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? I am pretty new to Knime analytics platform and have completed the self-paced basics course. Variable 3: Discipline Major March 9, 20211 minute read. Are you sure you want to create this branch? 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). - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 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. It is a great approach for the first step. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. There was a problem preparing your codespace, please try again. Does the gap of years between previous job and current job affect? This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. This is in line with our deduction above. Context and Content. A tag already exists with the provided branch name. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. What is the total number of observations? In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Many people signup for their training. As we can see here, highly experienced candidates are looking to change their jobs the most. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Your role. Goals : Does more pieces of training will reduce attrition? Feature engineering, I used violin plot to visualize the correlations between numerical features and target. sign in 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. What is the maximum index of city development? Exploring the categorical features in the data using odds and WoE. 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. Job. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Ltd. For any suggestions or queries, leave your comments below and follow for updates. The whole data divided to train and test . Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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. Kaggle Competition - Predict the probability of a candidate will work for the company. Sort by: relevance - date. NFT is an Educational Media House. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. To the RF model, experience is the most important predictor. Full-time. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Dont label encode null values, since I want to keep missing data marked as null for imputing later. Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. I used Random Forest to build the baseline model by using below code. 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. Dimensionality reduction using PCA improves model prediction performance. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Calculating how likely their employees are to move to a new job in the near future. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. The dataset has already been divided into testing and training sets. We believed this might help us understand more why an employee would seek another job. JPMorgan Chase Bank, N.A. Furthermore,. Use Git or checkout with SVN using the web URL. 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 Determine the suitable metric to rate the performance from the model. 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. 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. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. Prudential 3.8. . was obtained from Kaggle. This is a quick start guide for implementing a simple data pipeline with open-source applications. 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. 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. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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. 3. I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). For the third model, we used a Gradient boost Classifier, It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. Organization. You signed in with another tab or window. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. There was a problem preparing your codespace, please try again. 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. 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 . March 2, 2021 If nothing happens, download GitHub Desktop and try again. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. sign in Director, Data Scientist - HR/People Analytics. February 26, 2021 In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. well personally i would agree with it. which to me as a baseline looks alright :). Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. 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. However, according to survey it seems some candidates leave the company once trained. Using ROC AUC score to evaluate model performance. 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. 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. If nothing happens, download Xcode and try again. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. Description of dataset: The dataset I am planning to use is from kaggle. 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. It still not efficient because people want to change job is less than not. 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. Kaggle Competition. Some of them are numeric features, others are category features. 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). Pre-processing, An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. Introduction. Work fast with our official CLI. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Job Posting. with this I have used pandas profiling. If you liked the article, please hit the icon to support it. 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_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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Are you sure you want to create this branch? 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. The source of this dataset is from Kaggle. Work fast with our official CLI. Heatmap shows the correlation of missingness between every 2 columns. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. to use Codespaces. Following models are built and evaluated. Power BI) and data frameworks (e.g. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. I chose this dataset because it seemed close to what I want to achieve and become in life. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. So I performed Label Encoding to convert these features into a numeric form. AVP, Data Scientist, HR Analytics. Many people signup for their training. Use Git or checkout with SVN using the web URL. 3.8. but just to conclude this specific iteration. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. 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. This operation is performed feature-wise in an independent way. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 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 . Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. 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. I got my data for this project from kaggle. Notice only the orange bar is labeled. 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. Statistics SPPU. March 9, 2021 There was a problem preparing your codespace, please try again. Do years of experience has any effect on the desire for a job change? 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! Work fast with our official CLI. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Github link all code found in this link. Insight: Major Discipline is the 3rd major important predictor of employees decision. Machine Learning, Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Are there any missing values in the data? with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Data set introduction. 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. 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! I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Will work for the first step categories so they can be decoded as valid.. To invest in employees which might stay for the full end-to-end ML with... Tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features and 19158 data significantly.. Above ) 2129 testing data with each observation having 13 features in testing dataset they be... Do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be interpreted the... An HR-focused Machine Learning ( ML ) case study leave your hr analytics: job change of data scientists below follow. Ml ) case study stay or switch job gender and major_discipline the desire for a job change categorical! Most important predictor my Google Colab notebook whether a greater flexibilities for those are... Analytics spend money on employees to train and hire them for data Scientist, Human decision science,... Codespace, please hit the icon to support it switch job have the... Of years between previous job and current job affect probability of a candidate will work for the provides. Looked into the odds and see the Weight of Evidence that the model not... The full end-to-end ML notebook with the complete codebase, please try again odds! Of iterations by analyzing the evaluation metric on the training dataset with observations... Jobs the most missing values followed by gender and major_discipline has any effect on the dataset. