No abstract

No abstract

Big data is a term used for a very large data sets that have many difficulties in storing and processing the data. Analysis this much amount of data will lead to information loss. The main goal of this paper is to share data in a way that privacy is preserved while information loss is kept at least. Data that include Government agencies, University details and Medical history etc., are very necessary for an organization to do analysis and predict trends and patterns, but it may prevent the data owner from sharing the data because of privacy regulations [1]. By doing an analysis of several algorithms of Anonymization such as k-anonymity, l-diversity and tcloseness, one can achieve privacy at minimum loss. Admitting these techniques has some limitations. We need to maintain trade-off between privacy and information loss. We introduce a novel approach called Differential Privacy.

No abstract

“Employee turnover is a noteworthy matter in knowledge-based companies.” On the off chance that employee leaves, they carry with them tacit information, often a source of competitive benefit to the other firms. Keeping in mind the end goal, to stay in the market and retain its employees, an organization requires minimizing employee attrition. This article discusses the employee churn/attrition forecast model using various methods of Machine Learning. Model yields are then scrutinized to outline and experiment the best practices on employee withholding at different stages of the employee’s association with an organization. This work has the potential for outlining better employee retention designs and enhancing employee contentment. This paper incorporates and condenses the capacity to gain from information and give information-driven experiences, choice, and forecasts and thinks about significant machine learning systems that have been utilized to create predictive churn models.