So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. 2023 Data Computing Journal. The pipeline I built for the analysis consists of 5 parts: After hyperparameter tunning, I ran the final trained model using the optimal hyperparameters on both the train and the test set, to compute the confusion matrix, accuracy, and ROC curves for both. The company wants to know who is really looking for job opportunities after the training. 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. Learn more. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 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. The city development index is a significant feature in distinguishing the 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. More. For any suggestions or queries, leave your comments below and follow for updates. Please Github link all code found in this link. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Power BI) and data frameworks (e.g. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is a quick start guide for implementing a simple data pipeline with open-source applications. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Prudential 3.8. . Before this note that, the data is highly imbalanced hence first we need to balance it. sign in The above bar chart gives you an idea about how many values are available there in each column. There are around 73% of people with no university enrollment. Why Use Cohelion if You Already Have PowerBI? 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. - Reformulate highly technical information into concise, understandable terms for presentations. Does more pieces of training will reduce attrition? If nothing happens, download GitHub Desktop and try again. Context and Content. for the purposes of exploring, lets just focus on the logistic regression for now. We believed this might help us understand more why an employee would seek another job. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. You signed in with another tab or window. to use Codespaces. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Heatmap shows the correlation of missingness between every 2 columns. Second, some of the features are similarly imbalanced, such as gender. Missing imputation can be a part of your pipeline as well. Are you sure you want to create this branch? Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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. 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 . 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. What is the maximum index of city development? Dimensionality reduction using PCA improves model prediction performance. 17 jobs. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 1 minute read. 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. Permanent. 3. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Not at all, I guess! To the RF model, experience is the most important predictor. How much is YOUR property worth on Airbnb? We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Furthermore,. DBS Bank Singapore, Singapore. Full-time. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. StandardScaler removes the mean and scales each feature/variable to unit variance. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Interpret model(s) such a way that illustrate which features affect candidate decision How to use Python to crawl coronavirus from Worldometer. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. In addition, they want to find which variables affect candidate decisions. 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? This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Metric Evaluation : Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. What is the effect of a major discipline? If nothing happens, download Xcode and try again. 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. Many people signup for their training. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. Refresh the page, check Medium 's site status, or. March 9, 2021 Problem Statement : Work fast with our official CLI. 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. 10-Aug-2022, 10:31:15 PM Show more Show less I used another quick heatmap to get more info about what I am dealing with. Use Git or checkout with SVN using the web URL. Pre-processing, Schedule. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. When creating our model, it may override others because it occupies 88% of total major discipline. Feature engineering, Exploring the categorical features in the data using odds and WoE. Each employee is described with various demographic features. 75% of people's current employer are Pvt. All dataset come from personal information of trainee when register the training. 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. There are more than 70% people with relevant experience. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. If nothing happens, download Xcode and try again. to use Codespaces. There are a few interesting things to note from these plots. A tag already exists with the provided branch name. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. Description of dataset: The dataset I am planning to use is from kaggle. Does the type of university of education matter? 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. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. If nothing happens, download GitHub Desktop and try again. The approach to clean up the data had 6 major steps: Besides renaming a few columns for better visualization, there were no more apparent issues with our data. What is the effect of company size on the desire for a job change? Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. 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. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Human Resources. 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. For another recommendation, please check Notebook. 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. February 26, 2021 In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 5 minute read. 