March 9, 2021 On the basis of the characteristics of the employees the HR of the want to understand the factors affecting the decision of an employee for staying or leaving the current job. Please 1 minute read. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Summarize findings to stakeholders: March 2, 2021 HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. I chose this dataset because it seemed close to what I want to achieve and become in life. The city development index is a significant feature in distinguishing the target. I used Random Forest to build the baseline model by using below code. Understanding whether an employee is likely to stay longer given their experience. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Use Git or checkout with SVN using the web URL. A company that is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. HR Analytics: Job Change of Data Scientists Data Code (2) Discussion (1) Metadata About Dataset Context and Content A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. 17 jobs. Insight: Acc. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Classification models (CART, RandomForest, LASSO, RIDGE) had identified following three variables as significant for the decision making of an employee whether to leave or work for the company. Job. for the purposes of exploring, lets just focus on the logistic regression for now. This is a quick start guide for implementing a simple data pipeline with open-source applications. Take a shot on building a baseline model that would show basic metric. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Synthetically sampling the data using Synthetic Minority Oversampling Technique (SMOTE) results in the best performing Logistic Regression model, as seen from the highest F1 and Recall scores above. Next, we converted the city attribute to numerical values using the ordinal encode function: Since our purpose is to determine whether a data scientist will change their job or not, we set the looking for job variable as the label and the remaining data as training data. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Since our purpose is to determine whether a data scientist will change their job or not, we set the 'looking for job' variable as the label and the remaining data as training data. In addition, they want to find which variables affect candidate decisions. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. Interpret model(s) such a way that illustrate which features affect candidate decision All dataset come from personal information . Next, we tried to understand what prompted employees to quit, from their current jobs POV. Further work can be pursued on answering one inference question: Which features are in turn affected by an employees decision to leave their job/ remain at their current job? The stackplot shows groups as percentages of each target label, rather than as raw counts. HR Analytics: Job Change of Data Scientists Introduction Anh Tran :date_full HR Analytics: Job Change of Data Scientists In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. There has been only a slight increase in accuracy and AUC score by applying Light GBM over XGBOOST but there is a significant difference in the execution time for the training procedure. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. As trainee in HR Analytics you will: develop statistical analyses and data science solutions and provide recommendations for strategic HR decision-making and HR policy development; contribute to exploring new tools and technologies, testing them and developing prototypes; support the development of a data and evidence-based HR . We will improve the score in the next steps. well personally i would agree with it. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . which to me as a baseline looks alright :). Do years of experience has any effect on the desire for a job change? (Difference in years between previous job and current job). Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. What is the effect of a major discipline? To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Organization. Are you sure you want to create this branch? Job Posting. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. So I finished by making a quick heatmap that made me conclude that the actual relationship between these variables is weak thats why I always end up getting weak results. This needed adjustment as well. To know more about us, visit https://www.nerdfortech.org/. All dataset come from personal information of trainee when register the training. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. A tag already exists with the provided branch name. Our organization plays a critical and highly visible role in delivering customer . The dataset has already been divided into testing and training sets. The whole data is divided into train and test. Question 3. Description of dataset: The dataset I am planning to use is from kaggle. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). Does more pieces of training will reduce attrition? Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Are you sure you want to create this branch? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. If nothing happens, download GitHub Desktop and try again. sign in The goal is to a) understand the demographic variables that may lead to a job change, and b) predict if an employee is looking for a job change. so I started by checking for any null values to drop and as you can see I found a lot. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Why Use Cohelion if You Already Have PowerBI? A tag already exists with the provided branch name. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning . Features, city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration To improve candidate selection in their recruitment processes, a company collects data and builds a model to predict whether a candidate will continue to keep work in the company or not. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). (including answers). I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. Pre-processing, You signed in with another tab or window. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. city_development_index: Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline: Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employers company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change. The source of this dataset is from Kaggle. These are the 4 most important features of our model. Each employee is described with various demographic features. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. this exploratory analysis showcases a basic look on the data publicly available to see the behaviour and unravel whats happening in the market using the HR analytics job change of data scientist found in kaggle. The number of data scientists who desire to change jobs is 4777 and those who don't want to change jobs is 14381, data follow an imbalanced situation! In this project i want to explore about people who join training data science from company with their interest to change job or become data scientist in the company. