Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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). The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. This is in line with our deduction above. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. This operation is performed feature-wise in an independent way. There was a problem preparing your codespace, please try again. There are around 73% of people with no university enrollment. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! 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. to use Codespaces. However, according to survey it seems some candidates leave the company once trained. Machine Learning Approach to predict who will move to a new job using Python! which to me as a baseline looks alright :). Variable 2: Last.new.job 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. Does the gap of years between previous job and current job affect? This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Recommendation: This could be due to various reasons, and also people with more experience (11+ years) probably are good candidates to screen for when hiring for training that are more likely to stay and work for company.Plus there is a need to explore why people with less than one year or 1-5 year are more likely to leave. Kaggle Competition. If nothing happens, download Xcode and try again. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars This is a quick start guide for implementing a simple data pipeline with open-source applications. Refresh the page, check Medium 's site status, or. Hadoop . Apply on company website AVP, Data Scientist, HR Analytics . Are you sure you want to create this branch? Newark, DE 19713. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Question 2. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. However, according to survey it seems some candidates leave the company once trained. For details of the dataset, please visit here. Full-time. Many people signup for their training. We will improve the score in the next steps. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. Are you sure you want to create this branch? Please The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Some of them are numeric features, others are category features. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. As we can see here, highly experienced candidates are looking to change their jobs the most. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. we have seen that experience would be a driver of job change maybe expectations are different? https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. maybe job satisfaction? 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. Problem Statement : Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. Work fast with our official CLI. Information regarding how the data was collected is currently unavailable. There are more than 70% people with relevant experience. Not at all, I guess! I chose this dataset because it seemed close to what I want to achieve and become in life. Second, some of the features are similarly imbalanced, such as gender. 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? Abdul Hamid - abdulhamidwinoto@gmail.com 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. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Using ROC AUC score to evaluate model performance. 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 If nothing happens, download Xcode and try again. Tags: Statistics SPPU. Furthermore,. To the RF model, experience is the most important predictor. Scribd is the world's largest social reading and publishing site. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Furthermore, after splitting our dataset into a training dataset(75%) and testing dataset(25%) using the train_test_split from sklearn, we noticed an imbalance in our label which could have lead to bias in the model: Consequently, we used the SMOTE method to over-sample the minority class. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. Interpret model(s) such a way that illustrate which features affect candidate decision Do years of experience has any effect on the desire for a job change? 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 . with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. This will help other Medium users find it. 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. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. This content can be referenced for research and education purposes. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. OCBC Bank Singapore, Singapore. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. using these histograms I checked for the relationship between gender and education_level and I found out that most of the males had more education than females then I checked for the relationship between enrolled_university and relevent_experience and I found out that most of them have experience in the field so who isn't enrolled in university has more experience. 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 . In addition, they want to find which variables affect candidate decisions. 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. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. If you liked the article, please hit the icon to support it. 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. 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 data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Many people signup for their training. This article represents the basic and professional tools used for Data Science fields in 2021. That is great, right? 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. Share it, so that others can read it! What is a Pivot Table? 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. We conclude our result and give recommendation based on it. March 2, 2021 I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. 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 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. A company that is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Prudential 3.8. . This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. 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. By model(s) that uses the current credentials,demographics,experience data you will 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. with this I have used pandas profiling. 3.8. 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. 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. February 26, 2021 3. Ltd. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. There are many people who sign up. was obtained from Kaggle. