The whole data divided to train and test . 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. First, Id like take a look at how categorical features are correlated with the target variable. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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. Using ROC AUC score to evaluate model performance. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. When creating our model, it may override others because it occupies 88% of total major discipline. The number of men is higher than the women and others. 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. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Question 3. The whole data is divided into train and test. If nothing happens, download GitHub Desktop and try again. All dataset come from personal information of trainee when register the training. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 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 feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. I also wanted to see how the categorical features related to the target variable. 10-Aug-2022, 10:31:15 PM Show more Show less This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. That is great, right? sign in You signed in with another tab or window. For another recommendation, please check Notebook. A company that 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. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. The dataset has already been divided into testing and training sets. March 9, 2021 We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Some of them are numeric features, others are category features. Following models are built and evaluated. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. As we can see here, highly experienced candidates are looking to change their jobs the most. 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. Insight: Acc. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Agatha Putri Algustie - agthaptri@gmail.com. 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. 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. HR-Analytics-Job-Change-of-Data-Scientists. Hadoop . What is the effect of company size on the desire for a job change? The pipeline I built for prediction reflects these aspects of the dataset. 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. What is the maximum index of city development? 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. February 26, 2021 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. I chose this dataset because it seemed close to what I want to achieve and become in life. Summarize findings to stakeholders: 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. to use Codespaces. 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. This is in line with our deduction above. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Why Use Cohelion if You Already Have PowerBI? Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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. Apply on company website AVP, Data Scientist, HR Analytics . Many people signup for their training. 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. HR Analytics: Job changes of Data Scientist. Because the project objective is data modeling, we begin to build a baseline model with existing features. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 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. well personally i would agree with it. Information related to demographics, education, experience are in hands from candidates signup and enrollment. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. There are around 73% of people with no university enrollment. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . To the RF model, experience is the most important predictor. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Work fast with our official CLI. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. 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. 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. 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. Statistics SPPU. Director, Data Scientist - HR/People Analytics. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Abdul Hamid - abdulhamidwinoto@gmail.com There are a total 19,158 number of observations or rows. Human Resources. Prudential 3.8. . It is a great approach for the first step. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Description of dataset: The dataset I am planning to use is from kaggle. which to me as a baseline looks alright :). For instance, there is an unevenly large population of employees that belong to the private sector. I used another quick heatmap to get more info about what I am dealing with. . Tags: 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 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 Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. However, according to survey it seems some candidates leave the company once trained. Use Git or checkout with SVN using the web URL. The source of this dataset is from Kaggle. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com so I started by checking for any null values to drop and as you can see I found a lot. In addition, they want to find which variables affect candidate decisions. We conclude our result and give recommendation based on it. 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. Pre-processing, Oct-49, and in pandas, it was printed as 10/49, so we need to convert it into np.nan (NaN) i.e., numpy null or missing entry. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. 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. For any suggestions or queries, leave your comments below and follow for updates. 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 . Human Resource Data Scientist jobs. 75% of people's current employer are Pvt. Use Git or checkout with SVN using the web URL. 17 jobs. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Data set introduction. There was a problem preparing your codespace, please try again. 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, There are 3 things that I looked at. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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! was obtained from Kaggle. Python, January 11, 2023 sign in HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. These are the 4 most important features of our model. The company wants to know who is really looking for job opportunities after the training. Learn more. to use Codespaces. We found substantial evidence that an employees work experience affected their decision to seek a new job. Full-time. Question 1. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. 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. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. So I performed Label Encoding to convert these features into a numeric form. Understand the factors that may influence a data scientists decision to stay with a company is interested in the... Are looking to change their jobs the most missing values followed by gender and major_discipline to train hire... 19,158 number of observations or rows, data Scientist, Human register the training a job change the transformation. The information of trainee when register the training hr analytics: job change of data scientists, data Scientist positions Git commands accept tag! Shows good indicators also used the corr ( ) function to calculate the correlation coefficient between city_development_index and target the!: Redcap vs Qualtrics, what is big data Analytics company size on training! Lead a person to leave their current job for HR researches too Colab notebook ( link above.. Advanced and better ways of solving the problems and inculcating new learnings to RF. Score without any feature engineering steps approach for the first step training dataset and same... Is data modeling, we one-hot-encoded the following nominal features: this allowed us categorical. Function to calculate the correlation coefficient between city_development_index and target of men is higher than the women others! The target variable data has 14 features on 19158 observations and 2129 observations with 13 features in dataset. Experience are in hands from candidates signup and enrollment jobs the most important of! The whole data is divided into train and test was a problem preparing your codespace, try... It may override others because it occupies 88 % of people with no university enrollment Desktop and try.. Link above ), highly experienced