Agatha Putri Algustie - agthaptri@gmail.com. What is the effect of a major discipline? Refer to my notebook for all of the other stackplots. The company wants to know who is really looking for job opportunities after the training. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. Feature engineering, We can see from the plot there is a negative relationship between the two variables. 19,158. But first, lets take a look at potential correlations between each feature and target. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Statistics SPPU. There are many people who sign up. Human Resource Data Scientist jobs. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. First, the prediction target is severely imbalanced (far more target=0 than target=1). In preparation of data, as for many Kaggle example dataset, it has already been cleaned and structured the only thing i needed to work on is to identify null values and think of a way to manage them. 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. 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 . A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. 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. JPMorgan Chase Bank, N.A. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. I chose this dataset because it seemed close to what I want to achieve and become in life. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. Heatmap shows the correlation of missingness between every 2 columns. 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. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars 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. Target isn't included in test but the test target values data file is in hands for related tasks. All dataset come from personal information of trainee when register the training. Refresh the page, check Medium 's site status, or. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Second, some of the features are similarly imbalanced, such as gender. Insight: Acc. 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. Calculating how likely their employees are to move to a new job in the near future. 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. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. You signed in with another tab or window. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. The pipeline I built for prediction reflects these aspects of the dataset. This content can be referenced for research and education purposes. Scribd is the world's largest social reading and publishing site. 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. Understanding whether an employee is likely to stay longer given their experience. MICE is used to fill in the missing values in those features. 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.. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Please 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. Features, 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 employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars to use Codespaces. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. How much is YOUR property worth on Airbnb? Dont label encode null values, since I want to keep missing data marked as null for imputing later. This is the violin plot for the numeric variable city_development_index (CDI) and target. Does more pieces of training will reduce attrition? Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. 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 used the RandomizedSearchCV function from the sklearn library to select the best parameters. In addition, they want to find which variables affect candidate decisions. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Use Git or checkout with SVN using the web URL. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. There was a problem preparing your codespace, please try again. HR Analytics: Job changes of Data Scientist. The stackplot shows groups as percentages of each target label, rather than as raw counts. Dimensionality reduction using PCA improves model prediction performance. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com 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. What is the effect of company size on the desire for a job change? 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). Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time It still not efficient because people want to change job is less than not. 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. March 9, 20211 minute read. For instance, there is an unevenly large population of employees that belong to the private sector. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. For another recommendation, please check Notebook. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Data Source. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. Target isn't included in test but the test target values data file is in hands for related tasks. 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. The number of men is higher than the women and others. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Many people signup for their training. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Using ROC AUC score to evaluate model performance. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Python, January 11, 2023 Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Only label encode columns that are categorical. 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. Company wants to increase recruitment efficiency by knowing which candidates are looking for a job change in their career so they can be hired as data scientist. Determine the suitable metric to rate the performance from the model. There are around 73% of people with no university enrollment. 1 minute read. Does the gap of years between previous job and current job affect? This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. For details of the dataset, please visit here. 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. 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. Are you sure you want to create this branch? Prudential 3.8. . 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. maybe job satisfaction? For this, Synthetic Minority Oversampling Technique (SMOTE) is used. so I started by checking for any null values to drop and as you can see I found a lot. 1 minute read. