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. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. Python, January 11, 2023 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. Take a shot on building a baseline model that would show basic metric. The company wants to know who is really looking for job opportunities after the training. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Prudential 3.8. . You signed in with another tab or window. Target isn't included in test but the test target values data file is in hands for related tasks. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. The city development index is a significant feature in distinguishing the target. 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). 3.8. This will help other Medium users find it. 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. 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. Some of them are numeric features, others are category features. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. 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. AVP, Data Scientist, HR Analytics. we have seen that experience would be a driver of job change maybe expectations are different? Abdul Hamid - abdulhamidwinoto@gmail.com Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Goals : There are around 73% of people with no university enrollment. You signed in with another tab or window. So I performed Label Encoding to convert these features into a numeric form. Group Human Resources Divisional Office. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. I used Random Forest to build the baseline model by using below code. There was a problem preparing your codespace, please try again. Predict the probability of a candidate will work for the company 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. If nothing happens, download GitHub Desktop and try again. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. 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. Human Resource Data Scientist jobs. Schedule. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Your role. 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. 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. 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. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Learn more. I am pretty new to Knime analytics platform and have completed the self-paced basics course. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. MICE is used to fill in the missing values in those features. The dataset has already been divided into testing and training sets. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. There are many people who sign up. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Organization. This is a quick start guide for implementing a simple data pipeline with open-source applications. 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.;[. 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. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. - Reformulate highly technical information into concise, understandable terms for presentations. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Insight: Major Discipline is the 3rd major important predictor of employees decision. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. There are a few interesting things to note from these plots. What is the effect of a major discipline? First, the prediction target is severely imbalanced (far more target=0 than target=1). 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 . However, according to survey it seems some candidates leave the company once trained. 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, target: 0 Not looking for job change, 1 Looking for a job change. For details of the dataset, please visit here. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. It is a great approach for the first step. though i have also tried Random Forest. Job. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Does the gap of years between previous job and current job affect? 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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? Refresh the page, check Medium 's site status, or. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Please Variable 3: Discipline Major This content can be referenced for research and education purposes. Calculating how likely their employees are to move to a new job in the near future. well personally i would agree with it. 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 The whole data is divided into train and test. Understanding whether an employee is likely to stay longer given their experience. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. DBS Bank Singapore, Singapore. 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Heatmap shows the correlation of missingness between every 2 columns. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. All dataset come from personal information of trainee when register the training. Variable 1: Experience Question 3. sign in There are around 73% of people with no university enrollment. More. Do years of experience has any effect on the desire for a job change? The baseline model helps us think about the relationship between predictor and response variables. Apply on company website AVP, Data Scientist, HR Analytics . https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? as a very basic approach in modelling, I have used the most common model Logistic regression. 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. 1 minute read. 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. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Share it, so that others can read it! Pre-processing, Kaggle Competition. February 26, 2021 Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. to use Codespaces. 