We found substantial evidence that an employees work experience affected their decision to seek a new job. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars March 9, 20211 minute read. HR can focus to offer the job for candidates who live in city_160 because all candidates from this city is looking for a new job and city_21 because the proportion of candidates who looking for a job is higher than candidates who not looking for a job change, HR can develop data collecting method to get another features for analyzed and better data quality to help data scientist make a better prediction model. 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. Introduction. The number of men is higher than the women and others. What is the effect of a major discipline? 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. This is the violin plot for the numeric variable city_development_index (CDI) and target. Second, some of the features are similarly imbalanced, such as gender. This dataset is designed to understand the factors that lead a person to leave current job for HR researches too and involves using model(s) to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). 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. Before this note that, the data is highly imbalanced hence first we need to balance it. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Kaggle Competition. 10-Aug-2022, 10:31:15 PM Show more Show less Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Description of dataset: The dataset I am planning to use is from kaggle. First, the prediction target is severely imbalanced (far more target=0 than target=1). AUCROC tells us how much the model is capable of distinguishing between classes. There are around 73% of people with no university enrollment. 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. 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. 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. As we can see here, highly experienced candidates are looking to change their jobs the most. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. This operation is performed feature-wise in an independent way. 3.8. Problem Statement : As seen above, there are 8 features with missing values. 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. Many people signup for their training. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. What is the total number of observations? 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 . All dataset come from personal information of trainee when register the training. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. I used another quick heatmap to get more info about what I am dealing with. I used Random Forest to build the baseline model by using below code. Heatmap shows the correlation of missingness between every 2 columns. There was a problem preparing your codespace, please try again. Job. Refer to my notebook for all of the other stackplots. Apply on company website AVP, Data Scientist, HR Analytics . The city development index is a significant feature in distinguishing the target. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. March 9, 2021 The simplest way to analyse the data is to look into the distributions of each feature. Feature engineering, HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Determine the suitable metric to rate the performance from the model. MICE is used to fill in the missing values in those features. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. Many people signup for their training. HR Analytics: Job changes of Data Scientist. Variable 1: Experience - Reformulate highly technical information into concise, understandable terms for presentations. 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. 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. The number of STEMs is quite high compared to others. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Are you sure you want to create this branch? So I performed Label Encoding to convert these features into a numeric form. Understanding whether an employee is likely to stay longer given their experience. First, Id like take a look at how categorical features are correlated with the target variable. To know more about us, visit https://www.nerdfortech.org/. For another recommendation, please check Notebook. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. However, according to survey it seems some candidates leave the company once trained. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. 19,158. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? - Build, scale and deploy holistic data science products after successful prototyping. 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 . 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.. Interpret model(s) such a way that illustrate which features affect candidate decision Use Git or checkout with SVN using the web URL. A violin plot plays a similar role as a box and whisker plot. It is a great approach for the first step. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. to use Codespaces. 3. The dataset has already been divided into testing and training sets. AVP, Data Scientist, HR Analytics. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Learn more. The whole data divided to train and test . Are you sure you want to create this branch? We hope to use more models in the future for even better efficiency! 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. Power BI) and data frameworks (e.g. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. 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. HR-Analytics-Job-Change-of-Data-Scientists. Context and Content. Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability how..., Id like take a look at how categorical features are similarly,! Values in those features to survey it seems some candidates leave the once., and may belong to a fork outside of the features are correlated with the of! 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