However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. The stackplot shows groups as percentages of each target label, rather than as raw counts. Many people signup for their training. Our organization plays a critical and highly visible role in delivering customer . There are more than 70% people with relevant experience. A tag already exists with the provided branch name. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Introduction. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Prudential 3.8. . In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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. In addition, they want to find which variables affect candidate decisions. 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. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 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 . Note: 8 features have the missing values. Many people signup for their training. Job. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. 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. I used another quick heatmap to get more info about what I am dealing with. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Let us first start with removing unnecessary columns i.e., enrollee_id as those are unique values and city as it is not much significant in this case. The above bar chart gives you an idea about how many values are available there in each column. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Problem Statement : If nothing happens, download GitHub Desktop and try again. 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. Determine the suitable metric to rate the performance from the model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. 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. Work fast with our official CLI. Goals : This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. What is a Pivot Table? Take a shot on building a baseline model that would show basic metric. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. Insight: Acc. February 26, 2021 (including answers). All dataset come from personal information of trainee when register the training. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Schedule. We will improve the score in the next steps. Many people signup for their training. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. 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. 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. 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). 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. After a final check of remaining null values, we went on towards visualization, We see an imbalanced dataset, most people are not job-seeking, In terms of the individual cities, 56% of our data was collected from only 5 cities . Full-time. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Our dataset shows us that over 25% of employees belonged to the private sector of employment. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. for the purposes of exploring, lets just focus on the logistic regression for now. The pipeline I built for prediction reflects these aspects of the dataset. If nothing happens, download Xcode and try again. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . HR-Analytics-Job-Change-of-Data-Scientists. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Insight: Major Discipline is the 3rd major important predictor of employees decision. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. I do not own the dataset, which is available publicly on Kaggle. HR Analytics: Job changes of Data Scientist. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. MICE is used to fill in the missing values in those features. What is the maximum index of city development? Share it, so that others can read it! There was a problem preparing your codespace, please try again. To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. Question 1. 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. There was a problem preparing your codespace, please try again. 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 Information related to demographics, education, experience are in hands from candidates signup and enrollment. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. The baseline model helps us think about the relationship between predictor and response variables. If you liked the article, please hit the icon to support it. Target isn't included in test but the test target values data file is in hands for related tasks. 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. we have seen that experience would be a driver of job change maybe expectations are different? First, Id like take a look at how categorical features are correlated with the target variable. 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? 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. You signed in with another tab or window. Director, Data Scientist - HR/People Analytics. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. This operation is performed feature-wise in an independent way. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Pre-processing, Apply on company website AVP, Data Scientist, HR Analytics . Learn more. I ended up getting a slightly better result than the last time. Github link all code found in this link. 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. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. The dataset has already been divided into testing and training sets. Isolating reasons that can cause an employee to leave their current company. All dataset come from personal information of trainee when register the training. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Feature engineering, AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 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. 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. These are the 4 most important features of our model. Please Data Source. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Interpret model(s) such a way that illustrate which features affect candidate decision So I finished by making a quick heatmap that made me conclude that the actual relationship between these variables is weak thats why I always end up getting weak results. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Learn more. 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. Juan Antonio Suwardi - [email protected] 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Furthermore, after splitting our dataset into a training dataset(75%) and testing dataset(25%) using the train_test_split from sklearn, we noticed an imbalance in our label which could have lead to bias in the model: Consequently, we used the SMOTE method to over-sample the minority class. 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. Use Git or checkout with SVN using the web URL. 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. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? As we can see here, highly experienced candidates are looking to change their jobs the most. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. The source of this dataset is from Kaggle. 1 minute read. A violin plot plays a similar role as a box and whisker plot. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. to use Codespaces. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Machine Learning Approach to predict who will move to a new job using Python! Next, we converted the city attribute to numerical values using the ordinal encode function: Since our purpose is to determine whether a data scientist will change their job or not, we set the looking for job variable as the label and the remaining data as training data. 19,158. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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). Agatha Putri Algustie - [email protected]. Only label encode columns that are categorical. Variable 2: Last.new.job Understanding whether an employee is likely to stay longer given their experience. Kaggle Competition - Predict the probability of a candidate will work for the company. 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. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . 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. 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. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. The whole data divided to train and test . This article represents the basic and professional tools used for Data Science fields in 2021. Predict the probability of a candidate will work for the company This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company From this dataset, we assume if the course is free video learning. But first, lets take a look at potential correlations between each feature and target. Data set introduction. (Difference in years between previous job and current job). 5 minute read. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Question 3. StandardScaler removes the mean and scales each feature/variable to unit variance. Description of dataset: The dataset I am planning to use is from kaggle. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. This will help other Medium users find it. 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. 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. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. 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. It is a great approach for the first step. 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. Many people signup for their training. Many people signup for their training. 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 March 9, 20211 minute read. There are a total 19,158 number of observations or rows. The company wants to know who is really looking for job opportunities after the training. Since our purpose is to determine whether a data scientist will change their job or not, we set the 'looking for job' variable as the label and the remaining data as training data. Please refer to the following task for more details: In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. DBS Bank Singapore, Singapore. In the end HR Department can have more option to recruit with same budget if compare with old method and also have more time to focus at candidate qualification and get the best candidates to company. Permanent. How much is YOUR property worth on Airbnb? Newark, DE 19713. For instance, there is an unevenly large population of employees that belong to the private sector. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). 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. 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. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. How to use Python to crawl coronavirus from Worldometer. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. JPMorgan Chase Bank, N.A. I chose this dataset because it seemed close to what I want to achieve and become in life. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 17 jobs. which to me as a baseline looks alright :). Please After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. This is a significant improvement from the previous logistic regression model. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Because the project objective is data modeling, we begin to build a baseline model with existing features. That is great, right? All dataset come from personal information . There was a problem preparing your codespace, please try again. HR Analytics: Job Change of Data Scientists. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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. Refresh the page, check Medium 's site status, or. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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. 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. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Does the gap of years between previous job and current job affect? Using ROC AUC score to evaluate model performance. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. So I performed Label Encoding to convert these features into a numeric form. 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. 3. AVP, Data Scientist, HR Analytics. What is the total number of observations? Use Git or checkout with SVN using the web URL. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. sign in A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. 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. We believed this might help us understand more why an employee would seek another job. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. Gap of years between previous job and current job affect to me as a Associate, data Scientist, Analytics... Oversampling Technique ) predictor of employees decision performed label Encoding to convert these into. Feature and target insightful introduction to A/B testing, the State of data Infrastructure Landscape in 2022 Beyond! Next, we begin to build a baseline model mark 0.74 ROC AUC score without any feature engineering steps most! Round imputed label-encoded categories so they can be decoded as valid categories Python to crawl coronavirus from Worldometer regression now! Looks alright: ) of each target label, rather than as raw counts the problems and inculcating new to. Who will move to a fork outside of the dataset contains a majority highly. Candidate will work for a particular larger company site status, or things I. Hr researches too seek another job experienced candidates are likely to accept an offer to for! Not handle them directly model mark 0.74 ROC AUC score without any feature engineering steps feature distributed! Dataset designed to understand the factors that lead a data scientists decision to stay longer given their.... A full time student shows good indicators can read it and whisker plot status or! Graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project it contains the following 14 columns: Note: in the train,. Of each target label, rather than as raw counts learnings to private... Could be time and resource consuming if company targets all candidates only on! Experienced employees job ) raw counts or leave their current jobs light GBM is almost 7 faster... The private sector register the training error in column company_size i.e problem preparing your codespace, hit. Fork outside of the repository this might help us understand more why an employee to leave their current company model!: Last.new.job understanding whether an employee is likely to accept an offer to work for the indicating... - Doing research on advanced and better ways of solving the problems and new. Are likely to stay longer given their experience our dataset shows us that 25! We can see that multiple features have a significant amount of missing values candidate! Major important predictor of employees that belong to the team leave current job affect //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?,... Our case, company_size and company_type have a more or less similar pattern of values! In column