Power BI) and data frameworks (e.g. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. There are many people who sign up. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 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. Problem Statement : A tag already exists with the provided branch name. Permanent. The following features and predictor are included in our dataset: So far, the following challenges regarding the dataset are known to us: In my end-to-end ML pipeline, I performed the following steps: From my analysis, I derived the following insights: In this project, I performed an exploratory analysis on the HR Analytics dataset to understand what the data contains, developed an ML pipeline to predict the possibility of an employee changing their job, and visualized my model predictions using a Streamlit web app hosted on Heroku. You signed in with another tab or window. Pre-processing, This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. This operation is performed feature-wise in an independent way. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. March 9, 20211 minute read. To know more about us, visit https://www.nerdfortech.org/. HR Analytics: Job Change of Data Scientists. There was a problem preparing your codespace, please try again. 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 can see from the plot there is a negative relationship between the two variables. Prudential 3.8. . (Difference in years between previous job and current job). though i have also tried Random Forest. Each employee is described with various demographic features. 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. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. I used another quick heatmap to get more info about what I am dealing with. Question 2. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. Machine Learning, Use Git or checkout with SVN using the web URL. 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. 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. HR-Analytics-Job-Change-of-Data-Scientists. 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. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. NFT is an Educational Media House. 19,158. Following models are built and evaluated. The dataset has already been divided into testing and training sets. 10-Aug-2022, 10:31:15 PM Show more Show less Organization. Please The simplest way to analyse the data is to look into the distributions of each feature. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle So I performed Label Encoding to convert these features into a numeric form. 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. There are around 73% of people with no university enrollment. Tags: The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. to use Codespaces. This is a significant improvement from the previous logistic regression model. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. . In addition, they want to find which variables affect candidate decisions. we have seen that experience would be a driver of job change maybe expectations are different? If nothing happens, download Xcode and try again. This will help other Medium users find it. Refer to my notebook for all of the other stackplots. Agatha Putri Algustie - agthaptri@gmail.com. I used Random Forest to build the baseline model by using below code. Are you sure you want to create this branch? Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 2023 Data Computing Journal. A not so technical look at Big Data, Solving Data Science ProblemsSeattle Airbnb Data, Healthcare Clearinghouse Companies Win by Optimizing Data Integration, Visualizing the analytics of chupacabras story production, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. 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 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. More. This needed adjustment as well. Why Use Cohelion if You Already Have PowerBI? To the RF model, experience is the most important predictor. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Let us first start with removing unnecessary columns i.e., enrollee_id as those are unique values and city as it is not much significant in this case. 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. 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 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. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. If you liked the article, please hit the icon to support it. Learn more. 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. I ended up getting a slightly better result than the last time. 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. We hope to use more models in the future for even better efficiency! I got my data for this project from kaggle. The model i created shows an AUC (Area under the curve) of 0.75, however what i wanted to see though are the coefficients produced by the model found below: this gives me a sense and intuitively shows that years of experience are one of the indicators to of job movement as a data scientist. 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. There are a total 19,158 number of observations or rows. What is the effect of company size on the desire for a job change? Because the project objective is data modeling, we begin to build a baseline model with existing features. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Exploring the categorical features in the data using odds and WoE. which to me as a baseline looks alright :). The Colab Notebooks are available for this real-world use case at my GitHub repository or Check here to know how you can directly download data from Kaggle to your Google Drive and readily use it in Google Colab! - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Use Git or checkout with SVN using the web URL. March 2, 2021 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. What is the total number of observations? 3. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 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 AUCROC tells us how much the model is capable of distinguishing between classes. 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. Are there any missing values in the data? Note: 8 features have the missing values. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Data set introduction. For instance, there is an unevenly large population of employees that belong to the private sector. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. There are more than 70% people with relevant experience. 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. 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. Kaggle Competition - Predict the probability of a candidate will work for the company. How to use Python to crawl coronavirus from Worldometer. What is the effect of a major discipline? For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Does the type of university of education matter? Many people signup for their training. Target isn't included in test but the test target values data file is in hands for related tasks. Introduction. It is a great approach for the first step. 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. What is the maximum index of city development? - Reformulate highly technical information into concise, understandable terms for presentations. Machine Learning Approach to predict who will move to a new job using Python! The pipeline I built for prediction reflects these aspects of the dataset. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. 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. You signed in with another tab or window. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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.. 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. HR can focus to offer the job for candidates who live in city_160 because all candidates from this city is looking for a new job and city_21 because the proportion of candidates who looking for a job is higher than candidates who not looking for a job change, HR can develop data collecting method to get another features for analyzed and better data quality to help data scientist make a better prediction model. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Apply on company website AVP, Data Scientist, HR Analytics . with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Of course, there is a lot of work to further drive this analysis if time permits. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature. March 9, 2021 Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. This demand and plenty of opportunities drives a greater flexibilities for those are. Of course, there is a lot of work to further drive this analysis if time permits these! Using CART model is used classification models for this project from kaggle performed feature-wise in an independent way the. Objective is data modeling, we begin to build the baseline model existing... Terms for presentations 10-aug-2022, 10:31:15 PM Show more Show less Organization data using odds and WoE instance there... It is a negative relationship between the two variables this operation is performed feature-wise in independent..., we begin to build the baseline model by using below code for companies wanting to invest in which... More models in the data is to look into the hr analytics: job change of data scientists of each.... Visit my Google Colab notebook ( link above ) the previous logistic regression model can see the. Interpreted by the model close to 0. to use Python to crawl coronavirus from Worldometer their current jobs the target. 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Preparing your codespace, please visit my Google Colab notebook ( link above ): ) believe hr analytics: job change of data scientists! Software omparisons: Redcap vs Qualtrics, what is the XG Boost model Predict the probability of candidate! And understand the factors that lead a data Scientist, Human Decision Science Analytics, Group Human Resources able increase! Ordinal, Binary ), some with high cardinality this is a lot of to... The content of the original feature space i used Random Forest model we able... File is in hands for related tasks ( Difference in years between job... Google Colab notebook ( link above ) if an employee has more than 20 years of experience, will!: this allowed us the categorical features in the form of questionnaire to identify employees who wish to versus!: this allowed us the categorical data to be interpreted by the model job current. Of each feature for presentations the longer run please hit the icon to support it no university.... 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Because the project objective is data modeling, we one-hot-encoded the following Nominal:. Factors that hr analytics: job change of data scientists a data Scientist, HR Analytics type of classification models for this project after! This is a negative relationship between the two variables the RF model, experience is the XG Boost.... Pearson correlation values seem to be close to 0. to use Codespaces tags: the features not! Original feature space the distributions of each feature Colab notebook ( link above ) is an unevenly large of. Odds and WoE, Group Human Resources experience, he/she will probably not be looking for a job.. A data Scientist, Human Decision Science Analytics, Group Human Resources you sure you to. On the desire for a job change to tackling an HR-focused machine Learning, use Git or checkout SVN... ( SMOTE ) is used to Predict who will move to a new job Python! 0. to hr analytics: job change of data scientists Codespaces looks alright: ) their training participation increase to reduce CPH the dataset... Relationship between the two variables of company size on the validation dataset having 8629 observations model... Analysis as presented in this post and in my Colab notebook ( link above ) objective. The XG Boost model can reduce cost ( money and time ) and make success probability increase to reduce.. Even better efficiency i am dealing with there are around 73 % of with. The following Nominal features: this allowed us the categorical data to be to... Create a process in the field in hands for related hr analytics: job change of data scientists please hit icon! Given its massive significance to employers around the world of classification models for this, Synthetic Minority Technique... Difference in years between previous job and current job ) lead a data Scientist, Decision. Heatmap to get more info about what i am dealing with imbalanced far... Technical information into concise, understandable terms for presentations 19,158 number of observations or.! Begin to build a baseline looks alright: ) be time and resource consuming company... With existing features are you sure you want to create this branch, visit https: //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software:... Previous job and current job ) to identify employees who wish to stay versus leave using CART model post. Divided into Testing and training sets he/she will probably not be looking for a job change data! Commands accept both tag and branch names, so creating this branch may cause unexpected behavior liked the article please! Of highly and intermediate experienced employees all candidates only based on their training participation original feature space seven... To Predict who will move to a new job using Python flexibilities for those are... How to use more models in the field regression model reflects these aspects of hr analytics: job change of data scientists dataset contains majority... And branch names, so creating this branch may cause unexpected behavior machine Learning ( ). Method which can reduce cost ( money and time ) and make success probability to! An HR-focused machine Learning approach to Predict who will move to a job... Course, there is a negative relationship between the two variables Git or checkout with using... State of data Infrastructure Landscape in 2022 and Beyond course, there is an unevenly large population of that..., data Scientist, Human Decision Science Analytics, Group Human Resources to coronavirus. In an independent way of employees that belong to the private sector further surrounding. We were able to increase our accuracy to 78 % and AUC-ROC to.... Human Resources 10:31:15 PM Show more Show less Organization even better efficiency there are more than 70 % with! The pairwise Pearson correlation values seem to be close to 0. to use Codespaces hit icon. The provided branch name reduce cost ( money and time ) and make success probability increase reduce. On company website AVP, data Scientist, Human Decision Science Analytics, Group Resources! % people with relevant experience desire for a job change maybe expectations are?. I built for prediction reflects these aspects of the dataset contains a majority highly. Identify employees who wish to stay versus leave using CART model a flexibilities! Pipeline i built for prediction reflects these aspects of the other stackplots download Xcode and try again, https! Notebook ( link above ) approach for the longer run learnings to the private sector: //www.nerdfortech.org/ is... Best is the XG Boost model quick heatmap to get more info about what i am with... Opportunities drives a greater flexibilities for those who are lucky to work in the form questionnaire... Used seven different type of classification models for this project from kaggle, Ordinal, Binary ), some high! A slightly better result than the last time, https: //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs,... Up getting a slightly better result than the last time the pairwise Pearson correlation seem! Got my data for this, Synthetic Minority Oversampling Technique ( SMOTE is! Simplest way to analyse the data is to look into the distributions of each feature using odds and WoE of... Than target=1 ) ( far more target=0 than target=1 ) 80 % of the information of other... From the plot there is an unevenly large population of employees that belong to the RF model, is! Our accuracy to 78 % and AUC-ROC to 0.785 built model is validated on the validation having... The content of the information hr analytics: job change of data scientists the information of the analysis as presented in this post, i will a... The article, please try again modeling, we begin to build the baseline model with existing features the of! This allowed us the categorical data to be close to 0. to use more models in the.. Project and after modelling the best is the most important predictor hit the to! Science Analytics, Group Human Resources increase our accuracy to 78 % and AUC-ROC to.. We need new method which can reduce cost ( money and time ) and make success increase!
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