hr analytics: job change of data scientists

Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Only label encode columns that are categorical. If you liked the article, please hit the icon to support it. Does the type of university of education matter? This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. For any suggestions or queries, leave your comments below and follow for updates. 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. 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. What is the total number of observations? 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 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. 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. 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. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. Variable 2: Last.new.job Human Resources. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. This needed adjustment as well. Variable 3: Discipline Major March 9, 2021 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. maybe job satisfaction? How much is YOUR property worth on Airbnb? It still not efficient because people want to change job is less than not. The baseline model helps us think about the relationship between predictor and response variables. You signed in with another tab or window. 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. That is great, right? 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. AVP, Data Scientist, HR Analytics. All dataset come from personal information . This is in line with our deduction above. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Prudential 3.8. . Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Does more pieces of training will reduce attrition? StandardScaler removes the mean and scales each feature/variable to unit variance. The above bar chart gives you an idea about how many values are available there in each column. First, Id like take a look at how categorical features are correlated with the target variable. We will improve the score in the next steps. to use Codespaces. Summarize findings to stakeholders: Refresh the page, check Medium 's site status, or. 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. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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 . Use Git or checkout with SVN using the web URL. What is the maximum index of city development? Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. As we can see here, highly experienced candidates are looking to change their jobs the most. 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. 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. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. How to use Python to crawl coronavirus from Worldometer. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. I do not own the dataset, which is available publicly on Kaggle. 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. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. There are a few interesting things to note from these plots. What is the effect of company size on the desire for a job change? 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. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Deciding whether candidates are likely to accept an offer to work for a particular larger company. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. was obtained from Kaggle. You signed in with another tab or window. Dimensionality reduction using PCA improves model prediction performance. 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. Variable 1: Experience Each employee is described with various demographic features. 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. This will help other Medium users find it. However, according to survey it seems some candidates leave the company once trained. Organization. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Exploring the categorical features in the data using odds and WoE. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Refer to my notebook for all of the other stackplots. Missing imputation can be a part of your pipeline as well. Question 1. Are there any missing values in the data? This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. March 2, 2021 Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. We conclude our result and give recommendation based on it. Problem Statement : A violin plot plays a similar role as a box and whisker plot. Many people signup for their training. There was a problem preparing your codespace, please try again. A company that is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. I ended up getting a slightly better result than the last time. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). If nothing happens, download Xcode and try again. MICE is used to fill in the missing values in those features. 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. Ltd. It is a great approach for the first step. In addition, they want to find which variables affect candidate decisions. Schedule. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? There was a problem preparing your codespace, please try again. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. Are you sure you want to create this branch? The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Many people signup for their training. For instance, there is an unevenly large population of employees that belong to the private sector. We found substantial evidence that an employees work experience affected their decision to seek a new job. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Learn more. Newark, DE 19713. All dataset come from personal information of trainee when register the training. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Many people signup for their training. 1 minute read. 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. . If nothing happens, download GitHub Desktop and try again. A tag already exists with the provided branch name. 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). 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. This operation is performed feature-wise in an independent way. 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. sign in 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. 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. 5 minute read. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. Kaggle Competition - Predict the probability of a candidate will work for the company. 2023 Data Computing Journal. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Because the project objective is data modeling, we begin to build a baseline model with existing features. Predict the probability of a candidate will work for the company Pre-processing, Determine the suitable metric to rate the performance from the model. Feature engineering, You signed in with another tab or window. We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). 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 believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 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. sign in This is a quick start guide for implementing a simple data pipeline with open-source applications. 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.