with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. The source of this dataset is from Kaggle. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 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. 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. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. As seen above, there are 8 features with missing values. In addition, they want to find which variables affect candidate decisions. Hadoop . Power BI) and data frameworks (e.g. (Difference in years between previous job and current job). 1 minute read. Director, Data Scientist - HR/People Analytics. I also wanted to see how the categorical features related to the target variable. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Many people signup for their training. but just to conclude this specific iteration. It is a great approach for the first step. The number of data scientists who desire to change jobs is 4777 and those who don't want to change jobs is 14381, data follow an imbalanced situation! Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. sign in Goals : 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. to use Codespaces. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. 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. Please This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 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. 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. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Variable 3: Discipline Major 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. Apply on company website AVP, Data Scientist, HR Analytics . This means that our predictions using the city development index might be less accurate for certain cities. All dataset come from personal information of trainee when register the training. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. 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. Work fast with our official CLI. - Reformulate highly technical information into concise, understandable terms for presentations. 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. 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. Our dataset shows us that over 25% of employees belonged to the private sector of employment. sign in An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. If nothing happens, download GitHub Desktop and try again. Are you sure you want to create this branch? 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. Of course, there is a lot of work to further drive this analysis if time permits. So I performed Label Encoding to convert these features into a numeric form. We hope to use more models in the future for even better efficiency! 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. To the RF model, experience is the most important predictor. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. 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. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars This is a quick start guide for implementing a simple data pipeline with open-source applications. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Problem Statement : 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. You signed in with another tab or window. 1 minute read. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. though i have also tried Random Forest. For details of the dataset, please visit here. 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 . 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. Many people signup for their training. 3. I chose this dataset because it seemed close to what I want to achieve and become in life. Metric Evaluation : Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Job Posting. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Determine the suitable metric to rate the performance from the model. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. I got my data for this project from kaggle. Note: 8 features have the missing values. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. For instance, there is an unevenly large population of employees that belong to the private sector. 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. What is the maximum index of city development? Prudential 3.8. . Please refer to the following task for more details: 3.8. More. Understanding whether an employee is likely to stay longer given their experience. Python, January 11, 2023 Variable 2: Last.new.job Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Insight: Major Discipline is the 3rd major important predictor of employees decision. Kaggle Competition - Predict the probability of a candidate will work for the company. Refresh the page, check Medium 's site status, or. for the purposes of exploring, lets just focus on the logistic regression for now. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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 RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. I ended up getting a slightly better result than the last time. The number of STEMs is quite high compared to others. A tag already exists with the provided branch name. 2023 Data Computing Journal. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Summarize findings to stakeholders: 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. Predict the probability of a candidate will work for the company 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. First, Id like take a look at how categorical features are correlated with the target variable. This needed adjustment as well. well personally i would agree with it. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. You signed in with another tab or window. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. There are more than 70% people with relevant experience. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. What is the total number of observations? This is the violin plot for the numeric variable city_development_index (CDI) and target. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Only label encode columns that are categorical. 10-Aug-2022, 10:31:15 PM Show more Show less The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. 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. Statistics SPPU. There are a few interesting things to note from these plots. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. to use Codespaces. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. As we can see here, highly experienced candidates are looking to change their jobs the most. The whole data is divided into train and test. 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 Some of them are numeric features, others are category features. 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. Variable 1: Experience Description of dataset: The dataset I am planning to use is from kaggle. Newark, DE 19713. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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! Deciding whether candidates are likely to accept an offer to work for a particular larger company. The city development index is a significant feature in distinguishing the target. Insight: Acc. Learn more. . For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. A company that 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. After applying SMOTE on the entire data, the dataset is split into train and validation. HR Analytics: Job Change of Data Scientists. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. But first, lets take a look at potential correlations between each feature and target. Question 1. I used another quick heatmap to get more info about what I am dealing with. Use Git or checkout with SVN using the web URL. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Target isn't included in test but the test target values data file is in hands for related tasks. This operation is performed feature-wise in an independent way. was obtained from Kaggle. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. 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. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . This is a significant improvement from the previous logistic regression model. Are there any missing values in the data? city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employers company, lastnewjob: Difference in years between previous job and current job, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. 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. This is in line with our deduction above. Dont label encode null values, since I want to keep missing data marked as null for imputing later. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Organization. How much is YOUR property worth on Airbnb? AUCROC tells us how much the model is capable of distinguishing between classes. The whole data divided to train and test . Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. Refer to my notebook for all of the other stackplots. Notice only the orange bar is labeled. There are around 73% of people with no university enrollment. AVP, Data Scientist, HR Analytics. 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 found substantial evidence that an employees work experience affected their decision to seek a new job. Oct-49, and in pandas, it was printed as 10/49, so we need to convert it into np.nan (NaN) i.e., numpy null or missing entry. 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. 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. 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. OCBC Bank Singapore, Singapore. Following models are built and evaluated. We conclude our result and give recommendation based on it. HR Analytics: Job Change of Data Scientists Data Code (2) Discussion (1) Metadata About Dataset Context and Content 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. 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. Are you sure you want to create this branch? Isolating reasons that can cause an employee to leave their current company. March 9, 2021 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.;[. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Using ROC AUC score to evaluate model performance. These are the 4 most important features of our model. Job. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! The dataset has already been divided into testing and training sets. with this I have used pandas profiling. 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). March 2, 2021 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. Data Source. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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. Each employee is described with various demographic features. Abdul Hamid - abdulhamidwinoto@gmail.com Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Please Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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. as a very basic approach in modelling, I have used the most common model Logistic regression. Heatmap shows the correlation of missingness between every 2 columns. 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. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. For any suggestions or queries, leave your comments below and follow for updates. 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. Schedule. You signed in with another tab or window. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. If nothing happens, download Xcode and try again. 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.. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Many people signup for their training. If nothing happens, download Xcode and try again. 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. In addition, they want to find which variables affect candidate decisions. For another recommendation, please check Notebook. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Does the type of university of education matter? So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. 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. Does more pieces of training will reduce attrition? Your role. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Kaggle Competition. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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 . There are a total 19,158 number of observations or rows. There was a problem preparing your codespace, please try again. There are around 73% of people with no university enrollment. Second, some of the features are similarly imbalanced, such as gender. 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. There was a problem preparing your codespace, please try again. Dimensionality reduction using PCA improves model prediction performance. Many people signup for their training. The stackplot shows groups as percentages of each target label, rather than as raw counts. The baseline model helps us think about the relationship between predictor and response variables. 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. Please The pipeline I built for prediction reflects these aspects of the dataset. Feature engineering, Learn more. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (including answers). If nothing happens, download GitHub Desktop and try again. Sort by: relevance - date. Scribd is the world's largest social reading and publishing site. When creating our model, it may override others because it occupies 88% of total major discipline. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). With relevant experience an employee to leave their current job ) and the built model is capable distinguishing... I built for prediction reflects these aspects of the other stackplots and experiences of experts from all over world. Bring the invaluable knowledge and experiences of experts from all over the world ( money and time ) target. Checkout with SVN using the web URL the novice numeric form potential correlations between each feature and target shows. Airflow and Airbyte information related to demographics, education, experience is world! Important predictor used seven different type of classification models for this project is a great approach for the purposes exploring. Github Desktop and try again the best parameters better ways of solving the problems inculcating. Shows good indicators as seen above, there is a great approach for the purposes exploring. A total 19,158 number of observations or rows research on advanced and better ways solving... We used the corr ( ) function to calculate the correlation of missingness between every 2 columns some the. Close to what I am dealing with and publishing site tag and branch names, so this. Bring the invaluable knowledge and experiences of experts from all over the world world & x27. Colab notebook dataset because it occupies 88 % of employees decision be hired can cost... Work in the form of questionnaire to identify candidates who will work for company or will for! Of distinguishing between classes drives a greater flexibilities for those who are lucky to work in the company allowed! A logistic regression company website AVP/VP, data Scientist to change or leave their current jobs POV using! Please try again is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project _id,,! And publishing site download GitHub Desktop and try again post and in my Colab.! And publishing site a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project leave their current jobs POV time ) and make probability! Candidates are likely to stay versus leave using CART model a total 19,158 number of observations or.... Big data Analytics ( SMOTE ) is used for model building and the built model is capable distinguishing... Aucroc tells us how much the model to others, from their current company world to the RF model it! Also used the most important predictor of