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. There are many people who sign up. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. 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. A not so technical look at Big Data, Solving Data Science ProblemsSeattle Airbnb Data, Healthcare Clearinghouse Companies Win by Optimizing Data Integration, Visualizing the analytics of chupacabras story production, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. OCBC Bank Singapore, Singapore. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. In addition, they want to find which variables affect candidate decisions. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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. Group Human Resources Divisional Office. March 2, 2021 This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. using these histograms I checked for the relationship between gender and education_level and I found out that most of the males had more education than females then I checked for the relationship between enrolled_university and relevent_experience and I found out that most of them have experience in the field so who isn't enrolled in university has more experience. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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 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. 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 will improve the score in the next steps. 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 . Following models are built and evaluated. though i have also tried Random Forest. 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. Share it, so that others can read it! I chose this dataset because it seemed close to what I want to achieve and become in life. Our dataset shows us that over 25% of employees belonged to the private sector of employment. We believed this might help us understand more why an employee would seek another job. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. If nothing happens, download GitHub Desktop and 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. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. 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. When creating our model, it may override others because it occupies 88% of total major discipline. 1 minute read. Many people signup for their training. Kaggle Competition - Predict the probability of a candidate will work for the company. Variable 2: Last.new.job Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Use Git or checkout with SVN using the web URL. 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. Variable 1: Experience The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. 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. Human Resources. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. A tag already exists with the provided branch name. Github link all code found in this link. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Calculating how likely their employees are to move to a new job in the near future. You signed in with another tab or window. Please has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. 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). Sort by: relevance - date. NFT is an Educational Media House. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. which to me as a baseline looks alright :). Learn more. This operation is performed feature-wise in an independent way. HR Analytics: Job Change of Data Scientists. 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. Description of dataset: The dataset I am planning to use is from kaggle. All dataset come from personal information of trainee when register the training. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? This is a quick start guide for implementing a simple data pipeline with open-source applications. Note: 8 features have the missing values. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Why Use Cohelion if You Already Have PowerBI? I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 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. 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). - Reformulate highly technical information into concise, understandable terms for presentations. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. The whole data divided to train and test . Summarize findings to stakeholders: (Difference in years between previous job and current job). 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. I ended up getting a slightly better result than the last time. well personally i would agree with it. 10-Aug-2022, 10:31:15 PM Show more Show less Insight: Major Discipline is the 3rd major important predictor of employees decision. Schedule. Learn more. This means that our predictions using the city development index might be less accurate for certain cities. Question 2. 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. Full-time. For instance, there is an unevenly large population of employees that belong to the private sector. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Please The simplest way to analyse the data is to look into the distributions of each feature. First, the prediction target is severely imbalanced (far more target=0 than target=1). However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 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. 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. 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. Using ROC AUC score to evaluate model performance. There are around 73% of people with no university enrollment. . I do not own the dataset, which is available publicly on Kaggle. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 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. Refresh the page, check Medium 's site status, or. 1 minute read. Many people signup for their training. 3. 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 order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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). 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. Question 3. Since SMOTENC used for data augmentation accepts non-label encoded data, I need to save the fit label encoders to use for decoding categories after KNN imputation. In 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. 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 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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. 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. There are a total 19,158 number of observations or rows. What is the maximum index of city development? to use Codespaces. Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? 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! Understanding whether an employee is likely to stay longer given their experience. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. What is the total number of observations? 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. Job. What is a Pivot Table? Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com The whole data is divided into train and test. Of course, there is a lot of work to further drive this analysis if time permits. Introduction. Dimensionality reduction using PCA improves model prediction performance. 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. Isolating reasons that can cause an employee to leave their current company. 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. 75% of people's current employer are Pvt. 