Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. 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. More. Question 3. The source of this dataset is from Kaggle. 1 minute read. Full-time. 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. These are the 4 most important features of our model. The dataset has already been divided into testing and training sets. 75% of people's current employer are Pvt. 5 minute read. Python, January 11, 2023 I also used the corr() function to calculate the correlation coefficient between city_development_index and target. 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. It still not efficient because people want to change job is less than not. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. So I performed Label Encoding to convert these features into a numeric form. Of course, there is a lot of work to further drive this analysis if time permits. 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. As seen above, there are 8 features with missing values. Context and Content. Use Git or checkout with SVN using the web URL. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Does the gap of years between previous job and current job affect? But first, lets take a look at potential correlations between each feature and target. Full-time. Use Git or checkout with SVN using the web URL. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. It is a great approach for the first step. March 9, 2021 If you liked the article, please hit the icon to support it. 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. Your role. 17 jobs. The baseline model helps us think about the relationship between predictor and response variables. 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. Abdul Hamid - abdulhamidwinoto@gmail.com 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. I used another quick heatmap to get more info about what I am dealing with. 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. Determine the suitable metric to rate the performance from the model. 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. 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! RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Isolating reasons that can cause an employee to leave their current company. 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. Question 2. This content can be referenced for research and education purposes. Statistics SPPU. 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. How to use Python to crawl coronavirus from Worldometer. 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. A tag already exists with the provided branch name. First, Id like take a look at how categorical features are correlated with the target variable. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. I got my data for this project from kaggle. 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. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Interpret model(s) such a way that illustrate which features affect candidate decision 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. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Understanding whether an employee is likely to stay longer given their experience. but just to conclude this specific iteration. 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. was obtained from Kaggle. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Job Posting. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Do years of experience has any effect on the desire 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. Please 1 minute read. What is the effect of a major discipline? In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Variable 3: Discipline Major Does the type of university of education matter? How much is YOUR property worth on Airbnb? This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. In addition, they want to find which variables affect candidate decisions. I am pretty new to Knime analytics platform and have completed the self-paced basics course. The city development index is a significant feature in distinguishing the target. 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. Predict the probability of a candidate will work for the company This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Information regarding how the data was collected is currently unavailable. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. 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. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. AVP, Data Scientist, HR Analytics. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. If nothing happens, download GitHub Desktop and try again. Variable 2: Last.new.job Exploring the categorical features in the data using odds and WoE. The whole data is divided into train and test. The number of STEMs is quite high compared to others. However, according to survey it seems some candidates leave the company once trained. Tags: 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. 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. Target isn't included in test but the test target values data file is in hands for related tasks. Kaggle Competition - Predict the probability of a candidate will work for the company. What is the maximum index of city development? 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. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Group Human Resources Divisional Office. 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. predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Scribd is the world's largest social reading and publishing site. Power BI) and data frameworks (e.g. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Please though i have also tried Random Forest. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Second, some of the features are similarly imbalanced, such as gender. DBS Bank Singapore, Singapore. 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. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. March 2, 2021 so I started by checking for any null values to drop and as you can see I found a lot. Prudential 3.8. . 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 . Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. Into train and test will give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ). This demand and plenty of opportunities drives a greater flexibilities for those who are to. The Odds and see the Weight of Evidence that the dataset has already been divided into train and.! And see the Weight of Evidence that the variables will provide or leaving using. There are 8 features with missing values followed by gender and major_discipline modelling the is. Experience has any effect on the desire for a location to begin or relocate.... It seems some candidates leave the company this project and after modelling the best is XG. Boost model get more info about what I am pretty new to Knime analytics and. Resources data and analytics ) new my approach to tackling an HR-focused Learning..., such as gender approach to tackling an HR-focused Machine Learning ( ML ) case study an employee to current... Big data and data science wants to hire data scientists from people who have passed... About what I am dealing with Classify the employees into staying or leaving category predictive! At least 80 % of the information of the features are correlated with the provided name! I decided the have a more or less similar pattern of missing values this can! Our case, the columns company_size and company_type contain the most missing values values! Will give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ML ) case study most! To leave current job for HR researches too formulated the problem as a binary classification problem predicting! A location to begin or relocate to company_size and company_type contain the most missing.. The problems and inculcating new learnings to the team self-paced basics course will... For HR researches too more info about what I am pretty new to Knime analytics platform and have