Something relating to the history of the Holocaust Research Paper

Something relating to the history of the Holocaust - Research Paper Example The contrary will be shown. It will be shown that they had a class system. They had classified the types of citizens as early as 1936. The infrastructure had been created and the fascilities were built before the Germans even entered Dutch soil enabling the Germans to come in and murder over 100 000 people in less than 3 years. Three stages will be examined is this essay. From 1936-1939, when the national decree dictated who was a dutch citizen and the creation of refugee centers. From 1939 to 1940, when Westerbrok was voted into Parliament as a center for the "legal refugees". To conclude with the capitulation of the Netherlands government within 5 days in 1940 and the consequences it had on the Shoah. Please note that in the sources there is much conflicting information due to the age of the survivors and the difference in translations and countries methods of notations.. 1936-139 The Jewish population of Amsterdam represented approximately 10% of the population. The attitude was r ather avant garde, agnostic, assimilated and had benefited greatly from the WWI attitude of being a neutral state.(Hillesum 1999) There was a sense of safty of being Dutch before being Jewish. The general consensus was accepting the census as a natural govermental process. Upon registering in 1936, Jews were told that as citizens they would be protected. (Vanderwerff 2010)The atmosphere as explained by Etty Hillesum, in her Letters of Westerbork, was that she had no desire for organised religion. Life was absurd. God was helpless (12/07/1942) She was born into an agnostic family. Before 1941, she was lost in the different intellectual circles of Amsterdam. She had failed her exam to get into law school. She studied Slavic studies and then went on to tutor. This is an insight into the Jewish population of Amsterdam. The intellectual assimilation would eventually be the demise of the Jews of Amsterdam. The felt themeselves more protected and superior over the German Jews who were ofte n poorer and less educated then the Dutch Jews. They had jobs and lived in proper housing. They were not touched by the refugee housing or economic situation. As in other European nations, they considered themselves citizens of the nation of their birth. In 1936, by Royal Decree it was voted that a national census would require new identity cards in order to define who were Dutch citizens. Religion was required on the last line of the card. (Vanderwerff 2010) In 1939, Refugees were forced to register. Legal Refugee Jews (Stateless) were defined by having been born in a country that no longer existed because of World War I and having been born in Poland. Illegal Refugee Jews were those who came into the Netherlands without any visas. Illegal refugees were sent back to Germany. (Vanderwerff 2010) In World War I, The Netherlands had remained a neutral State. It was common knowledge that the Netherlands was a state that had had an open door policy. Because of the depression, lack of job s and overall anti-semitism, German Jews and Stateless Jews were considered secondary citizen to Dutch citizens. The geo-political economic situation of Europe has changed the map. Dutch citizens were given precedents over refugees in employment and housing. What had been refugee homes all over the country since 1936 had become internment camps in

Credit scoring model Coursework Example | Topics and Well Written Essays - 5000 words

Credit scoring model - Coursework Example As a way of solving classification issues and also decreases Type I errors, typical of many credit scoring models, this piece attempts to describe or rather come up with an appropriate credit scoring model via two stages. Classification stage involves development and construction of an ANN-based credit scoring model, which basically classifies applicants into two categories, which are, those who have acceptable credit (good) and those who have unacceptable credit (bad). In the second stage, which will also be referred to as the re-assigning stage, attempt is made to lower Type I error through reassignment of the unaccepted applicants with good credit to a conditionally accepted category making use of a CBR-based classification approach. In a bid to demonstrate the effectiveness of the model proposed in this paper, an analysis is run on a German dataset with assistance of SAS Enterprise Miner. The results will be expected to not only prove that the model is a more effective credit sco ring model but that it will also enhance the business revenues through its ability to lower both Type I and Type II error system scoring errors. Introduction Data mining is a process that involves search and analysis of data so as to find implicit, although substantially vital information. It covers selection, exploration and modeling of large data volumes with the aim of uncovering previously unrecognized patterns, and in the end generate understandable information, from huge databases. It generally employs an extensive range of computational techniques which include approaches such as statistical analysis, decision trees analysis, neural networks review, rule induction and refinement approach, as well as graphic visualization. Of the various mentioned methods, the classification aspect has an important role in decision making within businesses mainly as a result of the extensive applications when it comes to financial forecasting, detection of fraud, development of a marketing str ategy, credit scoring, to mention just but a few. The aim of developing credit scoring models is to assist financial institutions to detect good credit applicants who are more likely to honor their debt obligation. Often such systems are based on multiple variables including the applicant’s age, their credit limit, income levels, as well as marital status, among others. Conventionally, there are many distinct credit scoring models which have been developed by financial as well as researchers in a bid to unravel the mysteries behind classification problem. Such include linear discriminant analysis, logistic regression, multivariate adaptive regression splines, classification, as well as regression tree, case based reasoning, and of course the artificial neural networks. Normally, linear discriminant analysis, logistic regression, and artificial neural networks are utilized in construction of credit scoring models. LDA is amongst the earliest forms of credit scoring model and e njoy widespread usage across the globe. Nonetheless, its use has often been subjected to criticism based on its assumption of existence of a linear relationship between the input variables and the output variables. Sadly, this is an assumption that seldom holds, and is rather sensitive to deviations arising from assumption of multivariate normality (West, 2000). Like LDA, LR is also a rather common alternative employed in performance of credit scoring assessments. In essence, the LR model has stood out as the best