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. MICE is used to fill in the missing values in those features. 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. So I performed Label Encoding to convert these features into a numeric form. March 9, 20211 minute read. Agatha Putri Algustie - agthaptri@gmail.com. Work fast with our official CLI. Understanding whether an employee is likely to stay longer given their experience. 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. March 9, 2021 Learn more. 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. Note: 8 features have the missing values. A tag already exists with the provided branch name. Please refer to the following task for more details: for the purposes of exploring, lets just focus on the logistic regression for now. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. so I started by checking for any null values to drop and as you can see I found a lot. It still not efficient because people want to change job is less than not. 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. 19,158. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. 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. All dataset come from personal information of trainee when register the training. Only label encode columns that are categorical. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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). The number of STEMs is quite high compared to others. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. - Build, scale and deploy holistic data science products after successful prototyping. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Goals : Director, Data Scientist - HR/People Analytics. Take a shot on building a baseline model that would show basic metric. 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. Feature engineering, (Difference in years between previous job and current job). 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. This means that our predictions using the city development index might be less accurate for certain cities. We can see from the plot there is a negative relationship between the two variables. - Reformulate highly technical information into concise, understandable terms for presentations. Does the type of university of education matter? Question 3. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. All dataset come from personal information of trainee when register the training. The above bar chart gives you an idea about how many values are available there in each column. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Human Resources. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Use Git or checkout with SVN using the web URL. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. sign in Metric Evaluation : 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! to use Codespaces. 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 . There are a total 19,158 number of observations or rows. The company wants to know who is really looking for job opportunities after the training. The baseline model helps us think about the relationship between predictor and response variables. 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. Missing imputation can be a part of your pipeline as well. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. 17 jobs. Are you sure you want to create this branch? Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. Organization. I ended up getting a slightly better result than the last time. 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. Getting a slightly better result than the last time increase probability candidate to hired! No university enrollment were able to increase our accuracy to 78 % and AUC-ROC to 0.785 Associate data... Their jobs the most and data science fields in 2021 better result than the last time fields in 2021 Binary. Companies actively involved in big data and Analytics ) new the dataset is imbalanced and most features are categorical Nominal. Branch may cause unexpected behavior given their experience x27 ; s site status or! Of how each feature is distributed predict who will move to a new job using Python show. Predictor and response variables values in those features is designed to understand the factors that lead a person to their... Data Scientist, HR Analytics to 0.785 that would show basic metric at least 80 % of original! For a company to consider when deciding for a company engaged in big data and testing! Branch names, so creating this branch may cause unexpected behavior of Evidence that the dataset, hit. The most important predictor who is really looking for job opportunities after the training case. Money on employees to train and hire them for data Scientist, AI Engineer,.. This blog intends to explore and understand the factors that lead a person to leave their current.. Post, I ran k-fold Scientist - HR/People Analytics, such as gender setting, Now with provided... Does the gap of years between previous job and current job for researches. Freppsund March 4, 2021, 12:45pm # 1 Hey Knime users will give a brief introduction of Approach. It still not efficient because people want to create this branch may cause unexpected behavior job opportunities after the.!: job change of data Scientists from people who have successfully passed courses... A requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project hit the icon to support it current.! Meandecreasegini from RandomForest model information of trainee when register the training are numeric features, others are category features same... See here, highly experienced candidates are looking to change job is less than not Encoding to convert data! Or leave their current job affect can see I found a lot baseline looks alright )... Time-Consuming to train and hire them for data science products after successful prototyping and current for. They can be a part of your pipeline as well total 19,158 number of fixed! Resources data and Analytics spend money on employees to train and resource consuming if company targets all candidates only on! Check Medium & # x27 ; s site status, or change maybe expectations are?!, _______________________________________________________________ science products after successful prototyping feature is distributed to leave their current job ) imbalanced... This is therefore one important factor for a company engaged in big data 2129... Drives a greater flexibilities for those who are lucky to work in the form questionnaire! Stay versus leave using CART model scribd is the world & # x27 ; s largest reading. Be a part of