Predicting the probability of customer churn using deep survival models
Researcher: Mafanedza Nephawe, University of the Witwatersrand, Johannesburg
Supervisor: Dr. M. Arasomwan

Researcher: Mafanedza Nephawe, University of the Witwatersrand, Johannesburg
Supervisor: Dr. M. Arasomwan
Researcher: Dzhivhuho Asakundwi Praisethelord, University of Venda
Supervisor: Dr. M. Arasomwan
Researcher: Vincent Mohale, Sol Plaatjie University
Supervisor: Dr Ibidun Obagbuwa
Bloemfontein, a city, where wheat has long been a culinary cornerstone, faces a silent transformation which is underway. Wheat, the bedrock of staples like bread, cereals, and the essence of beverages such as beer and whiskey, now faces a challenging foe: climatechange.
In this study, we delve into the heart of this global staple, unravelling the complex web of connections that link climate change to wheat yield in Bloemfontein.
Researcher: Jaden Pieterse, Sol Plaatjie University
Supervisor: Dr Martins Arosamwan, University of the Witwatersrand, Johannesburg
The need for accurate demand and sales forecasting is important for companies to perform better. Due to rapid technological development, e-commerce platforms face challenges with finding proper models to deal with market demand. LSTM is considered to perform best in predicting demand.
Researcher: Joey J. Assabil
Supervisor: Dr C. Obagbuwa
Credit Card Fraud (CCF) hasbeen a worldwide conundrumresulting in millions of losses.The research compares theefficiency of Traditional algorithms (LogisticRegression, Decision Trees,K-Nearest Neighbor) andEnsemble algorithms(Random Forest, Adaboost,Xgboost) in detectingfraudulent patterns withindatasets.
Researcher: Kgoale Tshiamo Mahlako
Supervisor: Dr A Whata
Survival analysis is a vital field in statistics, offering valuable insights into various domains, from healthcare to epidemiology. It allows us to understand the factors that influence time-to-event outcomes. The analysis of survival data comes with its own set of challenges, especially when it involves censoring.
Censoring occurs when we don’t have complete information about the event times, making it difficult to draw causal inferences. The application of pseudo-observations, which is a crucial tool for enhancing causal inference, will be the subject of our particular attention. We apply the G-formula and IPTW, methods from causal inference, on these pseudo-observations. And estimate the ATE for a completely observed outcome and censored data.
Researcher: Emelia Thembile Kammies
Supervisor: Dr Lyson Chaka
Data mining, a vital machine learning technique, extracts valuable information from raw data across diverse fields like healthcare, retail, logistics, military, banking, and sports. It employs supervised and unsupervised methods, with classification being the most popular. This study evaluated six common machine learning classification models across six different datasets and identified their strengths.
Researcher: Kenneth Matsimbi, University of Limpopo
Supervisors: Dr. Martins A. Arasomwan, University of the Witwatersrand, Johannesburg
Researcher: Siphosihle Lebea, University of the Witwatersrand, Johannesburg
Supervisors: Dr. Martins A. Arasomwan, University of the Witwatersrand, Johannesburg