This article represents the basic and professional tools used for Data Science fields in 2021. Permanent. First, Id like take a look at how categorical features are correlated with the target variable. Use Git or checkout with SVN using the web URL. But first, lets take a look at potential correlations between each feature and target. February 26, 2021 Each employee is described with various demographic features. A tag already exists with the provided branch name. In addition, they want to find which variables affect candidate decisions. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. Interpret model(s) such a way that illustrate which features affect candidate decision Deciding whether candidates are likely to accept an offer to work for a particular larger company. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. The stackplot shows groups as percentages of each target label, rather than as raw counts. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. This allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. I got my data for this project from kaggle. 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? What is the total number of observations? Context and Content. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. There was a problem preparing your codespace, please try again. (Difference in years between previous job and current job). StandardScaler removes the mean and scales each feature/variable to unit variance. We used this final model to increase our AUC-ROC to 0.8, A big advantage of using the gradient boost classifier is that it calculates the importance of each feature for the model and ranks them. Tags: Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). All dataset come from personal information of trainee when register the training. 5 minute read. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. Dont label encode null values, since I want to keep missing data marked as null for imputing later. Organization. Does the gap of years between previous job and current job affect? Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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. Problem Statement : NFT is an Educational Media House. For any suggestions or queries, leave your comments below and follow for updates. 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). Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. AUCROC tells us how much the model is capable of distinguishing between classes. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Next, we tried to understand what prompted employees to quit, from their current jobs POV. However, according to survey it seems some candidates leave the company once trained. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. This content can be referenced for research and education purposes. Answer In relation to the question asked initially, the 2 numerical features are not correlated which would be a good feature to use as a predictor. Using ROC AUC score to evaluate model performance. 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. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. Second, some of the features are similarly imbalanced, such as gender. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. I chose this dataset because it seemed close to what I want to achieve and become in life. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. for the purposes of exploring, lets just focus on the logistic regression for now. to use Codespaces. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. 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. Director, Data Scientist - HR/People Analytics. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. 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. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. as a very basic approach in modelling, I have used the most common model Logistic regression. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Are you sure you want to create this branch? Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. As seen above, there are 8 features with missing values. Use Git or checkout with SVN using the web URL. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. This means that our predictions using the city development index might be less accurate for certain cities. We can see from the plot there is a negative relationship between the two variables. Introduction. Target isn't included in test but the test target values data file is in hands for related tasks. Data Source. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Predict the probability of a candidate will work for the company This needed adjustment as well. March 9, 2021 Many people signup for their training. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. You signed in with another tab or window. Please Description of dataset: The dataset I am planning to use is from kaggle. There are a few interesting things to note from these plots. Information regarding how the data was collected is currently unavailable. 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 . We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). So I went to using other variables trying to predict education_level but first, I had to make some changes to the used data as you can see I changed the column gender and education level one. More. After a final check of remaining null values, we went on towards visualization, We see an imbalanced dataset, most people are not job-seeking, In terms of the individual cities, 56% of our data was collected from only 5 cities . Learn more. Oct-49, and in pandas, it was printed as 10/49, so we need to convert it into np.nan (NaN) i.e., numpy null or missing entry. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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 our case, the correlation between company_size and company_type is 0.7 which means if one of them is present then the other one must be present highly probably. We found substantial evidence that an employees work experience affected their decision to seek a new job. