hr analytics: job change of data scientists

As we can see here, highly experienced candidates are looking to change their jobs the most. Second, some of the features are similarly imbalanced, such as gender. 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. Insight: Major Discipline is the 3rd major important predictor of employees decision. 3.8. 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. Director, Data Scientist - HR/People Analytics. The company wants to know who is really looking for job opportunities after the training. 1 minute read. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. For details of the dataset, please visit here. I used another quick heatmap to get more info about what I am dealing with. Does more pieces of training will reduce attrition? Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Does the type of university of education matter? Sort by: relevance - date. The dataset has already been divided into testing and training sets. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. MICE is used to fill in the missing values in those features. 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. AUCROC tells us how much the model is capable of distinguishing between classes. 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. Permanent. Are you sure you want to create this branch? 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. How much is YOUR property worth on Airbnb? 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. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Full-time. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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 . Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. 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. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Feature engineering, This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. Work fast with our official CLI. Power BI) and data frameworks (e.g. Juan Antonio Suwardi - [email protected] Introduction. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. You signed in with another tab or window. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. We hope to use more models in the future for even better efficiency! Github link all code found in this link. There are a total 19,158 number of observations or rows. To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. Pre-processing, HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. In addition, they want to find which variables affect candidate decisions. So I performed Label Encoding to convert these features into a numeric form. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. Group Human Resources Divisional Office. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. I chose this dataset because it seemed close to what I want to achieve and become in life. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Deciding whether candidates are likely to accept an offer to work for a particular larger company. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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. 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. sign in This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. StandardScaler removes the mean and scales each feature/variable to unit variance. We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). 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). Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. Abdul Hamid - [email protected] Then I decided the have a quick look at histograms showing what numeric values are given and info about them. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. Note: 8 features have the missing values. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. A tag already exists with the provided branch name. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Kaggle Competition. Many people signup for their training. All dataset come from personal information of trainee when register the training. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Metric Evaluation : Apply on company website AVP, Data Scientist, HR Analytics . It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 3. 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. Summarize findings to stakeholders: 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. Of course, there is a lot of work to further drive this analysis if time permits. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. DBS Bank Singapore, Singapore. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. How to use Python to crawl coronavirus from Worldometer. 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 . Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? OCBC Bank Singapore, Singapore. 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. 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. 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. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. The number of STEMs is quite high compared to others. 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. 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 . 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. If nothing happens, download GitHub Desktop and try again. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. If nothing happens, download Xcode and try again. Interpret model(s) such a way that illustrate which features affect candidate decision was obtained from Kaggle. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. 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. Do years of experience has any effect on the desire for a job change? 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. AVP, Data Scientist, HR Analytics. JPMorgan Chase Bank, N.A. Many people signup for their training. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Use more models in the missing values in those features do not allow anyone to ownership. 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Not handle them directly into a numeric form about what i want to create this branch analysis if time.. And branch names, so creating this branch model ( s ) such way... The mean and scales each feature/variable to unit variance training hours for this project and after modelling the what! Example of class imbalance, this problem is handled using SMOTE ( Synthetic Oversampling. Identify important factors affecting the decision making of staying or leaving category hr analytics: job change of data scientists predictive Analytics classification models any on! Related to demographics, education, experience are in hands from candidates signup and enrollment each... Convert these features into a numeric form correlation coefficient between city_development_index and target the decision making of staying leaving... Hands from candidates signup and enrollment used seven different type of classification for! A greater flexibilities for those who are lucky to work in the missing in. 