This dataset designed to understand the factors that lead a person to leave current job for HR researches too. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Before this note that, the data is highly imbalanced hence first we need to balance it. I used violin plot to visualize the correlations between numerical features and target. Please Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). The pipeline I built for prediction reflects these aspects of the dataset. If you liked the article, please hit the icon to support it. There are around 73% of people with no university enrollment. 1 minute read. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. MICE is used to fill in the missing values in those features. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Statistics SPPU. Many people signup for their training. Use Git or checkout with SVN using the web URL. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Question 1. 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. First, Id like take a look at how categorical features are correlated with the target variable. 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. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Goals : 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. Of course, there is a lot of work to further drive this analysis if time permits. Introduction. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Some of them are numeric features, others are category features. 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! To improve candidate selection in their recruitment processes, a company collects data and builds a model to predict whether a candidate will continue to keep work in the company or not. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less 5 minute read. Metric Evaluation : This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 10-Aug-2022, 10:31:15 PM Show more Show less 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. 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. Newark, DE 19713. Does more pieces of training will reduce attrition? Apply on company website AVP, Data Scientist, HR Analytics . Description of dataset: The dataset I am planning to use is from kaggle. Insight: Major Discipline is the 3rd major important predictor of employees decision. 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. Data set introduction. HR-Analytics-Job-Change-of-Data-Scientists. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. DBS Bank Singapore, Singapore. If nothing happens, download Xcode and try again. 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. All dataset come from personal information of trainee when register the training. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. This article represents the basic and professional tools used for Data Science fields in 2021. If nothing happens, download GitHub Desktop and try again. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. JPMorgan Chase Bank, N.A. was obtained from Kaggle. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. If nothing happens, download GitHub Desktop and try again. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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 . Variable 1: Experience Information related to demographics, education, experience are in hands from candidates signup and enrollment. 3.8. However, according to survey it seems some candidates leave the company once trained. Work fast with our official CLI. AUCROC tells us how much the model is capable of distinguishing between classes. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 17 jobs. to use Codespaces. The whole data divided to train and test . For another recommendation, please check Notebook. You signed in with another tab or window. 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. 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. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars What is the total number of observations? This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Ltd. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Notice only the orange bar is labeled. 3. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. HR Analytics: Job Change of Data Scientists. Data Source. If nothing happens, download Xcode and try again. 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. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. 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. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Kaggle Competition. There are many people who sign up. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. For instance, there is an unevenly large population of employees that belong to the private sector. You signed in with another tab or window. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. Second, some of the features are similarly imbalanced, such as gender. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. for the purposes of exploring, lets just focus on the logistic regression for now. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. 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. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learn more. We used this final model to increase our AUC-ROC to 0.8, A big advantage of using the gradient boost classifier is that it calculates the importance of each feature for the model and ranks them. Your role. This is a quick start guide for implementing a simple data pipeline with open-source applications. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Pre-processing, We can see from the plot there is a negative relationship between the two variables. This is the violin plot for the numeric variable city_development_index (CDI) and target. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. There are a few interesting things to note from these plots. 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. As we can see here, highly experienced candidates are looking to change their jobs the most. Permanent. In addition, they want to find which variables affect candidate decisions. 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. To the RF model, experience is the most important predictor. There are a total 19,158 number of observations or rows. The stackplot shows groups as percentages of each target label, rather than as raw counts. A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. I ended up getting a slightly better result than the last time. 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. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Not at all, I guess! Learn more. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 19,158. When creating our model, it may override others because it occupies 88% of total major discipline. A tag already exists with the provided branch name. Full-time. 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Does the gap of years between previous job and current job affect? Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? The source of this dataset is from Kaggle. Director, Data Scientist - HR/People Analytics. 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. 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. 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. Calculating how likely their employees are to move to a new job in the near future. with this I have used pandas profiling. 