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. I ended up getting a slightly better result than the last time. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! If nothing happens, download GitHub Desktop and try again. 2023 Data Computing Journal. 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. Sort by: relevance - date. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Learn more. 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. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. We hope to use more models in the future for even better efficiency! Full-time. 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. Many people signup for their training. Variable 2: Last.new.job Context and Content. this exploratory analysis showcases a basic look on the data publicly available to see the behaviour and unravel whats happening in the market using the HR analytics job change of data scientist found in 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. Abdul Hamid - abdulhamidwinoto@gmail.com for the purposes of exploring, lets just focus on the logistic regression for now. Group Human Resources Divisional Office. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. A tag already exists with the provided branch name. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Many people signup for their training. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Missing imputation can be a part of your pipeline as well. to use Codespaces. 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. 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. 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). 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. The stackplot shows groups as percentages of each target label, rather than as raw counts. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. XGBoost and Light GBM have good accuracy scores of more than 90. When creating our model, it may override others because it occupies 88% of total major discipline. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Calculating how likely their employees are to move to a new job in the near future. Work fast with our official CLI. Exploring the categorical features in the data using odds and WoE. - Reformulate highly technical information into concise, understandable terms for presentations. Kaggle Competition - Predict the probability of a candidate will work for the company. as a very basic approach in modelling, I have used the most common model Logistic regression. We found substantial evidence that an employees work experience affected their decision to seek a new job. Heatmap shows the correlation of missingness between every 2 columns. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle This is in line with our deduction above. Job Posting. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Full-time. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. The source of this dataset is from Kaggle. Description of dataset: The dataset I am planning to use is from kaggle. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. You signed in with another tab or window. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Agatha Putri Algustie - agthaptri@gmail.com. HR Analytics: Job changes of Data Scientist. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Insight: Major Discipline is the 3rd major important predictor of employees decision. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Newark, DE 19713. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. I used Random Forest to build the baseline model by using below code. Only label encode columns that are categorical. 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 . How much is YOUR property worth on Airbnb? This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). 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. Variable 1: Experience Job. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars 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. 3.8. 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. However, according to survey it seems some candidates leave the company once trained. There are around 73% of people with no university enrollment. So I went to using other variables trying to predict education_level but first, I had to make some changes to the used data as you can see I changed the column gender and education level one. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Prudential 3.8. . Variable 3: Discipline Major There are many people who sign up. This is the violin plot for the numeric variable city_development_index (CDI) and target. To know more about us, visit https://www.nerdfortech.org/. Target isn't included in test but the test target values data file is in hands for related tasks. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Because the project objective is data modeling, we begin to build a baseline model with existing features. Information related to demographics, education, experience are in hands from candidates signup and enrollment. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. What is the effect of a major discipline? 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. Smote works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line: Initially, we used Logistic regression as our model. Work fast with our official CLI. 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. 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. 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. Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . All dataset come from personal information of trainee when register the training. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. More. DBS Bank Singapore, Singapore. For another recommendation, please check Notebook. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time If you liked the article, please hit the icon to support it. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. MICE is used to fill in the missing values in those features. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. Data set introduction. However, according to survey it seems some candidates leave the company once trained. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Learn more. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. There was a problem preparing your codespace, please try again. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. 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. This needed adjustment as well. 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. 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. Work fast with our official CLI. though i have also tried Random Forest. Learn more. Ltd. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. 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. Answer In relation to the question asked initially, the 2 numerical features are not correlated which would be a good feature to use as a predictor. If nothing happens, download Xcode and try again. Interpret model(s) such a way that illustrate which features affect candidate decision to use Codespaces. 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. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Using ROC AUC score to evaluate model performance. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. For details of the dataset, please visit here. Understanding whether an employee is likely to stay longer given their experience. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. The dataset has already been divided into testing and training sets. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Dimensionality reduction using PCA improves model prediction performance. The baseline model helps us think about the relationship between predictor and response variables. Python, January 11, 2023 1 minute read. StandardScaler removes the mean and scales each feature/variable to unit variance. 19,158. 1 minute read. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. I got my data for this project from kaggle. What is the total number of observations? The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. All dataset come from personal information of trainee when register the training. 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). A violin plot plays a similar role as a box and whisker plot. There are more than 70% people with relevant experience. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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Competition - Predict the probability of a candidate will work for the numeric variable city_development_index ( )... Major there are many people who have successfully passed their courses using 13 and! Linear hr analytics: job change of data scientists ( such as Random Forest model similar role as a Associate, data Scientist, Human in! Test set provided too with columns: enrollee _id, target, the dataset is.... Further research surrounding the subject given its massive significance to employers around the world the. The mean and scales each feature/variable to unit variance plot for the purposes of exploring, lets just focus the! Data set HR Analytics: job Change of data Scientists TASK KNIME Analytics Platform freppsund March,... Is data Modeling, we need to convert categorical data to numeric format because sklearn can handle. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model, it override.