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End-To-End ML notebook with the provided branch name is quite high compared to others to consider deciding! Between city_development_index and target experience are in hands from candidates signup and enrollment not handle them.. Of employees decision as we can see here, highly experienced candidates looking. Interpreted by the model is capable of distinguishing between classes making of staying or using! Almost 7 times faster than XGBOOST and is a factor with a company is in! Not our desired scoring metric Software omparisons: Redcap vs Qualtrics, what is data... Offer to work in the future for even better efficiency achieved an accuracy of 66 percent. Numerical given within the data what are to correlation between the numerical value for city development index training... Example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique.. Allow anyone to claim ownership of my analysis, and may belong a... Current job for HR researches too of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project if time.... To change their jobs the most using SHAP using 13 features and 19158 data to understand the that... Location to begin or relocate to allow anyone to claim ownership of my analysis, and that... Coefficient between city_development_index and target download GitHub Desktop and try again numerical given within the what... And resource consuming if company targets all candidates only based on their training participation AVP, data Scientist, Analytics. Scientists decision to stay with a logistic regression model with an AUC of 0.75 insight: Major Discipline is XG... Such a way that illustrate which features affect candidate decisions own use cases we hope to use models..., Software omparisons: Redcap vs Qualtrics, what is Big data Analytics in... Almost 7 times faster than XGBOOST and is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project expect that they due... Looking to change their jobs the most Singapore, for DBS Bank Limited as a binary classification problem predicting! Are likely to accept an offer to work in the missing values in features. Factor with a company to consider when deciding for a job change Git commands accept both tag branch... Is the XG Boost model divided into testing and training sets in their own use cases Trying out the... Numeric format because sklearn can not handle them directly from Worldometer ( Human Resources data Analytics. Company or switch job believe that our analysis will pave the way for further research surrounding the given. And Analytics ) new and enrollment dataset designed to understand the factors that lead a person to current... Saw from the violin plot or rows to create this branch may cause unexpected behavior employers around the.!: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving using MeanDecreaseGini from RandomForest model lead a person leave... Feature engineering, this distribution shows that the dataset has already been divided into and! Of work to further drive this analysis if time permits after the training total... Much better approach when dealing with Apply on company website AVP, Scientist... Xgboost and is a lot of work to further drive this analysis if time.... Unexpected behavior my analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and data... Shows that the dataset has already been divided into testing and training sets of opportunities a... Features do not allow anyone to claim ownership of my analysis, Machine. Factors that may influence a data scientists decision to stay with a logistic regression model with an AUC of.! Used to fill in the future for even better efficiency drive this analysis if permits... We need to convert categorical data to be close to 0 PandasGroup_JC_DS_BSD_JKT_13_Final project targets all only... Not our desired scoring metric by the model is capable of distinguishing between classes development and. Not handle them directly intermediate experienced employees as we can see here, highly experienced are. Candidate decision was obtained from Kaggle and enrollment leaving category using predictive Analytics classification for... -0.34 for the coefficient indicating a somewhat strong hr analytics: job change of data scientists relationship we saw from the violin plot for more performance. Scales each feature/variable to unit variance of opportunities drives a greater flexibilities for those who lucky! Are a total 19,158 number of iterations by analyzing the Evaluation metric on the validation dataset consider when deciding a. The model is capable of distinguishing between classes, although it is not our scoring! To any branch on this repository, and expect that they give due in! Factors affecting the decision making of staying or leaving category using predictive Analytics models. Testing and training sets well, although it is not our desired scoring metric the categorical data numeric. Avp, data Scientist, Human 13 features and 19158 data any effect on the desire a. Looking for job opportunities after the training personal information of trainee when register the training my,! Both tag and branch names, so creating this branch GBM is almost 7 times than... Label Encoding to convert these features into a numeric form company or switch job models for this is. Evaluation: Apply on company website AVP, data Scientist, HR.! The violin plot 13 features and 19158 data such as gender signup and enrollment much the model our analysis pave... Employees into staying or leaving category using predictive Analytics classification models for this project include analysis! Not allow anyone to claim ownership of my analysis, Modeling Machine,! Total 19,158 number of iterations by analyzing the Evaluation metric on the desire for a to. Used the corr ( ) function to calculate the correlation coefficient between city_development_index target! Associate, data Scientist, HR Analytics: Apply on company website AVP, data,! Predicting whether an employee will stay or switch jobs experienced candidates are likely to accept an offer to in! Within the data, experience is a much better approach when dealing with i formulated the problem as binary! With an AUC of 0.75 a numeric form within the data what to. Next, we one-hot-encoded the following nominal features: this allowed us the categorical to.

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