CREDIT SCORING USING LOGISTIC REGRESSION

This report presents an approach to predict the credit scores of customers using the Logistic Regression machine learning algorithm. The research objective of this project is to perform a comparative study between feature selection and feature extraction, against the same dataset using the Logistic Regression machine learning algorithm. For feature selection, we have used Stepwise Logistic Regression. For feature extraction, we have used Singular Value Decomposition (SVD) and Weighted Singular Value Decomposition (SVD). In order to test the accuracy obtained using feature selection and feature extraction, we used a public credit dataset having 11 features and 150,000 records. After performing feature reduction, Logistic Regression algorithm was used for classification. In our results, we observed that Stepwise Logistic Regression gave a 14% increase in accuracy as compared to Singular Value Decomposition (SVD) and a 10% increase in accuracy as compared to Weighted Singular Value Decomposition (SVD). Thus, we can conclude that Stepwise Logistic Regression performed significantly better than both Singular Value Decomposition (SVD) and Weighted Singular Value Decomposition (SVD). The benefit of using feature selection was that it helped us in identifying important features, which improved the prediction accuracy of the classifier

A Survey on Water Marking techniques for Secure Sharing of Medical Data

The technique of transmitting biomedical signals through communication channels have become an important issue in many clinical practice related application. So a digital large capacity watermarking technique for singular value decomposition (SVD) is used for hiding these secure, confidential, private data. The Singular value decomposition (SVD) is used to realize the compression of watermark in the watermarking pre processing stage. Discrete Wavelet Transform technique is applied for image compression for better quality. For JPEG, BMP and PNG images this method is essential for construction of accurate targeted and blind steganalysis methods. DOI: 10.17762/ijritcc2321-8169.150616

Constraint elimination in dynamical systems

Large space structures (LSSs) and other dynamical systems of current interest are often extremely complex assemblies of rigid and flexible bodies subjected to kinematical constraints. A formulation is presented for the governing equations of constrained multibody systems via the application of singular value decomposition (SVD). The resulting equations of motion are shown to be of minimum dimension

Tensor renormalization group in bosonic field theory

We compute the partition function of a massive free boson in a square lattice using a tensor network algorithm. We introduce a singular value decomposition (SVD) of continuous matrices that leads to very accurate numerical results. It is shown the emergence of a CDL fixed point structure. In the massless limit, we reproduce the results of conformal field theory including a precise value of the central charge.Comment: 5+8 pages, 4+2 figure