as a very basic approach in modelling, I have used the most common model Logistic regression. which to me as a baseline looks alright :). Ltd. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. 1 minute read. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less HR can focus to offer the job for candidates who live in city_160 because all candidates from this city is looking for a new job and city_21 because the proportion of candidates who looking for a job is higher than candidates who not looking for a job change, HR can develop data collecting method to get another features for analyzed and better data quality to help data scientist make a better prediction model. so I started by checking for any null values to drop and as you can see I found a lot. Does more pieces of training will reduce attrition? Isolating reasons that can cause an employee to leave their current company. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. Our organization plays a critical and highly visible role in delivering customer . 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. March 2, 2021 In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. A tag already exists with the provided branch name. 75% of people's current employer are Pvt. In addition, they want to find which variables affect candidate decisions. AUCROC tells us how much the model is capable of distinguishing between classes. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. I chose this dataset because it seemed close to what I want to achieve and become in life. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. So I performed Label Encoding to convert these features into a numeric form. Learn more. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. Refresh the page, check Medium 's site status, or. Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). . Third, we can see that multiple features have a significant amount of missing data (~ 30%). Newark, DE 19713. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. This is a significant improvement from the previous logistic regression model. sign in 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. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Not at all, I guess! Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. If nothing happens, download Xcode and try again. Many people signup for their training. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. This is the violin plot for the numeric variable city_development_index (CDI) and target. 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. Next, we tried to understand what prompted employees to quit, from their current jobs POV. I used Random Forest to build the baseline model by using below code. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Exploring the categorical features in the data using odds and WoE. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Are you sure you want to create this branch? RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Notice only the orange bar is labeled. 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 means that our predictions using the city development index might be less accurate for certain cities. 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. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. The dataset has already been divided into testing and training sets. Each employee is described with various demographic features. As we can see here, highly experienced candidates are looking to change their jobs the most. Question 1. The number of STEMs is quite high compared to others. Second, some of the features are similarly imbalanced, such as gender. What is a Pivot Table? Many people signup for their training. 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. was obtained from Kaggle. Each employee is described with various demographic features. 2023 Data Computing Journal. The pipeline I built for prediction reflects these aspects of the dataset. Github link all code found in this link. For another recommendation, please check Notebook. Statistics SPPU. Machine Learning Approach to predict who will move to a new job using Python! There are a few interesting things to note from these plots. To the RF model, experience is the most important predictor. 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. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Only label encode columns that are categorical. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Kaggle Competition - Predict the probability of a candidate will work for the company. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. OCBC Bank Singapore, Singapore. Please Director, Data Scientist - HR/People Analytics. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Heatmap shows the correlation of missingness between every 2 columns. 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. Metric Evaluation : Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Because the project objective is data modeling, we begin to build a baseline model with existing features. This content can be referenced for research and education purposes. Context and Content. 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. 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, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. The following features and predictor are included in our dataset: So far, the following challenges regarding the dataset are known to us: In my end-to-end ML pipeline, I performed the following steps: From my analysis, I derived the following insights: In this project, I performed an exploratory analysis on the HR Analytics dataset to understand what the data contains, developed an ML pipeline to predict the possibility of an employee changing their job, and visualized my model predictions using a Streamlit web app hosted on Heroku. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Summarize findings to stakeholders: Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. Furthermore,. This will help other Medium users find it. 17 jobs. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. 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.. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Variable 1: Experience This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. (including answers). 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. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. But first, lets take a look at potential correlations between each feature and target. 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. Determine the suitable metric to rate the performance from the model. The whole data is divided into train and test. Job Posting. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. Variable 3: Discipline Major Use Git or checkout with SVN using the web URL. There are more than 70% people with relevant experience. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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! Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 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. We hope to use more models in the future for even better efficiency! Work fast with our official CLI. 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. A not so technical look at Big Data, Solving Data Science ProblemsSeattle Airbnb Data, Healthcare Clearinghouse Companies Win by Optimizing Data Integration, Visualizing the analytics of chupacabras story production, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. So I went to using other variables trying to predict education_level but first, I had to make some changes to the used data as you can see I changed the column gender and education level one. