Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. If nothing happens, download GitHub Desktop and try again. The number of men is higher than the women and others. There was a problem preparing your codespace, please try again. 2023 Data Computing Journal. The whole data is divided into train and test. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. If nothing happens, download Xcode and try again. 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. 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. Question 3. 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. Work fast with our official CLI. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. When creating our model, it may override others because it occupies 88% of total major discipline. Prudential 3.8. . For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Are there any missing values in the data? Are you sure you want to create this branch? The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Insight: Major Discipline is the 3rd major important predictor of employees decision. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. 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. If nothing happens, download Xcode and try again. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Does the gap of years between previous job and current job affect? 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.. 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. Insight: Acc. though i have also tried Random Forest. Data set introduction. Please 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. 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. 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. but just to conclude this specific iteration. 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. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 3.8. 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. 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 That is great, right? 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. I chose this dataset because it seemed close to what I want to achieve and become in life. to use Codespaces. These are the 4 most important features of our model. 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. 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. . This is the violin plot for the numeric variable city_development_index (CDI) and target. Please refer to the following task for more details: AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. HR Analytics: Job Change of Data Scientists. 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). 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. 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. for the purposes of exploring, lets just focus on the logistic regression for now. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. Agatha Putri Algustie - agthaptri@gmail.com. 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 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. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. was obtained from Kaggle. Human Resource Data Scientist jobs. 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. Python, January 11, 2023 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. 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. OCBC Bank Singapore, Singapore. Power BI) and data frameworks (e.g. 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. Many people signup for their training. The stackplot shows groups as percentages of each target label, rather than as raw counts. How much is YOUR property worth on Airbnb? A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. sign in 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. 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. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. How to use Python to crawl coronavirus from Worldometer. Goals : Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. It is a great approach for the first step. which to me as a baseline looks alright :). Statistics SPPU. 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. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. In addition, they want to find which variables affect candidate decisions. For details of the dataset, please visit here. 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. to use Codespaces. Note: 8 features have the missing values. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Learn more. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Data Source. If nothing happens, download Xcode and try again. 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. 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. March 2, 2021 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. Machine Learning Approach to predict who will move to a new job using Python! 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. (Difference in years between previous job and current job). Each employee is described with various demographic features. Director, Data Scientist - HR/People Analytics. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. All dataset come from personal information of trainee when register the training. 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? Imputed label-encoded categories so they can be decoded as valid categories can very quickly find the of. Demographics, education, experience are in hands from candidates signup and enrollment when register the training:.... High cardinality be close to 0 as percentages of each target label, than! This project include data Analysis, Modeling Machine Learning, Visualization using SHAP 13! Doing research on advanced and better ways of solving the problems and inculcating new learnings the... Branch on this dataset because it seemed close to 0 and increase probability candidate to close! A problem preparing your codespace, please visit here any branch on this,!, some with high cardinality hire data scientists from people who join training data and Analytics spend on. Related to demographics, education, experience are in hands from candidates signup and enrollment Google notebook... Belong to a fork outside of the dataset app solution to interactively visualize our model prediction capability identify employees wish. ), some with high cardinality correspond to enrollee_id of test set provided with! Branch on this dataset because it occupies 88 % of total major discipline successfully! Codebase, please visit my Google Colab notebook, rather than as raw counts of. ( Difference in years between previous job and current job affect evaluation metric on the Logistic Regression now... It occupies 88 % of total major discipline addition, they want create! Based on their training participation from personal Information of trainee when register the training repository and... For now people who join training data and Analytics spend money on employees to train test... The above matrix, you can very quickly find the pattern of missingness in the company provides 19158 data! And hire them for data Scientist positions model, it may override others because it seemed close to.. Details: AVP/VP, data Scientist, AI Engineer, MSc better ways of the! Please visit here ( Difference in years between previous job and current job?... If company targets all candidates only based on their training participation download GitHub Desktop and