Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.

In the digital era and advent of artificial intelligence, digital retailing has emerged as a notable shift in commerce. It empowers e-tailers with data-driven insights and predictive models to navigate a variety of challenges, driving informed decision-making and strategic formulation. While predictive models are fundamental for making data-driven decisions, this thesis spotlights binary classifiers as a central focus. These classifiers reveal the complexities of two real-world problems, marked by their particular properties. Specifically, binary decisions are made based on predictions, relying solely on predicted class labels is insufficient because of the variations in classification accuracy. Furthermore, prediction outcomes have different costs associated with making different mistakes, which impacts the utility. To confront these challenges, probabilistic predictions, often unexplored or uncalibrated, is a promising alternative to class labels. Therefore, machine learning modelling and calibration techniques are explored, employing benchmark data sets alongside empirical studies grounded in industrial contexts. These studies analyse predictions and their associated probabilities across diverse data segments and settings. The thesis found, as a proof of concept, that specific algorithms inherently possess calibration while others, with calibrated probabilities, demonstrate reliability. In both cases, the thesis concludes that utilising top predictions with the highest probabilities increases the precision level and minimises the false positives. In addition, adopting well-calibrated probabilities is a powerful alternative to mere class labels. Consequently, by transforming probabilities into reliable confidence values through classification with a rejection option, a pathway emerges wherein confident and reliable predictions take centre stage in decision-making. This enables e-tailers to form distinct strategies based on these predictions and optimise their utility. This thesis highlights the value of calibrated models and probabilistic prediction and emphasises their significance in enhancing decision-making. The findings have practical implications for e-tailers leveraging data-driven decision support. Future research should focus on producing an automated system that prioritises high and well-calibrated probability predictions while discarding others and optimising utilities based on the costs and gains associated with the different prediction outcomes to enhance decision support for e-tailers. / <p>The current thesis is a part of the industrial graduate school in digital retailing (INSiDR) at the University of Borås and funded by the Swedish Knowledge Foundation.</p>

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.

以往針對客戶流失與轉換行為所研究的對象，多偏重以消費品產業為主，較少探討工業品產業客戶流失對於企業經營所造成之影響。本研究針對工業品產業中之營建工具業，探討其客戶流失之原因及行為表現，並期望透過相關研究，使業者可預先發現可能流失之客戶，並做為後續發展客戶慰留專案之參考。 本研究可分為五部份，第一部份首先將回顧與本論文有關之文獻。第二部份則提出本研究之研究架構及研究方法。第三部份則以SPSS軟體進行實證研究，統計方法係利用敘述性統計分析、因素分析、信度分析、單因子變異數分析及區別分析等進行資料分析。第四部分為討論前述之研究發現，並將其與消費品市場之客戶流失行為模式相比較。最後則為結論與建議。 研究結果發現：1.流失原因可萃取出產品及服務因素及價格因素兩大構面。轉購原因可萃取出服務及品牌策略、產品策略及價格策略三大構面。2.流失行為係以降低購買頻率及轉換新的供應商兩種方式表現。3.營建工具業與服務業客戶轉換行為模式有明顯差異。4.購買持續時間較長之客戶，對於送貨時間太久之重要性認知程度與購買持續時間較短之客戶有明顯差異。產品價格太高及採購頻率兩項變數可作為判別流失客戶是否會轉換供應商之模式。但由於區別力不甚良好，故並不適合以此兩項變數作為判斷預測之基準。 / A great deal of effort has been made on the causes of customer churn in the consumer products industry. What seems to be lacking, however, is this subject in the industrial products industry. This study will focus the discussion on the causes of customer churn and customer switching behavior in the construction products supplier, in order to provide guidance for developing retention and loyalty programs. This study can be divided into five parts; the first part reviews the literature on this subject. The second part introduces the methodology to be utilized throughout the study, first with structural diagram of study followed by study methods, and object in study. The third part utilizes using SPSS for Windows as the tool to conduct statistical analysis, including description statistical analysis, reliability test, Discriminant Analysis, Factor Analysis, and One-way ANOVA. The fourth part discusses the experimental result of this study, and compares it with customer switching behavior in the consumer products industry. The last part is a conclusion of the thesis. The results of this study show as follows. 1. The main causes of customer churn are product and service oriented or price oriented. The main causes of customer switch are service and brand strategy, product strategy or price strategy. 2. Customer switching behavior includes decreasing purchased frequency and transferring to a new service provider. 3. Customer switching behavioral model in the service industry is different from the model in the construction products supplier. 4. The customers who have longer purchasing duration have higher recognition of importance for deliver time. Purchasing frequency and product price are not the best variables to predict if the customers would churn or not.