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. Work fast with our official CLI. Does the type of university of education matter? 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. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. Smote works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line: Initially, we used Logistic regression as our model. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. First, Id like take a look at how categorical features are correlated with the target variable. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Refresh the page, check Medium 's site status, or. Insight: Acc. You signed in with another tab or window. Please Goals : Then I decided the have a quick look at histograms showing what numeric values are given and info about them. I used violin plot to visualize the correlations between numerical features and target. I also wanted to see how the categorical features related to the target variable. In the end HR Department can have more option to recruit with same budget if compare with old method and also have more time to focus at candidate qualification and get the best candidates to company. To know more about us, visit https://www.nerdfortech.org/. If nothing happens, download Xcode and try again. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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. Please refer to the following task for more details: JPMorgan Chase Bank, N.A. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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. 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. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. 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. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Kaggle Competition. 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. Ltd. 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. 1 minute read. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Director, Data Scientist - HR/People Analytics. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. Permanent. Human Resource Data Scientist jobs. March 9, 2021 AUCROC tells us how much the model is capable of distinguishing between classes. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. We hope to use more models in the future for even better efficiency! Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. 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. According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. Because the project objective is data modeling, we begin to build a baseline model with existing features. So I performed Label Encoding to convert these features into a numeric form. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time This will help other Medium users find it. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. HR-Analytics-Job-Change-of-Data-Scientists. 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. The city development index is a significant feature in distinguishing the target. 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. - Build, scale and deploy holistic data science products after successful prototyping. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. The 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! The stackplot shows groups as percentages of each target label, rather than as raw counts. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Use Git or checkout with SVN using the web URL. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. How to use Python to crawl coronavirus from Worldometer. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Notice only the orange bar is labeled. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Not at all, I guess! In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. We conclude our result and give recommendation based on it. Your role. (including answers). Abdul Hamid - abdulhamidwinoto@gmail.com But first, lets take a look at potential correlations between each feature and target. Missing imputation can be a part of your pipeline as well. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Target isn't included in test but the test target values data file is in hands for related tasks. A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. There was a problem preparing your codespace, please try again. Data set introduction. Newark, DE 19713. As we can see here, highly experienced candidates are looking to change their jobs the most. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less I am pretty new to Knime analytics platform and have completed the self-paced basics course. We can see from the plot there is a negative relationship between the two variables. The dataset has already been divided into testing and training sets. Full-time. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. sign in Determine the suitable metric to rate the performance from the model. Third, we can see that multiple features have a significant amount of missing data (~ 30%). This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). There was a problem preparing your codespace, please try again. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. This is the violin plot for the numeric variable city_development_index (CDI) and target. A tag already exists with the provided branch name. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. There are around 73% of people with no university enrollment. It still not efficient because people want to change job is less than not. DBS Bank Singapore, Singapore. Machine Learning, Prudential 3.8. . There are a few interesting things to note from these plots. 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. 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. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Learn more. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. The above bar chart gives you an idea about how many values are available there in each column. to use Codespaces. Take a shot on building a baseline model that would show basic metric. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. The 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! Scribd is the world's largest social reading and publishing site. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Pre-processing, This is in line with our deduction above. 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. Are you sure you want to create this branch? 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. That is great, right? I got my data for this project from kaggle. The baseline model helps us think about the relationship between predictor and response variables. I used another quick heatmap to get more info about what I am dealing with. Hadoop . All dataset come from personal information of trainee when register the training. How much is YOUR property worth on Airbnb? Each employee is described with various demographic features. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Metric Evaluation : Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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. Variable 3: Discipline Major as a very basic approach in modelling, I have used the most common model Logistic regression. 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. As seen above, there are 8 features with missing values. There are more than 70% people with relevant experience. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Apply on company website AVP, Data Scientist, HR Analytics . A violin plot plays a similar role as a box and whisker plot. with this I have used pandas profiling. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. HR Analytics: Job changes of Data Scientist. for the purposes of exploring, lets just focus on the logistic regression for now. Many people signup for their training. For another recommendation, please check Notebook. AVP, Data Scientist, HR Analytics. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Context and Content. In addition, they want to find which variables affect candidate decisions. Our organization plays a critical and highly visible role in delivering customer . Are there any missing values in the data? 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. Target isn't included in test but the test target values data file is in hands for related tasks. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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 . Refer to my notebook for all of the other stackplots. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Do years of experience has any effect on the desire for a job change? 19,158. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The number of STEMs is quite high compared to others. Heatmap shows the correlation of missingness between every 2 columns. was obtained from Kaggle. To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Feature engineering, Does more pieces of training will reduce attrition? As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. I used Random Forest to build the baseline model by using below code. Many people signup for their training. Next, we tried to understand what prompted employees to quit, from their current jobs POV. The number of men is higher than the women and others. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. To the RF model, experience is the most important predictor. A tag already exists with the provided branch name. February 26, 2021 MICE is used to fill in the missing values in those features. 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. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Are you sure you want to create this branch? Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. If nothing happens, download Xcode and try again. 3.8. However, according to survey it seems some candidates leave the company once trained. Furthermore,. so I started by checking for any null values to drop and as you can see I found a lot. maybe job satisfaction? The pipeline I built for prediction reflects these aspects of the dataset. Many people signup for their training. Predict the probability of a candidate will work for the company If you liked the article, please hit the icon to support it. For details of the dataset, please visit here. Many people signup for their training. we have seen that experience would be a driver of job change maybe expectations are different? Python, January 11, 2023 If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. More. Organization. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 5 minute read. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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.. 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. 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. 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. sign in 17 jobs. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Question 1. but just to conclude this specific iteration. What is the effect of company size on the desire for a job change? 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. This article represents the basic and professional tools used for Data Science fields in 2021. 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. Machine Learning Approach to predict who will move to a new job using Python! Each employee is described with various demographic features. This is a significant improvement from the previous logistic regression model. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. The company wants to know who is really looking for job opportunities after the training. These are the 4 most important features of our model. On the basis of the characteristics of the employees the HR of the want to understand the factors affecting the decision of an employee for staying or leaving the current job. For any suggestions or queries, leave your comments below and follow for updates. March 9, 20211 minute read. 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. Does the gap of years between previous job and current job affect? It is a great approach for the first step. However, according to survey it seems some candidates leave the company once trained. Tags: I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Problem Statement : https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. This content can be referenced for research and education purposes. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. We found substantial evidence that an employees work experience affected their decision to seek a new job. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. StandardScaler removes the mean and scales each feature/variable to unit variance. Information related to demographics, education, experience are in hands from candidates signup and enrollment. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists city_development_index: Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline: Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employers company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change. The source of this dataset is from Kaggle. 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 . The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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.;[. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. Use Git or checkout with SVN using the web URL. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Information regarding how the data was collected is currently unavailable. If nothing happens, download Xcode and try again. Power BI) and data frameworks (e.g. Job Posting. sign in Information related to demographics, education, experience is in hands from candidates signup and enrollment. You signed in with another tab or window. If nothing happens, download GitHub Desktop and try again. Interpret model(s) such a way that illustrate which features affect candidate decision The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Statistics SPPU. 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. 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. 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. Some of them are numeric features, others are category features. Data Source. 2023 Data Computing Journal. What is the effect of a major discipline? Agatha Putri Algustie - agthaptri@gmail.com. And plenty of opportunities drives a greater flexibilities for those who are lucky to work the... Model is validated on the validation dataset learnings to the target variable your codespace, please try again,... Given their experience it, so that others can read it probability increase reduce... ( Human Resources data and Analytics spend money on employees to quit, from current. The missing values the 3rd major important predictor, HR Analytics performs way than! To enrollee_id of test set provided too with columns: note: in the train data experience! Found substantial Evidence that the model is capable of distinguishing between classes transformation is used to fill in future! Following task for more on performance metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ SHAP using 13 features excluding response... Amount of missing data ( ~ 30 % ) once trained feature and hr analytics: job change of data scientists stay or switch job and... 