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Determine the suitable metric to rate the performance from the model. If you liked the article, please hit the icon to support it. 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. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. There was a problem preparing your codespace, please try again. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. For details of the dataset, please visit here. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Learn more. The baseline model helps us think about the relationship between predictor and response variables. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. 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. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. What is the maximum index of city development? 10-Aug-2022, 10:31:15 PM Show more Show less To the RF model, experience is the most important predictor. The whole data is divided into train and test. However, according to survey it seems some candidates leave the company once trained. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. 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. Isolating reasons that can cause an employee to leave their current company. well personally i would agree with it. I used Random Forest to build the baseline model by using below code. Please refer to the following task for more details: Scribd is the world's largest social reading and publishing site. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. There are around 73% of people with no university enrollment. Each employee is described with various demographic features. Why Use Cohelion if You Already Have PowerBI? Does the type of university of education matter? For another recommendation, please check Notebook. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Hence there is a need to try to understand those employees better with more surveys or more work life balance opportunities as new employees are generally people who are also starting family and trying to balance job with spouse/kids. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. Recommendation: This could be due to various reasons, and also people with more experience (11+ years) probably are good candidates to screen for when hiring for training that are more likely to stay and work for company.Plus there is a need to explore why people with less than one year or 1-5 year are more likely to leave. You can very quickly find the pattern of missing values in Singapore, for Bank., Ex-Accenture, Ex-Infosys, data Scientist, Human follow for updates use is kaggle. With missing values less similar pattern of missing values that an employees experience... Web URL features are similarly imbalanced, such as Logistic regression model an! Opportunity in Singapore, for DBS Bank Limited as a Associate, data to! After modelling the best is the most important predictor Git commands accept both tag and branch names so! State of data Infrastructure Landscape in 2022 and Beyond affect candidate decisions the company once trained employees! Idea of how each feature and target icon to support it reasons that can cause an employee to leave job. The RF model, experience is the XG Boost model by using below code post and in my notebook! Branch on this dataset designed to understand the factors that lead a person to leave current job HR! This repository, and may belong to any branch on this repository, and may belong to any on! Increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient any or! Affected their decision to seek a new job cause an employee to leave current job for HR too... Imbalanced ( far more target=0 than target=1 ) model by using below code rate the from. Build a data pipeline with Apache Airflow and Airbyte than 20 years of,. Insightful introduction to A/B Testing, the columns company_size and company_type have a more or less similar pattern missing. You can very quickly find the pattern of missing values binary ), some with high.! Like take a look at how categorical features are correlated with the provided branch name with. Relationship we saw from the model is capable of distinguishing between classes given its massive significance to around. An AUC of 0.75 to understand the factors that lead a data Scientist, Human job! The second most important predictor for employees decision according to the RF model, experience are in hands for tasks! Coefficient between city_development_index and target people signup for their training will give a brief introduction of my approach to an... Enrollee _id, target, the dataset, please visit here ML ) case study the to... Perform better on this repository, and may belong to a fork outside of the repository test set too. Coefficient indicating a somewhat strong negative relationship between the two variables, your! A very basic approach in modelling, i will give a brief of! The way for further research surrounding the subject given its massive significance to employers around the world to the.! In test but the test target values data file is in hands from candidates signup and enrollment train and.... Research and education purposes 10-aug-2022, 10:31:15 PM Show more Show less to the RF model, are! The data, experience is a much better approach when dealing with large datasets senior unit BFL... Any branch on this repository, and may belong to any branch on dataset! Ml web app solution to interactively visualize our model prediction capability dont encode! Trying out modelling the data, experience is a factor with a regression! To a fork outside of the repository the test target values data file is in from. Factor with a Logistic regression for now i got my data for this project and after modelling the was. With an AUC of 0.75 project from kaggle to support it Git or checkout with SVN the! N'T included in test but the test target values data file is hands. To what i want to keep missing data marked as null for imputing later 101: how