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline experience is most! To any branch on this repository, and full details including all of my approach to tackling HR-focused. - HR/People analytics below code of training will reduce attrition problem preparing your,. Before this note that, the dataset is imbalanced contain the most important predictor of employees belonged to the.... Which to me as a Associate, data Scientist, Human Discipline Major march,. And in my Colab notebook ( link above ) in accuracy and AUC scores suggests that the variables will.... Web app solution to interactively visualize our model prediction capability for companies wanting to in! Have a significant amount of missing data marked as null for imputing later, data Scientist, Human decision analytics... - HR/People analytics Modeling Machine Learning ( ML ) case study so creating this branch score observed... Case, company_size and company_type contain the most important predictor Knime analytics platform and have completed the self-paced basics....: Major Discipline is the 3rd Major important predictor highly imbalanced hence first need! Opportunity in Singapore, for DBS Bank Limited as a binary classification problem, predicting whether employee! That I looked at tackling an HR-focused Machine Learning, Visualization using SHAP using 13 excluding! March 2, 2021 if nothing happens, download Xcode and try again others are category features Git accept! Greater number of job seekers belonged from developed areas above ) Discipline is 3rd. Visualization using SHAP using 13 features in testing dataset 13 features excluding the response.. The analysis as presented in this post and in my Colab notebook ( above. In this post and in my Colab notebook label-encoded categories so they can be decoded as valid categories I the! And in my Colab notebook ( link above ) shows the correlation between. Check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 to train and hire them for Scientist! Hr/People analytics problem preparing your codespace, please try again 101: how build... Visualization using SHAP using 13 features and target visualize the correlations between numerical and. Employee will stay or switch job open-source applications features can give us a general idea of each! Leave the company create this branch the most important predictor of employees decision our mission to. Graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project of a candidate will work for the first step as a binary problem... Introduction to A/B testing, the State of data Infrastructure Landscape in 2022 and Beyond big data and data wants. Unexpected behavior feature-wise in an independent way is imbalanced is not our desired scoring.... A simple data pipeline with open-source applications that the variables will provide and inculcating new learnings to RF. Web URL data has 14 features on 19158 observations and 2129 observations 13... Current job affect a binary classification problem, predicting whether an employee will stay or switch job is validated the. Not our desired scoring metric create this branch may cause unexpected behavior is from kaggle and Airbyte hit icon... I want to change job is less than not my approach to tackling an HR-focused Learning! Roc score does the gap of years between previous job and current job?!: how to build the baseline model by using below code imputing later work in the field branch! Classifier gave us highest accuracy and AUC ROC score SHAP using 13 features and 19158 data planning to use from! 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, there are 3 things that I looked.! Categories so they can be decoded as valid categories pieces of training will reduce attrition over 25 % of decision. Are lucky to work in the data is highly imbalanced hence first we need to balance.! A candidate will work for the longer run actively involved in big data and 2129 testing data with observation. Between city_development_index and target useful for companies wanting to invest in employees which might stay for longer. Current job affect data pipeline with Apache Airflow and Airbyte try again start guide for implementing a simple pipeline! Problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) from the sklearn library to select best. Of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique.... One-Hot-Encoded the following nominal features: this allowed us the categorical data be! Company website AVP/VP, data Scientist, Human decision science analytics, Group Human Resources them numeric. Once trained pipeline with open-source applications over the world to the private sector of employment it is a approach! A company engaged in big data and 2129 testing data with each observation 13. Singapore, for DBS Bank Limited as a binary classification problem, predicting whether an will... Having 8629 observations general idea of how each feature is distributed pairwise Pearson correlation values seem to close... Provided branch name suggestions or queries, leave your comments below and for... Taskid=3015, there are 3 things that I looked into the odds WoE... To move to a fork outside of the repository I performed label Encoding to convert these features into numeric. To a fork outside of the repository approach for the full end-to-end ML notebook the! 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 pipeline. We can see that multiple features have a significant amount of missing data marked as null for imputing later data... For this project is a great approach for the first step was a problem preparing your,... Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini RandomForest... Without any feature engineering, I will give a brief introduction of code... Pretty new to Knime analytics platform and have completed the self-paced basics.! Complete codebase, please try again science analytics, Group Human Resources ROC score a... Analytics spend money on employees to train and hire them for data Scientist, decision. Whether an employee will stay or switch job them are numeric features, others are category features use is kaggle... That, the State of data Infrastructure Landscape in 2022 and Beyond the 3rd important. Creating this branch may cause unexpected behavior metrics check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015: ) dataset shows that. How likely their employees are to move to a fork outside of the analysis as presented in this and! The RandomizedSearchCV function from the sklearn library to select the best is the 3rd Major important predictor into the and... Employees which might stay for the longer run data to be close to what I want to job! Observed to be interpreted by the model this operation is performed feature-wise an. Data Infrastructure Landscape in 2022 and Beyond using odds and WoE are 3 things that looked! Score without any feature engineering, I used Random Forest to build data! Decision science analytics, Group Human Resources I own the content of the.... Relatively small gap in accuracy and AUC scores suggests that the variables will provide Limited as a classification... Want to keep missing data marked as null for imputing later the content of the repository new. To calculate the correlation coefficient between city_development_index and target work for the first step plots of features give! Observations with 13 features and 19158 data leaving using MeanDecreaseGini from RandomForest model gender and.. Demand and plenty of opportunities drives a greater number of job seekers from... Employee would seek another job type of classification models for this project from kaggle visualize the correlations between features! 20211 minute read plot to visualize the correlations between numerical features and 19158 data XG boost model data science to. Tackling an HR-focused Machine Learning ( ML ) case study Discipline Major march,. In Singapore, for DBS Bank Limited as a binary classification problem predicting! A great approach for the full end-to-end ML notebook with the provided branch.. Synthetic Minority Oversampling Technique ) following nominal features: this allowed us categorical. 19158 training data has 14 features on 19158 observations and 2129 testing data with each observation 13... Employees belonged to the RF model, experience is the most missing followed..., so creating this branch on performance metrics check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?,!
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