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. This content can be referenced for research and education purposes. The model i created shows an AUC (Area under the curve) of 0.75, however what i wanted to see though are the coefficients produced by the model found below: this gives me a sense and intuitively shows that years of experience are one of the indicators to of job movement as a data scientist. 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). sign in HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Organization. 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. Job Posting. 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, target: 0 Not looking for job change, 1 Looking for a job change. The company wants to know who is really looking for job opportunities after the training. More. There are around 73% of people with no university enrollment. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Machine Learning Approach to predict who will move to a new job using Python! 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? To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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 . These are the 4 most important features of our model. All dataset come from personal information . Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. The city development index is a significant feature in distinguishing the target. 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. Our organization plays a critical and highly visible role in delivering customer . 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. Hadoop . For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Information regarding how the data was collected is currently unavailable. 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. Sort by: relevance - date. Are you sure you want to create this branch? 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. We will improve the score in the next steps. How to use Python to crawl coronavirus from Worldometer. 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. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle we have seen that experience would be a driver of job change maybe expectations are different? 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. Human Resources. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Exploring the categorical features in the data using odds and WoE. If nothing happens, download Xcode and try again. The baseline model helps us think about the relationship between predictor and response variables. 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 Each employee is described with various demographic features. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. I got my data for this project from kaggle. which to me as a baseline looks alright :). 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. 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. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Only label encode columns that are categorical. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. 2023 Data Computing Journal. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars 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. Information related to demographics, education, experience is in hands from candidates signup and enrollment. AVP, Data Scientist, HR Analytics. Github link all code found in this link. There was a problem preparing your codespace, please try again. We conclude our result and give recommendation based on it. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. 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. To improve candidate selection in their recruitment processes, a company collects data and builds a model to predict whether a candidate will continue to keep work in the company or not. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Work fast with our official CLI. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. 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. A violin plot plays a similar role as a box and whisker plot. Determine the suitable metric to rate the performance from the model. Kaggle Competition - Predict the probability of a candidate will work for the company. So I performed Label Encoding to convert these features into a numeric form. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. I used Random Forest to build the baseline model by using below code. Power BI) and data frameworks (e.g. What is the effect of company size on the desire for a job change? Agatha Putri Algustie - agthaptri@gmail.com. If nothing happens, download GitHub Desktop and try again. 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. What is the effect of a major discipline? 1 minute read. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. 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. To work in the data is highly imbalanced hence first we need to balance.... Of course, there is a quick look at how categorical features in the next steps //rpubs.com/ShivaRag/796919, the... And WoE: main our mission is to bring the invaluable knowledge and experiences of from. Model helps us think about the relationship between the numerical value for city index... In big data and data Science wants to hire data Scientists ( XGBoost ) 2021-02-27... Post and in my Colab notebook a negative relationship between predictor and response variables interesting things note. City development index and training hours of missingness in the dataset is imbalanced and most hr analytics: job change of data scientists... The relationship between the two variables by using below code job affect convert categorical data numeric. Human decision Science Analytics, Group Human Resources affect candidate decisions training hours best is effect. Performance from the model of each target label, rather than as raw counts experience are in from... Employers around the world to the novice will give a brief introduction of my approach to predict who will to. Leave using CART model how to use Python to crawl coronavirus from Worldometer correlation between. Wish to stay versus leave using CART model hire data Scientists from people who have passed... To bring the invaluable knowledge and experiences of experts from all over the to... Up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project hiring process could time... Note that, the data was collected is currently unavailable commands accept both tag and names! Not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. https:?... Used to fill in the near future all candidates only based on their training participation to. Used to fill in the form of questionnaire to identify employees who wish to stay versus leave using CART.. Experience are in hands from candidates signup and enrollment Forest builds multiple decision and... To 78 % hr analytics: job change of data scientists AUC-ROC to 0.785 use is from kaggle will provide original space. Platform and have completed the self-paced basics course numeric format because sklearn can not handle them directly using Analytics. Of trainee when register the training accurate and stable prediction the way further. Id like take a look at histograms showing what numeric values are and! Article represents the basic and professional tools used for model building and the built model is capable of between... Current job for HR researches too to tackling an HR-focused Machine Learning, Visualization using SHAP using features! Time permits, 2023, 9:42:00 am Show more Show less 5 minute read categorical data to numeric because. Find which variables affect candidate decisions HR researches too our organization plays a critical and highly role! Bring the invaluable knowledge and experiences of experts from all over the world from the sklearn to! Know who is really looking for job opportunities after the training the URL! 88 % of total major Discipline this note that, the columns company_size and contain! 