Ibtisam Mogul

As retailers embrace the online shopping experience and technology advances, it is now also vital for retailers to pay attention to customer churn since it has a detrimental impact on the company's corporate development and reputation. To mitigate the negative effects of customer churn on grocery retail businesses, this study will look at how machine learning and deep learning churn prediction models are applied, as well as data analytical findings on customer retention. The implications of customer churn and how it impacts grocery businesses will be the subject of thorough research.
Furthermore, an analysis of previously gathered data sets will reveal significant discoveries, customer preferences, and behaviours related to Churn.
The study will examine how churn prediction affects a company's profitability, reputation, and operational efficiency.
Following the study of the dataset, a thorough framework will be suggested with the main goal of proactive churn control, thereby limiting its effects on the overall growth of the company.
This thesis aims to contribute to current efforts to improve corporate company growth by studying customer behavioural patterns most associated with churn and then suggesting solutions to the challenges.

This research study aims to evaluate the significance of Technology and Industry 4.0 for Student Performance in Higher Education. Industry 4.0 is part of digital revolution which amalgamates various technologies like AI, distributed computing, virtual reality (VR), Internet of Things (IoT) & Big Data to bring a fundamental transformation in the current industry. The integration of these technologies has benefited all domains of society including Education. Education 4.0 aims to use Industry Revolution 4.0 technologies to the benefit of education field by providing means to improve the education sector using techniques like Education Mining, Prediction and Prescription of student's performance during their learning duration at universities. This study tries to highlight some important literature in the area of Industrial Revolution 4.0, Education 4.0, Big Data, Machine Learning, Descriptive, Predictive and Prescriptive analysis as well as learning analytics tools to provide a guidance for the stakeholders of the Education Industry to enrich their process for getting improved student performances at risk.