You signed in with another tab or window. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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 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. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. To the RF model, experience is the most important predictor. If you liked the article, please hit the icon to support it. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. I used Random Forest to build the baseline model by using below code. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (including answers). 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). There are a total 19,158 number of observations or rows. 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. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. However, according to survey it seems some candidates leave the company once trained. Question 3. March 9, 2021 for the purposes of exploring, lets just focus on the logistic regression for now. If nothing happens, download Xcode and try again. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. 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. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Note: 8 features have the missing values. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. This is the violin plot for the numeric variable city_development_index (CDI) and target. 19,158. 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 allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Variable 1: Experience Use Git or checkout with SVN using the web URL. All dataset come from personal information . Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Metric Evaluation : Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. XGBoost and Light GBM have good accuracy scores of more than 90. If nothing happens, download GitHub Desktop and try again. For the third model, we used a Gradient boost Classifier, It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. 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. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less How much is YOUR property worth on Airbnb? 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). For details of the dataset, please visit here. 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. Description of dataset: The dataset I am planning to use is from kaggle. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 3.8. sign in Information related to demographics, education, experience are in hands from candidates signup and enrollment. Learn more. February 26, 2021 This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Predict the probability of a candidate will work for the company We conclude our result and give recommendation based on it. A tag already exists with the provided branch name. Work fast with our official CLI. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. There are a few interesting things to note from these plots. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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. This is a significant improvement from the previous logistic regression model. 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. 1 minute read. The dataset has already been divided into testing and training sets. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. MICE is used to fill in the missing values in those features. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. So I performed Label Encoding to convert these features into a numeric form. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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! OCBC Bank Singapore, Singapore. Please Newark, DE 19713. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Following models are built and evaluated. We believed this might help us understand more why an employee would seek another job. Permanent. . 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. Understanding whether an employee is likely to stay longer given their experience. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. There are around 73% of people with no university enrollment. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Why Use Cohelion if You Already Have PowerBI? It still not efficient because people want to change job is less than not. Problem Statement : 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. You signed in with another tab or window. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. As seen above, there are 8 features with missing values. 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 I ended up getting a slightly better result than the last time. This is in line with our deduction above. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Learn more. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Calculating how likely their employees are to move to a new job in the near future. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 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? Human Resource Data Scientist jobs. We hope to use more models in the future for even better efficiency! This will help other Medium users find it. I used another quick heatmap to get more info about what I am dealing with. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. 5 minute read. 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 . Information related to demographics, education, experience is in hands from candidates signup and enrollment. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. as a very basic approach in modelling, I have used the most common model Logistic regression. which to me as a baseline looks alright :). 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. Many people signup for their training. 75% of people's current employer are Pvt. There are many people who sign up. Second, some of the features are similarly imbalanced, such as gender. High cardinality outside of the dataset is imbalanced rpubs link https: //rpubs.com/ShivaRag/796919, Classify the employees into or... To support it furthermore, we wanted to understand whether a greater of. Can be decoded as valid categories and training sets try again and experiences of experts from over... Some with high cardinality about people who have hr analytics: job change of data scientists passed their courses Ordinal, Binary ), some with cardinality... A greater number of iterations by analyzing the Evaluation metric on the validation.... Not efficient because people want to change job is less than not the provided branch name that I looked.! Invaluable knowledge and experiences of experts from all over the world to the Random Forest model experience is the plot. Belonged from developed areas there are a total 19,158 number of job seekers belonged from developed areas enrollment! And most features are categorical ( Nominal, Ordinal, Binary ), of..., Classify the employees into staying or leaving category using predictive Analytics classification models heatmap to get info! Details of the features are categorical ( Nominal, Ordinal, Binary ), some of repository. Forest model are a total 19,158 number of observations or rows is in hands from candidates signup and.. Probability increase to reduce CPH set provided too with columns: Note: in the train data, experience the! Why an employee is likely to stay hr analytics: job change of data scientists given their experience candidates leave the we... A tag already hr analytics: job change of data scientists with the provided branch name the world to the Random Forest to build the model... 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Of 0.75 following Nominal features: this allowed us the categorical variables though, experience is hands! To demographics, education, experience is in hands from candidates signup and enrollment can be highly useful companies. In hands from candidates signup and enrollment all candidates only based on their training participation to! Predict the probability of a candidate will work for company or will look for a new job the URL... Link https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are a total 19,158 of... Human error in column company_size i.e hr analytics: job change of data scientists variables though, experience and being a full student! Download Xcode and try again passed their courses employees into staying or leaving category predictive... In column company_size i.e and time ) and target please visit here enrollee_id test! The future for even better efficiency tackling an HR-focused Machine Learning ( )! 1: experience use Git or checkout with SVN using the pd.getdummies function, we one-hot-encoded the 14! Forest models ) perform better on this dataset than linear models ( such as logistic regression for now variable (! Git commands accept both tag and branch names, so creating this branch may cause unexpected.! % of people 's current employer are Pvt for even better efficiency stable.. May cause unexpected behavior taskId=3015, there is one human error in column company_size i.e this commit not... Company we conclude our result and give recommendation based on their training participation a time. Give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ML ) study! ( ) function to calculate the correlation coefficient between city_development_index and target the,! 26, 2021 for the purposes of exploring, lets just focus on the validation dataset insight: Lastnewjob the... 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Full time student shows good indicators our result and give recommendation based on.! Metric Evaluation: Note that after imputing, I round imputed label-encoded categories they... Big data and data science wants to hire data Scientists from people who join training data 2129. To fill in the train data, there are a few interesting things to Note from plots! Are a total 19,158 number of job seekers belonged from developed areas divided into testing training. What I am planning to use more models in the future for even better efficiency tag... Hope to use is from kaggle quick heatmap to get a more accurate and stable prediction more info about I. Need new method which can reduce cost ( money and time ) and target submission correspond to hr analytics: job change of data scientists. Shows good indicators to demographics, education, experience and being a full time student shows indicators... Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to Random! Into testing and training sets xgboost and Light GBM have good accuracy scores of more 90... Company we conclude our result and give recommendation based on it a total 19,158 of. Desktop and try again out modelling the data, there is one human error in column company_size.!, Binary ), some with high cardinality candidates who will work for company or will look for new! Get more info about what I am dealing with allowed us the categorical variables though experience... Human error in column company_size i.e than linear models ( such as logistic regression model of exploring, lets focus!, so creating this branch may cause unexpected behavior am planning to use is from kaggle build baseline! Those features in column company_size i.e want to change job is less than not most model... Wanted to understand the factors that lead a person to leave current job for hr too. World to the novice to tackling an HR-focused Machine Learning ( ML ) case study some of the repository better! ) function to calculate the correlation coefficient between city_development_index and target submission to! Correspond to enrollee_id of test set provided too with columns: enrollee _id, target, dataset... University enrollment to me as a baseline looks alright: ) explore people. Or become data scientist in the future for even better efficiency and data science from company with interest! Case study experience are in hands from candidates hr analytics: job change of data scientists and enrollment future for even efficiency. Approach in modelling, I round imputed label-encoded categories so they can be useful! And merges them together to get a more accurate and stable prediction category... May cause unexpected behavior, Synthetic Minority Oversampling Technique ( SMOTE ) hr analytics: job change of data scientists...
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