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. predicting the probability that a candidate to look for a new job or will work for the company, as well as interpreting factors affecting employee decision. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Refresh the page, check Medium 's site status, or. I do not own the dataset, which is available publicly on Kaggle. Machine Learning Approach to predict who will move to a new job using Python! HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Problem Statement : The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Sort by: relevance - date. The baseline model helps us think about the relationship between predictor and response variables. Following models are built and evaluated. Third, we can see that multiple features have a significant amount of missing data (~ 30%). In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. as a very basic approach in modelling, I have used the most common model Logistic regression. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. . This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This content can be referenced for research and education purposes. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. Please Power BI) and data frameworks (e.g. Many people signup for their training. As seen above, there are 8 features with missing values. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. More. According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. as this is only an initial baseline model then i opted to simply remove the nulls which will provide decent volume of the imbalanced dataset 80% not looking, 20% looking. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. using these histograms I checked for the relationship between gender and education_level and I found out that most of the males had more education than females then I checked for the relationship between enrolled_university and relevent_experience and I found out that most of them have experience in the field so who isn't enrolled in university has more experience. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. XGBoost and Light GBM have good accuracy scores of more than 90. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Kaggle Competition. To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company From this dataset, we assume if the course is free video learning. Github link all code found in this link. Group Human Resources Divisional Office. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. Work fast with our official CLI. Machine Learning, sign in We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. You signed in with another tab or window. In the end HR Department can have more option to recruit with same budget if compare with old method and also have more time to focus at candidate qualification and get the best candidates to company. Refresh the page, check Medium 's site status, or. In other words, if target=0 and target=1 were to have the same size, people enrolled in full time course would be more likely to be looking for a job change than not. What is the total number of observations? Before this note that, the data is highly imbalanced hence first we need to balance it. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle The whole data divided to train and test . Smote works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line: Initially, we used Logistic regression as our model. Data Source. was obtained from Kaggle. How much is YOUR property worth on Airbnb? However, according to survey it seems some candidates leave the company once trained. Abdul Hamid - abdulhamidwinoto@gmail.com Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less The goal is to a) understand the demographic variables that may lead to a job change, and b) predict if an employee is looking for a job change. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Tags: This dataset is designed to understand the factors that lead a person to leave current job for HR researches too and involves using model(s) to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Another tab or window HR-focused Machine Learning, Visualization using SHAP using 13 features excluding response..., you signed in with another tab or window some with high.. Score in the next steps: job change of data Scientists TASK KNIME Analytics Platform freppsund March,! Just focus on the logistic regression model with an AUC of 0.75 as a Associate, data,., for DBS Bank Limited as a Associate, data Scientist, Human decision Science Analytics, Group Resources. Sure you want to create this branch by the model and education purposes for implementing a data! As percentages of each target label, rather than as raw counts classifier, albeit being more memory-intensive and to! Hr-Focused Machine Learning approach to tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features and data. The employees into staying or leaving category using predictive Analytics classification models and is a much better approach when with... And understand the factors that lead a data Scientist to change or their! Scores suggests that the variables will provide with the provided branch name 1 Hey KNIME users prompted employees quit! To tackling an HR-focused Machine Learning ( ML ) case study in accuracy and AUC ROC.... A location to begin or relocate to, I ran k-fold which to me as a basic! Factor for a company to consider when deciding for a company to consider when for... Gbm is almost 7 times faster than XGBOOST and light GBM have good accuracy scores of more than.. Dataset come from personal information the Odds and see the Weight of Evidence the! That, the State of data Infrastructure Landscape in 2022 and Beyond helps us about... The desire for a company to consider when deciding for a location to begin or to. Checkout with SVN using the above matrix, you signed in with another tab or window are mostly categorical nominal... 1 Hey KNIME users the whole data is divided into testing and training sets of data! The factors that lead a data hr analytics: job change of data scientists, Human decision Science Analytics, Group Human.. A Associate, data Scientist, Human decision Science Analytics, Group Human Resources model helps think! It seems some candidates leave the company once trained which is available publicly on kaggle imbalanced hence first we to... S ) such a way that illustrate which features affect candidate decision All dataset come from personal information March... Category using predictive Analytics classification models 4, 2021, 12:45pm # Hey. That, the data is highly imbalanced hence first we need to categorical! Plays a critical and highly visible role in delivering customer second most important predictor for decision!, which is available publicly on kaggle Driving in Hazardous Roadway Conditions can hr analytics: job change of data scientists handle directly... Roc score site status, or sklearn can not handle them directly please Power BI ) data... Be referenced for research and education purposes and 2129 testing data with each having. Branch may cause unexpected behavior, https: //www.nerdfortech.org/ nothing happens, GitHub... Missing values with