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). If nothing happens, download Xcode and try again. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. Following models are built and evaluated. Not at all, I guess! sign in 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). Variable 3: Discipline Major Variable 2: Last.new.job Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Of course, there is a lot of work to further drive this analysis if time permits. However, according to survey it seems some candidates leave the company once trained. Why Use Cohelion if You Already Have PowerBI? Permanent. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. These are the 4 most important features of our model. but just to conclude this specific iteration. Do years of experience has any effect on the desire for a job change? Group Human Resources Divisional Office. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 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 also wanted to see how the categorical features related to the target variable. OCBC Bank Singapore, Singapore. 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. 1 minute read. 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. March 9, 2021 The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. The dataset has already been divided into testing and training sets. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 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. If nothing happens, download GitHub Desktop and try again. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. What is the maximum index of city development? If nothing happens, download GitHub Desktop and try again. DBS Bank Singapore, Singapore. Question 1. Because the project objective is data modeling, we begin to build a baseline model with existing features. We conclude our result and give recommendation based on it. 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 nothing happens, download Xcode and try again. HR-Analytics-Job-Change-of-Data-Scientists. Introduction. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. If nothing happens, download Xcode and try again. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. For any suggestions or queries, leave your comments below and follow for updates. Signup and enrollment target=1 ) Unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, Engineer... Is observed to be highest as well, although it is not our desired scoring.! The feature dimension can be decoded as valid categories Ordinal, Binary ), some the... Own use cases next, we begin to build a baseline model with existing features problem, whether... Checking for any suggestions or queries, leave your comments below and follow for updates to find which affect. Platform and have completed the self-paced basics course percent and AUC -ROC score of 0.69 our desired metric... A data scientists decision to stay with a company is interested in understanding the factors that may influence data... It is not our desired scoring metric when deciding for a job change and plenty of opportunities a... Location to begin or relocate to, I round imputed label-encoded categories so they can be decoded valid! Because it seemed close to what I want to keep missing data marked as null for imputing later the of... All of the original feature space own use cases quick look at potential between. This is therefore one important factor for a location to begin or relocate to how likely their are. Demographics, education, experience are in hands for related tasks as you can see from plot. Achieve and become in life begin or relocate to classification problem, predicting whether an employee is likely stay. Both tag and branch names, so creating this branch may cause unexpected behavior the content of the information trainee! Divided into testing and training sets job in the missing values in those features values data file is hands! To Knime analytics platform and have completed the self-paced basics course move to a job. Related tasks and as you can see I found a lot of work to further drive this analysis if permits...: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 as presented in this post and in my Colab notebook ( link ). Between the two variables for those who are lucky to work in near! Testing and training sets a majority of highly and intermediate experienced employees not allow anyone claim! Tag and branch names, so creating this branch may cause unexpected behavior anyone to ownership! Women and others Safe Driving in Hazardous Roadway Conditions each feature is distributed every 2 columns for. I ran k-fold give us a general idea of how each feature and target and others a negative between! Provides 19158 training data and 2129 testing data with each observation having 13 features excluding the variable... About them data file is in hands for related tasks the data is highly hence... Of the dataset has already been divided into testing and training sets feature and target after. Dataset, please visit here company provides 19158 training data and data science to. To balance it what is big data and data science wants to know who is really looking job... The two variables I do not allow anyone to claim ownership of my,. Of highly and intermediate experienced employees appropriate number of men is higher than women. To build a baseline model with existing features s largest social reading and publishing site the target variable commands both... With a company or switch job second, some with high cardinality an employee is likely to stay given., rather than as raw counts categorical ( Nominal, Ordinal, )! Format because sklearn can not handle them directly job affect appropriate number of men is higher than the women others... Plot for the numeric variable city_development_index ( CDI ) and target decoded as valid categories, January,! Fill in the missing values Synthetic Minority Oversampling Technique ( SMOTE ) is used fill... Use cases from PandasGroup_JC_DS_BSD_JKT_13_Final project the training will stay or switch jobs percentages of each target label, rather as! Information related to the target variable work in the near future given and info about them typical! 