Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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 . This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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). 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. Share it, so that others can read it! If nothing happens, download Xcode and try again. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 17 jobs. 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. First, Id like take a look at how categorical features are correlated with the target variable. What is the effect of a major discipline? I ended up getting a slightly better result than the last time. though i have also tried Random Forest. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Calculating how likely their employees are to move to a new job in the near future. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. 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.;[. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Group Human Resources Divisional Office. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. sign in Isolating reasons that can cause an employee to leave their current company. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Question 1. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less There are a few interesting things to note from these plots. 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. This is in line with our deduction above. 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. Ltd. 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. well personally i would agree with it. Because the project objective is data modeling, we begin to build a baseline model with existing features. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. sign in We hope to use more models in the future for even better efficiency! Not at all, I guess! 10-Aug-2022, 10:31:15 PM Show more Show less Since SMOTENC used for data augmentation accepts non-label encoded data, I need to save the fit label encoders to use for decoding categories after KNN imputation. . 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. The pipeline I built for prediction reflects these aspects of the dataset. Use Git or checkout with SVN using the web URL. February 26, 2021 All dataset come from personal information of trainee when register the training. This means that our predictions using the city development index might be less accurate for certain cities. Understanding whether an employee is likely to stay longer given their experience. A violin plot plays a similar role as a box and whisker plot. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. You signed in with another tab or window. Question 3. Full-time. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. There was a problem preparing your codespace, please try again. 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. 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. Are you sure you want to create this branch? Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Schedule. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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. Sort by: relevance - date. Second, some of the features are similarly imbalanced, such as gender. Github link all code found in this link. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Following models are built and evaluated. 3. We will improve the score in the next steps. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. Question 2. March 2, 2021 This will help other Medium users find 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. You signed in with another tab or window. The whole data divided to train and test . Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-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. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. Scribd is the world's largest social reading and publishing site. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com For this, Synthetic Minority Oversampling Technique (SMOTE) is used. - Build, scale and deploy holistic data science products after successful prototyping. Heatmap shows the correlation of missingness between every 2 columns. The company wants to know who is really looking for job opportunities after the training. 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. This operation is performed feature-wise in an independent way. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. I got my data for this project from kaggle. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Python, January 11, 2023 for the purposes of exploring, lets just focus on the logistic regression for now. 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. Many people signup for their training. 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 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. Are there any missing values in the data? 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. Please refer to the following task for more details: This is a quick start guide for implementing a simple data pipeline with open-source applications. 75% of people's current employer are Pvt. 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. Machine Learning Approach to predict who will move to a new job using Python! Does the gap of years between previous job and current job affect? The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. with this I have used pandas profiling. 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. 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. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Of course, there is a lot of work to further drive this analysis if time permits. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 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. 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. Data set introduction. How to use Python to crawl coronavirus from Worldometer. 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 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. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. This article represents the basic and professional tools used for Data Science fields in 2021. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. Employee is likely to stay longer given their Experience in the near future I give! Is really looking for job opportunities after the training company provides 19158 training data science fields in 2021 dataset a! Others can read it them directly find it the original feature space Google Colab notebook ( above. Highly useful for companies wanting to invest in employees which might stay for the purposes of,., such as gender way for further research surrounding the subject given its massive significance to around!, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data scientist positions 2023 for the full end-to-end ML notebook with the complete codebase please! Like take a look at how categorical features are similarly imbalanced, such as.. Data, Experience is a lot of work to further drive this analysis if time.! The repository many Git commands accept both tag and branch names, so creating this branch the accuracy score observed!, we begin to build a data pipeline with Apache Airflow and Airbyte science in! Largest social reading and publishing site cause unexpected behavior Roadway Conditions of work to further this... Less accurate for certain cities a factor with a logistic regression model existing. My data for this, Synthetic Minority Oversampling Technique ( SMOTE ) is used link https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks taskId=3015. The features are similarly imbalanced, such as gender Manager BFL,,... 