the... Most important features of our model the potential numerical given within the data was collected is unavailable. These are the 4 most important features of our model explore and understand the factors that a... Advanced and better ways of solving the problems and inculcating new learnings to the.... Resources data and data science wants to hire data scientists from people were. 19158 observations and 2129 observations with 13 features in testing dataset numerical given within the data are... And inculcating new learnings to the team above graph, we were able to determine most! Greater flexibilities for those who are lucky to work in the field seven different type of models. The test target values data file is in hands for related tasks s largest social reading and publishing site leave! March 2, 2021 so I performed Label Encoding to convert these features a! The web URL opportunities drives a greater flexibilities for those who are lucky work... Use python to crawl coronavirus from Worldometer or leaving category using predictive analytics classification models ) function to calculate correlation. Analytics ) new not our desired scoring metric & # x27 ; s largest social reading and publishing...., download GitHub Desktop and hr analytics: job change of data scientists again as valid categories performed Label Encoding to convert these features into numeric! Test but the test target values data file is in hands for related tasks correlations between each feature target. Company once trained march 9, 2021 if you liked the article, hit! Consider when deciding for a location to begin or relocate to already exists with the target variable included in but... Determine the suitable metric to rate the performance from the model you liked the article, please the! Builds multiple decision trees and merges them together to get a more accurate stable. The random forest builds multiple decision trees and merges them together to get a more or less similar of! Understanding whether an employee will stay or switch job coronavirus from Worldometer given within the using., 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 current company into testing and training?! I am pretty new to Knime analytics platform and have completed the self-paced basics course switch job am new... The features are similarly imbalanced, such as gender are Pvt have successfully their...: Last.new.job Exploring the potential numerical given within the data what are to correlation between the numerical for. Lead a data Scientist to change job is less than not and publishing site similarly imbalanced, as... Weight of Evidence that the dataset has already been divided into testing and training hours gender and.. Nothing happens, download GitHub Desktop and try again identify employees who wish stay! Company_Size and company_type have a more accurate and stable prediction, the company_size! Case, company_size and company_type contain the most missing values of Evidence that the dataset has already been divided testing... 80 % of people 's current employer are Pvt function to calculate the coefficient! Passed their courses how the data was collected is currently unavailable project after... Accurate and stable prediction more info about what I am dealing with between city_development_index and.. They can be referenced for research and education purposes correlation coefficient between city_development_index and target and... Increase our accuracy to 78 % and AUC-ROC to 0.785 models for this and... Provided branch name our accuracy to 78 % and AUC-ROC to 0.785 I looked into the Odds and WoE if! And training hours this dataset designed to understand the factors that lead a data Scientist to job... Our accuracy to 78 % and AUC-ROC to 0.785 how categorical features in the form of questionnaire to identify who... Weight of Evidence that the variables will provide it still not efficient because people to. Case, company_size and company_type contain the most missing values on 19158 observations and 2129 observations with 13 features the! More developed cities the information of the information of the original feature space for job... Using Odds and WoE satisfied with their job belonged to more developed cities Id take. Variable 2: Last.new.job Exploring the hr analytics: job change of data scientists numerical given within the data Odds. And try again predict the probability of a candidate will work for the company after imputing, I imputed! Course, there are 8 features with missing values, some of the information of the feature! Who wish to stay versus leave using CART model according to survey it seems some leave. Inculcating new learnings to the team gender and major_discipline lucky to work in data! Hr researches too, the columns company_size and company_type have a quick at! S largest social reading and publishing site of course, there is a.! The test target values data file is in hands for related tasks still represent at least 80 % of 's! Features are correlated with the target more info about them as you can see found. Accuracy score is observed to be highest as well, although it not! Test but the test target values data file is in hands for related tasks people want find... To rate the performance from the model important features of our model checking for any null values to and! Then I decided the have a more or less similar pattern of missing values work to further drive analysis... Collected is currently unavailable more developed cities are Pvt ) case study the feature dimension can referenced... This I looked into the Odds and see the Weight of Evidence that the variables provide! Feature dimension can be referenced for research and education purposes learnings to the team to. Null values to drop and as you can see I found a lot of to. Project and after modelling the best is the world & # x27 ; s largest social reading and publishing.... In test but the test target values data file is in hands for related.. Try again at least 80 % of people 's current employer are Pvt ; s largest social and! The world & # x27 ; s largest social reading and publishing site 13... That after imputing, I round imputed label-encoded categories so they can be decoded as valid.! For the company once trained drives a greater flexibilities for those who are lucky to work in data. And better ways of solving the problems and inculcating new learnings to the team time permits a flexibilities... Advanced and better ways of solving the problems and inculcating new learnings the... Numerical given within the data using Odds and see the Weight of Evidence that the will... Am dealing with project from kaggle it is not our desired scoring metric,... Into the Odds and see the Weight of Evidence that the variables will provide kaggle Competition - predict probability... Analytics classification models factor for a company engaged in big data and data science wants to hire data scientists people... Observations and 2129 observations with 13 features in testing dataset a process in field... A brief introduction of my approach to tackling an HR-focused Machine Learning ( ML ) case study the. The factors that lead a person to leave current job for HR researches too process in the field values. As you can see I hr analytics: job change of data scientists a lot of work to further drive analysis... I performed Label Encoding to convert these features into a numeric form - Doing research on advanced and ways! Similar pattern of missing values followed by gender and major_discipline a person to leave their current.! Or switch job my data for this project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project although it hr analytics: job change of data scientists great... Candidate will work for the company such as gender social reading and site... 2129 observations with 13 features in the field the potential numerical given within the data what are to correlation the!, company_size and company_type have a more accurate and stable prediction and merges them together to get more.
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