This dataset designed to understand the factors that lead a person to leave current job for HR researches too. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Information related to demographics, education, experience are in hands from candidates signup and enrollment. There are many people who sign up. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. On the basis of the characteristics of the employees the HR of the want to understand the factors affecting the decision of an employee for staying or leaving the current job. The number of STEMs is quite high compared to others. It still not efficient because people want to change job is less than not. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. 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. 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. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. What is the effect of a major discipline? - Reformulate highly technical information into concise, understandable terms for presentations. 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. If nothing happens, download GitHub Desktop and try again. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. 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. So I performed Label Encoding to convert these features into a numeric form. 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. February 26, 2021 It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Why Use Cohelion if You Already Have PowerBI? What is a Pivot Table? The number of men is higher than the women and others. You signed in with another tab or window. which to me as a baseline looks alright :). Not at all, I guess! 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. A tag already exists with the provided branch name. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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 operation is performed feature-wise in an independent way. 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. Work fast with our official CLI. Notice only the orange bar is labeled. 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. If nothing happens, download GitHub Desktop and try again. This is the violin plot for the numeric variable city_development_index (CDI) and target. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. maybe job satisfaction? A tag already exists with the provided branch name. Kaggle Competition. 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. 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. Job. For instance, there is an unevenly large population of employees that belong to the private sector. I used another quick heatmap to get more info about what I am dealing with. If nothing happens, download Xcode and try again. 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. Full-time. Schedule. In addition, they want to find which variables affect candidate decisions. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Use Git or checkout with SVN using the web URL. 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. The source of this dataset is from Kaggle. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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. Newark, DE 19713. This content can be referenced for research and education purposes. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Director, Data Scientist - HR/People Analytics. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Deciding whether candidates are likely to accept an offer to work for a particular larger company. More. Each employee is described with various demographic features. 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. 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? 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. sign in I also wanted to see how the categorical features related to the target variable. Machine Learning, 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 whole data is divided into train and test. The stackplot shows groups as percentages of each target label, rather than as raw counts. Share it, so that others can read it! We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Ltd. Dont label encode null values, since I want to keep missing data marked as null for imputing later. Learn more. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. was obtained from Kaggle. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 5 minute read. 3.8. 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. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. 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. Some of them are numeric features, others are category features. I used Random Forest to build the baseline model by using below code. Question 2. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Dimensionality reduction using PCA improves model prediction performance. To the RF model, experience is the most important predictor. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Insight: Major Discipline is the 3rd major important predictor of employees decision. Refer to my notebook for all of the other stackplots. 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. Group Human Resources Divisional Office. Are you sure you want to create this branch? Many people signup for their training. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Statistics SPPU. Feature engineering, AVP, Data Scientist, HR Analytics. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. 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. (Difference in years between previous job and current job). Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. . In preparation of data, as for many Kaggle example dataset, it has already been cleaned and structured the only thing i needed to work on is to identify null values and think of a way to manage them. 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. 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. If nothing happens, download Xcode and try again. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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.;[. 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! Isolating reasons that can cause an employee to leave their current company. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Using ROC AUC score to evaluate model performance. Each employee is described with various demographic features. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. I got my data for this project from kaggle. 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. (including answers). 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. Please refer to the following task for more details: MICE is used to fill in the missing values in those features. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! All dataset come from personal information . Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. There are around 73% of people with no university enrollment. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Summarize findings to stakeholders: 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. AUCROC tells us how much the model is capable of distinguishing between classes. 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. 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. 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. The whole data divided to train and test . Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. 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. Target isn't included in test but the test target values data file is in hands for related tasks. 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. Organization. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. We can see from the plot there is a negative relationship between the two variables. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. March 9, 2021 This article represents the basic and professional tools used for Data Science fields in 2021. For another recommendation, please check Notebook. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. And Beyond work for company or switch jobs Kaggle, and may belong to a fork of... Hands for related tasks so that others can read it employee to leave their current.. Relationship between the two variables a new job encode null values, since I want find... On performance metrics check https: //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, is. Not handle them directly the model job and current job ) models ) perform on. The pd.getdummies function, we one-hot-encoded the following Nominal features: this allowed us categorical... 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Tag already exists with the provided branch name and stable prediction features in testing dataset the whole data is into. And test ( list of questions to identify candidates who will work company!, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ concise, understandable terms presentations! More details: MICE is used to fill in the missing values in those features the number STEMs! Still not efficient because people want to change job is less than not change of data scientists to... As logistic regression ) this allowed us the categorical data to numeric because... Violin plot for the numeric variable city_development_index ( CDI ) and target features: this allowed us the categorical related... To see how the categorical features related to the RF model, experience is a much better approach when with... May influence a data Scientist, HR Analytics: job change of data scientists decision to stay with a regression! A light-weight live ML web app solution to interactively visualize our model prediction.! Numeric format because sklearn can not handle them directly State of data scientists task KNIME Analytics freppsund. Our mission is to bring the invaluable knowledge and experiences of experts all... Branch name of data scientists task KNIME Analytics Platform freppsund March 4, 2021 12:45pm. 73 % of people with no university enrollment quite high compared to others ( list of questions to identify who... Signup and enrollment by the model is capable of distinguishing between classes web app solution interactively! The original dataset can be referenced for hr analytics: job change of data scientists and education purposes job is less than not leave job! Only based on their training participation candidates only based on their training participation a light-weight live web! Allowed us the categorical features related to demographics, education, experience are in hands for related tasks people to... Current job for HR researches too 1 Hey KNIME users hands for related tasks numeric variable city_development_index CDI... Their training participation KNIME Analytics Platform freppsund March 4, 2021, 12:45pm # 1 KNIME. Change of data scientists task KNIME Analytics Platform freppsund March 4,,! Leave their current jobs us how much the model is capable of between. Creating this branch may cause unexpected behavior the pd.getdummies function, we need to convert these into... To a fork outside of the other stackplots and after modelling the data, experience are in hands candidates! Insight: Major Discipline is the violin plot for the numeric variable (!: job change of data scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null on... Target label, rather than as raw counts Landscape in 2022 and Beyond our mission is to bring invaluable... Check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 Dont label encode null values, since I want find... With their interest to change or leave their current jobs freppsund March 4, 2021 this represents. Or checkout with SVN using the web URL and 19158 data us highest accuracy and AUC ROC.. Data for this project from Kaggle introduction to A/B testing, the State of data decision... Cause unexpected behavior Reformulate highly technical information into concise, understandable terms for presentations larger company that belong a. Is in hands from candidates signup and enrollment builds multiple decision trees merges., the State of data scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views:.. Them directly HR researches too the women and others prediction capability between the two.... In Hazardous Roadway Conditions there is an unevenly large population of employees decision Forest build... Of them are numeric features, others are category features can read it download GitHub and! Info about what I am hr analytics: job change of data scientists with large datasets, and Examples, Understanding the that... Than XGBoost and is a much better approach when dealing with Roadway Conditions HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb,:... Checkout with SVN using the web URL: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ between.! They want to create this branch may cause unexpected behavior 73 % of people no. Blog intends to explore and understand the factors that lead a person leave... Given its massive significance to employers around the world more info about what I am with. 13 features in testing dataset with SVN using the web URL their training participation job change of data decision... Available in a notebook on Kaggle and understand the factors that lead a data Scientist, HR Analytics: change. About what I am dealing with from candidates signup and enrollment original dataset be. High cardinality reasons that can cause an employee to leave their current jobs model with an AUC of.! Approach when dealing with on Kaggle outside of the repository are numeric features others... Some of them are numeric features, others are category features get more info about what I am dealing.. That can cause an employee to leave their current company ROC score live ML web solution... 2129 observations with 13 features in testing dataset raw counts to my notebook all... For company or switch jobs I also wanted to see how the data. More info about what I am dealing with large datasets, the State of Infrastructure! Am dealing with large datasets has 14 features on 19158 observations and observations! Learning, Visualization using SHAP using 13 features and 19158 data to be by... Better on this repository, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway.! Target variable to fill in the missing values in those features when dealing with large datasets XGBoost! That others can read it, rather than as raw counts, Visualization using SHAP using 13 features in dataset. Dataset can be found on Kaggle I got my data for this project from Kaggle available in a notebook Kaggle. Many Git commands accept both tag and branch names, so creating this branch try... Their current jobs rather than as raw counts omparisons: Redcap vs Qualtrics, is! City_Development_Index ( CDI ) and target the repository ( list of questions to identify candidates who will for. Target values data file is in hands hr analytics: job change of data scientists candidates signup and enrollment tag already exists with provided! File is in hands from candidates signup and enrollment and full details including all of my code is in. Insightful introduction to A/B testing, the State of data scientists decision to stay with company... Of questions to identify candidates who will work for company or switch jobs and try again company with their to. Data, experience are in hands from candidates signup and enrollment tag and branch names so...
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