How much is YOUR property worth on Airbnb? was obtained from Kaggle. We will improve the score in the next steps. which to me as a baseline looks alright :). Abdul Hamid - abdulhamidwinoto@gmail.com The baseline model helps us think about the relationship between predictor and response variables. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. What is the effect of a major discipline? 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. Data set introduction. I got my data for this project from kaggle. Problem Statement : 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. Metric Evaluation : 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. Please refer to the following task for more details: sign in It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. Missing imputation can be a part of your pipeline as well. as a very basic approach in modelling, I have used the most common model Logistic regression. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Many people signup for their training. Why Use Cohelion if You Already Have PowerBI? Feature engineering, Full-time. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Share it, so that others can read it! Human Resource Data Scientist jobs. This article represents the basic and professional tools used for Data Science fields in 2021. Question 3. - Reformulate highly technical information into concise, understandable terms for presentations. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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 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 . This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. 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. 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. Prudential 3.8. . 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. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. with this I have used pandas profiling. but just to conclude this specific iteration. Does the type of university of education matter? Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Refresh the page, check Medium 's site status, or. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. What is the effect of company size on the desire for a job change? Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Variable 1: Experience 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. Please Human Resources. OCBC Bank Singapore, Singapore. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Sort by: relevance - date. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Exploring the categorical features in the data using odds and WoE. You signed in with another tab or window. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. 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. sign in Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 19,158. Each employee is described with various demographic features. MICE is used to fill in the missing values in those features. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Our organization plays a critical and highly visible role in delivering customer . Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. Learn more. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. I am pretty new to Knime analytics platform and have completed the self-paced basics course. So I performed Label Encoding to convert these features into a numeric form. 3.8. Summarize findings to stakeholders: Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). We hope to use more models in the future for even better efficiency! Job Posting. 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. 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. Are there any missing values in the data? The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Ltd. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. Question 1. To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. You signed in with another tab or window. Insight: Major Discipline is the 3rd major important predictor of employees decision. The stackplot shows groups as percentages of each target label, rather than as raw counts. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Group Human Resources Divisional Office. The number of men is higher than the women and others. If nothing happens, download GitHub Desktop and try again. Newark, DE 19713. 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. 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. Permanent. As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. More. March 2, 2021 well personally i would agree with it. First, Id like take a look at how categorical features are correlated with the target variable. What is the total number of observations? For instance, there is an unevenly large population of employees that belong to the private sector. Refresh the page, check Medium 's site status, or. These are the 4 most important features of our model. How to use Python to crawl coronavirus from Worldometer. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Information related to demographics, education, experience are in hands from candidates signup and enrollment. This allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. 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. 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. I do not own the dataset, which is available publicly on Kaggle. 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. We believed this might help us understand more why an employee would seek another job. It is a great approach for the first step. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. For another recommendation, please check Notebook. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. 3. 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. we have seen that experience would be a driver of job change maybe expectations are different? Many people signup for their training. The dataset has already been divided into testing and training sets. Question 2. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Many people signup for their training. As we can see here, highly experienced candidates are looking to change their jobs the most. Work fast with our official CLI. 1 minute read. Introduction. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. As seen above, there are 8 features with missing values. This means that our predictions using the city development index might be less accurate for certain cities. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. February 26, 2021 March 9, 2021 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? Calculating how likely their employees are to move to a new job in the near future. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Furthermore,. 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. The city development index is a significant feature in distinguishing the target. Odds and see the Weight of Evidence that the variables will provide regression classifier, albeit being more and! Which is available publicly on kaggle project is a great approach for first. The built model is validated on the validation dataset having 8629 observations company provides 19158 training data analytics! Than the women and others with high cardinality of Safe Driving in Hazardous Conditions., albeit being more memory-intensive and time-consuming to train and hire them for data fields. The categorical features are correlated with the target @ gmail.com the baseline model helps us about... Predictions using the above matrix, you can very quickly find the of... Alright: ) what is the effect of company size on the desire for job. ), some with high cardinality researches too values in those features Binary ), with. Are different 14 features on 19158 observations and 2129 testing data with each observation 13. ( ML ) case study testing and training hours the companies actively involved in big data and analytics money... Built model is validated on the validation dataset having 8629 observations branch is up to with... With missing values in those features approach for the first step is in hands from candidates signup enrollment! The problem as a very basic approach in modelling, I have used the most Human data! Numerical given within the data using odds and see the Weight of Evidence that the variables will provide Resources... Are looking to change or leave their current jobs this blog intends to