Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Job Posting. 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. sign in Variable 1: Experience Kaggle Competition. How to use Python to crawl coronavirus from Worldometer. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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? However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. I used violin plot to visualize the correlations between numerical features and target. 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. We believed this might help us understand more why an employee would seek another job. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time As we can see here, highly experienced candidates are looking to change their jobs the most. In the end HR Department can have more option to recruit with same budget if compare with old method and also have more time to focus at candidate qualification and get the best candidates to company. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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. 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. 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. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. Why Use Cohelion if You Already Have PowerBI? Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. For details of the dataset, please visit here. The source of this dataset is from Kaggle. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Some of them are numeric features, others are category 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. A tag already exists with the provided branch name. but just to conclude this specific iteration. 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. 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. How much is YOUR property worth on Airbnb? 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). 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. 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. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. The company wants to know who is really looking for job opportunities after the training. There are around 73% of people with no university enrollment. If nothing happens, download GitHub Desktop and try again. predicting the probability that a candidate to look for a new job or will work for the company, as well as interpreting factors affecting 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. 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. 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. If nothing happens, download Xcode and try again. In addition, they want to find which variables affect candidate decisions. If nothing happens, download Xcode and try again. It still not efficient because people want to change job is less than not. Ltd. Many people signup for their training. 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.;[. Each employee is described with various demographic features. After applying SMOTE on the entire data, the dataset is split into train and validation. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 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. Director, Data Scientist - HR/People Analytics. A tag already exists with the provided branch name. Interpret model(s) such a way that illustrate which features affect candidate decision March 9, 2021 1 minute read. Please This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Take a shot on building a baseline model that would show basic metric. 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. These are the 4 most important features of our model. Learn more. 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. 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. What is the effect of company size on the desire for a job change? The Gradient boost Classifier gave us highest accuracy and AUC ROC score. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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.
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