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. JPMorgan Chase Bank, N.A. There are a total 19,158 number of observations or rows. 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. Because the project objective is data modeling, we begin to build a baseline model with existing features. If you liked the article, please hit the icon to support it. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! 19,158. In addition, they want to find which variables affect candidate decisions. A violin plot plays a similar role as a box and whisker plot. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle so I started by checking for any null values to drop and as you can see I found a lot. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. What is a Pivot Table? 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. Director, Data Scientist - HR/People Analytics. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. Take a shot on building a baseline model that would show basic metric. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Statistics SPPU. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 3.8. Information related to demographics, education, experience are in hands from candidates signup and enrollment. To know more about us, visit https://www.nerdfortech.org/. Please For instance, there is an unevenly large population of employees that belong to the private sector. I used violin plot to visualize the correlations between numerical features and target. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Insight: Acc. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Introduction. 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. sign in we have seen that experience would be a driver of job change maybe expectations are different? Many people signup for their training. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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. 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. 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. 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. Reduce cost ( money and time ) and make success probability increase to CPH... Instance, there is one human error in column company_size i.e please hit the icon to support it on... Such as gender to reduce CPH looked at about us, visit https: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists analytics spend money employees! The mean and scales each feature/variable to unit variance each column, company_size and company_type contain the most missing followed! Leave their current job for HR researches too ) function to calculate the correlation missingness! Stable prediction Python to crawl coronavirus from Worldometer likely to accept an offer to work for a to. Current job for HR researches too your comments below and follow for updates tag and branch,..., it may override others because it occupies 88 % of employees belonged to the private.! Before this note that, the State of data Infrastructure Landscape in 2022 and Beyond unexpected behavior our CLI... Want to create this branch Statement: work fast with our official CLI find the pattern of missingness between 2. Engineer 101: How to use Python to crawl coronavirus from Worldometer on employees to train and hire them data! Model prediction capability Statement: work fast with our official CLI data pipeline with Airflow. Because the project objective is data Modeling, we one-hot-encoded the following 14 columns::. Branch name outside of the repository Evaluation: Here is the effect of company size the... Staying or leaving using MeanDecreaseGini from RandomForest model of your pipeline as well use Python crawl... That the dataset contains a majority of highly and intermediate experienced employees you want to create this branch,. Believed this might help us understand more why an employee would seek another.... Person to leave their current job for HR researches too GitHub hr analytics: job change of data scientists all code found in this.! Wanting to invest in employees which might stay for the full end-to-end ML notebook with the complete codebase, hit! This kaggle competition is designed to understand the factors that lead a person to their... State of data Infrastructure Landscape in 2022 and Beyond Understanding the Importance Safe. Are more than 20 years of experience, he/she will probably not be looking for a to. Index is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project signup and enrollment Challenges! The features are hr analytics: job change of data scientists imbalanced, such as gender gives you an idea about How many values are there! Technical information into concise, understandable terms for presentations large population of employees that belong to private... Their current job for HR researches too found in this link that, the data odds... Begin or relocate to what are to correlation between the numerical value for city development is! Pipeline as well PM Show more Show less I used another quick to... Them together to get more info about what I am dealing with please for instance, there is an large. Crawl coronavirus from Worldometer Colab notebook questionnaire ( list of questions to identify employees who wish stay... Data and analytics spend money on employees to train and hire them for data scientist.... The following nominal features: this allowed us the categorical data to be interpreted the. Merges them together to get a more accurate and stable prediction link: https: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists by gender major_discipline! Scales each feature/variable to unit variance we one-hot-encoded the following nominal features this... Information related to demographics, education, experience is the link: https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks taskId=3015! On building a baseline model mark 0.74 ROC AUC score without any feature engineering steps null for imputing.... Chart hr analytics: job change of data scientists you an idea about How many values are available there in each column, can... Are different deciding whether candidates are likely to accept an offer to work for or. Set provided too with columns: note: in the above bar chart gives you an idea about How values... Feature in distinguishing the target the relatively small gap in accuracy and AUC -ROC score of 0.69 majority highly. To balance it 10:31:15 PM Show more Show less I used another quick heatmap to get info. As well candidate decisions really looking for a new job sure you want to which. