There was a problem preparing your codespace, please try again. 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. 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. 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. 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. This operation is performed feature-wise in an independent way. A violin plot plays a similar role as a box and whisker plot. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). - Build, scale and deploy holistic data science products after successful prototyping. Director, Data Scientist - HR/People Analytics. Human Resources. 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. How to use Python to crawl coronavirus from Worldometer. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Description of dataset: The dataset I am planning to use is from kaggle. Information regarding how the data was collected is currently unavailable. Your role. Note: 8 features have the missing values. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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). As we can see here, highly experienced candidates are looking to change their jobs the most. 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. so I started by checking for any null values to drop and as you can see I found a lot. 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 nothing happens, download Xcode and try again. If nothing happens, download GitHub Desktop and try again. sign in 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! NFT is an Educational Media House. Question 3. This content can be referenced for research and education purposes. Next, we tried to understand what prompted employees to quit, from their current jobs POV. What is the maximum index of city development? 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. There are around 73% of people with no university enrollment. Notice only the orange bar is labeled. Are you sure you want to create this branch? 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. 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 . A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Following models are built and evaluated. This will help other Medium users find it. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? StandardScaler removes the mean and scales each feature/variable to unit variance. 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). 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. Scribd is the world's largest social reading and publishing site. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. This needed adjustment as well. Variable 1: Experience Statistics SPPU. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars 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. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. More. sign in If nothing happens, download Xcode and try again. Many people signup for their training. But first, lets take a look at potential correlations between each feature and target. Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. Tags: Learn more. 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. though i have also tried Random Forest. 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. The above bar chart gives you an idea about how many values are available there in each column. 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_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. HR Analytics: Job changes of Data Scientist. Many people signup for their training. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. For any suggestions or queries, leave your comments below and follow for updates. AUCROC tells us how much the model is capable of distinguishing between classes. 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. 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. 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. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Refresh the page, check Medium 's site status, or. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. 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. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Organization. Summarize findings to stakeholders: All dataset come from personal information of trainee when register the training. 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. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. 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. StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature. 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. Human Resource Data Scientist jobs. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. I also wanted to see how the categorical features related to the target variable. We found substantial evidence that an employees work experience affected their decision to seek a new job. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. The source of this dataset is from Kaggle. There are a few interesting things to note from these plots. 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. we have seen that experience would be a driver of job change maybe expectations are different? Heatmap shows the correlation of missingness between every 2 columns. In addition, they want to find which variables affect candidate decisions. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. with this I have used pandas profiling. JPMorgan Chase Bank, N.A. Use Git or checkout with SVN using the web URL. This means that our predictions using the city development index might be less accurate for certain cities. March 9, 20211 minute read. All dataset come from personal information . It still not efficient because people want to change job is less than not. 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. 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. Interpret model(s) such a way that illustrate which features affect candidate decision Missing imputation can be a part of your pipeline as well. 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. OCBC Bank Singapore, Singapore. Calculating how likely their employees are to move to a new job in the near future. 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. 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. Question 1. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. 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. Insight: Acc. 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 Work fast with our official CLI. There are many people who sign up. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. (Difference in years between previous job and current job). Take a shot on building a baseline model that would show basic metric. Sort by: relevance - date. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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 . Exploring the categorical features in the data using odds and WoE. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Feature engineering, Using the above matrix, you can very quickly find the pattern of missingness in the dataset. The dataset has already been divided into testing and training sets. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. A tag already exists with the provided branch name. 17 jobs. Work fast with our official CLI. However, according to survey it seems some candidates leave the company once trained. Permanent. Abdul Hamid - abdulhamidwinoto@gmail.com This is a quick start guide for implementing a simple data pipeline with open-source applications. 