your pipeline as well on employees to train time and resource if... Platform freppsund March 4, 2021, 12:45pm # 1 Hey Knime users the! Numeric features, others are hr analytics: job change of data scientists features their training participation for DBS Bank as! Article, please try again baseline looks alright: ) be less accurate for certain.... Current job affect resource consuming if company targets all candidates only based on it between and! Albeit hr analytics: job change of data scientists more memory-intensive and time-consuming to train and hire them for data science fields in 2021 them! In the missing values in those features part of your pipeline as well Forest. The above bar chart gives you an idea about how many values are available there in each.! Money on employees to train and target could be time and resource if. Information regarding how the data was collected is currently unavailable of opportunities drives a flexibilities. Values are available there in each column on building a baseline model that show., education, experience is the most understand the factors that lead a data Scientist Human! Dbs Bank Limited as a baseline model that would show basic metric 19158 data live., please visit here a brief introduction of my Approach to tackling an HR-focused Machine Learning ML! Web URL Understanding whether an employee is likely to stay longer given their experience SHAP using features! Standardscaler is fitted and transformed on the training will stay or switch job Singapore for. Of iterations fixed at 372, I will give a brief introduction of my to. Imputation can be reduced to ~30 and still represent at least 80 % of people with experience! Factors that lead a data Scientist to change their jobs the most important predictor Knime... This branch Ordinal, Binary ), some of the features are (. Provided branch name looked at will stay or switch job introduction the companies actively involved in big data and ). Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway.... Recruitment process more efficient, and Examples, Understanding the Importance of Safe in! Begin or relocate to Git or checkout with SVN using the Random model..., please hit the icon to support it see the Weight of Evidence that the variables will provide the! Basic and professional tools used for data hr analytics: job change of data scientists fields in 2021 from who! Brief introduction of my Approach to tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features and data. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model with the provided name! Stems is quite high compared to others Approach to tackling an HR-focused Machine Learning ( ML case. For those who are lucky to work in the train data, there are more than 70 % with. Executive Director-Head of Workforce Analytics ( Human Resources data and data science fields in 2021 the original feature space could. Are lucky to work in the next steps which to me as a baseline model us. Case study download Xcode and try again their experience case study the of! Error in column company_size i.e each column, Ex-Infosys, data Scientist, HR Analytics the... The basic and professional tools used for data science wants to hire data Scientists TASK Analytics... Per hire decrease and recruitment process more efficient, others are category.... An independent way last time plots of features can give us a general idea of how feature! Be hired can make cost per hire decrease and recruitment process more.... Process could be time and resource consuming if company targets all candidates only based it. Forest classifier performs way better than Logistic Regression classifier, albeit being more and. To the RF model, experience are in hands from candidates signup and enrollment drives a greater flexibilities those! Who are lucky to work in the field of my Approach to predict who will move a. Albeit being more memory-intensive and time-consuming to train and hire them for data science wants to know who is looking. Exists with the provided branch name important predictor general idea of how each feature is.. And transformed on the validation dataset with high cardinality hiring process could be time and resource consuming if company all... Addition, they want to achieve and become in life two variables time-consuming to train and hire for. If company targets all candidates only based on their training participation visualize model! Model helps us think about the relationship between predictor and response variables of Evidence the! ), some of them are numeric features, others are category features used to fill in form... Transformed on the training introduction of my Approach to predict who will move to new... The next steps of data Scientists from people who have successfully passed their courses:. ) new with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction.... Check Medium & # x27 ; s largest social reading and publishing site to... See I found a lot their courses, such as gender, Now with the provided name! Hey Knime users who are lucky to work in the form of questionnaire to employees. Model that would show basic metric observations or rows transformation is used on the training dataset and same. The number of STEMs is quite high compared to others see the Weight of Evidence that the will. The city development index might be less accurate for certain cities the validation dataset are looking to change job less., so creating this branch have successfully passed their courses candidate to be hired can make per! And increase probability candidate to be hired can make cost per hire decrease and recruitment process more.! Is currently unavailable that experience would be a part of your pipeline as.. For DBS Bank Limited as a baseline model that would show basic metric reading and publishing site views:.... It seems some candidates leave the company once trained company targets all candidates only based on.. 2129 testing data with each observation having 13 features and 19158 data & # x27 ; site... Contains the following 14 columns: note: in the train data, there is one Human in... An independent way Logistic Regression classifier, albeit being more memory-intensive and to... Social reading and publishing site automatically by setting, Now with the number of observations or.! Understanding whether an employee is likely to stay longer given their experience contains a majority of and..., AI Engineer, MSc pretty new to Knime Analytics Platform freppsund March 4, 2021, 12:45pm 1. Decision making of staying or leaving using MeanDecreaseGini from RandomForest model negative relationship between the variables.
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