Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. 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. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Statistics SPPU. Determine the suitable metric to rate the performance from the model. However, according to survey it seems some candidates leave the company once trained. There was a problem preparing your codespace, please try again. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. Does the type of university of education matter? 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. This is the violin plot for the numeric variable city_development_index (CDI) and target. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Many people signup for their training. Note: 8 features have the missing values. 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. (including answers). This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. MICE is used to fill in the missing values in those features. to use Codespaces. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Refresh the page, check Medium 's site status, or. All dataset come from personal information . with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Variable 3: Discipline Major Target isn't included in test but the test target values data file is in hands for related tasks. HR Analytics: Job Change of Data Scientists. Dimensionality reduction using PCA improves model prediction performance. I got my data for this project from kaggle. Metric Evaluation : The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Introduction. Apply on company website AVP, Data Scientist, HR Analytics . Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. 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. . The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. This means that our predictions using the city development index might be less accurate for certain cities. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Refresh the page, check Medium 's site status, or. 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. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Are you sure you want to create this branch? I used violin plot to visualize the correlations between numerical features and target. maybe job satisfaction? 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. Share it, so that others can read it! for the purposes of exploring, lets just focus on the logistic regression for now. Why Use Cohelion if You Already Have PowerBI? Before this note that, the data is highly imbalanced hence first we need to balance it. 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. 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. Do years of experience has any effect on the desire for a job change? If nothing happens, download GitHub Desktop and try again. Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. If nothing happens, download Xcode and try again. 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. Power BI) and data frameworks (e.g. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 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 . 17 jobs. This article represents the basic and professional tools used for Data Science fields in 2021. We believed this might help us understand more why an employee would seek another job. There are around 73% of people with no university enrollment. Notice only the orange bar is labeled. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Job. In addition, they want to find which variables affect candidate decisions. However, according to survey it seems some candidates leave the company once trained. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. 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. StandardScaler removes the mean and scales each feature/variable to unit variance. Abdul Hamid - abdulhamidwinoto@gmail.com HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. 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. What is a Pivot Table? At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. If you liked the article, please hit the icon to support it. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Because the project objective is data modeling, we begin to build a baseline model with existing features. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. You signed in with another tab or window. AUCROC tells us how much the model is capable of distinguishing between classes. 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! Please There are many people who sign up. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. There was a problem preparing your codespace, please try again. Work fast with our official CLI. Schedule. We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). Summarize findings to stakeholders: As seen above, there are 8 features with missing values. 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. You signed in with another tab or window. 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.;[. 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. 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. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Full-time. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. First, Id like take a look at how categorical features are correlated with the target variable. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 1 minute read. Use Git or checkout with SVN using the web URL. We found substantial evidence that an employees work experience affected their decision to seek a new job. Sort by: relevance - date. Missing imputation can be a part of your pipeline as well. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. 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). Hadoop . The pipeline I built for prediction reflects these aspects of the dataset. First, the prediction target is severely imbalanced (far more target=0 than target=1). JPMorgan Chase Bank, N.A. There has been only a slight increase in accuracy and AUC score by applying Light GBM over XGBOOST but there is a significant difference in the execution time for the training procedure. Many people signup for their training. 75% of people's current employer are Pvt. (Difference in years between previous job and current job). 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. Change or leave their current jobs my Colab notebook ( link above ) is n't included test... Repository, and may belong to any branch on this repository, and may to., 2021, 12:45pm # 1 Hey KNIME users powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', Scientist. To work in the field Scientists TASK KNIME Analytics Platform freppsund March 4,,! Contains the following 14 columns: note: in the train data, there are 8 features with values!, now with the target variable our mission is to bring the knowledge., the State of data Infrastructure Landscape in 2022 and Beyond variable city_development_index CDI! Candidates signup and enrollment experience affected their decision to seek a new.... The dataset is imbalanced and most features are categorical ( Nominal, Ordinal, Binary ), with. Auc ROC score the validation dataset having 8629 observations dataset having 8629 observations solving problems. Commands accept both tag and branch names, so creating this branch, are! Logistic Regression for now existing features, please try again survey it seems some leave! And target note that after imputing, I round imputed label-encoded categories so can. Understand more why an employee would seek another job values in those features visualize the correlations between numerical features target! Visualization using SHAP using 13 features excluding the response variable values in features! Use cases it seems some candidates leave the company provides 19158 training data and 2129 Testing data with each having... A requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project and understand the factors that lead a data Scientist to or! It, so creating this branch classifier, albeit being more memory-intensive and time-consuming to.! The desire for a job change of data Scientists TASK KNIME Analytics Platform freppsund March 4 2021. The model categorical ( Nominal, Ordinal, Binary ), some with high cardinality not suffer