Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Refresh the page, check Medium 's site status, or. All dataset come from personal information . 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. Job. 17 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. How to use Python to crawl coronavirus from Worldometer. Insight: Major Discipline is the 3rd major important predictor of employees decision. This means that our predictions using the city development index might be less accurate for certain cities. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. 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. When creating our model, it may override others because it occupies 88% of total major discipline. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. AVP, Data Scientist, HR Analytics. There was a problem preparing your codespace, please try again. Human Resource Data Scientist jobs. 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. There are a total 19,158 number of observations or rows. The stackplot shows groups as percentages of each target label, rather than as raw counts. 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. I got my data for this project from kaggle. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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. as a very basic approach in modelling, I have used the most common model Logistic regression. Only label encode columns that are categorical. 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. Data Source. Variable 3: Discipline Major A violin plot plays a similar role as a box and whisker plot. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars 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. was obtained from Kaggle. The pipeline I built for prediction reflects these aspects of the dataset. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars The dataset has already been divided into testing and training sets. Many people signup for their training. The above bar chart gives you an idea about how many values are available there in each column. Using ROC AUC score to evaluate model performance. to use Codespaces. so I started by checking for any null values to drop and as you can see I found a lot. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Information related to demographics, education, experience are in hands from candidates signup and enrollment. NFT is an Educational Media House. Juan Antonio Suwardi - [email protected] I ended up getting a slightly better result than the last time. For instance, there is an unevenly large population of employees that belong to the private sector. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. Feature engineering, with this I have used pandas profiling. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Why Use Cohelion if You Already Have PowerBI? Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. 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. Ltd. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. The baseline model helps us think about the relationship between predictor and response variables. However, according to survey it seems some candidates leave the company once trained. 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. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars Apply on company website AVP, Data Scientist, HR Analytics . Many people signup for their training. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. This content can be referenced for research and education purposes. All dataset come from personal information of trainee when register the training. Please This article represents the basic and professional tools used for Data Science fields in 2021. 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. 1 minute read. 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. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. MICE is used to fill in the missing values in those features. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. 5 minute read. 75% of people's current employer are Pvt. Learn more. Work fast with our official CLI. 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. 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. It is a great approach for the first step. As seen above, there are 8 features with missing values. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. 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. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. It still not efficient because people want to change job is less than not. I also wanted to see how the categorical features related to the target variable. 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. Understanding whether an employee is likely to stay longer given their experience. 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. Question 2. Missing imputation can be a part of your pipeline as well. 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. Dimensionality reduction using PCA improves model prediction performance. 1 minute read. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Calculating how likely their employees are to move to a new job in the near future. Many people signup for their training. 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.;[. There was a problem preparing your codespace, please try again. Interpret model(s) such a way that illustrate which features affect candidate decision All dataset come from personal information of trainee when register the training link https: //rpubs.com/ShivaRag/796919, Classify the into! And AUC -ROC score of 0.69 Medium & # x27 ; s site status, or answer Trying out the. 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