Our dataset shows us that over 25% of employees belonged to the private sector of employment. 5 minute read. 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. The dataset has already been divided into testing and training sets. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Please refer to the following task for more details: Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Question 1. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. This means that our predictions using the city development index might be less accurate for certain cities. HR-Analytics-Job-Change-of-Data-Scientists. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. JPMorgan Chase Bank, N.A. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. 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. was obtained from Kaggle. 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.. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. The number of men is higher than the women and others. If you liked the article, please hit the icon to support it. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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. I also wanted to see how the categorical features related to the target variable. . 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. These are the 4 most important features of our model. 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? This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. sign in I do not own the dataset, which is available publicly on Kaggle. So I finished by making a quick heatmap that made me conclude that the actual relationship between these variables is weak thats why I always end up getting weak results. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Learn more. There was a problem preparing your codespace, please try again. Furthermore,. Take a shot on building a baseline model that would show basic metric. There was a problem preparing your codespace, please try again. Feature engineering, The baseline model helps us think about the relationship between predictor and response variables. Predict the probability of a candidate will work for the company We conclude our result and give recommendation based on it. Data Source. You signed in with another tab or window. For details of the dataset, please visit here. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. - Reformulate highly technical information into concise, understandable terms for presentations. 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. What is a Pivot Table? Many people signup for their training. 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 This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Determine the suitable metric to rate the performance from the model. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Apply on company website AVP, Data Scientist, HR Analytics . Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 10-Aug-2022, 10:31:15 PM Show more Show less Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. It is a great approach for the first step. For any suggestions or queries, leave your comments below and follow for updates. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. How much is YOUR property worth on Airbnb? Job Posting. 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. 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. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. 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 Gradient boost Classifier gave us highest accuracy and AUC ROC score. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. with this I have used pandas profiling. Problem Statement : 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. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. to use Codespaces. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. This is the violin plot for the numeric variable city_development_index (CDI) and target. 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. Understanding whether an employee is likely to stay longer given their experience. 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. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Calculating how likely their employees are to move to a new job in the near future. Human Resource Data Scientist jobs. 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. 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. 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. Your role. MICE is used to fill in the missing values in those features. So I performed Label Encoding to convert these features into a numeric form. Agatha Putri Algustie - agthaptri@gmail.com. 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 From this dataset, we assume if the course is free video learning. What is the maximum index of city development? Interpret model(s) such a way that illustrate which features affect candidate decision Variable 2: Last.new.job A tag already exists with the provided branch name. Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). There are around 73% of people with no university enrollment. A tag already exists with the provided branch name. Human Resources. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Group Human Resources Divisional Office. In addition, they want to find which variables affect candidate decisions. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. There are more than 70% people with relevant experience. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. 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. This needed adjustment as well. AUCROC tells us how much the model is capable of distinguishing between classes. Metric Evaluation : Of course, there is a lot of work to further drive this analysis if time permits. though i have also tried Random Forest. 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. HR Analytics: Job changes of Data Scientist. Question 2. Missing imputation can be a part of your pipeline as well. Dont label encode null values, since I want to keep missing data marked as null for imputing later. The source of this dataset is from Kaggle. Github link all code found in this link. Following models are built and evaluated. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. Notice only the orange bar is labeled. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. Some of them are numeric features, others are category features. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Work fast with our official CLI. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. When creating our model, it may override others because it occupies 88% of total major discipline. Second, some of the features are similarly imbalanced, such as gender. XGBoost and Light GBM have good accuracy scores of more than 90. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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. 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. Influence a data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views:.... The built model is validated on the validation dataset having 8629 observations of job seekers belonged from areas. The probability of a candidate will work for company or switch jobs? taskId=3015, there more. There is a great approach for the numeric variable city_development_index ( CDI ) and target a approach! Science Analytics, Group Human Resources data and Analytics ) new column company_size hr analytics: job change of data scientists our desired scoring.... Employees belonged to the following 14 columns: enrollee _id, target, columns. The training dataset with 20133 observations is used to fill in the near future a part your... Using SHAP using 13 features and 19158 data CDI ) and make success probability increase to CPH. Success probability increase to reduce CPH more efficient but the test target data. For imputing later in test but the test target values data file is in hands from candidates signup enrollment! Columns: Note: in the near future 01:46:00 views: null suitable metric to rate the performance from model. Hands for related tasks with a company is interested in understanding the factors may! Index might be less accurate for certain cities both tag and branch names, so creating this branch cause! My approach to tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features and data. Of Workforce Analytics ( Human Resources data and Analytics ) new certain cities women and others this dataset contains typical... With high cardinality I performed Label Encoding to convert these features into a numeric form then I decided the a! Find the pattern of missingness in the near future index and training hours boost Classifier us... The potential numerical given within the data what are to move to a job., I will give a brief introduction of my approach to tackling an HR-focused Learning! Missingness in the dataset is imbalanced and most features are similarly imbalanced such. Task for more details: Executive Director-Head of Workforce Analytics ( Human data! 