This study investigates the performance of Logistic Regression, k-Nearest Neighbors (KNN), and Random Forest algorithms in predicting customer churn within an e-commerce platform. The choice of the mentioned algorithms was due to the unique characteristics of the dataset and the unique perception and value provided by each algorithm. Iterative models ‘examinations, encompassing preprocessing techniques, feature engineering, and rigorous evaluations, were conducted. Logistic Regression showcased moderate predictive capabilities but lagged in accurately identifying potential churners due to its assumptions of linearity between log odds and predictors. KNN emerged as the most accurate classifier, achieving superior sensitivity and specificity (98.22% and 96.35%, respectively), outperforming other models. Random Forest, with sensitivity and specificity (91.75% and 95.83% respectively) excelled in specificity but slightly lagged in sensitivity. Feature importance analysis highlighted "Tenure" as the most impactful variable for churn prediction. Preprocessing techniques differed in performance across models, emphasizing the importance of tailored preprocessing. The study's findings underscore the significance of continuous model refinement and optimization in addressing complex business challenges like customer churn. The insights serve as a foundation for businesses to implement targeted retention strategies, mitigating customer attrition, and promote growth in e-commerce platforms.

Adaptive antenna systems (AAS's) are traditionally of interest only in radar and sonar applications. However, since the onset of the explosive growth in demand for wireless communications during the 1990's, researchers are giving increasing attention to the use of AAS technology to overcome practical challenges in providing the service. The main benefit of the technology lies in its ability to exploit the spatial domain, on top of the temporal and frequency domains, to improve on transceiver performance. This thesis presents a unified study on two classes of preprocessing techniques for uniform circular arrays (UCA's). UCA's are of interest because of their natural ability to provide a full azimuth (i.e. 360') coverage found in typical scenarios for sensor array applications, such as radar, sonar and wireless communications. The two classes of preprocessing techniques studied are the Davies transformation and the interpolated array transformations. These techniques yield a mathematically more convenient form - the Vandermonde form - for the array steering vector via a linear transformation. The Vandermonde form is useful for different applications such as direction-of-arrival (DOA) estimation and optimum or minimum variance distortionless response (MVDR) beamforming in correlated signal environment and beampattem synthesis. A novel interpolated array transformation is proposed to overcome limitations in the existing interpolated array transformations. A disadvantage of the two classes of preprocessing techniques for UCA's with omnidirectional elements is the lack of robustness in the transformed array steering vector to array imperfections under certain conditions. In order to mitigate the robustness problem, optimisation problems are formulated to modify the transformation matrices. / Suitable optimisation techniques are then applied to obtain more robust transformations. The improved transformations are shown to improve robustness but at the cost of larger transformation errors. The benefits of the robustification procedure are most apparent in DOA estimation. In addition to the algorithm level studies, the thesis also investigates the use of AAS technology with respect to two different third generation (3G) mobile communications systems: Enhanced Data rates for Global Evolution (EDGE) and Wideband Code Division Multiple Access (WCDMA). EDGE, or more generally GSM/EDGE Radio Access Network (GERAN), is the evolution of the widely successful GSM system to provide 3G mobile services in the existing radio spectrum. It builds on the TDMA technology of GSM and relies on improved coding and higher order modulation schemes to provide packet-based services at high data rates. WCDMA, on the other hand, is based on CDMA technology and is specially designed and streamlined for 3G mobile services. For WCDMA, a single-user approach to DOA estimation which utilises the user spreading code and the pulse-shaped chip waveform is proposed. It is shown that the proposed approach produces promising performance improvements. The studies with EDGE are concerned with the evaluation of a simple AAS at the system and link levels. / Results from, the system and link level simulations are presented to demonstrate the effectiveness of AAS technology in the new mobile communications system. Finally, it is noted that the WCDMA and EDGE link level simulations employ the newly developed COST259 directional channel model, which is capable of producing accurate channel realisations of macrocell environments for the evaluation of AAS's.