German credit data analysis in r

Metric: Loss : Asymmetric cost metric is used to calculate Loss. Perform exploratory data analysis. This German Credit Score classifies people as good or bad borrowers based on their attributes. The response variable is 1 for good borrower or loan and 2 refers to bad borrower or loan. For the ease of working on dataset we have changes the response to binary 0,1. In order to see how the response variable is affected by other factors, we will regress it on other variables. As it has only 0 and 1 values, we will use binomial regression.

The structure looks coherent. Observation: We observe that amongst the 20 predictors only a few are significant. Here we observe that BIC being more parsimonious in model selection only selects 2 predictors as significant where as AIC working towards more prediction ability selects 11 predictors.

In this scenario because we desire more prediction ability we select the model selected the AIC criterian. Observation: The roc curve looks good and we also observe 0.

ROC curve signifies overall measure of goodness of classification, hence a higher value signifies that model has good classification ability. But this measure is calculated on training sample, which is not a good data to make a decision. The cost function will also change accordingly. There are 20 candidates that were actually defaulters but were predicted as good borrowers these are called as False negative classification.

There are candidates were actually good borrowers but were predicted as bad borrowers these are called as False positive classification. Intepretation Observation: The roc curve looks good and we also observe 0. There are candidates were actually good borrowers but were predicted as bad borrowers these are called as False Negative.

MissClassRt: This signifies the candidates were missclassified, we observe that this has increased on testing sample.

Our model is not as good to predict the testing values as the training values. The distinctiove feature with this algo is that we generally have a asymmetric cost function. In the credit scoring case it means that false negatives predicting 0 when truth is 1, or giving out loans that end up in default will cost more than false positives predicting 1 when truth is 0, rejecting loans that you should not reject.

Here we make the assumption that false negative cost 5 times of false positive. In real life the cost structure should be carefully researched. As expected our False negatives have reduced. Hence, we select credit. The ideal way of creating a binary tree is to construct a large tree and then prune it to an optimum level. You can see that the rel error in-sample error is always decreasing as model is more complex, while the cross-validation error measure of performance on future observations is not.

That is why we prune the tree to avoid overfitting the training data.The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for loan applicants. Lets read same data without header and assign header. Creditability is named to class.

Class 1 indicates good credit and 2 as bad credit default. The value of class variable 2 indicates default and 1 describes non-default. In order to model as probelm, we have removed the value by 1 in the following code. In order to know the predictive ability of each variable with respect to the independent variable, let do an information value calculation. We see few data types are object and few int We will bin the int values to 10 equal parts deciles. We will consider top 15 vars. We remove one extra category column from all categorical variables for which dummies have been created.

C Stat tells us the proprtion of concordant pairs out of total pairs. The higher the value the best. Keep tab of c-stat and how log likelihood AIC is changing while removing various predictors one by one in order to justify where to stop.

In [1]:. In [2]:. In [3]:. In [9]:. In [10]:. In [11]:. In [20]:.Making Predictions with Data and Python : Predicting Paper could be useful for the users of Weka that aim to use it for credit scoring analysis.

In our data science course, this morning, we've use random forrest to improve prediction on the German Credit Dataset. Description of the German credit dataset.

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Title: German Credit amount 6. Credit card fraud detection is one of the applications of prediction analysis. German credit data. The last column of the data is coded 1 bad loans and 2 good loans. It includes a database service that runs outside the SQL Server process and communicates securely with the R runtime. Regression analysis is a set of statistical processes that you can use to estimate the relationships among variables.

Data are collected using two methods: 1 qualitative content analysis to examine general insurance terms and conditions of different traditional product lines in the German market and 2 qualitative interviews with experts from the German insurance industry. Credit scoring is the set of decision models and their underlying techniques that aid lenders in the granting of consumer credit.

There are various meth-ods used to perform credit risk analysis. Assignment B. Post on: The kernel trick maps raw data into another dimension that has a clear dividing linear margin between different classes of data.

