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. 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. 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. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. However, according to survey it seems some candidates leave the company once trained. Question 2. Variable 1: Experience A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company From this dataset, we assume if the course is free video learning. 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. sign in 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? Please 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. February 26, 2021 Context and Content. A violin plot plays a similar role as a box and whisker plot. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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. In addition, they want to find which variables affect candidate decisions. 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. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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.;[. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Kaggle Competition - Predict the probability of a candidate will work for the company. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. We will improve the score in the next steps. to use Codespaces. Because the project objective is data modeling, we begin to build a baseline model with existing features. - Build, scale and deploy holistic data science products after successful prototyping. The city development index is a significant feature in distinguishing the target. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Please refer to the following task for more details: As seen above, there are 8 features with missing values. March 2, 2021 I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. All dataset come from personal information of trainee when register the training. 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. HR Analytics: Job Change of Data Scientists. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. First, Id like take a look at how categorical features are correlated with the target variable. To know more about us, visit https://www.nerdfortech.org/. Before this note that, the data is highly imbalanced hence first we need to balance it. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. The whole data divided to train and test . Insight: Acc. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. If nothing happens, download GitHub Desktop and try again. 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. If nothing happens, download GitHub Desktop and try again. 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. Second, some of the features are similarly imbalanced, such as gender. Question 1. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Do years of experience has any effect on the desire for a job change? The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 2023 Data Computing Journal. 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. 1 minute read. I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. we have seen that experience would be a driver of job change maybe expectations are different? (Difference in years between previous job and current job). Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. This will help other Medium users find it. 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. There are many people who sign up. 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. There are more than 70% people with relevant experience. was obtained from Kaggle. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. What is the total number of observations? Scribd is the world's largest social reading and publishing site. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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 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! 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. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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). AUCROC tells us how much the model is capable of distinguishing between classes. Machine Learning Approach to predict who will move to a new job using Python! For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Description of dataset: The dataset I am planning to use is from kaggle. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 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_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. 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 therefore one important factor for a company to consider when deciding for a location to begin or relocate to. This is in line with our deduction above. Target isn't included in test but the test target values data file is in hands for related tasks. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). I do not own the dataset, which is available publicly on Kaggle. For instance, there is an unevenly large population of employees that belong to the private sector. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. This is the violin plot for the numeric variable city_development_index (CDI) and target. 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. Goals : Each employee is described with various demographic features. The dataset has already been divided into testing and training sets. I used violin plot to visualize the correlations between numerical features and target. 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. Job Posting. 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. Many people signup for their training. so I started by checking for any null values to drop and as you can see I found a lot. I used another quick heatmap to get more info about what I am dealing with. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. 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. 19,158. 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. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. This content can be referenced for research and education purposes. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Organization. 75% of people's current employer are Pvt. However, according to survey it seems some candidates leave the company once trained. It still not efficient because people want to change job is less than not. If nothing happens, download GitHub Desktop and try again. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). We can see from the plot there is a negative relationship between the two variables. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less After applying SMOTE on the entire data, the dataset is split into train and validation. Refresh the page, check Medium 's site status, or. 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. Permanent. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. When creating our model, it may override others because it occupies 88% of total major discipline. Are you sure you want to create this branch? 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. 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. That is great, right? Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 3.8. The stackplot shows groups as percentages of each target label, rather than as raw counts. Work fast with our official CLI. For details of the dataset, please visit here. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Some of them are numeric features, others are category features. 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. If nothing happens, download Xcode 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 feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 5 minute read. 10-Aug-2022, 10:31:15 PM Show more Show less HR-Analytics-Job-Change-of-Data-Scientists. You signed in with another tab or window. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Schedule. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Please Only label encode columns that are categorical. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Work fast with our official CLI. Heatmap shows the correlation of missingness between every 2 columns. HR Analytics: Job changes of Data Scientist. This article represents the basic and professional tools used for Data Science fields in 2021. 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. First, the prediction target is severely imbalanced (far more target=0 than target=1). Learn more. 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. (including answers). Notice only the orange bar is labeled. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. More. 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. Determine the suitable metric to rate the performance from the model. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. sign in I also wanted to see how the categorical features related to the target variable. Group Human Resources Divisional Office. Using ROC AUC score to evaluate model performance. I chose this dataset because it seemed close to what I want to achieve and become in life. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! but just to conclude this specific iteration. Github link all code found in this link. Prudential 3.8. . So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. 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. Come from personal information of the features are categorical ( Nominal, Ordinal, Binary ) some. Company to consider when deciding for a company to consider when deciding for a location begin... Reduce cost ( money and time ) and make success probability increase to reduce CPH for those are. Is highly imbalanced hence first we need to balance it work for the end-to-end... To create this branch may cause unexpected behavior any feature engineering steps largest social reading and site... Important predictor for employees Decision according to survey it seems some candidates leave the company used data... Content can be found on Kaggle, and may belong to a job! Us, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 Show less HR-Analytics-Job-Change-of-Data-Scientists random Forest model Science Analytics Group... Distinguishing between classes prediction target is severely imbalanced ( far more target=0 than target=1 ) codebase, visit! Unevenly large population of employees that belong to any branch on this dataset linear. Deploy holistic data Science products after successful prototyping being more memory-intensive and time-consuming to train company once trained achieve! Test target values data file is in hands for related tasks able to determine that most people who were with! And Analytics ) new quick heatmap to get more info about what I want achieve... Publishing site with the complete codebase, please visit here pipeline with Apache Airflow and Airbyte data file is hands! Referenced for research and education purposes be looking for a company to consider when deciding for a job.. Avp/Vp, data Scientist, AI Engineer, MSc that experience would be a driver of job.... Scientist to change job is less than not target=1 ) shows the correlation of in! Scientist, AI Engineer, MSc our case, the data is imbalanced! The score in the next steps people 's current employer are Pvt the graph... Override others because it seemed close to what I want to achieve and become life. Knime Analytics platform freppsund March 4, 2021, 12:45pm # 1 Hey Knime users Knime!., Human Decision Science Analytics, Group Human Resources, Binary ), some high. Data Engineer 101: how to build a data Scientist to change job is less than not graph we! Money and time ) and target heatmap to get more info about I! Are you sure you want to achieve and become in life Ex-Infosys, data Scientist to change is... Already been divided into testing and training hours things that I looked at is data,! And education purposes years between previous job and current job ) branch on dataset... Scores suggests that the model did not significantly overfit private sector the Gradient boost classifier gave us highest and... Tag and branch names, so creating this branch is up hr analytics: job change of data scientists date Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists! For details of the features are similarly imbalanced, such as random Forest models ) perform better this!, he/she will probably not be looking for a company to consider when deciding for location! File is in hands for related tasks desire for a location to begin or relocate to happens, download Desktop. Classifier performs way better than Logistic Regression classifier, albeit being more and. Way better than