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Indian Premier League (IPL) is a famous Twenty-20 League conducted by the Board of Control for Cricket in India (BCCI). It was started in April 2008 and completed its fifteen seasons in 2022. The current, i.e., the fifteenth IPL season, was held in May 2022. IPL is a popular sport where it has a large set of the audience throughout the country. Therefore, every cricket fan would be eager to know and predict the IPL match results. This project is about a detailed exploratory data analysis of IPL matches conducted from the year 2008 till matches held in 2019. Here, we analyze the overall IPL match scores, best batting and bowling performances, the team with a more significant number of wins, the most successful IPL team, the most valuable players and their best performance range, and so on. The complete dataset is collected from officials of BCCI of IPL matches held from 2008-2019 through Kaggle. Here, the testing accuracy of SVM classifier is highest at 0.9159, and the next highest is the Decision Tree algorithm which gave 0.8225 accuracies. The second highest is Logistic Regression, which gave 0.8159 accuracies, and the Random Forest algorithm, with 0.7563 accuracies. As the SVM classifier has the highest accuracy of all the four models, we use that model to develop the analyzer model for the project. [ABSTRACT FROM AUTHOR]

Digitalization of music is the new trend, and preferences of individuals are highly rated. Millions of songs are being streamed in the music applications. The companies providing these services need to sort and arrange a wide range of music tastes for all of its users. On top of that, fresh music from various artists in a wide spectrum of genres are popping up every day. To keep track of all this, a classification system can be handy. So, we propose an RNN based model based on Natural Language processing to classify the songs based on their lyrics into different genres [1]. Additionally, this tool can be handy to the music lovers for quickly identifying which genre a particular song belongs to. In this paper, we apply Long Short Term Memory (LSTM) model with both Universal Serial Embedder (USE) and Bert embedders. A comparative study is performed to understand which combination of models works based to classify the genres based on lyrics. From our results, on the basis of accuracy of the model, we found that USE embedder with LSTM [2] gives a slightly better performance than Bert embedder. The LSTM model with USE embedding gave the highest accuracy of 83.42% when trained over a range of five folds. [ABSTRACT FROM AUTHOR]

Chatbot is an intelligent system which simply defines human-to-machine interaction and this chatbots are biggest development trend today. Contacting customer centers or going to bank for banking related queries invests lot of time and human effort, further the customer may get insufficient information and may have uncertainty through this process. This paper aims to convey the development of Banking Chatbot that gives guidance regarding the services provided by bank and provides detailed descriptions to the user's query. This Chatbot is user friendly to communicate with and it is an easy way to get response on time. To overcome the difficulty web application using Natural language processing and neural network is developed [1]. NLP is an added advantage to make chatbot understand user queries. The operations in this Banking Chatbot include viewing beneficiaries and post queries regarding banking services. [ABSTRACT FROM AUTHOR]

This is an era of successful implementation of cryptography and Steganographic technique in cloud environment where people share their data to each other over the internet using different protocols like Http/Https, web sockets, FTP, SMTP etc. For the security aspects of data cryptography plays an important role. And due to increase in usages of the internet the cyber-attacks have also increased over the past few years. Thus, this problem gives rise to steganography techniques that is some advanced secret sharing techniques are evolved to hide data in other files after applying cryptography to the confidential/secret data. This paper speaks about the different existing cryptography and steganography techniques in cloud environments. [ABSTRACT FROM AUTHOR]