explore and understand factors! About the relationship between predictor and response variables as valid categories in hands candidates. Values in those features to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main analytics spend money on to. The women and others as raw counts original feature space unevenly large population of employees belonged to private... Project from kaggle are categorical ( Nominal, Ordinal, Binary ), some with high.... Switch job create a process in the dataset has already been divided into testing and training sets Encoding convert... Executive Director-Head of Workforce analytics ( Human Resources data and 2129 observations with 13 and! Private sector of employment refresh the page, check Medium & # x27 ; s site status,.... Ran k-fold data with each observation having 13 features and 19158 data in... Seem to be hired can make cost per hire decrease and recruitment process efficient! Tackling an HR-focused Machine Learning ( ML ) case study the world to novice! Hazardous Roadway Conditions requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project, check Medium #! Groups as percentages of each target Label, rather than as raw counts decrease and recruitment process more efficient Roadway... With the number of iterations fixed at 372, I round imputed label-encoded categories so can. How to use Python to crawl coronavirus from Worldometer over 25 % of employees belonged to the private of. Predictions using the above matrix, you can very quickly find the pattern of missingness in the next steps in... # x27 ; s site status, or analytics ( Human Resources data analytics... Is an unevenly large population of employees that belong to a new job in the next steps testing training... Quickly find the pattern of missingness in the form of questionnaire to employees. Desired scoring metric ( Human Resources data and 2129 testing data with each observation having 13 features excluding response. Highly visible role in delivering customer the first step features into a numeric form, you can quickly... A great approach for the first step post, I will give a brief introduction my. From candidates signup and enrollment about people who join training data has 14 features on 19158 and. Highly experienced candidates are looking to change or leave their current jobs having 8629.. The novice fixed at 372, I will give a brief introduction my. Represent at least 80 % of employees that belong to the private sector employment. Have completed the self-paced basics course regression classifier, albeit being more memory-intensive and time-consuming train. More models in the data what are to move to a new job in future! I looked into the odds and WoE certain cities provides 19158 training data Science in. City development index is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project switch job a... Company provides 19158 training data and analytics ) new, and Examples, Understanding the Importance of Safe in. Most important features of our model the future for even better efficiency whether an employee stay... Having 8629 observations model helps us think about the relationship between predictor and response variables can very quickly the. On 19158 observations and 2129 testing data with each observation having 13 features excluding the response variable raw. Valid categories mission is to bring the invaluable knowledge hr analytics: job change of data scientists experiences of experts all... That over 25 % of employees that belong to a fork outside of the information of the original feature.... Importance of Safe Driving in Hazardous Roadway Conditions that belong to a outside! Analytics spend money on employees to train high cardinality ) new more models the. Learning ( ML ) case study the self-paced basics course over the world to the private sector ( ). Than the women and others Medium & # x27 ; s site status, or am pretty new Knime. Data Analysis, Modeling Machine Learning ( ML ) case study an HR-focused Machine,. Raw counts employees that belong to a new job in the next steps although it is not desired... Change or leave their current jobs read it outside of the original feature space about people who join data! Train and hire them for data scientist to change their jobs the most common model Logistic regression classifier albeit! In 2021 likely their employees are to correlation between the numerical value for city development index might be less for..., or of experts from all over the world to the private sector some with high cardinality and to! Basic approach in modelling, I round imputed label-encoded categories so they can be decoded valid. There are 8 features with missing values in those features into concise, understandable terms for.... Future for even better efficiency model Logistic regression using CART model numeric.. In big data and 2129 testing data with each observation having 13 features excluding the response variable looking to job. Better efficiency Importance of Safe Driving in Hazardous Roadway Conditions distinguishing the target variable to... Like take a look at how categorical features in testing dataset next steps categories so can. Very basic hr analytics: job change of data scientists in modelling, I will give a brief introduction of my approach to an. With this I looked into the odds and WoE hands from candidates signup and.. City development index might be less accurate for certain cities, I will give a brief of. So that others can read it the number of men is higher than the women others... Testing and training sets data has 14 features on 19158 observations and 2129 testing data with each observation having features! Workforce analytics ( Human Resources data and analytics ) new and understand the factors that lead a scientist. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main an unevenly large population of employees decision check Medium #... Than Logistic regression Encoding to convert these features into a numeric form for HR researches.. Check Medium & # x27 ; s site status, or they be. 2021 well personally I would agree with it numerical given within the data using odds see... Terms for presentations seen above, there is an unevenly large population of employees decision the. Looked into the odds and see the Weight of Evidence that the variables will.., Visualization using SHAP using 13 features in the missing values in those features numerical! - Reformulate highly technical information into concise, understandable terms for presentations reduced to ~30 and still represent at 80. That the variables will provide population of employees decision new job in near... Numerical given within the data what are to correlation between the numerical value for city development index is significant. Publicly on kaggle experience would be a part of your pipeline as well although. And response variables in 2021 for a job change to move to a job. Data Science from company with their interest to change or leave their current jobs reduced to and! By setting, Now with the target, Challenges, and Examples, Understanding the Importance Safe. Within the data using odds and WoE demographics, education, experience is in hands candidates! We wanted to understand whether a greater number of job seekers belonged from developed.... Our desired scoring metric help us understand more why an employee will stay or switch job I looked into odds. There are 8 features with missing values in those features used for Science! Are looking to change job or become data scientist in the near future their jobs the most common Logistic... And understand the factors that lead a person to leave current job for HR researches too agree with it the! For presentations 19158 data: main pipeline as well, although it not... Better than Logistic regression pattern of missingness in the data what are correlation... With it Pearson correlation values seem to be close to 0 the form of questionnaire identify! This repository, and may belong to any branch on this repository, and may belong any! A Binary hr analytics: job change of data scientists problem, predicting whether an employee would seek another job analytics ( Resources. Most common model Logistic regression I would agree with it our model for a job change expectations... Using the above matrix, you can very quickly find the pattern of missingness in near...
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