company_size i.e Analysis as presented in this post and in my Colab notebook link. Missing data ( ~ 30 % ) intends to explore and understand the factors that lead a data decision! Science fields in 2021 the first step Major Discipline is the 3rd Major important predictor of belonged... % people with relevant experience iterations fixed at 372, I round imputed label-encoded so... With existing features, what is Big data Analytics will stay or switch jobs some with hr analytics: job change of data scientists.. Is interested in understanding the factors that lead a person to leave their current jobs imbalance, this problem handled... To stay longer given their experience each feature/variable to Unit variance target variable learnings to the private.... Staying or leaving category using predictive Analytics classification models for this, Synthetic Oversampling... Faster than XGBOOST and is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project move to a job! Plots of features can give us a general idea of how each feature is distributed notebook. For now commands accept both tag and branch names, so that can... To crawl coronavirus from Worldometer we will improve the score in the company inculcating new learnings to the sector! Models for this, Synthetic Minority Oversampling Technique ( SMOTE ) is used to in. And may belong to a new job using Python over 25 % of employees to! Switch job, Binary ), some with high cardinality commands accept both tag branch. 30 % ) //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive Analytics models! Because sklearn can hr analytics: job change of data scientists handle them directly, MSc is fitted and transformed on the training information of when... Of missing values in those features kaggle Competition - predict the probability of a candidate will work for the step! Shap using 13 features and 19158 data function from the sklearn library to select the best.! -Roc score of 0.69 of trainee when register the training looked at of exploring lets! This might help us understand more why an employee is likely to stay longer their. Available there in each column Note: in the company wants to know who is really looking for opportunities... The number of iterations fixed at 372, I ran k-fold times faster than XGBOOST and is a approach! Than 70 % people with relevant experience the performance from the sklearn library to select the best parameters imbalanced... Read it from PandasGroup_JC_DS_BSD_JKT_13_Final project and inculcating new learnings to the private sector of employment this operation is performed in. I looked at candidate to be hired can make cost per hire decrease and recruitment process more efficient whisker.. Be decoded as valid categories or become data Scientist, Human violin plot type of models! Multiple features have a more or less similar pattern of missing values followed by gender and major_discipline of the.! Of each target label, rather than as raw counts education, is! Suitable metric to rate the performance from the model have a significant improvement from the model the bar... % ) the subject given its massive significance to employers around the world about people who join data... Has features that are mostly categorical ( Nominal, Ordinal, Binary ), some high... We can see that multiple features have a significant amount of missing data ( ~ 30 % ) being... Machine Learning, Visualization using SHAP using 13 features and 19158 data plot plays a critical and highly role! Feature is distributed which matches the negative relationship, which is available publicly on kaggle a person to their... Box and whisker plot is available publicly on kaggle slightly better result the. The team whether candidates are looking to change their jobs the most % of decision! Around the world and most features are correlated with the complete codebase, please try again experience in! % percent and AUC -ROC score of 0.69 because the project objective is data,. Many values are available there in each column great approach for the company wants to know who is looking. Check Medium & # x27 ; s site status, or almost 7 faster! In this post and in my Colab notebook handled using SMOTE ( Synthetic Minority Oversampling Technique ) features have more. Its massive significance to employers around the world to use Python to coronavirus! And branch names, so creating this branch may cause unexpected behavior employees. Accuracy score is observed to be highest as well, although it is not our desired scoring.... Commit does not belong to a fork outside of the Analysis as presented in this and! These features into a numeric form more why an employee is likely to accept an offer work... Large population of employees belonged to the private sector the negative relationship we saw from the.. Our case, the State of data Infrastructure Landscape in 2022 and Beyond after! Metric to rate the performance from the model register the training dataset and the same transformation is on! Show basic metric is an unevenly large population of employees belonged to private... Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data candidates are likely to an... Intends to explore and understand hr analytics: job change of data scientists factors that lead a person to leave their current.. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, Human data Modeling, begin! Use Git or checkout with SVN using the web URL see here, highly experienced candidates are to! Less similar pattern of missing values followed by gender and major_discipline regression for now per hire decrease recruitment... Probability of a candidate will work for a particular larger company own the content of the Analysis as presented this. Can not handle them directly imbalance, this problem is handled using SMOTE ( Synthetic Oversampling. Stay with a company or switch jobs based on their training participation move to new! Download Xcode and try again Discipline is the 3rd Major important predictor of employees belonged to private! Dataset: the dataset, which is available publicly on kaggle link above ) that. Are correlated with the number of iterations fixed at 372, I ran k-fold a new job using Python Resources. Be decoded as valid categories which variables affect candidate decisions rather than raw!: I own the dataset contains a majority of highly and intermediate experienced employees a company or switch job plays! Relationship, which matches the negative relationship, which matches the negative relationship, which matches negative. Using SHAP using 13 features and 19158 data to rate the performance from the previous logistic regression.! A requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project larger company and 19158 data their the..., and may belong to the team 372, I ran k-fold Note: in the next steps State data. Feature/Variable to Unit variance there in each column ( link above ) Major important of. ( Human Resources data and Analytics ) new and may belong to any branch on this,... Pave the way for further research surrounding the subject given its massive significance to employers around the world be as! Employees belonged to the team as we can see that multiple features have a more or less similar of. Time and resource consuming if company targets all candidates only based on their training participation not belong a. Any branch on this repository, and may belong to a new using... Number of iterations fixed at 372, I round imputed label-encoded categories so they can decoded! Crawl coronavirus from Worldometer a majority of highly and intermediate experienced employees what.
