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This document proposes using machine learning and sentiment analysis to predict employee service time. It introduces the problem of staff attrition rates affecting company performance. It then describes analyzing a dataset of over 36,000 examples with 10 variables related to federal employee records to build machine learning models for predicting average length of service. The models were evaluated on accuracy and AUC, with AdaBoosting performing best at 76% accuracy. Sentiment analysis of online comments found 78% supported extending work visas for STEM students. In future work, the authors aim to expand topics covered and optimize parameters to maximize company benefit.
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![Employee Service Time Forecasting by
Machine Learning Methods
Introduction
Staff attrition rate has a profound effect on the operation and
performance of a company. Statistics show that many
companies are now suffering heavy loss caused by sudden
employee attrition, resulting in urgent policy adjustment and
waste of budget resources.
Modeling
Conclusion
Business Intelligence & Analytics
http://www.stevens.edu/howe/academics/graduate/business-intelligence-analytics
Di Zhu, Tingting Jiang Nov 23, 2015
Instructor: Germán G. Creamer, Theodoros Lappas
Data Description Variables Description
agelvl Age
edlvl Education Level
gender Gender
patco Occupation Category
stemocc STEM Occupations
supervis Supervisory Status
toa Type of Appointment
worksch Work Schedule
workstat Work Status
los Average Length of Service
Data Source:
• www.opm.gov/data/
• www.regulations.gov/#!home
Target Variable:
• Average Length of Service
Length of the Data:
• 36,166 examples
• 10 variables
Raw Data
Cleaned Data
Optimal Algorithm
Logistic Regression
CART
AdaBoosting
Naïve Bayes
Data
Processing
Evaluation
Layer 3: Model in Use New Data Item Model Prediction
Data
Grabbing
Web Data Set
sentiment
Analysis
Text Mining Result
Layer 2: Sentimenl Analysis
Layer 1: Model Learning
A. Model Learning & Evaluation
Table 1. Variable Descriptions
Table 2. Testing Results
Figure 1. ROC and Precision-Recall Curves
References
[1] G Creamer and Y Freund, Automated Trading with Boosting
and Expert Weighting, Quantitative Finance, Vol. 4, No. 10, pp.
401–420
[2] G Creamer, Learning with decision trees: ADTrees, bagging,
and random forests, Stevens Institute of Technology, Oct, 2015
B. Sentiment Analysis
• We downloaded and
analyzed 3,977 comments
on Nov 6, 2015 when there
were over 4,000 comments.
• In this case, 78% of the net
users support this policy.
Adaboosting [1]:
Do you agree to
extend OPT for
F-1 students with
STEM degrees?
• We selected AdaBoosting algorithm because it yields the
best result in the testing data set, and reduces model bias
efficiently[2].
• The Accuracy Rate is 76%.
To deal with it, we
constructed a model to
predict staff service time
using both machine learning
and sentiment analysis.
We proposed a refined model to predict Employee Service
time applying machine learning and sentiment analysis
In future work:
• Involve more topics in order to yield an instructive and
comprehensive HR decision support system.
• Consider optimization analysis to optimize some specified
set of parameters leading to the maximum benefit of
company.
Algorithm Accuracy AUC Confusion Matrix
Logistic
Regression 0.728 0.808 3771 1414
1522 4079
CART 0.754 0.83 4286 1646
1007 3847
AdaBoosting 0.755 0.844 4195 1545
1098 3948
Naïve Bayes 0.698 0.768 4285 2245
1008 3248
78%
22%
Net Users’ Opinion Survey
Agree Disagree
Results](https://image.slidesharecdn.com/ab201a05-f2d5-429f-a30a-aa240cd2ed6e-160129153313/85/Di-Tingting_5-0-v-1-320.jpg)
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Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
Home assignment Tal Elor
This document outlines a proposal to build a model to predict employee churn risk using data from the messaging platform Slack. It discusses the motivation to retain employees, insights that could be gleaned from Slack data patterns, a roadmap and assumptions for the model, requirements for an MVP version, and metrics for measuring success including decreased churn rates and increased employee satisfaction.
Post Graduate Admission Prediction System
This document presents a post graduate admission prediction system built using machine learning algorithms. The system analyzes factors like GRE scores, TOEFL scores, undergraduate GPA, research experience etc. to predict the universities a student is likely to get admission in. Various machine learning models like multiple linear regression, random forest regression, support vector machine and logistic regression are implemented and evaluated on an admission prediction dataset. Logistic regression achieved the highest accuracy of 97%. A web application called PostPred is developed using the logistic regression model to help students predict suitable universities to apply to based on their profile.
