Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. If nothing happens, download GitHub Desktop and try again. Interpret model(s) such a way that illustrate which features affect candidate decision as this is only an initial baseline model then i opted to simply remove the nulls which will provide decent volume of the imbalanced dataset 80% not looking, 20% looking. Github link all code found in this link. A violin plot plays a similar role as a box and whisker plot. Job Posting. Each employee is described with various demographic features. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. (Difference in years between previous job and current job). We can see from the plot there is a negative relationship between the two variables. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Insight: Major Discipline is the 3rd major important predictor of employees decision. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 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. Please Third, we can see that multiple features have a significant amount of missing data (~ 30%). Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). HR-Analytics-Job-Change-of-Data-Scientists. Problem Statement : Tags: A tag already exists with the provided branch name. 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. March 2, 2021 Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. This is the violin plot for the numeric variable city_development_index (CDI) and target. Newark, DE 19713. As we can see here, highly experienced candidates are looking to change their jobs the most. This content can be referenced for research and education purposes. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. You signed in with another tab or window. Group Human Resources Divisional Office. to use Codespaces. In addition, they want to find which variables affect candidate decisions. Following models are built and evaluated. The simplest way to analyse the data is to look into the distributions of each feature. 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. 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. OCBC Bank Singapore, Singapore. 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! Information regarding how the data was collected is currently unavailable. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. If you liked the article, please hit the icon to support it. I also wanted to see how the categorical features related to the target variable. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. I used violin plot to visualize the correlations between numerical features and target. sign in To the RF model, experience is the most important predictor. 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. 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. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars which to me as a baseline looks alright :). But first, lets take a look at potential correlations between each feature and target. To know more about us, visit https://www.nerdfortech.org/. 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. Please For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. This is a quick start guide for implementing a simple data pipeline with open-source applications. This is a significant improvement from the previous logistic regression model. 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. Data set introduction. However, according to survey it seems some candidates leave the company once trained. Permanent. Use Git or checkout with SVN using the web URL. 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. 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. For any suggestions or queries, leave your comments below and follow for updates. 1 minute read. 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. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars I do not own the dataset, which is available publicly on Kaggle. I used another quick heatmap to get more info about what I am dealing with. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). (including answers). Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Insight: Acc. There are many people who sign up. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 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? Because the project objective is data modeling, we begin to build a baseline model with existing features. 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. HR Analytics: Job changes of Data Scientist. Please refer to the following task for more details: 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. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. When creating our model, it may override others because it occupies 88% of total major discipline. 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! Next, we tried to understand what prompted employees to quit, from their current jobs POV. we have seen that experience would be a driver of job change maybe expectations are different? 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 . Sort by: relevance - date. Introduction. Isolating reasons that can cause an employee to leave their current company. Missing imputation can be a part of your pipeline as well. It is a great approach for the first step. 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. Many people signup for their training. The whole data is divided into train and test. Question 2. I ended up getting a slightly better result than the last time. 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. Pre-processing, Exploring the categorical features in the data using odds and WoE. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. for the purposes of exploring, lets just focus on the logistic regression for now. Please 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. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists 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. 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 Does the gap of years between previous job and current job affect? There was a problem preparing your codespace, please try again. 10-Aug-2022, 10:31:15 PM Show more Show less If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. 2023 Data Computing Journal. Are you sure you want to create this branch? If nothing happens, download Xcode and try again. Statistics SPPU. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. For details of the dataset, please visit here. 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. It still not efficient because people want to change job is less than not. Many people signup for their training. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Determine the suitable metric to rate the performance from the model. Many people signup for their training. Description of dataset: The dataset I am planning to use is from kaggle. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Job. February 26, 2021 sign in Use Git or checkout with SVN using the web URL. 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. Ltd. However, according to survey it seems some candidates leave the company once trained. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. XGBoost and Light GBM have good accuracy scores of more than 90. 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. 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. 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. 1 minute read. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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). 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. Using ROC AUC score to evaluate model performance. We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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.;[. to use Codespaces. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. The baseline model helps us think about the relationship between predictor and response variables. Calculating how likely their employees are to move to a new job in the near future. As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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. Work fast with our official CLI. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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. Agatha Putri Algustie - agthaptri@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. 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 our case, company_size and company_type contain the most missing values followed by gender and major_discipline. - Build, scale and deploy holistic data science products after successful prototyping. 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. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Variable 2: Last.new.job 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. HR Analytics: Job Change of Data Scientists. Director, Data Scientist - HR/People Analytics. 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. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Dont label encode null values, since I want to keep missing data marked as null for imputing later. For instance, there is an unevenly large population of employees that belong to the private sector. All dataset come from personal information of trainee when register the training. 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. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. 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. 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 . 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. was obtained from Kaggle. Feature engineering, After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. There are a few interesting things to note from these plots. If nothing happens, download GitHub Desktop and try again. Variable 3: Discipline Major Why Use Cohelion if You Already Have PowerBI? Share it, so that others can read it! 17 jobs. AUCROC tells us how much the model is capable of distinguishing between classes. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 3.8. Kaggle Competition - Predict the probability of a candidate will work for the company. Data Source. 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 . Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Of course, there is a lot of work to further drive this analysis if time permits. Learn more. This will help other Medium users find it. Do years of experience has any effect on the desire for a job change? has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. Predict the probability of a candidate will work for the company 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. I chose this dataset because it seemed close to what I want to achieve and become in life. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Learn more. Our organization plays a critical and highly visible role in delivering customer . 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. The whole data divided to train and test . Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com StandardScaler removes the mean and scales each feature/variable to unit variance. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Machine Learning Approach to predict who will move to a new job using Python! The city development index is a significant feature in distinguishing the target. Target isn't included in test but the test target values data file is in hands for related tasks. Refresh the page, check Medium 's site status, or. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Work fast with our official CLI. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas.
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