The use of technology is at its peak. Many companies try to reduce the work and get an efficient result in a specific amount of time. But a large amount of data is being processed each day that is being stored and turned into large datasets. To get useful information, the dataset needs to be analyzed so that one can extract knowledge by training the machine. Thus, it is important to analyze and extract knowledge from a large dataset. In this paper, we have used two popular classification techniques-Decision tree and Naive Bayes to compare the performance of the classification of our data set. We have taken student performance dataset that has 480 observations. We have classified these students into different groups and then calculated the accuracy of our classification by using the R language. Decision tree uses a divide and conquer method including some rules that makes it easy for humans to understand. The Naive Bayes theorem includes an assumption that the pair of features being classified are independent. It is based on the Bayes theorem.

We live in a world where we consider a woman an equal personality to the man, but to what extent it is followed is unknown. Women suffer due to a lack of emotional or financial support. Nevertheless, after bearing all the sufferings, reaching their goals is the real woman empowerment. In this paper, we have highlighted the relevance of women entrepreneurs in the context of India and deduced how women could be empowered through entrepreneurship. For this, women's difficulties in bringing out their efforts and awareness about the government's policies in promoting women's entrepreneurship were studied. A questionnaire was formulated, and 200 women in the age group 16-25 years were surveyed. The data collected through our questionnaire was exhaustively cleaned and thoroughly analyzed to state the conclusion of our findings.

Data warehouses are decision support systems that are specifically designed for the business managers and executives for reporting and business analysis. Data warehouse is a database that stores enterprise-wide data that can be used to deduce useful information. Business organizations can achieve a great level of competitive advantage by analyzing its historical data and learning from it. However data warehouse concept is still maturing as a technology. In order to effectively design and implement a data warehouse for an organization, its goal needs to be understood and requirement must be analyzed in the perspective of the identified goal. In this paper we present a goal structured model for requirements engineering that also enables its users to manage traceability between the goals, decisions, business strategy and the corresponding business model

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