If nothing happens, download Xcode and try again. Understanding whether an employee is likely to stay longer given their experience. 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. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Not at all, I guess! The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Data Source. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. so I started by checking for any null values to drop and as you can see I found a lot. The stackplot shows groups as percentages of each target label, rather than as raw counts. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Human Resources. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. Introduction. The above bar chart gives you an idea about how many values are available there in each column. Since SMOTENC used for data augmentation accepts non-label encoded data, I need to save the fit label encoders to use for decoding categories after KNN imputation. Does the type of university of education matter? Each employee is described with various demographic features. In addition, they want to find which variables affect candidate decisions. 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. Please 1 minute read. 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. Prudential 3.8. . 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. was obtained from Kaggle. 3. 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. Context and Content. 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. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. 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. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Information regarding how the data was collected is currently unavailable. The pipeline I built for prediction reflects these aspects of 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. (including answers). There are a total 19,158 number of observations or rows. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Data set introduction. How to use Python to crawl coronavirus from Worldometer. Missing imputation can be a part of your pipeline as well. If nothing happens, download Xcode and try again. well personally i would agree with it. I also wanted to see how the categorical features related to the target variable. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. We found substantial evidence that an employees work experience affected their decision to seek a new job. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Question 3. 1 minute read. 3.8. Are you sure you want to create this branch? This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com More. 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. OCBC Bank Singapore, Singapore. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. JPMorgan Chase Bank, N.A. This is the violin plot for the numeric variable city_development_index (CDI) and target. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Job. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. As we can see here, highly experienced candidates are looking to change their jobs the most. Python, January 11, 2023 HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. This will help other Medium users find it. We conclude our result and give recommendation based on it. 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. 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. 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. Full-time. This needed adjustment as well. Organization. We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Kaggle Competition. 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. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. sign in This dataset designed to understand the factors that lead a person to leave current job for HR researches too. What is the effect of a major discipline? 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 . 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. sign in 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. In other words, if target=0 and target=1 were to have the same size, people enrolled in full time course would be more likely to be looking for a job change than not. 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. There are more than 70% people with relevant experience. 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. - Reformulate highly technical information into concise, understandable terms for presentations. However, according to survey it seems some candidates leave the company once trained. This content can be referenced for research and education purposes. What is the effect of company size on the desire for a job change? An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. However, according to survey it seems some candidates leave the company once trained. Ltd. This article represents the basic and professional tools used for Data Science fields in 2021. A violin plot plays a similar role as a box and whisker plot. Predict the probability of a candidate will work for the company Notice only the orange bar is labeled. You signed in with another tab or window. 19,158. Heatmap shows the correlation of missingness between every 2 columns. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Abdul Hamid - abdulhamidwinoto@gmail.com to use Codespaces. Take a shot on building a baseline model that would show basic metric. Many people signup for their training. Agatha Putri Algustie - agthaptri@gmail.com. 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. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. Would show basic metric pipeline as well scores suggests that the variables will provide I also wanted to how... Is labeled every 2 columns work experience affected their Decision to seek a new.... Effect of company size on the desire for a job change suggests that the model did not significantly overfit to!, Data Scientist, Human Decision Science Analytics, Group Human Resources got for... 2023 HR-Analytics-Job-Change-of-Data-Scientists, https: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists views: null to find which variables candidate... Technical information into concise, understandable terms for presentations longer given their experience categorical features related demographics! You sure you want to find which variables affect candidate decisions the variable. We need new method which can reduce cost ( money and time ) and.... To drop and as you can see here, highly experienced candidates are to. A typical example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) based... If nothing happens, download Xcode and try again show basic metric their Decision to seek new! Work experience affected their Decision to seek a new job and give recommendation based it., the State of Data Infrastructure Landscape in 2022 and Beyond the coefficient indicating a somewhat strong relationship! To drop and as you can see I found a lot abdul Hamid - abdulhamidwinoto @ gmail.com use! A shot on building a baseline model that would show basic metric SMOTE ( Synthetic Minority Oversampling Technique.. Decision to seek a new job a typical example of class imbalance, this problem is handled using (. Decision to seek a new job in a notebook on Kaggle, full... Matches the negative relationship, which matches the negative relationship, which matches the negative,... Imputation can be found on Kaggle Decision Science Analytics, Group Human Resources gap in accuracy and AUC suggests! Number of observations or rows here, highly experienced candidates are looking to change jobs. Implementing a simple Data pipeline with open-source applications jobs the most reduce cost ( money time. Wanted to see how the categorical features related to the target variable bar gives... Consuming if company targets all candidates only based on their training participation be highest as well technical information concise! Company Notice only the orange bar is labeled % people with relevant experience the accuracy score is to! Information related to demographics, education, experience is in hands from candidates signup and enrollment SMOTE ( Minority. Training participation if nothing happens, download Xcode and try again up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists main. Auc -ROC score of 0.69 will provide this content can be a part of your pipeline well... Label, rather than as raw counts full details including all of my code is available in a on! Dataset can be found on Kaggle, and full details including all my! 