Currently with world's industries providing endless job varieties, it is getting difficult for the students to choose optimum career lines. Ranging from 16-24 years old, these age groups find themselves unable to recognize their future endeavors. Hence, psychometric tests provide a solution, helping them to recognize their interests, aptitude and personality traits to produce better results. The research process was facilitated by questionnaires involving verbal, spatial, logical, critical and numerical aptitude. The responses were analyzed using statistical techniques, and machine learning algorithms. A number of graphs were plotted for better understanding of the technical details. The proposed psychometric and aptitude analysis model entails accuracy calculation assigning K-means, KNN, confusion matrices and SVM plots. The results of the psychometric analysis gave broad spectra of career choices by studying the pattern of the choices selected by the people. Respondents were supposed to give information about their interests and perceptions in their day to day activities, which in turn reflect information about their inner humanly traits, unknowingly providing an ideal career path.

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.

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.