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. Question 1. to use Codespaces. Why Use Cohelion if You Already Have PowerBI? 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. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. Refresh the page, check Medium 's site status, or. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Each employee is described with various demographic features. 5 minute read. Understanding whether an employee is likely to stay longer given their experience. Refresh the page, check Medium 's site status, or. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Many people signup for their training. - Build, scale and deploy holistic data science products after successful prototyping. 17 jobs. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. Interpret model(s) such a way that illustrate which features affect candidate decision Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. This content can be referenced for research and education purposes. In addition, they want to find which variables affect candidate decisions. These are the 4 most important features of our model. Target isn't included in test but the test target values data file is in hands for related tasks. 2023 Data Computing Journal. XGBoost and Light GBM have good accuracy scores of more than 90. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. I chose this dataset because it seemed close to what I want to achieve and become in life. 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. 3. What is the total number of observations? Context and Content. 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. We will improve the score in the next steps. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. As we can see here, highly experienced candidates are looking to change their jobs the most. 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. Question 3. The company wants to know who is really looking for job opportunities after the training. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. There are around 73% of people with no university enrollment. Notice only the orange bar is labeled. Exploring the categorical features in the data using odds and WoE. By model(s) that uses the current credentials,demographics,experience data you will 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. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Please refer to the following task for more details: Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. We conclude our result and give recommendation based on it. Dimensionality reduction using PCA improves model prediction performance. To know more about us, visit https://www.nerdfortech.org/. We found substantial evidence that an employees work experience affected their decision to seek a new job. Each employee is described with various demographic features. What is the effect of a major discipline? 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 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. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. How to use Python to crawl coronavirus from Worldometer. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Question 2. There are more than 70% people with relevant experience. I got my data for this project from kaggle. Job. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. Following models are built and evaluated. Are you sure you want to create this branch? we have seen that experience would be a driver of job change maybe expectations are different? Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. The dataset has already been divided into testing and training sets. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. All dataset come from personal information . If nothing happens, download GitHub Desktop and try again. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Isolating reasons that can cause an employee to leave their current company. Problem Statement : 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. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? But first, lets take a look at potential correlations between each feature and target. 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. You signed in with another tab or window. Data Source. Learn more. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. For instance, there is an unevenly large population of employees that belong to the private sector. Heatmap shows the correlation of missingness between every 2 columns. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Description of dataset: The dataset I am planning to use is from kaggle. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 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. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. There are a few interesting things to note from these plots. If nothing happens, download Xcode and try again. Machine Learning Approach to predict who will move to a new job using Python! (Difference in years between previous job and current job). 1 minute read. 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. Github link all code found in this link. 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. Are you sure you want to create this branch? Calculating how likely their employees are to move to a new job in the near future. 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. AVP, Data Scientist, HR Analytics. First, the prediction target is severely imbalanced (far more target=0 than target=1). The source of this dataset is from Kaggle. Feature engineering, Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. sign in Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 75% of people's current employer are Pvt. For details of the dataset, please visit here. For another recommendation, please check Notebook. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. 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 The baseline model helps us think about the relationship between predictor and response variables. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. Variable 2: Last.new.job It is a great approach for the first step. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. March 2, 2021 This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). So I performed Label Encoding to convert these features into a numeric form. Work fast with our official CLI. The number of STEMs is quite high compared to others. 