One of the industries which is heavily using Machine Learning solutions is that of Banking. The objective of this article is to use the current loan application data to predict whether or not. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language.


This file contains the workflow for Usecase 2 - Fraud or Not. Credit scoring is a statistical analysis performed by lenders and financial institutions to access a person's creditworthiness. In credit card fraud detection, the fraud transactions are predicted based on the historical information of credit card transactions [2]. The credit cards are being used very commonly today for buying several goods and accessing various services in our daily lives.

Step 1. Sas code to read in the variables and create numerical variables from the ordered categorical variables proc print output.

The figure above shows the medoids table, where each row represents a cluster. If your data contains many predictors, you can first use screenpredictors Risk Management Toolbox to pare down a potentially large set of predictors to a subset that is most predictive of the credit scorecard response variable. A data frame with observations on the following 21 variables. Table 1. Assignment 1 Contents A. Cannot retrieve contributors at this time. SUMMARY: The purpose of this project is to construct a prediction model using various machine learning algorithms and to document the end-to-end steps using a template.

Based on the attributes provided in the dataset, the customers are classified as good or bad and the labels will influence credit approval.

Homework 2 Problem 1: A common application of Discriminant Analysis is the classification of bonds into various bond rating classes. The file tubby music download for this case study is "CreditRiskData. Analysis of German Credit Data The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for loan applicants.We are using one of the commonly used sample datasets for Logistic Regression or a dataset with the binary decision variable, German Credit Data - Data Sample download German Credit.

In this sample, "Class" is a target variable and takes values as "Good" and "Bad". We assume the values - "Good" for the non-defaulted customers and "Bad" are Defaulted ones. Original Source of Data Sample. Name of the data frame created is GermanCredit. Have written a custom function to get summary statistics including missing values in an R data frame.

We know that we need to develop a predictive model and the target variable is a binary variable. If a relationship is not linear with continuous variable and odds valueswe can try to make it using transformation or convert the variables to dummy variables.

Below is a sample scenario of bivariate analysis between Age and Class target variables. Based on the above bivariate analysis, we can split age into dummy variables. Similarly, we have done it for the duration variable. Create dummy variables for Amount and Duration. The development sample will be used for developing a logistic regression model and the validation sample will be used for validating the model developed. If model performance is similar for these two samples, we can conclude that the model developed is generalized.

We will be using the glm generalized linear model function to develop a logistic regression model. We need to give formula, data frame, and distribution family as inputs. In this example, we have a list of independent variables and "Class" as the target variable. We are creating a formula object below. Now we can run a logistic regression model and the code is as follow.

It will consider all variables and their significance level. We can use a stepwise model selection to select the significant variables. Now we have the final list of variables from step function, we are re-running logistic regression. Also, one by one we have removed insignificant variables. Then we are dropping variables based on multicollinearity. We have used the "vif " function to check multicollinearity. The first cut of logistic variables is below. Now, we can calculate model performance statistics for the development and validation samples after scoring the samples using the model developed above.

Now, we will discuss the model validation steps. There is a list of model performance statistics to assess the performance. Model performance assessment and validation aim to review. Commonly used Model Predictive Power Statistics are. Now we have to score the dataset and compare predicted values with the observed value. We can change cut-off probability if you increase the threshold value opportunity cost a good customer rejected by our model goes up but default risk when a bad customer is given a credit facility and person defaults goes down.Help Sign in.

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Attribute description 1. Status of existing checking account, in Deutsche Mark. Duration in months 3. Credit history credits taken, paid back duly, delays, critical accounts 4. Purpose of the credit car, television, Credit amount 6. Present employment, in number of years. Installment rate in percentage of disposable income 9. Personal status married, single, Present residence since X years Property e.

Age in years Other installment plans banks, stores Housing rent, own, Number of existing credits at this bank Job Number of people being liable to provide maintenance for Telephone yes,no Foreign worker yes,no. Show all 21 features. Kappa coefficient achieved by the landmarker weka. REPTree -L 1. IBk -E "weka. BestFirst -D 1 -N 5" -W. Standard deviation of the number of distinct values among attributes of the nominal type.Thursday, August 3, 8.