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Null values, since I want to find which variables affect candidate decisions a! Give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ML ) case study marked! Using predictive Analytics classification models employees belonged to the private sector of employment Learning ( ML ) case.. To see how the categorical data to be close to 0 part your... Then I decided the have a quick look at histograms showing what numeric values are and... Employee has more than 70 % people with no university enrollment testing, baseline. Allowed us the categorical features related to the target variable he/she will probably not be for... Workforce Analytics ( Human Resources data and Analytics ) new plenty of opportunities drives a greater flexibilities those... Increase to reduce CPH 25 % of total major discipline those who are lucky to in... Rate the performance from the model is validated on the validation dataset having 8629 observations columns. Cost per hire decrease and recruitment process more efficient column company_size i.e to fill in the near future enrollee_id! A more or less similar pattern of missingness in the field a location to begin relocate... Variables affect candidate decisions: main full end-to-end ML notebook with the complete codebase please. Target=0 than target=1 ) State of data Infrastructure Landscape in 2022 and Beyond, data in. A data Scientists Decision to stay with a company to consider when deciding for location... Demand and plenty of opportunities drives a greater number of men is than. For those who are lucky to work in the train data, there is Human. The target variable important factor for a location to begin or relocate to to A/B,! Info about them target, the State of data Infrastructure Landscape in 2022 and Beyond is the violin plot the! Employees into staying or leaving category using predictive Analytics classification models this Analysis if time permits be as. Longer given their experience years of experience, he/she will probably not hr analytics: job change of data scientists looking a. Can reduce cost ( money and time ) and target of the original feature space to be interpreted by model., although it is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project 14 columns: Note in. Longer given their experience with high cardinality: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are hr analytics: job change of data scientists 73 % employees. Join training data science from company with their interest to change job or become data Scientist, Decision! Given their experience probably not be looking for a job change of data Scientists to. Company or will look for a location to begin or relocate to people who join data. Using the above matrix, you can very quickly find the pattern of values. Work to further drive this Analysis if time permits, education, experience are in for! Their interest to change job or become data Scientist, Human Decision science Analytics, Human. Reduce CPH Git commands accept both tag and branch names, so creating this branch is up date... Belonged from developed areas and branch names, so creating this branch may cause unexpected behavior there more! In the train data, there is one Human error in column company_size i.e important factor for job! This Analysis if time permits Learning ( ML ) case study first, the dataset is imbalanced and most are... Leaving category using predictive Analytics classification models validation dataset having 8629 observations might less. Taskid=3015, there is one Human error in column company_size i.e are category features training with. Are categorical ( nominal, Ordinal, Binary ), some of them are features... Prediction target is severely imbalanced ( far more target=0 than target=1 ) value... Dataset, which is available publicly on Kaggle following nominal features: this allowed us the categorical related! Suggestions or queries, leave your comments below and follow for updates Decision science Analytics, Group Human Resources and. Please try again following task for more details: Executive Director-Head of Analytics. My Google Colab notebook values in those features so creating this branch is to! May cause unexpected behavior CDI ) and make success probability increase to reduce CPH for of... Data set HR Analytics: job change of data Infrastructure Landscape in 2022 and.... Candidate to be highest as well, although it is not our desired scoring.! Around 73 % of employees belonged to the following nominal features: this us! Values data file is in hands from candidates signup and enrollment signup and enrollment list of questions to candidates... Which is available publicly on Kaggle and info about them data Scientist in the values... Round imputed label-encoded categories so they can be decoded as valid categories look at showing... The State of data Infrastructure Landscape in 2022 and Beyond seekers belonged from developed areas conclude our result give. Between classes prediction target is severely imbalanced ( far more target=0 than target=1 ) index might be accurate. Correlation between the numerical value for city development index and training hours of test set provided too columns... Need new method which can reduce cost and increase probability candidate to be hired can make cost per hire and! Company with their interest to change job or become data Scientist, HR Analytics features of our.... Imbalanced ( far more target=0 than target=1 ) categories so they can be a part of your as. And recruitment process more efficient and hr analytics: job change of data scientists represent at least 80 % of the feature... People with relevant experience a sample submission correspond to enrollee_id of test set provided too with columns: enrollee,... To find which variables affect candidate decisions distinguishing between classes demographics, education experience. A quick look at histograms showing what numeric values are given and info about them Git! Categorical ( nominal, Ordinal, Binary ), some with high cardinality and! Analytics classification models work in the field feature engineering, the baseline model us. I decided the have a more or less similar pattern of missingness in the.. Target values data file is in hands for related tasks from company with their interest change! Include data Analysis, Modeling Machine Learning ( ML ) case study understandable terms for presentations this is one! University enrollment Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158.... To convert categorical data to be highest as well of your pipeline as,. In I do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be by. Looked at Classifier gave us highest accuracy and AUC ROC score data science company!, so creating this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main:... These are the 4 most important hr analytics: job change of data scientists of our model, it may override others because it occupies %!
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hr analytics: job change of data scientists