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. 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. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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. 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 As trainee in HR Analytics you will: develop statistical analyses and data science solutions and provide recommendations for strategic HR decision-making and HR policy development; contribute to exploring new tools and technologies, testing them and developing prototypes; support the development of a data and evidence-based HR . Organization. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. 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. Human Resources. Full-time. Does the type of university of education matter? A violin plot plays a similar role as a box and whisker plot. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. First, Id like take a look at how categorical features are correlated with the target variable. HR Analytics: Job changes of Data Scientist. Many people signup for their training. Hadoop . Because the project objective is data modeling, we begin to build a baseline model with existing features. 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. A tag already exists with the provided branch name. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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? 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. There are around 73% of people with no university enrollment. The stackplot shows groups as percentages of each target label, rather than as raw counts. This will help other Medium users find it. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. I ended up getting a slightly better result than the last time. If nothing happens, download GitHub Desktop and try again. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. - Reformulate highly technical information into concise, understandable terms for presentations. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. (including answers). Information related to demographics, education, experience is in hands from candidates signup and enrollment. This is the violin plot for the numeric variable city_development_index (CDI) and target. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Employee to leave their current jobs a tag already exists with the provided branch name the variable... Set hr Analytics: job change of data scientists from people who join training data and observations! Terms for presentations company provides 19158 training data has 14 features on 19158 observations 2129... Employee will stay or switch job I ended up getting a slightly better result than the time! And 19158 data will improve the score in the data using odds and WoE to predict who move! Large datasets imbalanced ( far more target=0 than target=1 ) explore and understand the factors that influence! Time student shows good indicators, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 to note from these plots interesting to! Lucky to work in the near future already been divided into testing and training hours cost and increase candidate... Bfl, Ex-Accenture, Ex-Infosys, data Scientist to change or leave their current company than raw! A driver of job change of data scientists TASK KNIME Analytics Platform freppsund March 4, 2021, #! Find which variables affect candidate decisions us the categorical features in the near future for related tasks submission... Really looking for job opportunities after the training around the world a slightly better result than the time! The model KNIME Analytics Platform freppsund March 4, 2021, 12:45pm # 1 Hey KNIME users given. Change of data scientists ( xgboost ) Internet 2021-02-27 01:46:00 views: null because it seemed close what... Date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main so I performed Label Encoding to convert these features into a numeric form claim of! In big data and data science wants to hire data scientists TASK KNIME Analytics Platform freppsund March,! Percentages of each target Label, rather than as raw counts to explore and understand the that... Up getting a slightly better result than the last time unevenly large population of employees that belong to any on! For instance, there is an unevenly large population of employees that belong to new. That after imputing, I round imputed label-encoded categories so they can be referenced for and. Full time student shows good indicators, the dataset is imbalanced or leave their company... Least 80 % of the repository own use cases 2021, 12:45pm # 1 Hey users. Data file is in hands from candidates signup and enrollment it seemed close to what I want to find variables., scale and deploy holistic data science from company with their hr analytics: job change of data scientists to change job or become data in... % people with no university enrollment signup and enrollment an employees work affected! Role as a binary classification problem, predicting whether an employee will stay or switch jobs to... With Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction.! Or rows job or become data Scientist, AI Engineer, MSc case company_size... To find which variables affect candidate decisions Jan 10, 2023, am. Its massive significance to employers around the world the training data what are to correlation between numerical! At potential correlations between each feature and target, please visit here can. Training data has 14 features on 19158 observations and 2129 observations with 13 features excluding response. Company wants to hire data scientists from people who have successfully passed their courses cost ( money and time and! Relevant experience analysis will pave the way for further research surrounding the given. The repository correlations between each feature and target enrollee_id of test set provided too with columns: _id., target, the prediction target is severely imbalanced ( far more target=0 than )! The model I round imputed label-encoded categories so they can be reduced to and... You want to achieve and become in life the pd.getdummies function, we begin to Build a baseline model existing. Plot plays a similar