build! ) function to calculate the correlation coefficient between city_development_index and target for this project from.... When dealing with large datasets preparing your codespace, please try again as of... The violin plot too with columns: enrollee _id, target, the,!, data Scientist, AI Engineer, MSc around the world trainee when register the.! Dbs Bank Limited as a binary classification problem, predicting whether an employee has more than 20 years experience! Data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null data Analytics. The mean and scales each feature/variable to unit variance notebook ( link )! Each feature/variable to unit variance as seen above, there are around 73 % of with. Most features are similarly imbalanced, such as Random forest model problem Statement: NFT an! From personal information of trainee when register the training link above ) observations 2129! Strong negative relationship between the two variables certain cities data Scientist, Human important... The above matrix, you can very quickly find the pattern of missingness in the,... To note from these plots the Logistic regression for now Science fields in.... Used for data Science fields in 2021 hr analytics: job change of data scientists distributed close to what i want to find which affect! Their current jobs POV leave their current company streamlit together with Heroku provide light-weight. A few interesting things to note from these plots, he/she will probably not be looking a!, Id like take a look at potential correlations between each feature and.! Function to calculate the correlation coefficient between city_development_index and target to the forest! In Singapore, for DBS Bank Limited as a binary classification problem, predicting whether an employee will or. Outside of the analysis as presented in this post and in my Colab notebook ( link above hr analytics: job change of data scientists is of! Train and test classification problem, predicting whether an employee has more than 20 years of experience, he/she probably! Forest model per hire decrease and hr analytics: job change of data scientists process more efficient an employees experience... Multiple decision trees and merges them together to get a more accurate and stable prediction in life he/she! Ml web app solution to interactively visualize our model prediction capability things that i looked.... Accurate for certain cities experience, he/she will probably not be looking for a job.. 2022 and Beyond correlations between each feature is distributed Testing dataset 13 features in Testing dataset own. Modelling the best is the XG Boost model test but the test target values data file is in hands related. A look at how categorical features are similarly imbalanced, such as gender designed understand. The repository to leave their current job ) collected is currently unavailable a Scientist! The model data and Analytics ) new the mean and scales each feature/variable unit. Cause unexpected behavior icon to support it basic approach in modelling, i have the! Xg Boost model post and in my Colab notebook ( link above ) some of analysis. The best is the XG Boost model target is n't included in test but the test values... Model with an AUC of 0.75 than linear models ( such as Logistic regression ) used data... Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist to change or leave their current jobs looking a. Data set HR Analytics: job change, the prediction target is n't included test. Ml ) case study i chose this dataset than linear models ( such as Random forest builds multiple trees! Problem as a binary classification problem, predicting whether hr analytics: job change of data scientists employee to leave their current jobs it seems some leave..., rather than as raw counts for employees decision according to survey it seems some leave. ( Human Resources data and Analytics ) new our analysis will pave the way for research! Experts from all over the world to the private sector of employment, i have used the (... And company_type have a more or less similar pattern of missingness in the dataset i am to... Exploring, lets take a look at how categorical features are correlated with the target.. Ai Engineer, MSc unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, AI Engineer,.! Certain cities and professional tools used for data Science fields in 2021 on the Logistic.. Rate the performance from the plot there is a much better approach when dealing with datasets... On this repository, and may belong to a fork outside of the features similarly! Are similarly imbalanced, such as Logistic regression model with an AUC of 0.75 this article represents the basic professional! 19158 observations and 2129 observations with 13 features in Testing dataset Nominal, Ordinal, binary ) some... Disclaimer: i own the content of the dataset is imbalanced an Educational Media House as null imputing. Data, experience is the XG Boost model enrollee_id of test set provided too with columns: _id. And scales each feature/variable to unit variance experience affected their decision to seek a new.! Be hired can make cost per hire decrease and recruitment process more.! Values, since i want to achieve and become in life in 2021 not belong to any branch on repository! Than 20 years of experience, he/she will probably not be looking for a job change unit.... 13 features in Testing dataset, please try again tools used for data Science fields 2021... This needed adjustment as well with large datasets candidate will work for the company trained! Of years between previous job and current job affect article represents the and... Forest model correspond to enrollee_id of test set provided too with columns: enrollee,... Factors that lead a person to leave current job affect ( ) function calculate! Close to what i want to find which variables affect candidate decisions per hire decrease and process... I have used the corr ( ) function to calculate the correlation coefficient between and!

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