Module Overview Careers in Analytics In this module, we .docx
Module Overview | Careers in
Analytics
In this module, we will evaluate the various quantitative data collection and analysis methods in
standard industry practice. These methods are what will be used throughout this program, so
you should become familiar with the terminology.
The second part of this module presents a variety of career paths for data analysts and an
overview of how several industries are currently using data analytics. Pay special attention to
the intersection of skills necessary for a data analyst to possess, and think of the steps you can
take to gain or improve on these in your own skill set. This may give you an idea of the career
path and industry you would like to pursue, or enhance your understanding of a career path and
industry you have already chosen.
Industry Practice
Learning Objectives
Explain the technical elements and steps associated with analytics practices and processes
Explore industry practice of data analytics
Typical Quantitative Techniques Used in Advanced Analytics
Several quantitative techniques apply to analytics projects, including:
Type Description
Simulation
Randomized repetitions of a set of discrete events in order to
model real-world systems and phenomena (e.g., queues)
Optimization
Algorithm selects the best possible outcome, subject to
satisfying constraints
Matrix Algebra
Calculations involving matrices solve multidimensional
problems
Fitting Functions to Data
Also called “curve fitting,” using numerical methods to
interpolate data
Survival Analysis
Originally used by life scientists, but adopted by marketers and
actuaries
Time Series
When data are “auto-correlated,” such as time-dependent data
(also called “Box-Jenkins”)
Predictive Analytics and Machine Learning
Classical Statistics
Descriptive: calculates metrics to characterize the distribution of
values of data (mean, standard deviation, range, etc.)
Predictive: estimates parameters using historical data and
making predictions of future outcomes (multivariate regression,
generalized linear regression, etc.)
Learning
Unsupervised learning: characterizes the data to establish
classes without using explicit metrics, e.g., k-means clustering
Supervised learning: Classify and describe the data with pre-
defined ‘labels,’ e.g., decision trees
Bayesian
Used to augment classical analysis when there is prior
knowledge about how the data was generated
Typical Challenges and Pitfalls in an Analytics Project
1. Poorly defined problem
• Unclear goal of problem-solving
• Scope is unclear, e.g., how many SKUs to analyze
• Mixed objectives, e.g., economic analysis of a product category promotion for retailer versus
CPG mixed
2. Limited IT resources
• Cloud data can’t be acquired off-line within a reasonable time
• Can’t run the complete model due to computation limitation
• Too slow to generate results in real time
• Can’t share.
Module Overview Careers in Analytics In this module, we .docx
Module Overview | Careers in
Analytics
In this module, we will evaluate the various quantitative data collection and analysis methods in
standard industry practice. These methods are what will be used throughout this program, so
you should become familiar with the terminology.
The second part of this module presents a variety of career paths for data analysts and an
overview of how several industries are currently using data analytics. Pay special attention to
the intersection of skills necessary for a data analyst to possess, and think of the steps you can
take to gain or improve on these in your own skill set. This may give you an idea of the career
path and industry you would like to pursue, or enhance your understanding of a career path and
industry you have already chosen.
Industry Practice
Learning Objectives
Explain the technical elements and steps associated with analytics practices and processes
Explore industry practice of data analytics
Typical Quantitative Techniques Used in Advanced Analytics
Several quantitative techniques apply to analytics projects, including:
Type Description
Simulation
Randomized repetitions of a set of discrete events in order to
model real-world systems and phenomena (e.g., queues)
Optimization
Algorithm selects the best possible outcome, subject to
satisfying constraints
Matrix Algebra
Calculations involving matrices solve multidimensional
problems
Fitting Functions to Data
Also called “curve fitting,” using numerical methods to
interpolate data
Survival Analysis
Originally used by life scientists, but adopted by marketers and
actuaries
Time Series
When data are “auto-correlated,” such as time-dependent data
(also called “Box-Jenkins”)
Predictive Analytics and Machine Learning
Classical Statistics
Descriptive: calculates metrics to characterize the distribution of
values of data (mean, standard deviation, range, etc.)
Predictive: estimates parameters using historical data and
making predictions of future outcomes (multivariate regression,
generalized linear regression, etc.)
Learning
Unsupervised learning: characterizes the data to establish
classes without using explicit metrics, e.g., k-means clustering
Supervised learning: Classify and describe the data with pre-
defined ‘labels,’ e.g., decision trees
Bayesian
Used to augment classical analysis when there is prior
knowledge about how the data was generated
Typical Challenges and Pitfalls in an Analytics Project
1. Poorly defined problem
• Unclear goal of problem-solving
• Scope is unclear, e.g., how many SKUs to analyze
• Mixed objectives, e.g., economic analysis of a product category promotion for retailer versus
CPG mixed
2. Limited IT resources
• Cloud data can’t be acquired off-line within a reasonable time
• Can’t run the complete model due to computation limitation
• Too slow to generate results in real time
• Can’t share ...
Work Measurement and Operational Effectiveness
Work measurement is a systematic process that has been used since the late 1800s to improve productivity. It involves directly observing work tasks and breaking them into elements to determine standard times. These standards provide benchmarks for tasks and help identify areas for improvement. Work measurement provides data to help with staffing needs, process design, cost analysis, and measuring performance against goals. The success of work measurement relies on accurately capturing all task times, applying appropriate allowances, and using the standards to drive continuous improvement.
Ibm test data_management_v0.4
This document discusses test data management strategies and IBM's approach. It begins by explaining how test data management has become essential for software development. A key challenge is ensuring high quality test data. The document then outlines goals for a test data management strategy, such as producing reusable, consumable, and scalable results. It proposes analyzing needs, crafting data models, and establishing governance. IBM's approach involves engaging consultants, conducting a proof of concept, piloting the strategy, and full implementation using test data management tools. The overall goal is to improve testing efficiency and effectiveness.
Scalable HR Integrations for Better Data Analytics: Challenges & Solutions
Scalable HR Integrations for Better Data Analytics: Challenges & SolutionsHarbinger Systems - HRTech Builder of Choice
Find out the opportunities to build integrations that facilitate seamless exchange of information across internal and external HR applications.
In this webcast we have discussed the below pointers,
• Need for Data-Driven HR and its relevance in organizations
• Aspects of Workplace Analytics Maturity Model and how to correlate it with your needs
• Understand typical challenges in data integrations and how to solve them
• How to decide on an integration approach for your organization
This webcast is in collaboration with HR.com
Personnel Productivity System - Updated 6-6-2013
Workflow Management System designed to examine work requests, measure staff workload volumes and skills, interact with Personnel Management, Automated Training System, Personnel Recruitment (Employee and Consultant), and Management Reporting to improve information sharing and employee career path and loyalty.
Data Mining and Analytics
The document discusses how analytics and data mining can be used to gain insights from data generated by business processes. It describes how event data from processes can be analyzed in real-time for monitoring and over time to identify patterns and opportunities for process improvement. Key applications discussed include predictive modeling, simulation, optimization, and automated recommendations for resource allocation and process changes.
Tbla 2016
The document provides an overview of using big data analytics to help the Palm Group, a hospitality company, achieve its ambitious growth plans. It begins with an analysis of the hospitality industry outlook and Palm Group's situation. This is followed by a presentation of insights gained from analyzing Palm Group's HR data to identify priority areas for transformation. The recommendation is to build an analytics platform to leverage both internal and external data for talent acquisition, retention, and other HR processes. A strategic roadmap and business case are provided outlining the potential benefits, investments, and payback period of implementing a big data and analytics driven approach for HR.
IRJET - Employee Performance Prediction System using Data Mining
This document summarizes a research paper that uses data mining techniques to build a classification model to predict employee performance. The researchers collected data on employee attributes like education, experience, and personal qualities. They then used classification algorithms like decision trees, K-nearest neighbors, and naive Bayes to analyze the data and identify patterns that affect performance. The best performing model could help human resources professionals evaluate employees more objectively and make data-driven decisions to improve performance.
Day 1 (Lecture 2): Business Analytics
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Descriptive Statistics and Interpretation Grading GuideQNT5.docx
This document outlines a sampling and data collection plan to test whether implementing a Total Quality Management (TQM) system will increase product quality at PhoenixSolar. The target population includes production workers, managers, engineers, technicians, and customers who will provide insights through focus groups and surveys. A sample size of 385 is needed for a 95% confidence level. Internal employees will participate in exploratory focus groups, while external groups like technicians and customers will complete paper, email, and installation surveys. Validity, reliability, and privacy protocols are defined. The plan is to analyze responses over six months to determine if TQM increases quality and customer satisfaction at PhoenixSolar.
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...Value Amplify Consulting
This document discusses building a predictive system using machine learning. It describes predicting income using census data with four machine learning algorithms: Two-Class Decision Jungle, Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, and Two-Class Locally-Deep Support Vector Machine. It also discusses tuning hyperparameters, combining results, and benchmarking performance. Additional sections cover predictive analytics processes, digital transformation, and predictive maintenance maturity models.-linkedin
This document summarizes a capstone project on automated data science. It discusses the data science pipeline, which includes preparation, analysis, and integration phases. Activities within the preparation phase like data cleansing, dimension reduction, correlation analysis, and feature synthesis have varying levels of automation maturity. Algorithms and techniques for automating tasks in each phase are presented. The document also examines challenges of incorporating automated data science like change management and skills transition. Finally, it discusses vendor capabilities in different phases and factors to consider for in-house vs outsourced integration solutions.
Machine Learning for Business - Eight Best Practices for Getting Started
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Developing a framework for
The document describes developing a framework to predict human performance using ensemble techniques. It discusses collecting data on project personnel attributes and performance ratings. A random forest model is developed using attributes like programming skills, reasoning ability, domain knowledge, time efficiency, GPA, and communication skills. The random forest improves on previous studies by enhancing prediction accuracy. Results show attributes like programming skills and domain knowledge are highly important for predicting performance. The model achieves high accuracy rates based on evaluation metrics.
Paper 30 decision-support_system_for_employee_candidate(1)
This document summarizes a research paper that developed a decision support system using the Analytic Hierarchy Process (AHP) and Profile Matching (PM) methods to help a company select the best prospective employees. The system was tested on four candidates applying for a position, and produced a ranking of the candidates based on their scores on 11 criteria such as work motivation, intelligence, and experience. Evaluation of the system found it to be well accepted by users and effective at integrating both qualitative and quantitative data to recommend the highest ranking candidates.
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ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...
Module Overview Careers in Analytics In this module, we .docx
Module Overview Careers in Analytics In this module, we .docx
Module Overview Careers in Analytics In this module, we .docx
Module Overview Careers in Analytics In this module, we .docx
Scalable HR Integrations for Better Data Analytics: Challenges & Solutions
Scalable HR Integrations for Better Data Analytics: Challenges & Solutions
IRJET - Employee Performance Prediction System using Data Mining
IRJET - Employee Performance Prediction System using Data Mining
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Descriptive Statistics and Interpretation Grading GuideQNT5.docx
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
Machine Learning for Business - Eight Best Practices for Getting Started
Machine Learning for Business - Eight Best Practices for Getting Started
Paper 30 decision-support_system_for_employee_candidate(1)
Paper 30 decision-support_system_for_employee_candidate(1)
Di&Tingting_5.0 v
- 1. Employee Service Time Forecasting by Machine Learning Methods Introduction Staff attrition rate has a profound effect on the operation and performance of a company. Statistics show that many companies are now suffering heavy loss caused by sudden employee attrition, resulting in urgent policy adjustment and waste of budget resources. Modeling Conclusion Business Intelligence & Analytics http://www.stevens.edu/howe/academics/graduate/business-intelligence-analytics Di Zhu, Tingting Jiang Nov 23, 2015 Instructor: Germán G. Creamer, Theodoros Lappas Data Description Variables Description agelvl Age edlvl Education Level gender Gender patco Occupation Category stemocc STEM Occupations supervis Supervisory Status toa Type of Appointment worksch Work Schedule workstat Work Status los Average Length of Service Data Source: • www.opm.gov/data/ • www.regulations.gov/#!home Target Variable: • Average Length of Service Length of the Data: • 36,166 examples • 10 variables Raw Data Cleaned Data Optimal Algorithm Logistic Regression CART AdaBoosting Naïve Bayes Data Processing Evaluation Layer 3: Model in Use New Data Item Model Prediction Data Grabbing Web Data Set sentiment Analysis Text Mining Result Layer 2: Sentimenl Analysis Layer 1: Model Learning A. Model Learning & Evaluation Table 1. Variable Descriptions Table 2. Testing Results Figure 1. ROC and Precision-Recall Curves References [1] G Creamer and Y Freund, Automated Trading with Boosting and Expert Weighting, Quantitative Finance, Vol. 4, No. 10, pp. 401–420 [2] G Creamer, Learning with decision trees: ADTrees, bagging, and random forests, Stevens Institute of Technology, Oct, 2015 B. Sentiment Analysis • We downloaded and analyzed 3,977 comments on Nov 6, 2015 when there were over 4,000 comments. • In this case, 78% of the net users support this policy. Adaboosting [1]: Do you agree to extend OPT for F-1 students with STEM degrees? • We selected AdaBoosting algorithm because it yields the best result in the testing data set, and reduces model bias efficiently[2]. • The Accuracy Rate is 76%. To deal with it, we constructed a model to predict staff service time using both machine learning and sentiment analysis. We proposed a refined model to predict Employee Service time applying machine learning and sentiment analysis In future work: • Involve more topics in order to yield an instructive and comprehensive HR decision support system. • Consider optimization analysis to optimize some specified set of parameters leading to the maximum benefit of company. Algorithm Accuracy AUC Confusion Matrix Logistic Regression 0.728 0.808 3771 1414 1522 4079 CART 0.754 0.83 4286 1646 1007 3847 AdaBoosting 0.755 0.844 4195 1545 1098 3948 Naïve Bayes 0.698 0.768 4285 2245 1008 3248 78% 22% Net Users’ Opinion Survey Agree Disagree Results