted, data-driven recommendations for capacity investment to improve network performance and optimise resource allocation.

Data Analysis Exam

E5 Anna K

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This study applies data analytics to LTE network performance data to identify the key drivers of customer experience degradation during peak traffic periods. Using a structured approach combining exploratory data analysis, visualisation, hypothesis testing, correlation, and regression modelling, the analysis demonstrates that network congestion—driven primarily by high resource utilisation and traffic demand—is the dominant factor reducing throughput. A Capacity Stress Framework was developed to identify high-risk cells, enabling targeted, data-driven recommendations for capacity investment to improve network performance and optimise resource allocation.

This analysis examines behavioural and financial patterns across 250,000 customers in the Business Banking Group's April 2026 portfolio. Five analytical techniques — Exploratory Data Analysis (EDA), Data Visualisation, Hypothesis Testing, Correlation Analysis, and Logistic Regression — are applied to a 26-variable dataset spanning digital engagement, transaction volumes, credit ratings, and deposit balances.

Criação e Manipulação de dados usando pyplot e ferramentas nativas do rstudio.