Placement Analysis using Data Mining

Education data mining is one of the growing fields of the present time as it grows many issues to improve system comes in the notice one of them is improvement of the placement. Placement is a very important issue for any educational organization. Every organization wants to improve its placement. Success of any educational institute is measured by the placed student of the organization. This paper actually deals with the application of neural network to the educational data to improve placement. In today’s world all organization faces one of the big problems is recruit right candidate for the suitable position, Organization ready to invest a huge amount to recruitment process but till now they failed to recruit. In this paper, we apply the data mining techniques for placement prediction. To predict the performance of a student’s is the great concern for the organization, as they seek knowledgeable, talented and qualified professionals to need to fill up their positions. According to the survey, the corporate companies spend a sum of 1800crores for choosing candidates to fill up their vacancies. The Majority of the companies recruiting the candidates via on-campus recruitment and to fill up them positions. Our method is very useful for corporate companies, consultancies. This method is the best way to get the right candidate at the right time in the corporate world. The industry gets the best talent candidates from different institutes/universities, and the students also get a chance to kick start their career with some of the best organization. But the students facing some difficulties in getting placements. To overcome the problem we apply the Improved Decision Tree classification algorithms on these data, we have predicted which students placed in Recruitment Drives. Corporate companies need only knowledgeable and skilled persons for the vacant position. To find that particularly skilled person there's a question that how can the companies identify them. In order to overcome this problem in this paper, we provide a complete solution to the recruitment process. Actual challenges appear when they will develop real-world software. Training develops confidence in whatever School, Colleges, Universities will train. But all corporate expect skilled, confidence with active persons. We are giving to find the right candidate for the right job in this development. After recruiting employees corporate feel much confident about their development. So as much as possible, clear them confusions and get new ideas about their project training and become confident about their work

ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS

Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Minin

Preserving talent: Employee churn prediction in higher education

Retaining employees in a knowledge-based organisation, such as a university, is a significant challenge, especially as the need to keep knowledgeable workers is key to sustaining their competitive advantage. Knowledge is the organisations’ and employees\u27 most valuable and productive asset, but this intrinsic character leads to a high employee turnover. Often, universities learn about employees\u27 imminent departure too late. To prevent the loss of high-performing employees and to detect the warning signs early, business firms have been using advanced data mining techniques to predict “customer churn”. Recently these techniques have been used with “employee churn” in various industries, but not in higher education. This research bridges this gap by applying data mining techniques to predict employee churns in a university. The contributions of this research will be: 1) to identify critical factors that lead to talent losses; 2) to help universities devise appropriate strategies to retain their employees’ talents

We Are What We Generate - Understanding Ourselves Through Our Data

AbstractWe have tendency to exhibit ourselves through the data we share about ourselves including, liking, friendship, follows, disliking, pictures, audio, videos, causes, blogs and sites. Such data about us have already been used by big data companies to create customized ads and marketing tactics. However, while such data being in unstructured and noisy format, utilization and research is at its early stages. In this paper, we elaborate on the idea of understanding individuals through lens of data they produce in context of our main research work for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT) algorithm engine. We illustrate some of research problems in relevance of such data and identify research problem as ground for this paper. We present sub set of our framework including algorithm and math constructs, for the problem we identify. We conclude that such analytics and cognitive research can help to improve education, healthcare, Job economy, crime control, etc. Thus we coin the phrase “we are what we generate”, with our work in this paper. We suggest future work and opportunities in relevant directions

Business analytics in sport talent acquisition: methods, experiences, and open research opportunities

Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts' opinions but without systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed

Implementation of a Training Courses Recommender System based on k-means algorithm

Providing the right professional training courses for employees is a critical issue for organizations as well as employees. Its necessity stemmed out on the fulfillment of the organization and employees need. Thus, building a recommender system that would help in the decision making process and planning of the training course offered by organizations. This can be performed using various techniques and methodologies, where the most important one is data mining. Data mining is a process of looking for specific patterns and knowledge from large databases and carrying out predictions for outputs. Therefore, this project aims to build a web-based application for predicting appropriate training recommenders for Princess Norah University employees based on their education and professional information. This helps the university in suggesting the most optimal training recommender for employees, which in turn can enhance their performance and develop their career and working levels. Employees’ data was gathered from the Human Resource of the university and then clustered using the WEKA program to find the centroids of clusters to be then used in the developed application. The developed web-based application is used to suggest the most suitable training recommender for each employee. Results demonstrate that the developed web-based application effectively suggests the most appropriate training courses for employees based on the previously taken courses, evaluation of courses and probability for promotion. Furthermore, this web-based application can be used for describing the appropriate training courses for new employees based on their levels. The achieved accuracy of the developed system was 73.33%