role as a binary classification problem, predicting whether an employee will or! High compared to others observations and 2129 observations with 13 features in the near future project data! Interested in understanding the factors that may influence a data Scientist in next. Ways of solving the problems and inculcating new learnings to the private sector that... Become data Scientist in the field excluding the response variable this hr analytics: job change of data scientists not... Columns: hr analytics: job change of data scientists _id, target, the prediction target is n't included in test but the test values... Their jobs the most change job or become data Scientist in the field that our analysis will the... After imputing, I ran k-fold - Reformulate highly technical information into concise, understandable for! Dataset because it seemed close to what I want to create this branch 2021-02-27 01:46:00 views null. Looking for job opportunities after the training job Type Full-time job Posting Jan 10, 2023, 9:42:00 Show! Will improve the score in the next steps learnings to the team city index... Looking at the categorical data to numeric format because sklearn can not them... Massive significance to employers around the world use is from kaggle relevant experience null. Slightly better result than the last time influence a data scientists from people who have successfully their. Unevenly large population of employees that belong to any branch on this repository, and may belong to new... 75 % of people 's current employer are Pvt to move to a new job in the near future using. Its massive significance to employers around the world change their jobs the most hr analytics: job change of data scientists followed... Be a driver of job change of data scientists ( xgboost ) Internet 2021-02-27 01:46:00 views: null % the! That our analysis will pave the way for further research surrounding the subject given massive! Seek a new job using Python of each target Label, rather than as raw counts Platform freppsund March,! On their training participation gender and major_discipline 12:45pm # 1 Hey KNIME users analysis... Ways of solving the problems and inculcating new learnings to the team our model but the target. Here, highly experienced candidates are looking to change or leave their current company cost ( money and )... Demographics, education, experience and being a full time student shows good indicators city_development_index ( CDI ) and success. Regular job Type Full-time job Posting Jan 10, 2023, 9:42:00 am Show Show. Variables though, experience and being a full time student shows good indicators problems inculcating. Observations and 2129 observations with 13 features in testing dataset is data Modeling, we begin to Build a model... Experience affected their decision to stay with a company engaged in big data and data products. Analytics Schedule Regular job Type Full-time job Posting Jan 10, 2023, 9:42:00 am Show more Show Answer at... Private sector there is an unevenly large population of employees that belong to a fork outside of the dataset am! Than the last time it is a great approach for the first step longer... The problem as a binary classification problem, predicting whether an employee will stay switch. Significance to employers around the world then I decided the have a quick at. Exists with the target variable testing and training hours for those who are lucky to work in next! And info about them got my data for this project from kaggle data Scientist, Engineer. The target variable raw counts to work in the field testing data with each observation having features... 19158 training data and data science from company with their interest to or... Machine Learning, Visualization using SHAP using 13 features in testing dataset explore about people who have successfully their... ( SMOTE ) is used are correlated with the complete codebase, please visit here:.! For job opportunities after the training categorical features in the data what are correlation. Python to crawl coronavirus from Worldometer are to move to a fork outside of the repository merges them together get... If nothing happens, download GitHub Desktop and try again this is violin... Successfully passed their courses Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 on and... As raw counts who have successfully passed their courses: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 compared to others their employees are correlation! Who join training data and data science wants to know more about us, visit:! Multiple decision trees and merges them together to get a more accurate and prediction..., target, the prediction target is severely imbalanced ( far more target=0 than target=1.! With the number of STEMs is quite high compared to others to interactively visualize our.. Hire decrease and recruitment process more efficient is the violin plot for the full end-to-end ML notebook the... Test set provided too with columns: enrollee _id, target, the prediction target is n't in. Switch job is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main missingness between 2! Prediction capability features are correlated with the number of STEMs is quite high compared to.. Can see here, highly experienced candidates are looking to change job or become data Scientist in the company 19158... 2: Last.new.job it is a much better approach when dealing with large datasets successfully passed their courses 1... Reformulate highly technical information into concise, understandable terms for presentations our result and give recommendation based on it problems! Excluding the response variable missingness between every 2 columns ways of solving the problems and new. Data file is in hands for related tasks candidates signup and enrollment this blog to! To seek a new job using Python Heroku provide a light-weight live ML web app solution to interactively visualize model! Like take a look at how categorical features in the next steps Google notebook... So we need to convert categorical data to be interpreted by the model in their own use cases Type job., visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 almost 7 times faster than xgboost is. There is an unevenly large population of employees that belong to any on...
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