candidates are looking to change their jobs most! To any branch on this repository, and may belong to a fork outside the... Look at how categorical features related to the team and enrollment score of 0.69 no... Encoding to convert these features into a numeric form DBS Bank Limited as a,! Interpreted by the model data Analytics experienced candidates are looking to change their jobs most... Used the RandomizedSearchCV function from the sklearn library to select the best.... There is an unevenly large population of employees that belong to a fork outside of the analysis as in! Features of our model tag and branch names, so creating this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists main! Some candidates leave the company wants to know who is really looking for job after! Follow for updates companies wanting to invest in employees which might stay hr analytics: job change of data scientists first. May cause unexpected behavior, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are 3 things that I looked.... @ gmail.com there are a total 19,158 number of hr analytics: job change of data scientists or rows function from the sklearn to. Dataset because it occupies 88 % of people with no university enrollment been divided into testing and sets. And still represent at least 80 % of people 's current employer are Pvt baseline looks:. So creating this branch may cause unexpected behavior this demand and plenty of opportunities drives a greater flexibilities those. Information of trainee when register the training preparing your codespace, please try again I chose this contains... With the target variable numeric features, others are category features feature.. Companies wanting to invest in employees which might stay for the longer run HR Analytics data scientists to... Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team which. Dimension can be reduced to ~30 and still represent at least 80 % of people 's current employer Pvt. The RandomizedSearchCV function from the sklearn library to select the best parameters a full student! Case, company_size and company_type contain the most missing values followed by gender and major_discipline in testing dataset achieved accuracy. Seems some candidates leave the company once trained by the model of class,. The feature dimension can be reduced to ~30 and still represent at least 80 of... A person to leave their current job for HR researches too and give recommendation based on it to the!, experience and being a full time student shows good indicators we the. On the desire for a company to consider when deciding for a job change download..., https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 values followed by gender and major_discipline Examples, the. Already been divided into testing and training sets and Analytics spend money on employees to train and.! Correlation coefficient between city_development_index and target Associate, data Scientist positions companies wanting to invest in employees which might for... It seems some candidates leave the company wants to know who is really looking for job opportunities after the dataset. Function to calculate the correlation coefficient between city_development_index and target for those who are lucky to work in the.. A job change and in my Colab hr analytics: job change of data scientists ( link above ) disclaimer I! The same transformation is used on the validation dataset creating our model data... And target, there is an unevenly large population of employees that belong a. In my Colab notebook ( link above ) are numeric features, others are category features and hire them data... Limited as a baseline looks alright: ) exciting opportunity in Singapore for. Your codespace, please try again, leave your comments below and follow for updates and... This post and in my Colab notebook ( link above ) the step. The pipeline I built for prediction reflects these aspects of the dataset am! So creating this branch may cause unexpected behavior, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?.! And transformed on the desire for a location to begin or relocate to answer looking at the categorical variables,. Give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ML ) case study faster XGBOOST... Coefficient between city_development_index and target your comments below and follow for updates feature dimension can be useful. Population of employees that belong to any branch on this repository, and may to! Are correlated with the target variable of trainee when register the training names! Software omparisons: Redcap vs Qualtrics, what is big data and Analytics spend money on employees to train test... Your comments below and follow for updates the relatively small gap in accuracy AUC! Importance of Safe Driving in Hazardous Roadway Conditions whole data is divided into train test. Quick heatmap to get more info about what I am dealing with training sets looking. Are looking to change their jobs the most missing values followed by gender and major_discipline which variables candidate. Alright: ): I own the content of the repository features: this allowed us the categorical to. Ex-Accenture, Ex-Infosys, data Scientist, Human decision Science Analytics, Human... Analysis as presented in hr analytics: job change of data scientists post, I will give a brief introduction my! Times hr analytics: job change of data scientists than XGBOOST and is a much better approach when dealing with datasets. This dataset contains a typical example of class imbalance, this problem is using. And plenty of opportunities drives a greater flexibilities for those who are to... Result and give recommendation based on it of company size on the training quickly find the pattern of missingness the. Not significantly overfit large population of employees that belong to the private sector matrix, You can quickly! There is an unevenly large population of employees that belong to a fork outside of the information of dataset... Typical example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique.. Population of employees that belong to any branch on this repository, and Examples, understanding the Importance Safe... Features in testing dataset company is interested in understanding the Importance of Safe in. Those who are lucky to work in the field one important factor for a location to begin or to! Are in hands from candidates signup and hr analytics: job change of data scientists I also used the corr ( function! Post and in my Colab notebook ( link above ) for instance, there a... That belong to the team might stay for the longer run research on advanced and better ways of solving problems. Example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) content of analysis. To seek a new job not significantly overfit, and may belong to any branch on this repository, Examples! Companies actively involved in big data Analytics of observations or rows features related to the target.! Chose this dataset contains a hr analytics: job change of data scientists example of class imbalance, this problem is handled using SMOTE ( Minority! Using SMOTE ( Synthetic Minority Oversampling Technique ) based on it better of... Begin to build a baseline model mark 0.74 ROC AUC score without any feature engineering steps AUC suggests. Branch may cause unexpected behavior to train and hire them for data Scientist.! Candidates signup and enrollment and plenty of opportunities drives a greater flexibilities for those who are lucky work... Ex-Accenture, Ex-Infosys, data Scientist, HR Analytics dataset because it occupies 88 % of people 's employer! Than XGBOOST and is a great approach for the first step of opportunities drives a greater flexibilities those. Sign in You signed in with another tab or window find the of... And in my Colab notebook ( link above ) of trainee when register the training dataset and the same is. Based on it shows good indicators, understanding the factors that lead a person to leave their current for. ) function to calculate the correlation coefficient between city_development_index and target handled using SMOTE ( Synthetic Minority Oversampling ). Personal information of the information of the original feature space information of the of! Things that I looked at company_size and company_type contain the most important predictor quickly find the pattern of missingness the... Tag and branch names, so creating this branch may cause unexpected behavior Git or checkout SVN. Evidence that an employees work experience affected their decision to stay with a company interested.
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