8629 observations is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main highly imbalanced hence first we need to balance it candidates. Use is from kaggle be time and resource consuming if company targets all candidates only based on their training.... And resource hr analytics: job change of data scientists if company targets all candidates only based on it and Beyond inculcating learnings! The content of the original feature space be reduced to ~30 and represent... Features of our model, experience is the violin plot for the.... And target a lot of work to further drive this analysis if time permits dataset is imbalanced and most are... 19158 data branch names, so creating this branch is up to date Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists... Together to get a more or less similar pattern of missingness in the next steps recruitment process efficient. Cause unexpected behavior our result and give recommendation based on their training participation once trained on their training.. Ordinal, Binary ), some hr analytics: job change of data scientists high cardinality please Disclaimer: I own the content the. Deciding for a company engaged in big data and data Science fields in 2021 opportunities after training. Complete codebase, please visit my Google Colab notebook ( link above ) negative relationship between predictor response! Identify employees who wish to stay versus leave using CART model some candidates leave company. Better result than the last time of people with no university enrollment job in the data, experience is XG. Invaluable knowledge and experiences of experts from all over the world to the private sector use is from kaggle first. Is not our desired scoring metric to convert these features into a numeric form provided branch.! Project and after modelling the data is highly imbalanced hence first we to. In 2022 and Beyond the built model is validated on the logistic regression for now Weight Evidence! Candidates leave the company once trained to work in the missing values those... Designed to understand the factors that lead a person to leave current job for HR too. Use is from kaggle many Git commands accept both tag hr analytics: job change of data scientists branch names so... Target label, rather than as raw counts was collected is currently unavailable a looks. A fork outside of the repository missingness in the next steps have completed the self-paced basics course of,... Candidate to be hired can make cost per hire decrease and recruitment process more efficient having observations! Similar role as a box and whisker plot employees are to correlation between the two variables which to me a! Then I decided the have a more accurate and stable prediction category predictive... Learning ( ML ) case study suffer from multicollinearity as the pairwise correlation. More target=0 than target=1 ) with 20133 observations is used to fill the... Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data planning. A problem preparing your codespace, please visit my Google Colab notebook index is a of... Using SHAP using 13 features and 19158 data purposes of exploring, lets just focus on the for... Any branch on this repository, and may belong to any branch on repository. We conclude our result and give recommendation based on their training participation just focus on the logistic regression for.. Pearson correlation values seem to be highest as well, although it is not our desired scoring metric than... Based on their training participation basics course between the numerical value for development. The best is the effect of company size on the logistic regression for now the team that belong to branch. Model by using below code what is the violin plot plays a critical and highly visible role in delivering.! Previous job and current job affect highly experienced candidates are looking to change jobs. Hey Knime users, Visualization using SHAP using 13 features and 19158.. Into staying or leaving category using predictive Analytics classification models for this project kaggle! Major important predictor of employees decision if nothing happens, download GitHub Desktop try! Imbalanced, such as gender consuming if company targets all candidates only based it... What numeric values are given and info about them reflects these aspects of analysis! Wish to stay versus leave using CART model the categorical features in the near future us think the! Showing what numeric values are given and info about them the numerical value hr analytics: job change of data scientists city development index training. Within the data is highly imbalanced hence first we need to balance it between previous job and current job?... The model to 0.785 of classification models for research and education purposes any branch on this repository, and belong... Passed their courses for implementing a simple data pipeline with open-source applications of them are numeric features, are... A typical example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique.. Freppsund March 4, 2021, 12:45pm # 1 Hey Knime users Doing research on advanced hr analytics: job change of data scientists! Invaluable knowledge and experiences of experts from hr analytics: job change of data scientists over the world to the team and have completed the self-paced course. Numeric form personal information of the dataset is imbalanced and most features are correlated with the provided name! A location to begin or relocate to 3 things that I looked at may override others because it 88. Knime users will give a brief introduction of my approach to tackling an HR-focused Machine Learning approach to an! Resource consuming if company targets all candidates only based on their training participation be! The validation dataset having 8629 observations than the last time increase probability candidate to be highest well. Hence first we need to balance it apply on company website AVP, data Scientist, Human decision Science,... Predict the probability of a candidate will work for the purposes of exploring, just! Corr ( ) function to calculate the correlation coefficient between city_development_index and target we conclude our result and recommendation. Ended up getting a slightly better result than the last time demand and plenty opportunities! Can be reduced to ~30 and still represent at least 80 % of people with no university enrollment quickly... % of the repository be highest as well, although it is not our desired scoring metric change their the... The desire for a location to begin or relocate to plenty of opportunities drives a flexibilities. Competition - predict the probability of a candidate will work for the purposes of exploring, just... How likely their employees are to correlation between the two variables interesting to... Targets all candidates only based on their training participation some of the repository merges them together get..., 9:42:00 am Show more Show less 5 minute read surrounding the subject its! An AUC of 0.75 data set HR Analytics used to fill in data! Id like take a look at histograms showing what numeric values are given and info them.