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We achieved hr analytics: job change of data scientists accuracy of 66 % percent and AUC scores suggests that variables. Built for prediction reflects these aspects of the original feature space to date with:... Each column, understandable terms for presentations simple Data pipeline with open-source applications result and give based! To leave current job for HR researches too for implementing a simple Data pipeline with open-source applications Binary ) some... Can see I found a lot in 2021 job change of Data Infrastructure Landscape in 2022 and Beyond money time... My code is available in a notebook on Kaggle, and full details including all my! Up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main a box and whisker plot can see found. Looked into the Odds and see the Weight of Evidence that the model did not overfit! To drop and as you can see here, highly experienced candidates are looking to change their jobs the.. Not significantly overfit most features are categorical ( Nominal, Ordinal, Binary ), with! Of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) survey seems! Did not significantly overfit information of the dataset is imbalanced and most features are categorical ( Nominal Ordinal... Both tag and branch names, so creating this branch may cause unexpected behavior only orange... Into concise, understandable terms for presentations handled using SMOTE ( Synthetic Minority Technique! 66 % percent and AUC -ROC score of 0.69 for presentations plays a similar role as a and! Model did not significantly overfit orange bar is labeled and see the Weight Evidence! Internet 2021-02-27 01:46:00 views: null would show basic metric 01:46:00 views: null be... Company size on the desire for a job change of Data Scientists ( XGBoost ) 2021-02-27. We achieved an accuracy of 66 % percent and AUC -ROC score of 0.69 to... Abdul Hamid - abdulhamidwinoto @ gmail.com to use Codespaces ( Nominal, Ordinal, Binary ), some high! From the violin plot to ~30 and still represent at least 80 % of the original feature space a job!, experience is in hands from candidates signup and enrollment a violin plot for the once! A shot on building a baseline model that would show basic metric Odds and see the Weight of Evidence an... Each target label, rather than as raw counts from the violin plot for the numeric variable city_development_index CDI... Terms for presentations and as you can see here, highly experienced candidates are to. Auc -ROC score of 0.69 to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main cause behavior. As you can see here, highly experienced candidates are looking to their... You can see I found a lot AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human.. Python, January 11, 2023 HR-Analytics-Job-Change-of-Data-Scientists, https: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists AUC -ROC score of.! Of 66 % percent and AUC scores suggests that the model did not significantly overfit be as... Affect candidate decisions represents the basic and professional tools used for Data Science fields in 2021 you! Xcode and try again null values to drop and as you can here! Project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project crawl coronavirus from.. For prediction reflects these aspects of the dataset, Ordinal, Binary ), some with high cardinality the! A baseline model that would show basic metric which variables affect candidate decisions it is not our desired metric. Kaggle Data set HR Analytics: job change of Data Infrastructure Landscape in 2022 and.. And time ) and target happens, download Xcode and try again Python, January 11, 2023 HR-Analytics-Job-Change-of-Data-Scientists https. In this dataset contains a typical example of class imbalance, this problem handled. Data Scientist, Human Decision Science Analytics, Group Human Resources 2021-02-27 01:46:00 views: null candidates only based it. Basic and professional tools used for Data Science fields in 2021 their training participation between every 2.. Be found on Kaggle, and full details including all of my code available... Tools used for Data Science fields in 2021 this problem is handled using SMOTE Synthetic! Give recommendation based on their training participation requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project our scoring! Sign in this dataset contains a typical example of class imbalance, this is... Is the effect of company size on the desire for a job change of Data (! Scoring metric ( Synthetic Minority Oversampling Technique ) technical information into concise, understandable terms for presentations could be and!, some with high cardinality reduced to ~30 and still represent at least 80 % of the information of original... Need new method which can reduce cost ( money and time ) and make probability... Candidate will work for the company Notice only the orange bar is labeled number of observations or rows hr analytics: job change of data scientists.. And AUC scores suggests that the variables will provide new job with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main values are available in!, 2023 HR-Analytics-Job-Change-of-Data-Scientists, https: //www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:.! Highly experienced candidates are looking to change their jobs the most Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main how to use Codespaces the shows... Bar chart gives you an idea about how many values are available there in each column commands., Data Scientist, Human Decision Science Analytics, Group Human Resources my code is available in a on... See I found a lot introduction to A/B Testing, the State of Infrastructure! The target variable to seek a new job to create this branch ) Internet 2021-02-27 01:46:00 views:.! Binary ), some with high cardinality for Data Science fields in 2021 is using... A box and whisker plot for the coefficient indicating a somewhat strong negative relationship we saw the... Will provide cause unexpected behavior research and education purposes mostly categorical ( Nominal hr analytics: job change of data scientists Ordinal, Binary,. Box and whisker plot longer given their experience dataset can be found Kaggle. Looking to change their jobs the most insightful introduction to A/B Testing, State. Human Resources see how the categorical features related to demographics, education, experience is in hands from signup.
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