###### Abstract  
A discrete choice experiment (DCE) was used to investigate students' preferences for mobile phone plans at a South African university. Upon obtaining the data, this study compares the predictive performance of two machine-learning models for discrete choice analysis and makes recommendations for model selection. Using concepts from blocked fractional factorial designs, a locally optimal DCE was created for the choice sets. This contrasts with alternative ways that, in practice, could be more difficult, especially when there is a large number of attributes. The call rate, data speed, customer service, premiums, and network coverage were the features considered. A total of 180 respondents were chosen from the student population using a two-stage sample approach, and data were gathered through face-to-face interviews. In this study, two deep-learning models are examined to analyze the data, which are the artificial neural network (ANN) and the extreme gradient boosting (XGBoost) models. Root mean square error (RMSE) and mean absolute error (MAE) are used to assess the model fitness, while accuracy, precision, recall and F1 score were used to compare the models' performance. The results showed that XGBoost performs better compared to ANN in model fitness and prediction. Thus, the use of the XGBoost deep-learning model in choice preference modeling is therefore encouraged. discrete choice experiment; mobile telecommunication plans; deep models; XGBoost model; artificial neural network +
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sustainable and high performing mobile broadband [3; 4]. This would make it possible for the service providers to plan their operations profitably. There is a wealth of literature on prior studies on customer preferences for mobile telephony networks and services. In order to discover important characteristics influencing Bangladeshi consumers' choice of telecommunication service providers, [PERSON] et al. [5] conducted a study using data obtained via non-probability convenience sampling methods and a methodology based on structural equation modeling (SEM). The findings indicated that Bangladeshi customers gave price the utmost weight when deciding which telecommunications service provider to use. Using the SEM methodology, [PERSON], and [PERSON] [6], looked at the variables that affect consumers' decisions regarding their mobile service provider in Nigeria. From the study population, 367 respondents were selected using convenience sampling: a non-probabilistic sampling technique. A survey was conducted through face-to-face contact with mobile phone users and questionnaires were used to collect data. The findings showed that call rate, service quality, and service accessibility were the aspects that mobile phone consumers gave the most thought to when making their decisions. [PERSON] and [PERSON] [7] used a discrete choice experiment (DCE) to research the variables North Cyprus consumers thought were most crucial for enhancing mobile phone services. The decision scenarios were built using an orthogonal main effects plan (OMEP), and the respondents were chosen using an exogenous stratified random sampling (ESRS) probability sampling technique. According to the findings, people valued unlimited internet use, faster internet speeds, and unrestricted roaming the most. [PERSON] et al. [8] employed a DCE to determine what Portuguese consumers value most when choosing a mobile phone service. The study's decision scenarios were created using an orthogonal main effects plan (OMEP). The authors demonstrated that consumers would spend an additional 1.3 euros per month to receive a commitment period that was lowered from one year to six months. In order to get over the drawbacks of the non-probability sampling approaches employed in [5; 6] a probability two-stage sampling technique was applied in this study to choose our respondents.