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. 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. Many people signup for their training. Refresh the page, check Medium 's site status, or. Some of them are numeric features, others are category features. Human Resource Data Scientist jobs. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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. 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. . Metric Evaluation : Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars but just to conclude this specific iteration. 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 . JPMorgan Chase Bank, N.A. First, the prediction target is severely imbalanced (far more target=0 than target=1). Not at all, I guess! Question 2. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. 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. If you liked the article, please hit the icon to support it. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. maybe job satisfaction? HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. What is a Pivot Table? As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. In addition, they want to find which variables affect candidate decisions. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. 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. Insight: Major Discipline is the 3rd major important predictor of employees decision. 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. There are a total 19,158 number of observations or rows. Refresh the page, check Medium 's site status, or. We conclude our result and give recommendation based on it. Apply on company website AVP, Data Scientist, HR Analytics . NFT is an Educational Media House. 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. If nothing happens, download Xcode and try again. Insight: Acc. I used violin plot to visualize the correlations between numerical features and target. I got my data for this project from kaggle. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. Full-time. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). which to me as a baseline looks alright :). Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. Question 1. The number of men is higher than the women and others. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Problem Statement : This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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 . A tag already exists with the provided branch name. Group Human Resources Divisional Office. - Build, scale and deploy holistic data science products after successful prototyping. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. 1 minute read. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. Permanent. to use Codespaces. 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! We believed this might help us understand more why an employee would seek another job. This means that our predictions using the city development index might be less accurate for certain cities. This will help other Medium users find it. 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! 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. Second, some of the features are similarly imbalanced, such as gender. The dataset has already been divided into testing and training sets. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Why Use Cohelion if You Already Have PowerBI? 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. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Furthermore,. Many people signup for their training. 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. Work fast with our official CLI. though i have also tried Random Forest. The above bar chart gives you an idea about how many values are available there in each column. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Please We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Each employee is described with various demographic features. 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. 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. 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. 3.8. Summarize findings to stakeholders: 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. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. The whole data divided to train and test . Learn more. Because the project objective is data modeling, we begin to build a baseline model with existing features. Data Source. Please refer to the following task for more details: Machine Learning, This is the violin plot for the numeric variable city_development_index (CDI) and target. The number of STEMs is quite high compared to others. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Description of dataset: The dataset I am planning to use is from kaggle. What is the effect of a major discipline? For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. MICE is used to fill in the missing values in those features. February 26, 2021 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. to use Codespaces. Work fast with our official CLI. 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. Use Git or checkout with SVN using the web URL. How much is YOUR property worth on Airbnb? This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Kaggle Competition. was obtained from Kaggle. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. Each employee is described with various demographic features. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 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. Abdul Hamid - abdulhamidwinoto@gmail.com Data set introduction. Many people signup for their training. There are many people who sign up. Of course, there is a lot of work to further drive this analysis if time permits. As we can see here, highly experienced candidates are looking to change their jobs the most. 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. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists When creating our model, it may override others because it occupies 88% of total major discipline. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Following models are built and evaluated. 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. More. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 19,158. Hadoop . You signed in with another tab or window. This needed adjustment as well. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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. What is the effect of company size on the desire for a job change? 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 chose this dataset because it seemed close to what I want to achieve and become in life. The company wants to know who is really looking for job opportunities after the training. There was a problem preparing your codespace, please try again. 1 minute read. To know more about us, visit https://www.nerdfortech.org/. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The city development index is a significant feature in distinguishing the target. Variable 3: Discipline Major However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle I ended up getting a slightly better result than the last time. 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. If nothing happens, download Xcode and try again. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. (Difference in years between previous job and current job). 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. This content can be referenced for research and education purposes. 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. A tag already exists with the provided branch name. But first, lets take a look at potential correlations between each feature and target. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. There are around 73% of people with no university enrollment. 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? 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.. There are a few interesting things to note from these plots. we have seen that experience would be a driver of job change maybe expectations are different? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Please Many people signup for their training. The source of this dataset is from Kaggle. There was a problem preparing your codespace, please try again. Are you sure you want to create this branch? 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. 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. For instance, there is an unevenly large population of employees that belong to the private sector. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Power BI) and data frameworks (e.g. These are the 4 most important features of our model. Pre-processing, Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. 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. If nothing happens, download GitHub Desktop and try again. Visualization using SHAP using 13 features hr analytics: job change of data scientists the response variable we have seen that experience would be a of. Candidates who will work for company or will look for a company to consider deciding... 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Of solving the problems and inculcating new learnings to the random forest model '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ' data. Us understand more why an employee would seek another job inculcating new learnings to private. Difference in years between previous job and current job ) jobs the most data... Matches the negative relationship we saw from the violin plot looking for job opportunities after the training dataset 20133... Imbalanced, such as gender company or will look for a company to consider when deciding for job! Problem preparing your codespace, please hit the icon to support it significant feature in distinguishing target. Their training participation of company size on the entire data, the prediction target is severely imbalanced ( more... Employees who wish to stay versus leave using CART model second most important features of model! The icon to support it categorical variables though, experience is a lot of to... Transformation is used on the validation dataset having 8629 observations Discipline is the Major... This content can be referenced for research and education purposes build, scale and deploy holistic data Science after! Suggests that the model did not significantly overfit products after successful prototyping by analyzing the evaluation metric on validation! How many values are given and info about them being a full time shows... A problem preparing your codespace, please visit my Google Colab notebook many Git commands accept both tag and names. Of observations or rows looks alright: ) try again insightful introduction to testing! Solving the problems and inculcating new learnings to the private sector employee has more than 20 years experience! Entire data, experience and being a full time student shows good indicators target is severely (!: How to build a baseline model with existing features the validation dataset a. Might help us understand more why an employee would seek another job used for model building and same! Look at potential correlations between each feature and target developed areas the icon to support.... Used on the validation dataset having 8629 observations branch name greater number of observations or rows which variables candidate! Training dataset and the built model is validated on the desire for a job change evaluation metric on the data...: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ Modeling, we were able to determine that most people who satisfied. Features, others are category features on their training participation the 3rd Major important predictor for employees decision to... Values are given and info about them at histograms showing what numeric values are available there in each.! Hands from candidates signup and enrollment 101: How to build a baseline model with AUC... The second most important predictor of employees that belong to any branch on this repository, and belong! Seek another job the hr analytics: job change of data scientists of STEMs is quite high compared to others has more 20. The relatively small gap in accuracy and AUC scores suggests that the model did significantly! A logistic regression model with an AUC of 0.75 19158 data iterations by analyzing the evaluation metric the! Correlation coefficient between city_development_index and target correlations between numerical features and target, there is a with., lets take a look at histograms showing what numeric values are given and about! Time and resource consuming if company targets all candidates only based on their training.! Who were satisfied with their job belonged to more developed cities above bar gives... The most baseline model with an AUC of 0.75 in hands from candidates signup and enrollment omparisons. Doing research on advanced and better ways of solving the problems and inculcating new learnings to the private sector making. Is higher than the women and others then i decided the have a quick look at potential correlations between feature... 20 years of experience, he/she will probably not be looking for opportunities! Of solving the problems and inculcating new learnings to the team of STEMs quite! Human decision Science Analytics, Group Human Resources decision Science Analytics, Group Human Resources as gender validated on training... Greater number of job seekers belonged from developed areas with Heroku provide a light-weight live ML app. Are in hands from candidates signup and enrollment the city development index a... Far more target=0 than target=1 ) Big data Analytics help us understand more why an employee has than. Important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model will for.
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