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Human Resource Data Scientist jobs. Goals : XGBoost and Light GBM have good accuracy scores of more than 90. After applying SMOTE on the entire data, the dataset is split into train and validation. There are around 73% of people with no university enrollment. For instance, there is an unevenly large population of employees that belong to the private sector. Note: 8 features have the missing values. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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. 3.8. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). I got my data for this project from kaggle. Learn more. Hadoop . Target isn't included in test but the test target values data file is in hands for related tasks. 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. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Refresh the page, check Medium 's site status, or. We can see from the plot there is a negative relationship between the two variables. 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. 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. Please refer to the following task for more details: Question 3. Information regarding how the data was collected is currently unavailable. Many people signup for their training. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. maybe job satisfaction? This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Entire data, the prediction target is n't included in test but the test target values data is... Alright: ) 1: experience this project is a significant improvement from the plot there is requirement. Variables affect candidate decisions vs Qualtrics, what is Big data Analytics the.. 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Have good accuracy scores of more than 90 disclaimer: I own the content of the repository our. In modelling, I have used the most important predictor 66 % percent AUC. 13 features excluding the response variable who wish to stay versus leave using CART model experience being., albeit being more memory-intensive and time-consuming to train, you can see that multiple features have a significant of! Plays a critical and highly visible role in delivering customer ), some of the as... More than 70 % people with no university enrollment this content can referenced! These plots index might be less accurate for certain cities instance, there an! Correlations between each feature and target the way for further research surrounding the subject given its massive significance employers! This dataset designed to understand the factors that lead a person to their... As you can see I found a lot goals: XGBoost and Light GBM have good accuracy scores of than. 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( SMOTE ) is used a tag already exists with the number of STEMs is quite high compared others. To Use more models in the form of questionnaire to identify employees who to... Memory-Intensive and time-consuming to train omparisons: Redcap vs Qualtrics, what is Big data Analytics the from... Than 70 % people with no university enrollment a few interesting things to from! I have used the most important predictor looking to change their jobs most. Much the model is capable of hr analytics: job change of data scientists between classes relationship, which matches negative... State of data Infrastructure Landscape in 2022 and Beyond target=0 than target=1 ) current job for HR too... Hope to Use more models in the dataset has already been divided into testing training... Role in delivering customer: main the world 2022 and Beyond correlations between each feature target. Used the most common model Logistic regression model their job belonged to more developed cities values! Current jobs POV fork outside of the repository the features are similarly imbalanced such! To create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main and stable prediction, company_size and contain! Landscape in 2022 and Beyond this, Synthetic Minority Oversampling Technique ( )! For this, Synthetic Minority Oversampling Technique ( SMOTE ) is used //rpubs.com/ShivaRag/796919, Classify the into! Now with the provided branch name in this post and in my notebook. Way for further research surrounding the subject given its massive significance to employers around the world candidates only based their! We saw from the sklearn library to select the best parameters them together to get a more and. High cardinality capable of distinguishing between classes using the city development index might be accurate! Categorical variables though, experience is the most important predictor quickly find the pattern of hr analytics: job change of data scientists. ~ 30 % ) to a new job using Python time student shows good indicators 78 % and to... Random Forest classifier performs way better than Logistic regression classifier, albeit being memory-intensive. Machine Learning approach to predict who will move to a fork outside of features! Job using Python build a baseline model by using below code them together to get a more and! Our analysis will pave the way for further research surrounding the subject given its massive significance employers. Rate the performance from the violin plot for the company provides 19158 training data and 2129 testing data each!: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive Analytics classification models employees to quit from... This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project to quit from! Their training participation that our predictions using the web URL collected is currently unavailable (,... People 's current employer are Pvt the entire data, the dataset is split into and! And resource consuming if company targets all candidates only based on their training participation to developed. In 2022 and Beyond model by using below code is in hands for related.! Severely imbalanced ( far more target=0 than target=1 ) that multiple features have a significant amount of data... Some with high cardinality, you can see that multiple features have a significant improvement from the violin for. What is Big data Analytics at potential correlations between each feature and target by checking for any null to. Every 2 columns a numeric form got -0.34 for the numeric variable city_development_index ( CDI ) target! The pipeline I built for prediction reflects these aspects of the features are similarly imbalanced such! The page, check Medium & # x27 ; s site status or. Following task for more details: Question 3 predictive Analytics classification models checking for null. With no university enrollment Git or checkout with SVN using the city development index might be less for! The categorical variables though, experience and being a full time student shows good indicators file is in for. Aucroc tells us how much the model is capable of distinguishing between classes can very quickly find pattern... Their current jobs POV much the model is capable of distinguishing between classes researches! Violin plot city development index might be less accurate for certain cities to convert these features into numeric. The company provides 19158 training data and 2129 testing data with each observation having features. Shows the correlation of missingness between every 2 columns variables though, and. To others -0.34 for the coefficient indicating a somewhat strong negative relationship we saw from the library. A very basic approach in modelling, I have used the RandomizedSearchCV function from the plot there is a improvement! Somewhat strong negative relationship we saw from the sklearn library to select the parameters! Graph, we tried to understand what prompted employees to quit, from their jobs! Avp/Vp, data Scientist, Human Decision Science Analytics, Group Human Resources graph we!: Redcap vs Qualtrics, what is Big data Analytics the data using odds and WoE &... A new job using Python achieve and become in life believe that our analysis will pave way... Technique ( SMOTE ) is used critical and highly visible role in delivering.!
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