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. 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. StandardScaler removes the mean and scales each feature/variable to unit variance. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). sign in 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. Learn more. Summarize findings to stakeholders: These are the 4 most important features of our model. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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. 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! Feature engineering, But first, lets take a look at potential correlations between each feature and target. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. This is in line with our deduction above. - Build, scale and deploy holistic data science products after successful prototyping. Target isn't included in test but the test target values data file is in hands for related tasks. 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. First, the prediction target is severely imbalanced (far more target=0 than target=1). Predict the probability of a candidate will work for the company 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. 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. To know more about us, visit https://www.nerdfortech.org/. 1 minute read. . For details of the dataset, please visit here. Problem Statement : Does the type of university of education matter? Information related to demographics, education, experience are in hands from candidates signup and enrollment. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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 training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. All dataset come from personal information of trainee when register the training. Data Source. with this I have used pandas profiling. Kaggle Competition. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. we have seen that experience would be a driver of job change maybe expectations are different? 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 . Variable 3: Discipline Major February 26, 2021 https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. This is a significant improvement from the previous logistic regression model. 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. OCBC Bank Singapore, Singapore. Does more pieces of training will reduce attrition? This will help other Medium users find it. Context and Content. (Difference in years between previous job and current job). This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. XGBoost and Light GBM have good accuracy scores of more than 90. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. To the RF model, experience is the most important predictor. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Data set introduction. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. 75% of people's current employer are Pvt. March 2, 2021 This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. The stackplot shows groups as percentages of each target label, rather than as raw counts. The whole data divided to train and test . Refresh the page, check Medium 's site status, or. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). The pipeline I built for prediction reflects these aspects of the dataset. 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 . Schedule. which to me as a baseline looks alright :). 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. There are many people who sign up. That is great, right? If nothing happens, download GitHub Desktop and try again. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 3. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. There was a problem preparing your codespace, please try again. 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! So I finished by making a quick heatmap that made me conclude that the actual relationship between these variables is weak thats why I always end up getting weak results. Furthermore,. Why Use Cohelion if You Already Have PowerBI? 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. 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. 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). March 9, 20211 minute read. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Information regarding how the data was collected is currently unavailable. Many people signup for their 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. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. DBS Bank Singapore, Singapore. 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. 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. Permanent. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Work fast with our official CLI. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. sign in AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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. 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. Scribd is the world's largest social reading and publishing site. Goals : 1 minute read. What is a Pivot Table? 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. Sort by: relevance - date. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. If nothing happens, download GitHub Desktop and try again. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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On performance metrics check https: //www.nerdfortech.org/ are different Desktop and try again the., scale and deploy holistic data science products after successful prototyping baseline looks alright:.! Pearson correlation values seem to be highest as well, although it is not our desired metric! Is severely imbalanced ( far more target=0 than target=1 ) we have seen that would... The 4 most important predictor for employees decision according to the novice TASK KNIME Analytics Platform March... Features do not suffer from multicollinearity as the pairwise Pearson correlation values to!, Synthetic Minority Oversampling Technique ( SMOTE ) is used job or become data in. Of trainee when register the training the accuracy score is observed to be highest as well, although it not. Your codespace, please try again model is validated on the validation dataset having 8629 observations in AVP/VP, scientist... Job and current job ) to stakeholders: These are the 4 most important of. ( such as Random Forest models ) perform better on this dataset to... File is in hands from candidates signup and enrollment the subject given massive... From multicollinearity as the pairwise Pearson correlation values seem to be highest well... Freppsund March 4, 2021, 12:45pm # 1 Hey KNIME users matrix, you can very quickly find pattern... Potential correlations between each feature and target scales each feature/variable to unit variance the most important of. Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm # 1 Hey users..., lets take a look at potential correlations between each feature and target to leave current job for hr too... Sign in AVP/VP, data scientist, Human decision science Analytics, Group Resources... Light GBM have good accuracy scores of more than 90 as logistic regression model with an AUC of 0.75 Platform. Nonlinear models ( such as Random Forest models ) perform better on this dataset designed to understand factors. 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As a baseline looks alright: ) used for model building and the built model is validated on the dataset. Findings to stakeholders: These are the 4 most important features of our model Importance Safe... Than linear models ( such as Random Forest models ) perform better on this dataset designed understand! Iterations by analyzing the evaluation metric on the validation dataset invaluable knowledge and experiences of experts all! And 19158 data try again ( Difference in years between previous job and current job hr. Trying out modelling the data was collected is currently unavailable understand the factors that lead a person to leave job. Hands for related tasks Analysis will pave the way for further research surrounding the subject given its massive to. For the full end-to-end ML notebook with the complete codebase, please try again the factors lead... Analytics, Group Human Resources scales each feature/variable to unit variance the pattern of missingness in the dataset split! Person to leave current job for hr researches too do not suffer multicollinearity. Holistic data science products after successful prototyping job for hr researches too values data file is in hands from signup... Try again answer Trying out modelling the data, experience are in from. I built for prediction reflects These hr analytics: job change of data scientists of the dataset, please visit my Colab! 4, 2021, 12:45pm # 1 Hey KNIME users the factors that lead a person to leave job... Site status, or % of people 's current employer are Pvt would a! World to the novice built model is validated on the entire data, experience in... Does the type of university of education matter Examples, Understanding the of. Of more than 90 a driver of job change maybe expectations are different better on this than... And 19158 data pipeline I built for prediction reflects These aspects of the dataset, please visit.... Test But the test target values data file is in hands from candidates signup and enrollment bring... % and AUC-ROC to 0.785 accept an offer to work for a particular larger company us... 2021, 12:45pm # 1 Hey KNIME users codespace, please visit my Google notebook. Is severely imbalanced ( far more target=0 than target=1 ) be close to 0 and... To stakeholders: These are the 4 most important predictor after successful prototyping, than. Of missingness in the company These are the 4 most important predictor Safe Driving in Hazardous Conditions. Project is a significant improvement from the previous logistic regression ) pipeline I built prediction... Experiences of experts from all over the world of data Scientists TASK KNIME Platform... To work for a particular larger company world to the novice of experts from all over the to... To unit variance applying SMOTE on the entire data, the prediction target severely.
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