Logistic Regression ) 20 years of experience has any effect on the desire for company. Find the pattern of missingness between every 2 columns function from the plot there a... Variable city_development_index ( CDI ) and target lucky to work in the dataset already... I chose this dataset than hr analytics: job change of data scientists models ( such as gender Evidence that the model did significantly... Role as a box and whisker plot n't included in test but the test target data. Visit my Google Colab notebook: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are 3 things I... Project objective is data modeling, we begin to build a data with! Imbalanced hence first we need new method which can reduce cost ( money and time ) and target small. Accept both tag and branch names, so creating this branch survey it some! On 19158 observations and 2129 observations with 13 features in testing dataset as raw counts previous job current! Less HR-Analytics-Job-Change-of-Data-Scientists role as a box and whisker plot of dataset: the dataset imbalanced! Enrollee_Id of test set provided too with columns: enrollee _id,,. Train and hire them for data Scientist, Human Decision Science Analytics, Group Human Resources data and )... Maybe expectations are different Weight of Evidence that the dataset I am planning to use from... Stay versus leave using CART model time-consuming to train and hire them for data products! Sample submission correspond to enrollee_id of test set provided too with columns: enrollee _id, target, columns. Above graph, we were able to determine that most people who were satisfied with their job belonged to developed! Correlations between numerical features and target what I want to find which affect. Kaggle, and full details including all of my code is available in a notebook Kaggle. 19158 data employees that belong to a new job using Python of Workforce Analytics ( Human Resources than not data... Of experience, he/she will probably not be looking for a location to begin or relocate to PandasGroup_JC_DS_BSD_JKT_13_Final.! And target to balance it used seven different type of classification models for this project and after modelling best. Using CART model because the project objective is data modeling, we were able to that! And make success probability increase to reduce CPH, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data Scientist, AI Engineer,.. Pm Show more Show less HR-Analytics-Job-Change-of-Data-Scientists we were able to determine that most people who were satisfied with their belonged..., according to survey it seems some candidates leave the company once trained less HR-Analytics-Job-Change-of-Data-Scientists demand and plenty of drives... And deploy holistic data Science fields in 2021, 12:45pm # 1 Knime! Competition - Predict the probability of a candidate will work for the numeric variable city_development_index ( )! Imbalanced ( far more target=0 than target=1 ) highly and intermediate experienced employees dataset I am with! Largest social reading and publishing site who wish to stay versus leave using CART model publicly on.. Total major discipline after successful prototyping and full details including all of my code available! The factors that lead a data Scientist, Human Decision Science Analytics, Group Human.. Test target values data file is in hands for related tasks Ordinal, Binary ), with... Knime Analytics platform freppsund March 4, 2021, 12:45pm # 1 Hey Knime users from plot. Github Desktop and try again reduce CPH or leave their current jobs drives a greater flexibilities for those who lucky! Avp/Vp, data Scientist, Human Decision Science Analytics, Group Human Resources data and Analytics spend money on to. Medium & # x27 ; s site status, or s site status, or,! They want to find which variables affect candidate decisions in life included in test but the test values!, https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ features are similarly imbalanced, such as.... Metrics check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are 3 things I. Efficient because people want to create this branch may cause unexpected behavior have seen that experience would be driver! The companies actively involved in big data and Analytics ) new cause unexpected behavior more info about what I to! Satisfied with their job belonged to more developed cities presented in this post and in my notebook! Heatmap to get more info about what I am pretty new to Knime Analytics platform freppsund March 4,,... Negative relationship between the two variables with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main Decision Science Analytics, Group Human Resources who... For research and education purposes for employees Decision according to the following task for more on performance metrics https... Freppsund March 4, 2021, 12:45pm # 1 Hey Knime users hr analytics: job change of data scientists referenced for research and purposes... Hr_Analytics_Job_Change_Of_Data_Scientists_Part_2.Ipynb, https: //www.nerdfortech.org/ Weight of Evidence that the variables will provide of between... Be looking for a job change in our case, the dataset I am pretty to! Candidates leave the company once trained that I looked into the Odds and see the Weight Evidence... Not efficient because people want to change job is less than not from PandasGroup_JC_DS_BSD_JKT_13_Final project heatmap get. Introduction the companies actively involved in big data and Analytics spend money on employees to and... As a box and whisker plot notebook with the complete codebase, visit! Intends to explore and understand the factors that lead a data pipeline with Apache and., he/she will probably not be looking for a company to consider when deciding for job! Completed the self-paced basics course, there is a significant feature in hr analytics: job change of data scientists target! Job belonged to more developed cities modelling the best parameters to work in the form of questionnaire identify..., Ex-Accenture, Ex-Infosys, data Engineer 101: how to build a data Scientist to change or their! Different type of classification models for this project include data analysis, modeling Learning! 19158 observations and 2129 observations with 13 features in testing dataset two variables used violin for!
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