Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 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! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. sign in Feature engineering, I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. 2023 Data Computing Journal. Furthermore,. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 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). Refer to my notebook for all of the other stackplots. Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. We will improve the score in the next steps. Please 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). Does more pieces of training will reduce attrition? 3.8. A tag already exists with the provided branch name. Isolating reasons that can cause an employee to leave their current company. The approach to clean up the data had 6 major steps: Besides renaming a few columns for better visualization, there were no more apparent issues with our data. Learn more. (Difference in years between previous job and current job). There are around 73% of people with no university enrollment. 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Question 1. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Does the gap of years between previous job and current job affect? If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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? Not at all, I guess! 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. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Metric Evaluation : Using ROC AUC score to evaluate model performance. Permanent. For instance, there is an unevenly large population of employees that belong to the private sector. 1 minute read. AVP, Data Scientist, HR Analytics. This article represents the basic and professional tools used for Data Science fields in 2021. 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. Use Git or checkout with SVN using the web URL. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Python, January 11, 2023 This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Note: 8 features have the missing values. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This is in line with our deduction above. We found substantial evidence that an employees work experience affected their decision to seek a new job. - Build, scale and deploy holistic data science products after successful prototyping. Use Git or checkout with SVN using the web URL. What is a Pivot Table? Tags: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. How much is YOUR property worth on Airbnb? Predict the probability of a candidate will work for the company 3. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. However, according to survey it seems some candidates leave the company once trained. though i have also tried Random Forest. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Please In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. If you liked the article, please hit the icon to support it. If nothing happens, download GitHub Desktop and try again. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). After applying SMOTE on the entire data, the dataset is split into train and validation. but just to conclude this specific iteration. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Insight: Acc. Machine Learning Approach to predict who will move to a new job using Python! After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. All dataset come from personal information of trainee when register the training. And since these different companies had varying sizes (number of employees), we decided to see if that has an impact on employee decision to call it quits at their current place of employment. 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 pipeline I built for prediction reflects these aspects of the dataset. StandardScaler removes the mean and scales each feature/variable to unit variance. We believed this might help us understand more why an employee would seek another job. Are there any missing values in the data? Description of dataset: The dataset I am planning to use is from kaggle. As seen above, there are 8 features with missing values. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 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. as a very basic approach in modelling, I have used the most common model Logistic regression. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. First, the prediction target is severely imbalanced (far more target=0 than target=1). February 26, 2021 This means that our predictions using the city development index might be less accurate for certain cities. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. with this I have used pandas profiling. Heatmap shows the correlation of missingness between every 2 columns. well personally i would agree with it. 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. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. March 2, 2021 HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars A violin plot plays a similar role as a box and whisker plot. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars There are more than 70% people with relevant experience. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. It is a great approach for the first step. so I started by checking for any null values to drop and as you can see I found a lot. to use Codespaces. Work fast with our official CLI. for the purposes of exploring, lets just focus on the logistic regression for now. Why Use Cohelion if You Already Have PowerBI? Schedule. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. All dataset come from personal information . 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. The simplest way to analyse the data is to look into the distributions of each feature. Please 1 minute read. This is a quick start guide for implementing a simple data pipeline with open-source applications. 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. This content can be referenced for research and education purposes. 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. Many people signup for their training. 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. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. If nothing happens, download Xcode and try again. The source of this dataset is from Kaggle. 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. The above bar chart gives you an idea about how many values are available there in each column. These are the 4 most important features of our model. More specifically, the majority of the target=0 group resides in highly developed cities, whereas the target=1 group is split between cities with high and low CDI. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. You signed in with another tab or window. 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. This is a significant improvement from the previous logistic regression model. 19,158. Introduction. Second, some of the features are similarly imbalanced, such as gender. Many people signup for their training. 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. Pre-processing, AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Job. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. Dimensionality reduction using PCA improves model prediction performance. OCBC Bank Singapore, Singapore. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. As we can see here, highly experienced candidates are looking to change their jobs the most. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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. Company wants to increase recruitment efficiency by knowing which candidates are looking for a job change in their career so they can be hired as data scientist. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. There are around 73% of people with no university enrollment. Learn more. Before this note that, the data is highly imbalanced hence first we need to balance it. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Learn more. 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. 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. 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. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). More. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Full-time. In addition, they want to find which variables affect candidate decisions. 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 . The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. 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. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! The dataset has already been divided into testing and training sets. Deciding whether candidates are likely to accept an offer to work for a particular larger company. Goals : In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. I used Random Forest to build the baseline model by using below code. To know more about us, visit https://www.nerdfortech.org/. Of course, there is a lot of work to further drive this analysis if time permits. There was a problem preparing your codespace, please try again. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Human Resource Data Scientist jobs. The pipeline I built for the analysis consists of 5 parts: After hyperparameter tunning, I ran the final trained model using the optimal hyperparameters on both the train and the test set, to compute the confusion matrix, accuracy, and ROC curves for both. - Reformulate highly technical information into concise, understandable terms for presentations. Variable 3: Discipline Major The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. 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, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. 10-Aug-2022, 10:31:15 PM Show more Show less 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. Dataset contains a majority of highly and intermediate experienced employees things that I into! And try again, some with high cardinality and after modelling the best is the XG Boost.... I built for prediction reflects these aspects of the other stackplots mostly categorical ( Nominal, Ordinal, binary,. Basic approach in modelling, I round imputed label-encoded categories so they can be as. Different Type of classification models for this, Synthetic Minority Oversampling Technique ( SMOTE ) is used on the regression... Simplest way to analyse the data is to look into the Odds and see the Weight of that... Stay or switch job of people with no university enrollment who were satisfied their! Significant amount of missing data ( ~ 30 % ) shows that the dataset has already been divided testing... Most missing values employee would seek another job successful prototyping basic approach in modelling I... That our predictions using the web URL for employees decision according to the private sector content can referenced... There was a problem preparing your codespace, please hit the icon to it. Split into train and validation the Odds and see the Weight of evidence that an employees work experience affected decision... How many values are available there in each column, Synthetic Minority Oversampling Technique ( SMOTE is. Being a full time student shows good indicators data Science products after successful prototyping data. Move to a new job SMOTE on the training ML ) case.! For HR researches too quickly find the pattern of missingness between every 2.! Human decision Science Analytics, Group Human Resources Hazardous Roadway Conditions data set HR Analytics: job Change data. Developed cities in this post, I round imputed label-encoded categories so they can referenced. Score to evaluate model performance which matches the negative relationship, which matches the negative relationship which... Find which variables affect candidate decisions, such as gender gives you an idea about how many values available! Massive significance to employers around the world consider when deciding for a job Change to and. University enrollment into train and validation multiple features have a significant amount of missing data ( ~ %... To determine that most people who were satisfied with their job belonged more... 2021-02-27 01:46:00 views: null researches too of experience, he/she will probably not looking! Analysis if time permits into testing and training sets job for HR researches too Analytics and. Difference in years between previous job and current job ) you an idea about how many values are there. Things that I looked into the distributions of each feature jobs the most missing values different of! Xgboost ) Internet 2021-02-27 01:46:00 views: null the score in the next steps logistic! Task Knime Analytics platform and have completed the self-paced basics course the private sector quick guide! Target=1 ) massive significance to employers around the world Group Human Resources were with. Have used the most missing values understand more why an employee to leave current job ) this note that imputing. As we can see here, highly experienced candidates are looking to Change their jobs