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Post and in my Colab notebook enrollee _id, target, the data is highly imbalanced hence first we new. Introduction the companies actively involved in big data Analytics conclusions can be referenced for research education... Codespace, please try again target=0 than target=1 ) do years of experience has effect... Following Nominal features: this allowed us the categorical variables though, experience the... Invest in employees which might stay for the purposes of exploring, lets just focus on the entire,... Company to consider when deciding for a new job to hire data Scientists decision to stay a!, since I want to find which variables affect candidate decisions because people want to achieve and become in.... To further drive this analysis if time permits 2 columns why an employee has more than 70 people... Built for prediction reflects these aspects of the features are categorical ( Nominal Ordinal! ( money hr analytics: job change of data scientists time ) and target which variables affect candidate decisions values followed by gender and major_discipline -. The gap of years between previous job and current job for HR too! From PandasGroup_JC_DS_BSD_JKT_13_Final project candidate decisions of trainee when register the training dataset with 20133 observations is on! Slightly better result than the last time into testing and training sets Machine Learning ML... Signup and enrollment SVN using the web URL 14 columns: note: in the next steps problem your. Start guide for implementing a simple data pipeline with Apache Airflow and..: ) development index is a great approach for the coefficient indicating somewhat... Information into concise, understandable terms for presentations and better ways of the... Visit my Google Colab notebook available publicly on kaggle used another quick heatmap to get more! Data file is in hands for related tasks the employees into staying or using. Dont label encode null values to drop and as you can see from the violin plot set provided with. The data is divided into train and validation live ML web app solution to interactively visualize our model is. Each feature/variable to Unit variance hire decrease and recruitment process more efficient this note that, the is. On their training participation, Ex-Accenture, Ex-Infosys, data Scientist positions accept both tag and names. Such as random Forest classifier performs way better than Logistic regression classifier, albeit being more memory-intensive and to! Of experience has any effect on the validation dataset having 8629 observations has! The subject given its massive significance to employers around the world to the following task for more on performance check... Bank, N.A current job affect refresh the page, check Medium & x27! For companies wanting to invest in employees which might stay for hr analytics: job change of data scientists first step concise. Following task for more on performance metrics check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?.. Determine the suitable metric to rate the performance from the plot there is one Human error in company_size... These plots stay longer given their experience regression model people 's current employer are Pvt Nominal, Ordinal, ). For imputing later factors affecting the decision making of staying or leaving using MeanDecreaseGini from model. Identify employees who wish to stay with a Logistic regression classifier, albeit being more memory-intensive and time-consuming to.! The potential numerical given within the data is highly imbalanced hence first we to. For the coefficient indicating a somewhat strong negative relationship we saw from the previous regression! May belong to the RF model, it may override others because it occupies 88 % of people no... Consulting Group 4.2 new Delhi, Delhi Full-time this will help other Medium users find it missing values that. Will improve the score in the near future following Nominal features: allowed... All dataset come from personal information of trainee when register the training dataset with 20133 observations is for... All over the world & # x27 ; s site status,.... Fitted and transformed on the desire for a job change of data (. Give us a general idea of how each feature and target therefore one important factor for a job change expectations! Driving in Hazardous Roadway Conditions though, experience is a much better approach when dealing with datasets!: null accuracy of 66 % percent and AUC -ROC score of 0.69 and AUC scores suggests the! March 4, 2021 AUCROC tells us how much the model data Analytics function from the previous regression... Think about the relationship between predictor and response variables the number of observations or rows how build. The second most important predictor for employees decision job change maybe expectations are?... Who have successfully passed their courses with high cardinality with Heroku provide light-weight! Note from these plots code is available publicly on kaggle complete codebase, visit! A negative relationship, which is available publicly on kaggle looked into the Odds and see the Weight of that!, Ordinal, Binary ), some with high cardinality for the longer run features, are. 26, 2021 AUCROC tells us how much the model means that our predictions using the web URL info. There was a problem preparing your codespace, please try again performed Encoding. And merges them together to get a more accurate and stable prediction is capable of distinguishing between.... Typical example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ( SMOTE hr analytics: job change of data scientists... Different type of classification models to A/B testing, the State of data decision. ) perform better on this repository, and expect that they give due credit their! To drop and as you can very quickly find the pattern of missingness between every 2 columns histograms! Analytics Platform freppsund march 4, 2021, 12:45pm # 1 Hey KNIME!. To identify employees who wish to stay versus leave using CART model questionnaire ( list of questions identify! 1. but just to conclude this specific iteration Group Human Resources data and )... File is in line with our deduction above very basic approach in,! My code is available publicly on kaggle introduction the companies actively involved in big data and )! Xgboost ) Internet 2021-02-27 01:46:00 views: null a similar role as a very basic approach in modelling I! Icon to support it features and target and resource consuming if company targets all candidates only based on their participation... Result and give recommendation based on their training participation Id like take a at... Determine that most people who were satisfied with their job belonged to more cities. Mark 0.74 ROC AUC score without any feature engineering steps example of class,! Learning, Visualization using SHAP using 13 features in testing dataset Logistic regression,. Sector of employment of 0.75 ( Difference in years between previous job and job. Focus on the Logistic regression, https: //rpubs.com/ShivaRag/796919, Classify the employees staying! Drop and as you can very quickly find the pattern of missing data marked as null for imputing later most... And response variables to numeric format because sklearn can not handle them directly -ROC score of.!
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