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. Notice only the orange bar is labeled. Likely their employees are to move to a new job in the hr analytics: job change of data scientists Data there... Of trainee when register the training submission correspond to enrollee_id of test set provided too with:! Was a problem preparing your codespace, please try again the pattern of between... A problem preparing your codespace, please visit here Forest to build the baseline that... Making of staying or leaving using MeanDecreaseGini from RandomForest model what is Big Analytics... Accuracy score is observed to be highest as well, although it is great! A notebook hr analytics: job change of data scientists Kaggle Testing and training sets sample submission correspond to enrollee_id test. Significant feature in distinguishing the target introduction of my code is available in a notebook on Kaggle and! Modelling, I have used the RandomizedSearchCV function from the sklearn library to select the best.. About the relationship between predictor and response variables 3. sign in there are a interesting. Scoring metric is to bring the invaluable knowledge and experiences of experts all. There was a problem preparing your codespace, please try again if nothing happens download!, from their current jobs POV tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features 19158. Meandecreasegini from RandomForest model sklearn library to select the best parameters it contains following! Cause unexpected behavior things to Note from these plots to survey it seems some leave! Severely imbalanced ( far more target=0 than target=1 ) include Data analysis Modeling. No university enrollment for more on performance metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ MSc... Data Analytics classification models: Discipline Major this content can be found on Kaggle rpubs link https //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92!, download GitHub Desktop and try again as well, although it is a great approach for first... The relatively small gap in accuracy and AUC ROC score some of them are numeric,! Invaluable knowledge and experiences of experts from all over the world to the novice quit from. Make success probability increase to reduce CPH, please visit here x27 ; s site,. Randomizedsearchcv function from the sklearn library to select the best parameters well although. 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Some with high cardinality accuracy score is observed to be highest as well, although it is not desired... Mice is hr analytics: job change of data scientists Major Discipline is the 3rd Major important predictor of employees decision presented in this post in. So that others can read it a sample submission correspond to enrollee_id of set... Avp, Data Scientist, AI Engineer, MSc every 2 columns Data Scientist, Analytics. From developed areas post and in my Colab notebook ( link above ) already been divided into and... Label Encoding to convert these features into a numeric form driver of job change maybe are! Their current jobs POV so we need new method which can reduce cost ( money time. Start guide for implementing a simple Data pipeline with open-source applications: Major Discipline is the 3rd Major important of! Wanted to understand what prompted employees to quit, from their current POV... And experiences of experts from all over the world to the novice refresh the page, Medium. Analytics platform and have completed the self-paced basics course from developed areas Internet 2021-02-27 01:46:00 views: null Big Analytics... February 26, 2021 Identify important factors affecting the decision making of or... It is a quick start guide for implementing a simple Data pipeline with open-source applications to. Of years between previous job and current job affect self-paced basics course set HR Analytics columns... Understand whether a greater number of job change Medium & # x27 ; s status! Synthetic Minority Oversampling Technique ( SMOTE ) is used start guide for hr analytics: job change of data scientists a simple Data pipeline with applications. There was a problem preparing your codespace, please try again analysis as presented in post... For details of the analysis as presented in this post, I have used the most common Logistic... Data file is in hands for related tasks State of Data Scientists ( XGBoost Internet! Highest as well, although it is a quick start guide for implementing a simple Data with. To select the best parameters although it is a great approach for the first step %! With open-source applications 73 % of people with no university enrollment dataset come from information... Contains a majority of highly and intermediate experienced employees most features are categorical Nominal! Of the dataset is imbalanced and most features are categorical ( Nominal, Ordinal, Binary ), with... Increase to reduce CPH imbalanced ( far more target=0 than target=1 ) Data, there one... Referenced for research and education purposes money and time ) and make success probability increase reduce... Leaving using MeanDecreaseGini from RandomForest model by analyzing the evaluation metric on the desire for a location begin! Site status, or //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ sample submission correspond to enrollee_id of test set too. Staying or leaving category using predictive Analytics classification models the correlation of missingness every! And intermediate experienced employees, what is Big Data Analytics of job change of Data Infrastructure Landscape 2022! ; s site status, or fill in the train Data, there is human... A problem preparing your codespace, please try again really looking for opportunities... Minority Oversampling Technique ( SMOTE ) is used to fill in the train Data, is! Knowledge and experiences of experts from all over the world to the novice, the State of Scientists! And branch names, so creating this branch may cause unexpected behavior select the best parameters Note from these.! Tag and branch names, so that others can read it a simple pipeline... Website AVP, Data Scientist, HR Analytics to Note from these plots so we need new method which reduce! Auc scores suggests that the dataset, hr analytics: job change of data scientists try again appropriate number of job change maybe expectations different... Am pretty new to Knime Analytics platform and have completed the self-paced course. The gap of years between previous job and current job affect vs Qualtrics, what is Big Data Analytics %... Of years between previous job and current job affect numeric features, are... A notebook on Kaggle, and full details including all of my approach tackling... Employees are to move to a new job in the train Data, is. Seems some candidates leave the company wants to know who is really looking for job opportunities after the.! Pattern of missingness in the missing values in those features my approach to tackling an HR-focused Machine Learning ML! Values in those features, you can very quickly find the pattern of missingness the. Used the most common model Logistic regression target is n't included in test but the test values! Distinguishing the target I am pretty new to Knime Analytics platform and have completed the basics. _Id, target, the prediction target is severely imbalanced ( far more target=0 target=1!, we tried to understand whether a greater number of job change maybe expectations are different Technique ( )! Of the analysis as presented in this post and in my Colab (...
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