Schedule. Does the type of university of education matter? This is a significant improvement from the previous logistic regression model. - Reformulate highly technical information into concise, understandable terms for presentations. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 17 jobs. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 3.8. Please refer to the following task for more details: 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Are you sure you want to create this branch? Learn more. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. If you liked the article, please hit the icon to support it. 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. 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. There was a problem preparing your codespace, please try again. Tags: Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. You signed in with another tab or window. Hadoop . StandardScaler removes the mean and scales each feature/variable to unit variance. Data set introduction. (Difference in years between previous job and current job). The whole data divided to train and test . What is the total number of observations? 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. 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? There are around 73% of people with no university enrollment. 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 What is the effect of company size on the desire for a job change? 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. MICE is used to fill in the missing values in those features. Before this note that, the data is highly imbalanced hence first we need to balance it. 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. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. What is a Pivot Table? 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. Newark, DE 19713. 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. Metric Evaluation : For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Problem Statement : March 9, 2021 has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. When creating our model, it may override others because it occupies 88% of total major discipline. I used another quick heatmap to get more info about what I am dealing with. 10-Aug-2022, 10:31:15 PM Show more Show less 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. was obtained from Kaggle. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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. The dataset has already been divided into testing and training sets. 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. The company wants to know who is really looking for job opportunities after the training. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Question 3. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Use Git or checkout with SVN using the web URL. Power BI) and data frameworks (e.g. If nothing happens, download Xcode and try again. sign in Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . A tag already exists with the provided branch name. 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. This is a quick start guide for implementing a simple data pipeline with open-source applications. 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. XGBoost and Light GBM have good accuracy scores of more than 90. The source of this dataset is from Kaggle. 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. There are a few interesting things to note from these plots. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Answer In relation to the question asked initially, the 2 numerical features are not correlated which would be a good feature to use as a predictor. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 2023 Data Computing Journal. Github link all code found in this link. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. Take a shot on building a baseline model that would show basic metric. . 5 minute read. 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. 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. HR Analytics: Job changes of Data Scientist. 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. Human Resources. I am pretty new to Knime analytics platform and have completed the self-paced basics course. We believed this might help us understand more why an employee would seek another job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. But first, lets take a look at potential correlations between each feature and target. 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. If nothing happens, download GitHub Desktop and try again. 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. Goals : In addition, they want to find which variables affect candidate decisions. 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 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. 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. Agatha Putri Algustie - agthaptri@gmail.com. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 1 minute read. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. We will improve the score in the next steps. AUCROC tells us how much the model is capable of distinguishing between classes. sign in A violin plot plays a similar role as a box and whisker plot. To know more about us, visit https://www.nerdfortech.org/. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. HR-Analytics-Job-Change-of-Data-Scientists. which to me as a baseline looks alright :). For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Heatmap shows the correlation of missingness between every 2 columns. HR Analytics: Job Change of Data Scientists Introduction Anh Tran :date_full HR Analytics: Job Change of Data Scientists In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Exploring the categorical features in the data using odds and WoE. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Full-time. Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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. More. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Provided branch name the dataset has already been divided into Testing and training sets Analysis, Modeling Learning! 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