Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . (including answers). OCBC Bank Singapore, Singapore. That is great, right? Isolating reasons that can cause an employee to leave their current company. 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. 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 . DBS Bank Singapore, Singapore. Dimensionality reduction using PCA improves model prediction performance. There are around 73% of people with no university enrollment. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Apply on company website AVP, Data Scientist, HR Analytics . Work fast with our official CLI. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. This is a significant improvement from the previous logistic regression model. Many people signup for their training. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Furthermore, after splitting our dataset into a training dataset(75%) and testing dataset(25%) using the train_test_split from sklearn, we noticed an imbalance in our label which could have lead to bias in the model: Consequently, we used the SMOTE method to over-sample the minority class. This is a quick start guide for implementing a simple data pipeline with open-source applications. Following models are built and evaluated. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Refresh the page, check Medium 's site status, or. we have seen that experience would be a driver of job change maybe expectations are different? 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Please . Agatha Putri Algustie - agthaptri@gmail.com. - Build, scale and deploy holistic data science products after successful prototyping. You signed in with another tab or window. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. I got my data for this project from kaggle. 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. Question 2. Ltd. Context and Content. The number of STEMs is quite high compared to others. Some of them are numeric features, others are category features. 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. 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. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Information related to demographics, education, experience are in hands from candidates signup and enrollment. There are many people who sign up. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. The whole data divided to train and test . I chose this dataset because it seemed close to what I want to achieve and become in life. 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, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. 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. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. sign in 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. 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 | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists Next, we tried to understand what prompted employees to quit, from their current jobs POV. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Power BI) and data frameworks (e.g. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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. To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. 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. Permanent. All dataset come from personal information . There are a few interesting things to note from these plots. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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 conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. What is the maximum index of city development? Does the gap of years between previous job and current job affect? 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. HR Analytics: Job changes of Data Scientist. 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. I used another quick heatmap to get more info about what I am dealing with. 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. 1 minute read. Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). 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. I ended up getting a slightly better result than the last time. 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). Deciding whether candidates are likely to accept an offer to work for a particular larger company. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. If nothing happens, download GitHub Desktop and try again. Many people signup for their training. 2023 Data Computing Journal. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle was obtained from Kaggle. Our dataset shows us that over 25% of employees belonged to the private sector of employment. maybe job satisfaction? 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 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. Why Use Cohelion if You Already Have PowerBI? Question 1. Predict the probability of a candidate will work for the company Abdul Hamid - abdulhamidwinoto@gmail.com Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Heatmap shows the correlation of missingness between every 2 columns. but just to conclude this specific iteration. Target isn't included in test but the test target values data file is in hands for related tasks. Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Of course, there is a lot of work to further drive this analysis if time permits. JPMorgan Chase Bank, N.A. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. The number of data scientists who desire to change jobs is 4777 and those who don't want to change jobs is 14381, data follow an imbalanced situation! 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. with this I have used pandas profiling. 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. There are more than 70% people with relevant experience. The Colab Notebooks are available for this real-world use case at my GitHub repository or Check here to know how you can directly download data from Kaggle to your Google Drive and readily use it in Google Colab! Share it, so that others can read it! For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Metric Evaluation : Second, some of the features are similarly imbalanced, such as gender. Hadoop . A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. to use Codespaces. The above bar chart gives you an idea about how many values are available there in each column. Newark, DE 19713. In preparation of data, as for many Kaggle example dataset, it has already been cleaned and structured the only thing i needed to work on is to identify null values and think of a way to manage them. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. I am pretty new to Knime analytics platform and have completed the self-paced basics course. You signed in with another tab or window. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior.