Employee Attrition Analysis
A leading organization would like to know why its best and most experienced employees are leaving early. Based on the previous data, classification was done to predict the employees who could leave early.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
The main goal of this slide is to leverage the power of data science to conduct an analysis on existing employee data to provide some interesting trends that may exists in data set, identify top factors that contribute to turnover and build a model to classify attrition and predict monthly income for the company, Alnylam Pharmaceuticals.
The goal of this project was to predict employee attrition and to determine key factors that might contribute to attrition. Four different classification models are evaluated and compared to determine the best classification model.
IBM HR Analytics Employee Attrition & PerformanceShivangiKrishna
- Help companies to be prepared for future employee-loss
- Evaluating possible trends and reasons for employee attrition, in order to prevent valuable employees from leaving.
- We analyzed the numeric and categorical data with the use of Machine Learning models to identify the main variables contributing to the attrition of employees
- This project was completed and carried out by three DSAI students Angelin Grace Wijaya, Agarwala Pratham, Krishna Shivangi
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
The main goal of this slide is to leverage the power of data science to conduct an analysis on existing employee data to provide some interesting trends that may exists in data set, identify top factors that contribute to turnover and build a model to classify attrition and predict monthly income for the company, Alnylam Pharmaceuticals.
The goal of this project was to predict employee attrition and to determine key factors that might contribute to attrition. Four different classification models are evaluated and compared to determine the best classification model.
IBM HR Analytics Employee Attrition & PerformanceShivangiKrishna
- Help companies to be prepared for future employee-loss
- Evaluating possible trends and reasons for employee attrition, in order to prevent valuable employees from leaving.
- We analyzed the numeric and categorical data with the use of Machine Learning models to identify the main variables contributing to the attrition of employees
- This project was completed and carried out by three DSAI students Angelin Grace Wijaya, Agarwala Pratham, Krishna Shivangi
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
An overview of HR analytics. The slide can be used by everyone for their learning purpose as well as in institute presentation at the last moment. All basics are being covered.
Best of Luck.
Employee turnover is a part of normal business activity; employees come and go as their life situations change. Employers realize this and, indeed, firms typically have entire departments devoted to the management of human resources in order to make the transition as painless as possible for both management and employee and to minimize the associated hiring and training costs.
Big Data, Business Intelligence, HR Analytics - How they are related?Shojibul Alam Shojib
Big data, business intelligence, and HR analytics are three buzzwords that are frequently talked about. Do you really know what they mean? And what added value does big data and business intelligence bring to the field of HR?
Machine Learning Approach for Employee Attrition Analysisijtsrd
"Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource HR attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform 1 . Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees’ satisfaction level as compared to other attributes. Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G. Naik ""Machine Learning Approach for Employee Attrition Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23065.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath"
The rate at which employees leave a company and are replaced by new Employees. One of the critically challenging issues in business world. Estimated probability that employees will stay or leave the organization. May triggered by - quits, attrition, exits, mobility, migration, succession. Obstacles toward achieving organizational objectives. Delay in innovation process & weak service consistency. Increasing pressure for the current employees in organization & Reflects poor organizational image. Overall bad impact on organizational performance & effectiveness.
Now a days every organization has HR Software but some of those don't know how AI will help to Human resource tasks.
You are may be already aware about what is AI but here we have explained about how Artificial Intelligence help to HR (Human resource) to make simplify task.
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
All organizations are really focusing on improving their processes and one of the key aspect for a project to succeed is its employees. Employees play a vital role in a business success. Therefore, the company wants to predict which employee will leave next and look for measures to retain him/her.
An overview of HR analytics. The slide can be used by everyone for their learning purpose as well as in institute presentation at the last moment. All basics are being covered.
Best of Luck.
Employee turnover is a part of normal business activity; employees come and go as their life situations change. Employers realize this and, indeed, firms typically have entire departments devoted to the management of human resources in order to make the transition as painless as possible for both management and employee and to minimize the associated hiring and training costs.
Big Data, Business Intelligence, HR Analytics - How they are related?Shojibul Alam Shojib
Big data, business intelligence, and HR analytics are three buzzwords that are frequently talked about. Do you really know what they mean? And what added value does big data and business intelligence bring to the field of HR?
Machine Learning Approach for Employee Attrition Analysisijtsrd
"Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource HR attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform 1 . Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees’ satisfaction level as compared to other attributes. Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G. Naik ""Machine Learning Approach for Employee Attrition Analysis"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23065.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath"
The rate at which employees leave a company and are replaced by new Employees. One of the critically challenging issues in business world. Estimated probability that employees will stay or leave the organization. May triggered by - quits, attrition, exits, mobility, migration, succession. Obstacles toward achieving organizational objectives. Delay in innovation process & weak service consistency. Increasing pressure for the current employees in organization & Reflects poor organizational image. Overall bad impact on organizational performance & effectiveness.
Now a days every organization has HR Software but some of those don't know how AI will help to Human resource tasks.
You are may be already aware about what is AI but here we have explained about how Artificial Intelligence help to HR (Human resource) to make simplify task.
Data Analytics PowerPoint Presentation SlidesSlideTeam
This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics PowerPoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
All organizations are really focusing on improving their processes and one of the key aspect for a project to succeed is its employees. Employees play a vital role in a business success. Therefore, the company wants to predict which employee will leave next and look for measures to retain him/her.
Global competition for talent, outsourcing labor, compliance legislation, remote workers, aging populations – these are just a few of the daunting challenges faced by HR organizations today.
Yet the most commonly monitored workforce metrics do very little to deliver true insight into these topics. Leaders need to graduate from metrics to analytics, surfacing the important connections and patterns in their data to make better workforce decisions. By graduating from metrics to analytics, HR professionals and leaders can better understand the contributing factors that are impacting their organization, and take the right actions to implement programs that will provide a true competitive advantage.