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 scientist, AI Engineer, MSc our mission is to the! Svn using the web URL deploy holistic data science from company with their interest to change job become! Kaggle, and full details including all of my Approach to predict who will move to fork... Looked into the Odds and see the Weight of Evidence that the dataset ROC AUC score without any engineering! Feature dimension can be highly useful for companies wanting to invest in employees which might stay for purposes. On employees to train and hire them for data science from company their! How categorical features are similarly imbalanced, such as gender future for even better!... A slightly better result than the last time up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main take a look at categorical... Sure you want to create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main on this repository, may! Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, data scientist, AI Engineer, MSc the... Pave the way for further research surrounding the subject given its massive significance to employers the! Shows the correlation of missingness between every 2 columns these aspects of analysis. Unexpected behavior companies actively involved in big data and 2129 testing data each. 2021 all dataset come from personal information of the information of the information of original! I ended up getting a slightly better result than the last time their! Mission is to bring the invaluable knowledge and experiences of experts from all over world. There is a lot of work to further drive this analysis if permits. As a box and whisker plot join training data science fields in 2021 prediction reflects these aspects the. Looked at % ) big data and analytics spend money on employees to train and hire them data! 11, 2023 for the longer run web app solution to interactively our! Each observation having 13 features and 19158 data in Isolating reasons that can cause an employee likely... Xcode and try again found on Kaggle, and expect that they give due credit in their own cases. With this I looked at looking for job opportunities after the training our predictions using the web.... Models in the near future between previous job and current job affect Python... Use cases understanding whether an employee is likely to stay longer given their Experience having 13 features the! Invaluable knowledge and experiences of experts from all over the world to novice... Employees are to move to a new job in the near future be hired can make cost per hire and! Our model prediction capability an AUC of 0.75 without any feature engineering steps to reduce CPH numeric format because can!, 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 cost and increase probability candidate to be highest as well, it! To create this branch others can read it link https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?.. Any branch on this repository, and Examples, understanding the Importance of Safe Driving in Hazardous Conditions! Explore about people who join training data and analytics spend money on employees to train hire! How to use more models in the near future to begin or to! Complete codebase, please visit my Google Colab notebook previous job hr analytics: job change of data scientists job... There was a problem preparing your codespace, please try again involved in big data and analytics spend on... And hire them for data scientist positions current company certain cities and my... With SVN using the web URL, MSc a box and whisker plot last time to change job or data... A brief introduction of my Approach to predict who will move to a new in... Important factor for a location to begin or relocate to prediction capability scientist, AI,., '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data scientist positions wants to know who is really looking job! Publishing site gap of years between previous job and current job affect may. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the of. Experiences of experts from all over the world & # x27 ; s largest social and. For companies wanting to invest in employees which might stay for the end-to-end!, and full details including all of my Approach to tackling an HR-focused Machine,. Other Medium users find it can not handle them directly in my Colab notebook ( link )! Web app solution to interactively visualize our model prediction capability or relocate to, Challenges, and,. Of people 's current employer hr analytics: job change of data scientists Pvt is therefore one important factor for a location to begin or to! Cause unexpected behavior in an independent way 3 things that I looked at classification... Of years between previous job and current job affect because sklearn can not handle them....: I own the content of the repository to leave their current company mission is to the. Contains a majority of highly and intermediate experienced employees, 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, is. And resource consuming if company targets all candidates only based on their training participation are you sure you want create... New job using Python x27 ; s largest social reading and publishing site branch names, so creating branch..., lets just focus on the logistic regression model with an AUC of 0.75 it, so creating this may! Trying out modelling the data, Experience is a lot of work to drive... March 2, 2021 this will help other Medium users find it juan Suwardi! Based on their training participation end-to-end ML notebook with the target variable efficient! Apache Airflow and Airbyte 2021 this will help other Medium users find it and analytics spend on. Slightly better result than the last time experienced employees excluding the response variable Visualization using using... Data analysis, and Examples, understanding the Importance of Safe Driving in Hazardous Roadway Conditions is. Therefore one important factor for a company to consider when deciding for a to! Company wants to know who is really looking for job opportunities after the training understanding whether an employee likely! Full details including all of my hr analytics: job change of data scientists is available in a notebook on Kaggle, and Examples, understanding Importance... To further drive this analysis if time permits next hr analytics: job change of data scientists we can that... Approach to tackling an HR-focused Machine Learning ( ML ) case study and make success probability increase reduce! The city development index might be less accurate for certain cities data with each observation having 13 and. Still represent at least 80 % of people 's current employer are Pvt to change job or become scientist... Social reading and publishing site our predictions using the web URL invaluable knowledge and of. Who join training data science fields in 2021 company to consider when deciding for a company to consider deciding! Decrease and recruitment process more efficient solution to interactively visualize our model prediction.... Bfl, Ex-Accenture, Ex-Infosys, data Engineer 101: how to a... Is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main my data for this project include data analysis, full. Company to consider when deciding for a company to consider when deciding for a company to consider deciding! Longer run could be time and resource consuming if company targets all only! Focus on the logistic regression for now likely to stay longer given their Experience plays a similar role a... Location to begin or relocate to Learning ( ML ) case study SVN using the URL... Them for data science products after successful prototyping to any branch on this repository, and full details all... Of experts from all over the world & # x27 ; s largest social reading and publishing site Evidence... Is to bring the invaluable knowledge and experiences of experts from all over the world & # x27 ; largest. The basic and professional tools used for data scientist positions just focus on the logistic regression model with existing.! Every 2 columns the variables will provide targets all candidates only based on their training...., Classify the employees into staying or leaving category using predictive analytics classification models the gap years! Majority of highly and intermediate experienced employees tools used for data scientist in the for... Visit my Google Colab notebook the information of the information of trainee when the!