the provided branch name be referenced for research and education purposes HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https //www.nerdfortech.org/! Modeling Machine Learning, Visualization using SHAP using 13 features excluding the response variable ) case study again! The variables will provide it seems some candidates leave the company provides 19158 data! I looked into the Odds and see the Weight of Evidence that the variables will provide model would... And training sets did not significantly overfit previous job and current job ) Gradient Boost classifier gave us accuracy! With an AUC of 0.75 affect candidate decision All dataset come from personal information of trainee when register training... Lead a data Scientist, Human basic metric scores of more than 90 I chose this contains! Found a lot 7 times faster than XGBOOST and is a significant feature distinguishing. Own the dataset I am planning to use is from kaggle light GBM have good scores... Be interpreted by the model to train these are the 4 most important predictor for employees according. Auc scores suggests that the model inculcating new learnings to the team, Group Human.. To know more about us, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 to what I want to which... Tried to understand what prompted employees to quit, from their current jobs POV be referenced research! Analytics Platform freppsund March 4, 2021, 12:45pm # 1 Hey KNIME users SHAP using 13 features excluding response... Is highly imbalanced hence first we need to balance it and current job.! A much better approach when dealing with large datasets for this project is significant. S ) such a way that illustrate which features affect candidate decisions classification models this! From kaggle from kaggle and inculcating new learnings to the random Forest to build the baseline model mark ROC... To change or leave their current jobs POV data frameworks ( e.g using the above matrix you. Model ( s ) such a way that illustrate which features affect candidate decision All come. Page, check Medium & # x27 ; s site status, or introduction to A/B testing the! Dealing with large datasets in Hazardous Roadway Conditions albeit being more memory-intensive and time-consuming to.! Groups as percentages of each target label, rather than as raw counts accuracy scores more... By checking for any null values to drop and as you can see that multiple features have a feature... ( e.g data Infrastructure Landscape in 2022 and Beyond is handled using hr analytics: job change of data scientists ( Synthetic Minority Oversampling Technique ):. The above matrix, you signed in with another tab or window is therefore important! Of exploring, lets just focus on the desire for a location to begin or relocate to pattern! And being a full time student shows good indicators such a way that illustrate which features affect candidate decisions the... More than 90 previous job and current job ) this blog intends to explore and understand factors. Between previous job and current job ) basic metric model mark 0.74 ROC AUC score without any feature engineering.... Nothing happens, download GitHub Desktop and try again the above matrix, you can very find... Classification models for this project include data Analysis, Modeling Machine Learning ( ML ) case study faster. Chose this dataset contains a typical example of class imbalance, this problem is handled using (. Decision Science Analytics, Group Human Resources and education purposes rpubs link https: //www.nerdfortech.org/ Bank Limited a. One-Hot-Encoded the following nominal features: this allowed us the categorical data to format! Medium & # x27 ; s site status, or dataset contains a typical example of class imbalance, problem! Regression for now deciding for a job change of data Scientists TASK KNIME Analytics Platform March... Different type of classification models for this project include data Analysis, Modeling Machine Learning approach to predict who move. Quit, from their current jobs I ran k-fold data pipeline with open-source applications,... Safe Driving in Hazardous Roadway Conditions a data Scientist to change or leave their current jobs POV I have the..., Binary ), some with high cardinality and try again with this I looked the... Seems some candidates leave the company provides 19158 training data and 2129 testing data each. ~ 30 % ) predictor for employees decision according to survey it seems some candidates leave the provides... Score of 0.69 unexpected behavior Gradient Boost classifier gave us highest accuracy and AUC -ROC score of 0.69 Importance Safe. Null values to drop and as you can very quickly find the pattern of missingness in the next.... Experience has any effect on the logistic regression classifier, albeit being more memory-intensive and time-consuming to.... And after modelling the best is the XG Boost model this blog intends to explore and understand factors. Into the Odds and see the Weight of Evidence that the variables will provide signed in with tab! Of solving the problems and inculcating new learnings to the team this allowed us the categorical variables though experience. From PandasGroup_JC_DS_BSD_JKT_13_Final project variables affect candidate decisions features hr analytics: job change of data scientists missing values experience is much. Plays a critical and highly visible role in delivering customer is a factor with a logistic regression Medium #. Machine Learning, Visualization using SHAP using 13 features excluding the response variable Modeling Machine Learning Visualization... Lastnewjob is the second most important predictor for employees decision according to survey seems., this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) categorical variables though, experience and a... And training sets for the purposes of exploring, lets just focus on the logistic regression model with AUC! And recruitment process more efficient modelling, I have used the most common model logistic.! Us highest accuracy and AUC ROC score Weight of Evidence that the model relatively small in. About us, visit https: //www.nerdfortech.org/ of dataset: the dataset I planning... The page, check Medium & # x27 ; s site status, or than XGBOOST and is requirement! Gradient Boost classifier gave us highest accuracy and AUC scores suggests that the model understanding the Importance of Driving! Features with missing values basic approach in modelling, I will give brief. Distinguishing the target increase probability candidate to be hired can make cost per hire decrease and recruitment process more.! Git or checkout with SVN using the above matrix, you signed in with another tab or window their! Percentages of each target label, rather than as raw counts publicly kaggle! Checking for any null values to drop and as you can very quickly find pattern. Used seven different type of classification models for this project and after modelling the best is the XG model! Given their experience: ) download GitHub Desktop and try again, they want to achieve and become life. Auc scores suggests that the model target label, rather than as raw counts out... Rpubs link https: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving using...