Data Science Project
The main goal here is to predict whether an employee will stay or leave within company.
Besides this,
Key drivers for attrition are to be identified
Employees shall be classified into High ,Medium and Low risk profiles in terms of attrition
Predicting thresholds for key drivers of attrition
Employee level risk analysis showing supporting and contradicting features
Role Of HR In Organizational Design PowerPoint Presentation SlidesSlideTeam
Grab Role Of HR In Organizational Design PowerPoint Presentation Slides to create a gripping presentation within moments. SlideTeam designers offer a comprehensive but concise PPT theme to elucidate the responsibilities of HR. This PowerPoint slideshow helps HR specialists to explain the elements and types of organizational design. Use our Human resources management PPT templates to elaborate on forms of departmentalization. You can effectively consolidate functional, geographical, product, process, and customer departmentalization. Human capital PowerPoint deck is ideal for explaining employee expenses, revenue per employee, and statement of operations. Absorbing data visualization tools of HR management PPT presentation portray complex information clearly. Using this HR organizational setup PowerPoint theme, you can illustrate the chain of command and line authority. Demonstrate levels and span of control with the help of our Human Resources role PPT slideshow. Communicate the functions and responsibilities of each position through this HR framework PowerPoint deck. So, hit the download button now and personalize this virtually-opulent PPT presentation. https://bit.ly/3oCvxJ2
Actionable results to enhance Employee satisfaction score analysis via TableauShruti Nigam (CWM, AFP)
Use Tableau for following Analysis:
The management of this organization is concerned about their employees’
satisfaction index and has been constantly measuring the same. They somehow feel
that improving the satisfaction scores shall ensure longevity of their employees
preventing unhealthy attrition.
• Analyze this dataset by finding the key drivers for employee satisfaction scores.
The attached document is a shortened Organizational Analysis showing an outline of the steps for performing a Needs Assessment and the consequential Gaps that were discovered and prioritized.
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Lean principles have been used for years in the manufacturing world, and have started to make an impact in the office as well. These tools can provide the foundation to building a systematic approach to improving your HR practice and lowering costs. In this session, we will review a sample structure for project generation, selection and governance. Additionally, we will apply these tools in an interactive session to create a list of potential actions attendees can use on their return to their organization. The intent is to provide a high level overview of the methodology, provide tools that can be taken and implemented, and provide experience applying the tools within the session.
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Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
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In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
3. BUSINESS PROBLEM
A leading organisation's business had been increasing quite well over the
past. But, there has been slowdown in terms of growth.
To predict, why best and most experienced employees are leaving based on
the given profile.
HRM Planning is very important for the companies to make sure the
continued retention of the high performers with the best talent.
5. Attributes
On to the Left
Numerical Attributes
On to the Right
Categorical Attributes
Target Variable
Left
6. Attribute – Range/Level
ATTRIBUTE RANGE/LEVEL
Satisfaction_level 0.09 to 1.00
Last_Evaluation 0.36 to 1.00
Number_Project 2 To 7
Average_monthly_hours 96 To 310
Time_spend_company 2 To 10
Department 10 Levels
Salary 3 Levels
Left, Work accident, Promotion_last_5years 0 , 1
7. Data Understanding
ATTRIBUTE 0’s 1’s
Left 11428 3571
Left (Distinct) 10000 1991
Total number of observations is 14999 with 9 independent attributes.
The given data has mix of Numerical & Categorical Attributes.
Target variable is left & it is discrete, which has high class imbalance.
8. Binary
Classification
(Approach)
Given, Target variable is left
& it is discrete which has two classes.
Where as ‘1’ is considered as left &
that of ‘0’ is considered as not left .
This is supervised learning classification
model building approach.
Error Metric to be worked on
is F1Score .
14. Avg. Satisfaction Level
v/s
Avg. Monthly Hours
Monthly hours does have effect
on satisfaction level
More monthly hours have less
satisfaction level
Reason :
Human capacity per day
16. INFERENCE
Common traits of Good people leaving
Experienced
Very low satisfaction level
Spend more time at work
Possible Reasons for people leaving
Experienced people may not be finding
any challenges in work. Hence they leave.
Work to Pay ratio may be high (because
we find clear correlation only in low and
medium salary ranges)
18. Data Preprocessing
Task
Duplicate Records
No Missing Values
Subsetting & Categorical conversion
Standardization
Handling Class Imbalance
Implementation
Using Distinct Function
--
Using as.factor() function
Using Range, Z-Score methods
Using SMOTE - 60:40, 70:30, 80:20
19. MODEL BUILDING
As Target Variable (left) is discrete, hence Various Classification Techniques has to be applied.
Started with Logistic regression and then with Decision Trees, RandomForest and XGBOOST
obtained predicted values.
MODEL APPLIED / F1 Score TRAIN VALIDATION TEST
LOGISTIC 0.5932136 0.5982906 0.5939249
DECISION TREES 0.9383260 0.9302899 0.9315540
RANDOMFOREST 0.9673754 0.9988067 0.9643624
XGBOOST 0.9818643 0.9953924 0.9693356
20. MODEL BUILDING
Using ROC curve
prob > 0.35 is taken
as 1’s & rest as 0’s
Logistic
Regression
Hyper paremeters :
ntrees = 200 to 500
Mtry = 4 (3 to 6)
Random
Forest
21. MODEL BUILDING
Tree Depth :
Reduced from 32 to 6
Decision
Trees Enabled Cross
Validation
Handling Missing
Values
Tree pruning
XGBOOST
22. FUTURE ENHANCEMENTS
With the use of functional/domain knowledge,
Feature engineering will be done by generating a new columns or attributes.
Other Classification models will be applied with the respective hyper parameter tuning.