Sales prediction in online banking

This master thesis seeks to explore how machine learning methods can be applied to predict the customers that are likely to purchase a credit card in Sparebank 1 SMN. The sales prediction problem has many similarities with customer churn prediction problems. We examine the current literature of both problems within the banking domain and adapt several techniques to our project. The experiment conducted follows an exploratory, result-driven approach with the primary goal of answering three research questions. We develop two machine learning models from data based on the event logs from interactions with the bank's online services and from customers' personal attributes. We define two pipelines, one for each dataset. In both pipelines we evaluate multiple classification algorithms. The first pipeline is exploratory of nature as little research has been done examining how sequential event data in the form of customer timelines can be used for training a classification model. The second pipeline is based on a traditional static customer attributes dataset commonly seen in state-of-the-art research. We apply various preprocessing and data aggregation techniques to optimise the datasets for further analysis. By performing sampling and feature selection techniques we measure the effect on model performance in terms of how well the models are able to identify likely credit card purchasers while reducing the number of incorrectly predicted purchasers. After finalising each pipeline, we examine whether a combination of the models produce better results than either model in isolation. Finally, we attempt to uncover customer segments that are likely to produce high confidence predictions. Our main findings show that the Random Forest algorithm achieves the highest performance for both datasets. The customer event timelines produced a higher performing model than the static customer attributes in terms of identifying likely credit card purchasers. The combination of the two models identifies a slightly lower amount of purchasers than either model in isolation, however greatly reduces the number of incorrectly predicted purchasers. Furthermore, by using sampling techniques to balance the proportion of purchasers to non-purchasers in the datasets, we are able to control the model's ratio between correctly and incorrectly identified purchasers

Pedogenic processes are reflected in the effective hydraulic properties in Sphagnum bog profiles

delimited, and horizon-specific soil physical and chemical properties can be specified. This is different for Sphagnum bog peatlands, because Sphagnum mosses grow continually upward in the Acrotelm and leave behind dead plant remnants, which are increasingly decomposed with increasing depth. Thus, a continuous change of soil properties is characteristic for these profiles. To be able to quantify the change of soil hydraulic properties (SHP) with depth in Sphagnum bog profiles, we conducted transient evaporation experiments in the laboratory on a series of samples from the entire profile of the acrotelm. The identified effective pore size densities for Sphagnum are trimodal in the upper part of the acrotelm. We present size classes defining a unifying nomenclature to be used when describing the pore size classes in Sphagnum moss and peat. These size classes refer to the inter-plant pore space which is constituted of the voids between individual mosses, the intra-plant pore space representing the voids between branches and leaves, and the inner-plant pore space which is the space constituted by the water bearing hyaline cells

Baysian and NonBaysian Methods to Estimate the two parameters of Logistic Distribution

In this paper ,the problem of point estimation for the two parameters of logistic distribution has been investigated using simulation technique. The rank sampling set estimator method which is one of the Non_Baysian procedure and Lindley approximation estimator method which is one of the Baysian method were used to estimate the parameters of logistic distribution. Comparing between these two mentioned methods by employing mean square error measure and mean absolute percentage error measure .At last simulation technique used to generate many number of samples sizes to compare between these methods

Comparison of 2D and 3D modeling for deriving effective hydraulic properties of stony soils

Stone, gravel or rock fragments that are embedded in a matrix of fine soil have a substantial effect on effective soil hydraulic and transport properties. Understanding the role of stones in soils is important not only for soil water transport processes such as infiltration, evaporation and redistribution, but also for related solute transport processes. A variety of models has been proposed in the past to predict the systematic effect of varying amounts of stones on effective saturated conductivity and water retention of a soil-stone mixture. Respective studies for unsaturated hydraulic conductivity are still missing. To test the accuracy and validity of such predictive models, and to expand them to unsaturated conductivity, the investigation of virtual porous media, which can be obtained by numerical forward modeling of water and solute transport in soil-stone mixtures is the method of choice. Furthermore, to test the postulate that effective homogeneous properties exist and can replace the heterogeneous system, the ability of a 1D model with assumed homogeneous soil properties to match “observed” state variables and fluxes of a higher-dimensional heterogeneous model under a variety of conditions is a necessary requirement. With few exceptions, such heterogeneous modeling studies have hitherto been performed only for simplified cases, i.e., either under fully saturated conditions, or with a reduced dimensionality, i.e., 2D simulations of soil/stone mixtures. In this work, we use the simulation tool HYDRUS-2D3D to investigate the systematic differences that occur when modeling partially unsaturated transient water in stony soils, based on the Richards equation. Specifically, we compare truly 3D with 2D simulations and discuss the implications for effective 1D hydraulic properties

Digital Platform Ecosystem Governance: Preliminary Findings and Research Agenda

This paper explores collaborative governance in digital platform ecosystems and the governance challenges that may occur in such environments. We analyze three different digital platform ecosystems and identify six unresolved key governance issues that we believe are central to the type of digital platform ecosystems we address. This paper has three contributions. First, we add to the literature on digital platform ecosystems by revealing a set of governance challenges regarding ecosystem forming and sustainability. Second, our findings may serve as recommendations for organizations that are planning to establish or that are already running an ecosystem based on a digital platform. Third, we contribute to digital platform ecosystem research by proposing an agenda for future research in this area

C9ORF72 interaction with cofilin modulates actin dynamics in motor neurons.

Intronic hexanucleotide expansions in C9ORF72 are common in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, but it is unknown whether loss of function, toxicity by the expanded RNA or dipeptides from non-ATG-initiated translation are responsible for the pathophysiology. We determined the interactome of C9ORF72 in motor neurons and found that C9ORF72 was present in a complex with cofilin and other actin binding proteins. Phosphorylation of cofilin was enhanced in C9ORF72-depleted motor neurons, in patient-derived lymphoblastoid cells, induced pluripotent stem cell-derived motor neurons and post-mortem brain samples from ALS patients. C9ORF72 modulates the activity of the small GTPases Arf6 and Rac1, resulting in enhanced activity of LIM-kinases 1 and 2 (LIMK1/2). This results in reduced axonal actin dynamics in C9ORF72-depleted motor neurons. Dominant negative Arf6 rescues this defect, suggesting that C9ORF72 acts as a modulator of small GTPases in a pathway that regulates axonal actin dynamics