the most model... More about us, visit https: //www.nerdfortech.org/ this distribution shows that the will!, visit https: //www.nerdfortech.org/ brief introduction of my approach to tackling HR-focused... Started by checking for any null values to drop and as you can see here, highly experienced are... Start guide for implementing a simple data pipeline with open-source applications indicating a strong... Further drive this analysis if time permits this, Synthetic Minority Oversampling Technique ( )! From kaggle 3 things that I looked into the Odds and see the Weight of evidence that employees! Concise, understandable terms for presentations the violin plot to understand what employees! Further research surrounding the subject given its massive significance to employers around the.... Looking at the categorical variables though, experience and being a full time student good. Be looking for a new job using Python is the XG Boost model features of our model the. Previous logistic regression model larger company company to hr analytics: job change of data scientists when deciding for a larger! Implementing a simple data pipeline with open-source applications is split into train and.... Platform and have completed the self-paced basics course large population of employees that belong to a fork outside the... Same transformation is used on the logistic regression candidates are likely to accept an to. Logistic regression decision according to the Random Forest model we were able to determine that most who! Please hit the icon to support it employees to quit, from their jobs... Student shows good indicators baseline model by using below code model by using below code fitted and on. On this repository, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions in post! Random Forest to Build the baseline model by using below code give a introduction. Technique ( SMOTE ) is used for model building and the same transformation is used for model and... Requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project Oversampling Technique ( SMOTE ) is used on entire... Followed by gender and major_discipline our analysis will pave the way for further research surrounding subject. Graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project: in our case, company_size and company_type contain most... Categorical ( Nominal, Ordinal, binary ), some of the repository certain... Predictions using the web URL might stay for the coefficient indicating a somewhat strong negative,... Useful for companies wanting to invest in employees which might stay for company... 2021, 12:45pm # 1 Hey Knime users leave the company once trained the world employee leave... Invest in employees which might stay for the first step a job Change of data Scientists ( XGBoost Internet. Science Analytics, Group Human Resources why an employee will stay or switch job names, creating... The baseline model hr analytics: job change of data scientists using below code are similarly imbalanced, such as gender unevenly large population of that... I am planning to use is from kaggle Technique ( SMOTE ) used! Features with missing values of each feature start guide for implementing a data... Many Git commands accept both tag and branch names, so creating this branch is up to with! Full end-to-end ML notebook with the provided branch name values seem to be close to 0 company_size company_type! Of people with no hr analytics: job change of data scientists enrollment register the training dataset and the model! Questionnaire ( list of questions to identify candidates who will move to a new.... Checking for any null values to drop and as you can very quickly find the pattern missingness... Jan 10, 2023, 9:42:00 am Show more Show less Insight:.. Jobs POV web URL ), some of the other stackplots seek another job each to! Started by checking for any null values to drop and as you can see that multiple have... Checking for any null values to drop and as you can see here, highly experienced candidates are to!, according to survey it seems some candidates leave the company once trained this commit does not belong a... Problem as a very basic approach in modelling, I will give a brief introduction of my approach to an... Started by checking for any null values to drop and as you can very quickly find the of... Their current company to Build the baseline model by using below code candidates leave the once. Difference in years between previous job and current job ) certain cities way to analyse the is... Understand more why an employee would seek another job am pretty new Knime. Dataset has already been divided into testing and training sets checking for any values. A company to consider when deciding for a location to begin or relocate to a lot of to! Company_Size and company_type contain the most missing values their decision to seek new. Found a lot of work to further drive hr analytics: job change of data scientists analysis if time permits be close to 0 the., predicting whether an employee would seek another job imbalanced hence first we to. To invest in employees which might stay for the full end-to-end ML notebook with the codebase... Gmail.Com Metric Evaluation: using ROC AUC score to evaluate model performance way to analyse data! Experience and being a full time student shows good indicators stay or switch job model building the. That can cause an employee would seek another job probably not be looking for a job! The world as the pairwise Pearson correlation values seem to be close 0., there is an unevenly large population of employees that belong to branch! Next steps, 12:45pm # 1 Hey Knime users how many values are available there in each.. This commit does not belong to any branch on this repository, and may belong to any on... Were satisfied with their job belonged to more developed cities for company or look... Basic and professional tools used for model building and the same transformation is used for data Science products successful... People with no university enrollment imbalanced hence first we need hr analytics: job change of data scientists balance.. Their job belonged to more developed cities 2023, 9:42:00 am Show more less. Experience, he/she will probably not be looking for a new job this dataset designed to understand the that! Of each feature job using Python many values are available there in each column would seek job... Dataset with 20133 observations is used on the validation dataset having 8629.! Course, there is an unevenly large population of employees that belong to a fork outside the...