If nothing happens, download Xcode and try again. How much is YOUR property worth on Airbnb? 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. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. we have seen that experience would be a driver of job change maybe expectations are different? Permanent. It is a great approach for the first step. The number of STEMs is quite high compared to others. Exploring the categorical features in the data using odds and WoE. 10-Aug-2022, 10:31:15 PM Show more Show less This needed adjustment as well. StandardScaler removes the mean and scales each feature/variable to unit variance. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. If nothing happens, download Xcode and try again. 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 There are around 73% of people with no university enrollment. What is the effect of company size on the desire for a job change? 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. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). 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. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Question 1. 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). 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 tried to understand what prompted employees to quit, from their current jobs POV. Learn more. as a very basic approach in modelling, I have used the most common model Logistic regression. Ltd. 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. 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. However, according to survey it seems some candidates leave the company once trained. Does the type of university of education matter? The whole data is divided into train and test. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Each employee is described with various demographic features. We believed this might help us understand more why an employee would seek another job. Context and Content. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. 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. so I started by checking for any null values to drop and as you can see I found a lot. After applying SMOTE on the entire data, the dataset is split into train and validation. 3. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Kaggle Competition. Using ROC AUC score to evaluate model performance. 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. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . to use Codespaces. More. 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.;[. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. All dataset come from personal information of trainee when register the training. Refresh the page, check Medium 's site status, or. Each employee is described with various demographic features. 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. AUCROC tells us how much the model is capable of distinguishing between classes. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. I used Random Forest to build the baseline model by using below code. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Use Git or checkout with SVN using the web URL. We can see from the plot there is a negative relationship between the two variables. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Please Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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 . Metric Evaluation : Kaggle Competition - Predict the probability of a candidate will work for the company. 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. Prudential 3.8. . That is great, right? Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 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. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. These are the 4 most important features of our model. (including answers). Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. A tag already exists with the provided branch name. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Job Posting. 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. We will improve the score in the next steps. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Dimensionality reduction using PCA improves model prediction performance. Machine Learning Approach to predict who will move to a new job using Python! Organization. I ended up getting a slightly better result than the last time. sign in HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! What is a Pivot Table? AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. There are more than 70% people with relevant experience. 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. Does the gap of years between previous job and current job affect? 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. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. 2023 Data Computing Journal. Statistics SPPU. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. 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. Furthermore,. was obtained from Kaggle. 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. 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. Question 2. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. I chose this dataset because it seemed close to what I want to achieve and become in life. 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. The source of this dataset is from Kaggle. All dataset come from personal information of trainee when register the training. as this is only an initial baseline model then i opted to simply remove the nulls which will provide decent volume of the imbalanced dataset 80% not looking, 20% looking. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com (Difference in years between previous job and current job). 1 minute read. 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. Agatha Putri Algustie - agthaptri@gmail.com. Not at all, I guess! StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. 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. Interpret model(s) such a way that illustrate which features affect candidate decision HR-Analytics-Job-Change-of-Data-Scientists. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Sort by: relevance - date. Following models are built and evaluated. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars OCBC Bank Singapore, Singapore. sign in Many people signup for their training. 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. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Before this note that, the data is highly imbalanced hence first we need to balance it. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). There was a problem preparing your codespace, please try again. 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. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? As we can see here, highly experienced candidates are looking to change their jobs the most. Variable 1: Experience Work fast with our official CLI. In addition, they want to find which variables affect candidate decisions. 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. This content can be referenced for research and education purposes. Python, January 11, 2023 Because the project objective is data modeling, we begin to build a baseline model with existing features. 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. 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