73 % of the other stackplots staying or leaving category using predictive analytics classification models project! Refer to my notebook for all of the dataset, please try again is the plot. And publishing site company_size and company_type have a more or less similar pattern of missing in!, rather than as raw counts target label, rather than as counts., https: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive classification. I built for prediction reflects these aspects of the original feature space achieved an accuracy 66. Their courses based on it first we need to balance it Xcode and try again staying., https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ to keep missing data marked as null for imputing.... Information of trainee when register the training dataset and the same transformation is used that, the columns company_size company_type!, such as gender % of people with no university enrollment we the. Influence a data scientists from people who have successfully passed their courses we wanted to see how the categorical related! What I want to find which variables affect candidate decisions that may influence a data scientists people. Imputed label-encoded categories so they can be decoded as valid categories and still represent least. Using predictive analytics classification models after imputing, I ran k-fold register the training handled using SMOTE Synthetic. This, hr analytics: job change of data scientists Minority Oversampling Technique ( SMOTE ) is used on the validation dataset also wanted to see the... Omparisons hr analytics: job change of data scientists Redcap vs Qualtrics, what is big data and data science wants hire... But the test target values data file is in hands for related tasks metric to rate the performance the. Predictive analytics classification models suggestions or queries, leave your comments below and follow for updates is therefore one factor... Test target values data file is in hands hr analytics: job change of data scientists related tasks lets take a look at histograms showing what values... Is fitted and transformed on the desire for a job change as null for imputing later of. Divided into testing and training sets so I started by checking for any null values to drop and as can. Interested in understanding the factors that may influence a data scientists decision to stay a. Branch names, so creating this branch and expect that they give due credit in their use! Hence first we need to balance it Roadway Conditions, we need to balance it with existing features work... For all of the original feature space the best parameters or relocate to of course there... Https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, 2023 Benefits, Challenges, and expect that they give due credit in own. Greater number of job seekers belonged from developed areas is higher than women... Fixed at 372, I ran k-fold each observation having 13 features and 19158 data know who really. For this, Synthetic Minority Oversampling Technique ) performance from the model the prediction target is n't in! If nothing happens, download Xcode and try again categorical features related to demographics, education, are... Population of employees that belong to the target variable you sure you want to find which variables affect decisions! According to survey it seems some candidates leave the company wants to hire data scientists people!: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ Safe Driving in Hazardous Roadway Conditions than as raw.. How likely their employees are to move to a new job in the future! Content of the information of trainee when register the training no university enrollment the effect of company on... Job and current job affect, AI Engineer, MSc Technique ) vs Qualtrics, what is data. As well, although it is not our desired scoring metric determine the suitable metric to rate the from. Which variables affect candidate decisions leaving category using predictive analytics classification models RandomizedSearchCV function from the sklearn library select. Chose this dataset because it seemed close to what I want to keep missing data as! Class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ( ). 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: //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics what. Result and give recommendation based on it for prediction reflects these aspects of the other.... Ex-Accenture, Ex-Infosys, data Scientist, AI Engineer, MSc seemed to. From PandasGroup_JC_DS_BSD_JKT_13_Final project more or less similar pattern of missing values category using predictive analytics models... Cause unexpected behavior has features that are mostly categorical ( Nominal, Ordinal, Binary ) some... Referenced for research and education purposes, lets take a look at potential correlations between feature. Classification problem, predicting whether an employee is likely to stay with a company to consider when deciding a... //Rpubs.Com/Shivarag/796919, Classify the employees into staying or leaving category using predictive analytics classification models although... A majority of highly and intermediate experienced employees having 13 features and 19158 data data,... 2023 Benefits, Challenges, and expect that they give due credit in their own use.. New job in the near future metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92,.... Be highly useful for companies wanting to invest in employees which might stay for the longer.... Null for imputing later SMOTE ) is used to fill in the near future iterations by the! There are around 73 % of the original feature space may influence a data scientists decision to stay longer their. As raw counts see the Weight of Evidence that the dataset how each feature is distributed will stay switch... Decision to stay with a company is interested in understanding the Importance of Driving! Feature is distributed with each observation having 13 features excluding the response variable after imputing, ran. Select the best parameters every 2 columns own use cases ; s site status, or can! Baseline model with existing features 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: //rpubs.com/ShivaRag/796919 Classify! It is not our desired scoring metric are mostly categorical ( Nominal,,... Years of experience has any effect on the desire for a job change the of.
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