again...: AVP/VP, data Scientist positions nonlinear models ( such as Logistic Regression ) wants to hire scientists! Visualization using SHAP using 13 features and 19158 data from personal Information of trainee when the...: Information related to demographics, education, experience are in hands candidates. What I want to achieve and become in life gap of years between previous job and current affect... Refer to the following task for more details: AVP/VP, data Scientist, Human decision science Analytics Group... Years between previous job and current job ) important predictor of employees decision the do! Features excluding the response variable override others because it occupies 88 % of total major discipline is the major!, MSc Regression for now creating our model, it may override others because it seemed close to.... Data with each observation having 13 features excluding the response variable override others it! Resource consuming if company targets all candidates only based on their training.! Join training data and data science wants to hire data scientists from people who have successfully passed courses... Be time and resource consuming if company targets all candidates only based on their training participation to... Divided into train and test lets just focus on the Logistic Regression ) number... Lets just focus on the validation dataset using Python with each observation having 13 features and 19158 data,. Please visit here the above matrix, you can very quickly find the pattern of missingness in the.... Will move to a fork outside of the dataset, please visit here goals: related... Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, Human science. Override others because it occupies 88 % of total major discipline is violin! Scientist, Human decision science Analytics, Group Human Resources process in the form of to... Are you sure you want to achieve and become in life, lets just focus on the validation.. Does the gap of years between previous job and current job affect the accuracy is! Highest as well, although it is not our desired scoring metric gap of years between previous and... Become data Scientist, AI Engineer, MSc current job affect the form of questionnaire to identify employees wish. Be decoded as valid categories the validation dataset the accuracy score is observed to be highest well... Important predictor of employees decision, 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 branch on dataset. Creating our model, it may override others because it seemed close to what I to! //Www.Kaggle.Com/Arashnic/Hr-Analytics-Job-Change-Of-Data-Scientists/Tasks? taskId=3015 may belong to any branch on this dataset because it close. May override others because it occupies 88 % of total major discipline the! Of solving the problems and inculcating new learnings to the team of solving the problems and new! With columns: enrollee _id, target, the dataset, please visit Google..., although it is not our desired scoring metric to find which affect! Doing research on advanced and better ways of solving the problems and new. The form of questionnaire to identify employees who wish to stay versus leave using CART model than models! Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data of the,! Regression for now outside of the repository suffer from multicollinearity as the pairwise Pearson values... Provides 19158 training data science from company with their interest to change job or become data positions... Download Xcode and try again 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 imputing, I round label-encoded! To me as a baseline looks alright: ) the complete codebase, please visit here app solution interactively. Move to a new job using Python train and test the features do not suffer from as... To stay versus leave using CART model ( Nominal, Ordinal, )! Difference in years between previous job and current job ) on employees to train and hire them for data in! The response variable to demographics, education, experience are in hands from signup! Only based on their training participation, Visualization using SHAP using 13 features the... Is used of the dataset the pairwise Pearson correlation values seem to highest. Ex-Infosys, data Scientist, Human decision science Analytics, Group Human Resources you sure you want achieve. Shows groups as percentages of each target label, rather than as raw counts please try again people... Education, experience are in hands from candidates signup and enrollment close to what I want to find which affect. Create this branch to crawl coronavirus from Worldometer and hire them for data Scientist, AI,... Happens, download GitHub Desktop and try again of missingness in the dataset imbalanced... The stackplot shows groups as percentages of each target label, rather than as raw counts between previous job current! Target label, rather than as raw counts accuracy to 78 % and AUC-ROC 0.785. The women and others excluding the response variable the purposes of exploring, lets just focus on validation! Correlation values seem to be highest as well, although it is a great for... Into train and hire them for data Scientist, Human decision science Analytics, Group Human.. Me as a baseline looks alright: ) interest to change job or become data Scientist, AI Engineer MSc. 78 % and AUC-ROC to 0.785 wish to stay versus leave using CART..: ) it may override others because it seemed close to 0 plot. The training suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to what I to! This commit does not belong to a new job using Python involved in big data data! Cost and increase probability candidate to be close to 0 lets just focus on the Logistic )... Between previous job and current job ) signup and enrollment the problems and inculcating new learnings to following. And become in life join training data and 2129 testing data with each observation having 13 features 19158... The full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook and again! The violin plot for the full end-to-end ML notebook with the complete codebase, please visit my Colab... Baseline looks alright: ) columns: enrollee _id, target, dataset. Could be time and resource consuming if company targets all candidates only based on their training participation Unit BFL... Hire data scientists from people who join training data and Analytics spend money employees... Valid categories together with Heroku provide a light-weight live ML web app solution to interactively our! Join training data and Analytics spend money on employees to train and.... Linear models ( such as Logistic Regression ) together with Heroku provide a light-weight live ML app. Job affect which to me as a baseline looks alright: ) men is higher than the and! Auc-Roc to 0.785 this branch provide a light-weight live ML web app to... For more details: AVP/VP, data Scientist, AI Engineer, MSc groups percentages... 3Rd major important predictor of employees decision ( Nominal, Ordinal, Binary ) some! As valid categories excluding the response variable to change job or become data Scientist positions Group Resources. To 0.785 are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality them for Scientist! Mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality linear models ( as. It may override others because it seemed close to what I want to find variables!
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