<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

The changes in IT sector constantly influencing the performance of banking sector in the world. The emergence of internet banking has changed the way of banks of how to offer the products and services to the customers. In order to survive in the rapidly changing technological environment, the banks are required to adapt such changes and to maintain and improve the services which they are offering to their customers in order to attain the customers satisfaction. Now the term quality does not only include the products but also the services. This paper deals with the internet banking operations and how it affects the service quality of the banks in Punjab. The research is much more of qualitative nature but to prove facts and figures quantitative approach is also used in the paper. The research is descriptive as well as explanatory. In order to arrive at the sample size, non probability method has been used. For the primary data collection a structured questionnaire is used to record the response of various respondents. Secondary data has been collected from annual reports, other published literature of the banks etc. In order to test the impact of internet banking on the service quality of banks seven service quality dimensions model is used. A model with seven dimensions service quality named reliability, assurance, responsiveness, empathy, tangibility, security and communication is used to complete the study. In these seven dimensions 37 variables are covered. For the data analysis the statistical package SPSS 20 is used.  Descriptive statistics is used to analyse the data. The research proves that all the dimensions which are included in the study have a positive impact on the service quality of banks providing internet banking services to their customers in Punjab. The recommendations are also discussed with which the service quality and customers satisfaction can be improved.

<p>Smart phone has various utilizations to various clients as per their necessities. With sensational rise in the usage of smart phone the individuals are considering different factors while purchasing a smart phone. This paper has put endeavor to reveal the fundamental factors which effect clients in picking up of the smart phone. A sample of 512 responses was taken through questionnaire. An organized questionnaire was planned with five point Likert scale was utilized to meeting respondent’s .Factor analysis and descriptive statistical tools were applied to extricate the basic variables influence cell phone acquiring choice. The result shows that the most important factors are physical attributes, apps and sounds while the less importance is given to other factors such as convenience, price which can also vary by age, service and gender. The future scope of this paper lies in the fact that whether age, occupation, gender makes any difference in purchasing decision of smart phone.</p>

Forecasting and making speculations about the financial market is intriguing and enticing for many of us. Predicting sentiments in the field of finance is a difficult thing as there is a special language that is used in financial markets and the data is unlabeled. Generalized models are not sufficient because the words that are used in financial markets have a completely different meaning when compared to their regular use. This paper represents the study of the stock price fluctuations and forecasting of the future stock prices using financial news about the big IT giants. NLP techniques should be applied to extract the correct sentiments out of the statements. This paper proposes a hybrid Machine Learning model DSM i.e. Decision Support Machine based on Support Vector Machine and Decision Tree. In this study news headlines dataset is preprocessed and then used for making predictions. The results show that the proposed model DSM got an accuracy of 79.75%. Results are then compared with the real-time stock market data for the same time duration, thus giving us a better picture of the actual changes. DSM is also compared with BERT, TextBlob, Decision Tree, Naïve Bayes, NLTK-Vader, SVM and KNN. The proposed model can further be extended if more datasets associated with investors’ sentiments can be used for training.

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