English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Predicting the Credit Defaulter is a perilous task of Financial Industrieslike Banks. Ascertaining non-payer before giving loan is a significant andconflict-ridden task of the Banker. Classification techniques are the betterchoice for predictive analysis like finding the claimant, whether he/she is anunpretentious customer or a cheat. Defining the outstanding classifier is arisky assignment for any industrialist like a banker. This allow computerscience researchers to drill down efficient research works through evaluatingdifferent classifiers and finding out the best classifier for such predictiveproblems. This research work investigates the productivity of LADTreeClassifier and REPTree Classifier for the credit risk prediction and comparestheir fitness through various measures. German credit dataset has been takenand used to predict the credit risk with a help of open source machine learningtool.

Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast