An Impact of Capital Adequacy Ratio on the Profitability of Private Sector Banks in India – A Study

Profitability being one of the cardinal principles of bank lending acts as a game changer for the survival and success of private sector banks in India. In order to stay profitable, banks have to capitalise on every penny advanced to yield the expected returns. However, considering the constraints laid down by the Reserve Bank of India, banks have to maintain a minimum capital adequacy ratio, as per the current BASEL III regulations active in India. With the mergers of public sector banks, the challenge has got just tougher for the private sector banks in India. Expansion and Diversification are the key strategies adopted by the key players from the private banking sector, however, with the minimum capital adequacy ratio observed by them, it is necessary to understand its actual impact on the bank’s profitability. This research paper aims to throw light upon the linkage that capital adequacy has with the bank’s profitability. It attempts to establish a relation between the Capital Adequacy Ratio with the Net profits of the bank. For the purpose of this study, data from the past 5 years of the leading private sector banks has been collected, namely, HDFC Bank, ICICI Bank, Kotak Mahindra Bank, AXIS Bank and YES Bank. The collected data has been analysed using Pearson’s Correlation to establish a relation between the CAR Ratio & the bank’s profitability. Hypothesis testing has been further done to study the quantum of proportionate change in the profitability with a change in the CAR Ratio for private sector banks using applicable research tools. The said research tools are applied to achieve the desired results while maintaining the required quantum of accuracy. It also aims to understand the proportionate impact of changes in CAR to the bank’s profitability, which can act as a suggested measure for banks to develop a reliable framework for efficient capital management and increase overall efficiency. The results derived from the data collected and analyzed aim to provide scope for further study on the subject matter

An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm

MadhurDixit13/MovieRecommender: MovieRecommender_v2.0.0

<p>Revamped UI with Integrated OMDB API!</p> <p>Experience our sleek, modern user interface with the power of the OMDB API seamlessly woven in. Now, you can easily access essential information from the movie database, including IMDb ratings and eye-catching posters. Say goodbye to the old and hello to the new and improved Movie Recommender! </p> <h2>What's Changed</h2> <ul> <li>Update README.md by @MadhurDixit13 in https://github.com/MadhurDixit13/MovieRecommender/pull/2</li> <li>Madhur by @MadhurDixit13 in https://github.com/MadhurDixit13/MovieRecommender/pull/1</li> <li>Madhur by @MadhurDixit13 in https://github.com/MadhurDixit13/MovieRecommender/pull/5</li> <li>Update workflow.yml by @MadhurDixit13 in https://github.com/MadhurDixit13/MovieRecommender/pull/8</li> <li>Madhur dixit13 patch 4 by @MadhurDixit13 in https://github.com/MadhurDixit13/MovieRecommender/pull/9</li> <li>Created Cards for predicted movies with poster and imdb ratings by @ATHARVA47 in https://github.com/MadhurDixit13/MovieRecommender/pull/15</li> </ul> <h2>New Contributors</h2> <ul> <li>@ATHARVA47 made their first contribution in https://github.com/MadhurDixit13/MovieRecommender/pull/15</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/MadhurDixit13/MovieRecommender/commits/version2.0</p&gt

raghavnarula/MovieRecommender: Sentiment Analysis on Coments

<p>Added sentiment analysis on comments so as to separate them into three categories:</p> <ul> <li>Supportive</li> <li>Critical</li> <li>Neutral</li> </ul> <h2>What's Changed</h2> <ul> <li>Added Sentiment Analysis by @AtharvaThorve in https://github.com/raghavnarula/MovieRecommender/pull/20</li> <li>Updated README.md by @raghavnarula in https://github.com/raghavnarula/MovieRecommender/pull/21</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/raghavnarula/MovieRecommender/compare/v2.0...v2.5</p&gt

nihitmittal/MovieRecommender: Web Scrapping of Comments from IMBD

As a part of our curriculum at NCSU CSC 510, we have a developed a movie-recommender, which will recommend movies. Movies are recommended by this

brwali/PopcornPicks: version 5

<p>Created additional user functionality and integrated a database to the system.</p&gt