arXiv subjects

Shenghuan Yang

Publications and source records attributed to Shenghuan Yang.

3 recordsLinked to original sources

arXiv2021

IBM Employee Attrition Analysis

In this paper, we analyzed the dataset IBM Employee Attrition to find the main reasons why employees choose to resign. Firstly, we utilized the correlation matrix to see some features that were not significantly correlated with other attributes and removed them from our dataset. Secondly, we selected important features by exploiting Random Forest, finding monthlyincome, age, and the number of companies worked significantly impacted employee attrition. Next, we also classified people into two clusters by using K-means Clustering. Finally, We performed binary logistic regression quantitative analysis: the attrition of people who traveled frequently was 2.4 times higher than that of people who rarely traveled. And we also found that employees who work in Human Resource have a higher tendency to leave.

cs.CY↗

arXiv2020

Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating

The credit rating is an evaluation of a company's credit risk that values the ability to pay back the debt and predict the likelihood of the debtor defaulting. There are various features influencing credit rating. Therefore, it is essential to select substantive features to explore the main reason for credit rating change. To address this issue, this paper exploited Principal Component Analysis and Factor Analysis as feature selection algorithms to select important features, summarized the similar features together, and obtained a minimum set of features for four sectors, Financial Sector, Energy Sector, Health Care Sector, Consumer Discretionary Sector. This paper used two data sets, Financial Ratio and Balance Sheet, with two mappings, Detailed Mapping, and Coarse Mapping, converting the target variable(credit rating) into categorical variable. To test the accuracy of credit rating prediction, Random Forest Classifier was used to test and train feature sets. The results showed that the accuracy of Financial Ratio feature sets was higher than that of Balance Sheet feature sets. In addition, Factor Analysis can reduce the number of features significantly to obtain almost the same accuracy that can decrease dramatically the time spent on analyzing data; we also summarized seven dominant factors and ten dominant factors affecting credit rating change in Financial Ratio and Balance Sheet by utilizing Factor Analysis, respectively, which can explain the reason of credit rating change better.

q-fin.ST↗

arXiv2020

Influence of Murder Incident of Ride-hailing Drivers on Ride-hailing User's Consuming Willingness in Nanchang

Due to the frequent murder incidents of ride-hailing drivers in China in 2018, ride-hailing companies took a series of measures to prevent such incidents and ensure ride-hailing passengers' safety. This study investigated users' willingness to use ride-hailing apps after murder incidents and users' attitudes toward Safety Rectification. We found that murder incidents of ride-hailing drivers had a significant adverse impact on people's usage of ride-hailing apps. Female users' consuming willingness was 0.633 times that of male users, such as" psychological harm" was more evident among females, and Safety Rectification had a calming effect for some users. Finally, we found that people were satisfied with ride-hailing apps' efficiency, but were not satisfied with safety and reliability, considered them important; female users were more concerned about the security than male users.

cs.CY↗