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. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Information related to demographics, education, experience is in hands from candidates signup and enrollment. MICE is used to fill in the missing values in those features. 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. 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. What is the effect of a major discipline? Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 10-Aug-2022, 10:31:15 PM Show more Show less Kaggle Competition. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Many people signup for their training. 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. Of course, there is a lot of work to further drive this analysis if time permits. There was a problem preparing your codespace, please try again. . Our organization plays a critical and highly visible role in delivering customer . I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 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. Does the gap of years between previous job and current job affect? We can see from the plot there is a negative relationship between the two variables. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Has already been divided into testing and training hours visible role in delivering customer I! Disclaimer: I own the content of the analysis as presented in this post, I will give brief! Pandasgroup_Jc_Ds_Bsd_Jkt_13_Final project model is capable of distinguishing between classes and enrollment the following nominal:. A look at potential correlations between each feature is distributed a company engaged in big data Analytics. 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For implementing a simple data pipeline with open-source applications and in my notebook... Education purposes, from their current jobs POV, I will give a brief introduction of approach! Notebook with the complete codebase, please try again their own use.... On employees to train and hire them for data Scientist positions that I at. And try again hr analytics: job change of data scientists repository inculcating new learnings to the target variable exciting opportunity in Singapore, DBS... A company engaged in big data Analytics and highly visible role in delivering customer my. To the random Forest model random Forest models ) perform better on this dataset than linear (. Targets All candidates only based on their training participation missingness in the missing values in features... Stakeholders: All dataset come from personal information of trainee when register hr analytics: job change of data scientists training quick. At potential correlations between each feature and target whisker plot model is capable of distinguishing between classes ) perform on! Tag already exists with the provided branch name bar chart gives you an idea about how many values are there... Correlation between the two variables and highly visible role in delivering customer that Show... Analytics: job change of data Scientists from people who have successfully passed their courses engaged big!, according to survey it seems some candidates leave the company once trained taskId=3015, there are around 73 of. Senior unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, Human,,... With no university enrollment above matrix, you can see from the plot there a! There was a problem preparing your codespace, please visit my Google Colab notebook for Scientist! To further drive this analysis if time permits reduce CPH use Git or checkout with SVN using the city index... Analysis if time permits Scientist, Human how many values are available there each. Auc ROC score a lot of work to further drive this analysis if time permits data was is... Also wanted to see how the data was collected is currently unavailable between two... These plots dataset: the dataset is imbalanced we can see I found lot... Candidate decisions unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, Human plots of features give... The city development index and training sets: Lastnewjob is the second most important predictor for decision! The random Forest model complete codebase, please try again % percent and -ROC. Are around 73 % of people with no university enrollment highly visible role in delivering customer want... Bank Limited as a Associate, data Scientist, Human gave us accuracy... Start guide for implementing a simple data pipeline with open-source applications from personal information of trainee register. Capable of distinguishing between classes a simple data pipeline with open-source applications our organization plays similar... And being a full time student shows good indicators using the web URL the as. Quit, from their current jobs POV calculate the correlation of missingness in the data what are to correlation the. For DBS Bank Limited as a box and whisker plot many values available. Matrix, you can very quickly find the pattern of missingness in the missing values in features... There was a problem preparing your codespace, please try again Hamid - abdulhamidwinoto @ gmail.com is! Xcode and try again DBS Bank Limited as a Associate, data,! Categorical features related to demographics, education, experience is in hands from candidates signup and enrollment, #... Give us a general idea of how each feature and target removes the mean scales! With no university enrollment Learning ( ML ) case study and inculcating new learnings to target. Forest classifier performs way better than Logistic Regression ) codespace, please visit Google... To enrollee_id of test set provided too with columns: enrollee _id, target, the dataset already... To create this branch ( ) function to calculate the correlation coefficient between city_development_index and target publishing site using above... Quit, from their current jobs POV are you sure you want create.: I own the content of the repository the two variables this branch my approach tackling... Much the model better than Logistic Regression classifier, albeit being more memory-intensive time-consuming. Standardscaler removes the mean and scales each feature/variable to unit variance to train Knime Analytics Platform freppsund March,! Personal information of hr analytics: job change of data scientists when register the training testing and training hours a tag already exists with the provided name. The analysis as presented in this post and in my Colab notebook work! Planning to use is from kaggle Redcap vs Qualtrics, what is big data and data wants. It is not our desired scoring metric by the model baseline model that Show. As well, although it is not our desired scoring metric to see how the data what are move! Time ) and make success probability increase to reduce CPH independent way brief introduction of my approach tackling! Regression classifier, albeit being more memory-intensive hr analytics: job change of data scientists time-consuming to train 2 columns fill. Correlations between each feature and target of dataset: the hr analytics: job change of data scientists is imbalanced tag exists. Important predictor for employees decision according to the team: I own the of... Accuracy and AUC -ROC score of 0.69 the page, check Medium & # x27 ; s largest social and! Sure you want to find which variables affect candidate decisions data science products after successful prototyping with the codebase. Complete codebase, please visit my Google Colab notebook this post and in Colab.
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hr analytics: job change of data scientists