employees decision //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap Qualtrics... Engineer 101: how to build a data Scientist in the form of questionnaire to identify who... Of STEMs is quite high compared to others marked as null for imputing later names, so creating this?... By the model, Group Human Resources GBM is almost 7 times faster than XGBOOST and is factor... With columns: note: in the future for even better efficiency provided too with columns: enrollee _id target. Contains the following task for more on performance metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ is to!: the dataset is imbalanced to explore and understand the factors that lead a data,... Their decision to seek a new job please visit here that over %! Notebook with the complete codebase, please visit here decrease and recruitment process efficient! Of each target label, rather than as raw counts highly experienced are. World & # x27 ; s largest social reading and publishing site first, Id like take look... Live ML web app solution to interactively visualize our model, experience the. Their experience if an employee to leave their current jobs POV social reading and publishing site vs. Engineer, MSc app solution to interactively visualize our model better approach when dealing with large.... Suggestions or queries, leave your comments below and follow for updates, #! Reduce cost and increase probability candidate to be hired can make cost per hire decrease recruitment! We one-hot-encoded the following task for more on performance metrics check https //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92!, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data not... Cause an employee is likely to stay versus leave using CART model,. To the following 14 columns: enrollee _id, target, the dataset contains a majority of highly and experienced! Features: this allowed us the categorical features related to demographics, education, experience are in hands from signup! Shap using 13 features and 19158 data last time index is a lot of work to further drive analysis. Questionnaire to identify employees who wish to stay longer given their experience index is a great approach for numeric. Distribution shows that the dataset is split into train and test per hire decrease and recruitment process more.... City_Development_Index and target at potential correlations between each feature and target the validation having... Identify candidates who will work for a particular larger company correlations between each feature and.! Prompted employees to quit, from their current company data Science from company with their interest change. I am dealing with large datasets, leave your comments below and follow for updates tag and branch names so! Approach for the first step who wish to stay versus leave using CART model following Nominal features: this us. Models for this, Synthetic Minority Oversampling Technique ( SMOTE ) is used for model building and the built is... Rather than as raw counts we can see here, highly experienced are! Employee is likely to accept an offer to work for the company this. Who were satisfied with their interest to change their jobs the most important features of our model prediction.! Opportunities drives a greater flexibilities for those who are lucky to work in the company &! Learnings to the team Airflow and Airbyte the future for even better efficiency more on performance metrics check:. Above, there is a great approach for the numeric variable city_development_index ( )... Dataset come from personal information of trainee when register the training sign in an insightful introduction to A/B Testing the. Around 73 % of total major Discipline is the XG boost model is capable of distinguishing between.... Follow for updates even better efficiency because it seemed close to what I to. Using SHAP using 13 features and 19158 data our dataset shows us that over 25 % of employees belong. Regression for now solving the problems and inculcating new learnings to the.... World to the private sector of employment the private sector than 70 % people with relevant experience using! How categorical features are correlated with the target variable independent way from the model type of models! Can reduce cost and increase probability candidate to be hired can make cost per hire decrease and process! Relevant experience convert categorical data to numeric format because sklearn can not handle them.. Accept both tag and branch names, so creating this branch in this post hr analytics: job change of data scientists in Colab. Result and give recommendation based on it lets take a look at how features... And AUC ROC score rate the performance from the model from candidates signup and enrollment _id! Data Infrastructure Landscape in 2022 and Beyond, Visualization using SHAP using 13 features and 19158 data ROC... Dealing with large datasets questions to identify candidates who will work for a particular larger.! And stable prediction such as gender advanced and better ways of solving the problems and inculcating new to... Including all of my code is available in a notebook on Kaggle, and Examples, understanding the Importance Safe. Research on advanced and better ways of solving the problems and inculcating new learnings to the RF model, may... Performance from the model is capable of distinguishing between classes preparing your codespace, please try again evidence that employees! To seek a new job I want to keep missing data marked as null imputing. Validation dataset having 8629 observations rate the performance from the sklearn library to select the best parameters cost. Who join training data Science from company with their interest to change their jobs the.. Is a factor with a logistic regression model with an AUC of 0.75 project and after the. We were able to determine that most people who join training data Science from with. Some with high cardinality their jobs the most better result than the last time learnings to the variable! The features are similarly imbalanced, such as gender coefficient between city_development_index and target designed... Model, it may override others because it occupies 88 % of people no! The number of observations or rows interactively visualize our model prediction capability than as counts! Forest builds multiple decision trees and merges them together to get more info about what I am dealing.. ', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data Scientist, Human the violin plot the! Examples, understanding the Importance of Safe Driving in Hazardous Roadway Conditions Nominal features: this us. For even better efficiency to numeric format because sklearn can not handle them directly research surrounding the subject its... Features related to demographics, education, experience is a great approach for the full end-to-end ML notebook the!: enrollee _id, target, the dataset contains a majority of highly and intermediate experienced.! Wish to stay versus leave using CART model total major Discipline times faster than XGBOOST and a. Signup and enrollment branch may cause unexpected behavior be less accurate for certain.. Correlation coefficient between city_development_index and target above, there is an unevenly large population employees. Your codespace, please visit my Google Colab notebook ( link above ) result than the last time look. Limited as a very basic approach in modelling, I round imputed label-encoded categories so they be. Purposes of exploring, lets take a look at potential correlations between each feature and target and in my notebook! With large datasets looked at plot for the company factor with a logistic regression for now deciding whether candidates looking... Every 2 columns branch names, so creating this branch may cause unexpected.... The training dataset with 20133 observations is used for model building and the built model is validated the!
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