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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found that can discriminate between customers that are likely to pay their invoice and customers that are not. However, the more advanced models only performed slightly better than the logistic regression.

Abstract not available/Sammanfattning ej tillgänglig.

In this thesis, the inverse distance weighting, different kriging methods, ordinary least squares and two variants of the geographically weighted regression was used to evaluate the spatial mapping abilities on an observed dataset and a simulated dataset. The two datasets contain the same bioclimatic variable, near-surface air temperature, uniformly distributed over the whole world. The observed dataset is the observed temperature of a global atmospheric reanalysis produced by ECMWF and the other being simulated temperature produced by SMHI’s climate model EC-earth 3.1. The data, initially containing space-time information during the time period 1993-2010 displayed no significant temporal variation when using a spatio-temporal variogram. However, each year displayed its own variation so each year was split where the different methods were used on the observed dataset to estimate a surface for each year that was then used to make comparisons to the simulated data. CLARA clustering was done on the observed geographical dataset in the hope to force the inverse distance weighting and the kriging methods to estimate a locally varying mean. However, the variograms produced displayed an irregular trend that would lead to inaccurate kriging weights. Geometric anisotropy variogram analysis was accounted for that displayed moderate anisotropy. Results show that the geographically weighted regression family outperformed the rest of the used methods in terms of root mean squared error, mean absolute error and bias. It was able to create a surface that had a high resemblance to the observed data.

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Vi har genomfört en enkätundersökning om restaurangers och serveringars ekonomiska utsatthet och anpassning under coronapandemin. 139 restauranger och serveringar från Stockholms kommun valdes slumpmässigt ut för att delta i studien. Varje servering fick tillgång till ett formulär, som bland annat inkluderade frågor relaterade till beställnings- och överlämningsmetoder samt försäljning. Svaren från respondenterna användes sedan för att göra punkt- och intervallskattningar om olika parametrar i populationen. Vi tar även fram en logistisk regressionsmodell för att skatta sannolikheter för att serveringar genomför olika anpassningsmetoder. Utifrån urvalet kan vi se att många serveringar har valt att införa någon sorts ny strategi som svar på pandemin. För de flesta har dock försäljningen minskat. Det verkar också som att serveringar som har fått färre kunder tenderar att ha en högre sannolikhet att anpassa sig. På grund av att bara runt 15,83 procent av de kontaktade serveringarna valde att svara, finns det stor osäkerhet i våra skattningar. / We have conducted a survey about the adaptation and economic vulnerability of restaurants and food services during the COVID-19 pandemic. 139 restaurants from Stockholm Municipality were randomly chosen to participate. Each restaurant was provided with a form that included questions related to different methods for ordering and serving food, and sales. The answers from the respondents were later used to form point and interval estimates for different population parameters. We have also used a logistic regression model to estimate the probability that food services utilize a new adaptation strategy. The results from the sample indicate that many food services have chosen some kind of new strategy in response to the pandemic. For most, sales have decreased. It also seems to be the case that food services that have had a decrease in customers tend to have higher probability for adaptation. Since only 15.83 percent of the food services that were included in the sample chose to participate, there exists large uncertainty in our estimates.

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