Employee Churn which is otherwise called representative turnover is an exorbitant issue for organizations. The genuine expense for supplanting a worker can frequently be very enormous. In this work, we aimed to understand why and when employees are most likely to leave a company i.e the probability of an active employee leaving the organization and the key factors of an employee leaving the organization. For this purpose, we created such standard dataset where we include those attributes that are helpful for our analysis to predict the factors that are responsible for an employee to leave a company. The attributes we used in the dataset are satisfaction level, last evaluation, a number of projects, monthly average hours, amount of time spend in the company, employees left the company, promotions in last 5years, departments, salary. Further, under these attributes, we include 603 data samples. It is also useful to the company to retain the employees' safety and secure without losing them in the organization for a long time. We applied various Machine Learning models such as, Logistic Regression Classifier, Random Forest Classifier, SVM to check that our dataset is resulting with accurate values or not and which model is predicting the best. Thus, after applying all the models to the dataset, the Random Forest Classifier is giving more accuracy that is about 97.2% when compared to all the other classification models. This Random Forest Classifier correctly depicts the factors responsible for an employee leaving the company.
Singular Value Decomposition (SVD) is trust-based matrix factorization technique for recommendations is proposed. Trust SVD integrates multiple information sources into the recommendation model to reduce the data sparsity and cold start problems and their deterioration of recommendation performance. An analysis of social trust data from four real-world data sets suggests that both the explicit and the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Trust SVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ uses the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted and trusting users on the guess of items for an active user. The proposed technique extends SVD++ with social trust information. Experimental results on the four data sets demonstrate that Trust SVD achieves accuracy than other recommendation techniques.
During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The APRIORI growth algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or APRIORI tree), and frequent item set is mining by using of APRIORI tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (APRIORI-Growth, COFI-Tree, CT-PRO) based upon the APRIORI- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on MEDICAL data to generate rules and patterns using Frequent Pattern (APRIORI)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on MEDICAL data.
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