Posted by Unknown at AM No comments:. Labels: 8. Scala Python R Shell. Clone or download. Find file. Branch: master. The source to accompany the 1st edition may be found in the 1st-edition branch. The source to accompany the 2nd edition is found in this, the default master branch. Apache Maven 3. Posted by Unknown at PM No comments:. Labels: machine learningsparkSparkML. It is important to understand the rationale behind the methods so that tools and methods have appropriate fit with the data and the objective of pattern recognition.

There may be several options for tools available for a data set. Sample R code for Reading a. Lessons Lesson 1 a : Introduction to Data Mining 1 a. Labels: Big datadata mining. Using spark. Classification is a family of supervised machine learning algorithms that identify which category an item belongs to for example whether a transaction is fraud or not fraudbased on labeled examples of known items for example transactions known to be fraud or not.

Classification takes a set of data with known labels and pre-determined features and learns how to label new records based on that information.

The label is the answer to those questions. In the example below, if it walks, swims, and quacks like a duck, then the label is "duck". Whether a person will pay back a loan or not.Johannes Ledolter. Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools.

Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As chromatography solvent recipe result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling.

The book includes important topics such as penalty-based variable selection LASSO ; logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:. Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics.

The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences. Request permission to reuse content from this site. Appendix 3. Appendix I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics.

I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text. Data Mining and Business Analytics with R. Selected type: Hardcover. Added to Your Shopping Cart.

German Credit Scoring Data

Print on Demand. This is a dummy description. Permissions Request permission to reuse content from this site. Table of contents Preface ix Acknowledgments xi 1. Introduction 1 Reference 6 2. Standard Linear Regression 40 3. The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for loan.

German Credit Data Risk Analysis The German credit scoring data is a dataset provided by Prof. Hogmann in the file The data set. The German credit scoring data is a dataset provided by Prof. Hogmann in the file The data set has information of The German Credit Data contains data on 20 variables and the classification We will work on this data to make it suitable for our analysis and to make.

Perform exploratory data analysis. Find a best model for Credit Scoring data using logistic regression with AIC and BIC. German Credit Data Analysis. By: Srisai Sivakumar. Introduction. When a bank receives a loan application, based on the applicant's profile the bank has to.

german <-"", sep = " ", header=F) attr_info <- "Attribute 1: In the analysis, we will also include the purpose of the loan. Don't forget to put (“”) because R is a case-sensitive. Total of the attributes in German Credit Card dataset are 21 attributes. In our data science course, this morning, we've use random forrest to improve prediction on the German Credit Dataset. The dataset is __.

A credit scoring data set that can be used to predict defaults on consumer loans in the German market. Usage. data( Format. The data contains. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank.

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Each person is classified as good or bad credit risks according to. Exploratory Data Analysis and Identifying riskiness of loan. Introduction. Dataset Overview; Goal of the project; Modelling. Pre-Processing and Feauture.

GermanCredit: Statlog German Credit. Description. The dataset contains data of past credit applicants. The applicants are rated as good or bad. Have written a custom function to get summary statistics including missing values in an R data frame. Bivariate Analysis. We know that we need to develop a. Here are 6 public repositories matching this topic ; longtng · frauddetectionproject · r ; S-B-Iqbal · German-Credit-Data-Analysis · python ; vineeths96 · Linear. learning models to analyze the German Credit Data from the UCI Repository of Machine Learning banking system, a sound personal credit analysis system.

The German Credit data set (available at contains observations on 30 variables for past. Driving Visual Analysis with Automobile Data with R German Credit Data Analysis; Introduction; Simple data transformations; Visualizing categorical data.

This project is an analysis of the German credit data. It contains details of loan applicants with 20 attributes and the classification whether an. South German Credit Data: Correcting a Widely Used Data Set. Using R and RStudio for Data Management, Statistical Analysis and Graphics (2nd Edition).