The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. DBS Bank Singapore, Singapore. Calculating how likely their employees are to move to a new job in the near future. Does the gap of years between previous job and current job affect? 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. . This operation is performed feature-wise in an independent way. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. For another recommendation, please check Notebook. Scribd is the world's largest social reading and publishing site. Permanent. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. What is the maximum index of city development? Please Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. What is the effect of company size on the desire for a job change? What is the total number of observations? 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. I do not own the dataset, which is available publicly on Kaggle. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. In addition, they want to find which variables affect candidate decisions. We can see from the plot there is a negative relationship between the two variables. to use Codespaces. How much is YOUR property worth on Airbnb? I ended up getting a slightly better result than the last time. Why Use Cohelion if You Already Have PowerBI? Question 2. 2023 Data Computing Journal. Group Human Resources Divisional Office. The city development index is a significant feature in distinguishing the target. I also wanted to see how the categorical features related to the target variable. (Difference in years between previous job and current job). However, according to survey it seems some candidates leave the company once trained. Data Source. All dataset come from personal information of trainee when register the training. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Some of them are numeric features, others are category features. well personally i would agree with it. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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! The whole data divided to train and test . First, Id like take a look at how categorical features are correlated with the target variable. 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. 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. 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. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Our dataset shows us that over 25% of employees belonged to the private sector of employment. According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. I 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. I chose this dataset because it seemed close to what I want to achieve and become in life. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Machine Learning, A tag already exists with the provided branch name. 75% of people's current employer are Pvt. To know more about us, visit https://www.nerdfortech.org/. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Features, city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration 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. Github link all code found in this link. Python, January 11, 2023 If nothing happens, download GitHub Desktop and try again. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Note: 8 features have the missing values. with this I have used pandas profiling. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Dimensionality reduction using PCA improves model prediction performance. 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. To the RF model, experience is the most important predictor. Understanding whether an employee is likely to stay longer given their experience. If nothing happens, download Xcode and try again. Your role. 17 jobs. All dataset come from personal information of trainee when register the training. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Kaggle Competition. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 5 minute read. 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. 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. 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. Refresh the page, check Medium 's site status, or. I used violin plot to visualize the correlations between numerical features and target. What is the effect of a major discipline? XGBoost and Light GBM have good accuracy scores of more than 90. 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. 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. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. though i have also tried Random Forest. Many people signup for their training. Description of dataset: The dataset I am planning to use is from kaggle. 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. 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. We believed this might help us understand more why an employee would seek another job. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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! 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. For instance, there is an unevenly large population of employees that belong to the private sector. sign in Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Introduction. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Target isn't included in test but the test target values data file is in hands for related tasks. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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. We conclude our result and give recommendation based on it. Tags: I used another quick heatmap to get more info about what I am dealing with. We will improve the score in the next steps. so I started by checking for any null values to drop and as you can see I found a lot. Target isn't included in test but the test target values data file is in hands for related tasks. 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. 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. Context and Content. This will help other Medium users find it. Data set introduction. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. The number of men is higher than the women and others. Many people signup for their training. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. The company wants to know who is really looking for job opportunities after the training. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. March 2, 2021 Job. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Use Git or checkout with SVN using the web URL. A tag already exists with the provided branch name. Insight: Major Discipline is the 3rd major important predictor of employees decision. Ltd. to use Codespaces. There are many people who sign up. Are there any missing values in the data? Hadoop . This means that our predictions using the city development index might be less accurate for certain cities. Question 1. 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. In addition, they want to find which variables affect candidate decisions. (including answers). For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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! 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. This is a significant improvement from the previous logistic regression model. I used Random Forest to build the baseline model by using below code. Problem Statement : 10-Aug-2022, 10:31:15 PM Show more Show less Before this note that, the data is highly imbalanced hence first we need to balance it. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. After applying SMOTE on the entire data, the dataset is split into train and validation. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Sort by: relevance - date. Organization. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Following models are built and evaluated. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 1 minute read. So I performed Label Encoding to convert these features into a numeric form. This is the violin plot for the numeric variable city_development_index (CDI) and target. Take a shot on building a baseline model that would show basic metric. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. For any suggestions or queries, leave your comments below and follow for updates. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Many people signup for their training. 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. 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. There was a problem preparing your codespace, please try again. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Metric Evaluation : Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. Each employee is described with various demographic features. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. 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. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Next, we tried to understand what prompted employees to quit, from their current jobs POV. but just to conclude this specific iteration. 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. Learn more. Dont label encode null values, since I want to keep missing data marked as null for imputing later. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Our organization plays a critical and highly visible role in delivering customer . 3.8. Please HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. 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. There are a total 19,158 number of observations or rows. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Second, some of the features are similarly imbalanced, such as gender. Hence there is a need to try to understand those employees better with more surveys or more work life balance opportunities as new employees are generally people who are also starting family and trying to balance job with spouse/kids. 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). - Build, scale and deploy holistic data science products after successful prototyping. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. If nothing happens, download Xcode and try again. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. OCBC Bank Singapore, Singapore. for the purposes of exploring, lets just focus on the logistic regression for now. This content can be referenced for research and education purposes. 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. You signed in with another tab or window. sign in Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Are you sure you want to create this branch? Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. What is a Pivot Table? Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. Predict the probability of a candidate will work for the company JPMorgan Chase Bank, N.A. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Each employee is described with various demographic features. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. Using ROC AUC score to evaluate model performance. Notice only the orange bar is labeled. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Feature engineering, 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 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. All dataset come from personal information . Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Human Resource Data Scientist jobs. There are more than 70% people with relevant experience. This needed adjustment as well. Of course, there is a lot of work to further drive this analysis if time permits. The stackplot shows groups as percentages of each target label, rather than as raw counts. 3. That is great, right? Abdul Hamid - abdulhamidwinoto@gmail.com 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.

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