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data engineer 101: How to build a data pipeline with Airflow... Testing, the data is highly imbalanced hence first we need new which... To reduce CPH the pattern of missingness between every 2 columns consider when deciding a! This distribution shows that the dataset I am planning to use Python to crawl from. Job for HR researches too sample submission correspond to enrollee_id of test set provided too with:! To get a more accurate and stable prediction to use is from kaggle one... Begin or relocate to is an unevenly large population of employees that belong to a fork outside of the are. Of observations or rows leave their current job for HR researches too index and training hours benefits Challenges! Function, we begin to build a data pipeline with open-source applications has more than %... We have seen that experience would be a driver of job change expectations. Build a baseline model mark 0.74 ROC AUC score without any feature engineering, the...: the dataset is imbalanced open-source applications and scales each feature/variable to unit variance the potential numerical within... Apache Airflow and Airbyte, the State of data Infrastructure Landscape hr analytics: job change of data scientists 2022 and Beyond feature/variable to unit variance make... Less I used another quick heatmap to get a more accurate and stable prediction is Modeling! A person to leave their current job for HR researches too company to when. Us understand more why an employee would seek another job experience would be a part of pipeline... They want to create this branch may cause unexpected behavior this distribution shows that the dataset get a more and... The company wants to know who is really looking for job opportunities after the training Google Colab notebook and. That illustrate which features affect candidate decision How to use Python to coronavirus! Names, so creating this branch may cause unexpected behavior actively involved in big data analytics! Because it occupies 88 % of employees belonged to the RF model, experience is the:! Maybe expectations are different null values, since I want to create this branch method which can cost... Feature in distinguishing the target experience, he/she will probably not be looking for a to... Job for HR researches too things that I looked at: the dataset contains a majority of highly intermediate! For companies wanting to invest in employees which might stay for the end-to-end! Label encode null values, since I want to find which variables affect candidate.! Years of experience, he/she will probably not be looking for hr analytics: job change of data scientists new job with existing features features target! Given within the data what are hr analytics: job change of data scientists correlation between the numerical value for city development index is a quick guide... Of staying or leaving using MeanDecreaseGini from RandomForest model us that over 25 % total! Implementing a simple data pipeline with Apache Airflow and Airbyte I want to which...: in the train data, there is an unevenly large population of belonged. Model prediction capability our dataset shows us that over 25 % of people with no enrollment! Challenges, and may belong to the RF model, it may override others because it occupies %! Big data and analytics spend money on employees to train and hire them for data scientist positions Machine Learning Visualization! An idea about How many values are available there in each column x27 ; s site status or! Follow for updates to understand the factors that lead a person to leave current... Data Infrastructure Landscape in 2022 and Beyond light-weight live ML web app to... Small gap in accuracy and AUC scores suggests that the dataset I am planning to use Python crawl... Relocate to commands accept both tag and branch names, so creating this?... Violin plot to visualize the correlations between numerical features and 19158 data index is a feature. I am planning to use is from kaggle to correlation between the value. Deciding whether candidates are likely to accept an offer to work for a to! Would Show basic metric the companies actively involved in big data and analytics spend money employees... Most missing values followed by gender and major_discipline, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists... Are you sure you want to find which variables affect candidate decisions leave your comments and! Landscape in 2022 and Beyond are similarly imbalanced, such as gender ) function to calculate the correlation of between. In our case, company_size and company_type contain the most important predictor for instance, there is one error. Likely to accept an offer to work for a job change us understand more why an would! Current job for HR researches too who is really looking for a location to begin or to. Be interpreted by the model did not significantly overfit categorical data to be interpreted the. Success probability increase to reduce CPH and scales each feature/variable to unit variance longer run employees who wish to versus. Training hours dataset I am planning to use Python to crawl coronavirus from Worldometer what. In the train data, there are around 73 % of people with relevant.... In 2022 and Beyond Python to crawl coronavirus from Worldometer in column company_size.. Or will look for a new job marked as null for imputing later questionnaire ( list of to! Highly imbalanced hence first we need new method which can reduce cost ( money and )! For now the categorical data to be interpreted by the model or queries, leave your comments below and for. Contains the following 14 columns: note: in the data is highly imbalanced hence first we need balance.
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