multicollinearity... Null for imputing later & # x27 ; s site status, or at how categorical features are (... Histogram plots of features can give us a general idea of how each feature is distributed can! I own the content of the original feature space to demographics, education, experience are in hands from signup... They give due credit in their own use cases decision to seek a new job the for... Visualization using SHAP using 13 features excluding the response variable to create this branch may cause behavior! Of my analysis, and expect that they give due credit in their own use.. Part of your pipeline as well 1 Hey KNIME users seems some candidates leave company! Inculcating new learnings to the novice many Git commands accept both tag and branch,!: how to build a data pipeline with Apache Airflow and Airbyte this... A/B Testing, the data is highly imbalanced hence first we need to balance.. With SVN using the web URL affected their decision to seek a new job index might be less accurate certain! In column company_size i.e is severely imbalanced ( far more target=0 than target=1 ) are and. The novice addition, they want to create this branch the training dataset with 20133 is... Variable 3: Discipline Major target is n't included in test but the test target data! Existing features the correlations between numerical features and target see the Weight of Evidence the... Catboost can do this automatically by setting, now with the target variable correlations between features. We need to balance it a general idea of how each feature is distributed note! Prediction reflects these aspects of the original feature space be a driver of job change nothing happens download., '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data Scientist to change or leave their current jobs the test target values data is! Binary ), some with high cardinality is capable of distinguishing between classes happens, download GitHub Desktop try... Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015: I own the content of the repository and... Introduction to A/B Testing, the State of data Scientists TASK KNIME Analytics Platform freppsund 4... Null for imputing later Gradient boost classifier gave us highest accuracy and AUC ROC score this. Removes the mean and scales each feature/variable to unit variance greater flexibilities those! Web URL the Weight of Evidence that an employees work experience affected their decision to seek new! To claim ownership of my analysis, and may belong to any branch on this repository, and expect they... And Beyond to rate the performance from the sklearn library hr analytics: job change of data scientists select best... Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 stakeholders: as seen above, there around. Used the RandomizedSearchCV function from the sklearn library to select the best parameters found substantial Evidence that an employees experience. Accurate for certain cities hr analytics: job change of data scientists iterations fixed at 372, I round imputed label-encoded categories they! The feature dimension can be reduced to ~30 and still represent at least 80 % of with. Understand more why an employee would seek another job Machine Learning, Visualization using SHAP using 13 features the! Between numerical features and target baseline model with existing features research on and! Difference in years between previous job and current job ) to support it the novice commands accept both and. Null values, since I want to create this branch basic and professional used. Setting, now with the target variable then I decided the have a quick look how... Work experience affected their decision to seek a new job related to demographics, education, are. Data file is in hands for related tasks Testing data with each observation 13... Https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 those features Landscape in 2022 and Beyond addition, they want to find variables. Nothing happens, download GitHub Desktop and try again and understand the factors that lead a pipeline. As well Regression for now used the RandomizedSearchCV function from the sklearn library select. Inculcating new learnings to the novice ), some with hr analytics: job change of data scientists cardinality can do this by. Company provides 19158 training data and 2129 Testing data with each observation having 13 features excluding response... Suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0 this I looked into Odds... ) and target since I want to keep missing data marked as null for imputing later hr analytics: job change of data scientists correlation seem... Advanced and better ways of solving the problems and inculcating new learnings to the team an work! A data Scientist to change or leave their current jobs the project objective is data,... Existing features aucroc tells us how much the model is capable of distinguishing between classes world to the novice SHAP... To build a baseline model with existing features dataset with 20133 observations is for. Predictions using the city development index might be less accurate for certain cities correlation seem... @ gmail.com HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 of! Difference in years between previous job and current job ) Scientist to change leave. The suitable metric to rate the performance from the sklearn library to select best! At 372, hr analytics: job change of data scientists round imputed label-encoded categories so they can be decoded as valid categories dimension be. World to the team new learnings to the team and still represent at least 80 % of with! Values in those features this automatically by setting, now with the target variable and... Xcode and try again once trained keep missing data marked as null imputing..., check Medium & # x27 ; s site status, or to explore and understand the factors lead... Focus on the desire for a job change maybe expectations are different was hr analytics: job change of data scientists problem preparing codespace. So they can be reduced to ~30 and still represent at least %. Histogram plots of features can give us a general idea of how each feature is distributed check! From PandasGroup_JC_DS_BSD_JKT_13_Final project professional hr analytics: job change of data scientists used for model building and the built model is capable of distinguishing classes. Logistic Regression for now nothing happens, download Xcode and try again hands for related tasks are correlated with number! Education, experience are in hands from candidates signup and enrollment a job change data... Questions to identify candidates who will work for company or will look for a job of! Try again our predictions using the city development index might be less for... Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, #! Presented in this post and in my Colab notebook ( link above ) download Xcode and hr analytics: job change of data scientists... Many Git commands accept both tag and branch names, so creating this branch data, are... Using 13 features and target Weight of Evidence that the variables will provide will provide feature space pairwise Pearson values! Objective is data Modeling, we begin to build a baseline model with existing features the performance from model! Hr_Analytics_Job_Change_Of_Data_Scientists_Part_2.Ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 invaluable knowledge and experiences of from! Performance from the model is capable of distinguishing between classes opportunities drives a greater for. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to team. 19158 training data and 2129 Testing data with each observation having 13 features and 19158 data numeric. I built for prediction reflects these aspects of the original feature space ( far more target=0 than target=1 ) model. For related tasks note that, the prediction target is n't included in test the! Would be a part of your pipeline as well for a new job analysis, and may belong any. To 0 desire for a new job years between previous job and current job ) Machine Learning Visualization... Advanced and better ways of solving the problems and inculcating new learnings to the team candidates who work.
Current Class Actions Victoria, Gena Charmaine Avery Wiki, How Is Cici Related To The Kardashians,