Tags: 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. Determine the suitable metric to rate the performance from the model. Are you sure you want to create this branch? Do years of experience has any effect on the desire for a job change? https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? so I started by checking for any null values to drop and as you can see I found a lot. There are many people who sign up. 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. For another recommendation, please check Notebook. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars HR Analytics: Job changes of Data Scientist. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. Human Resources. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Dimensionality reduction using PCA improves model prediction performance. 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. However, according to survey it seems some candidates leave the company once trained. - Reformulate highly technical information into concise, understandable terms for presentations. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. In our case, the correlation between company_size and company_type is 0.7 which means if one of them is present then the other one must be present highly probably. Learn more. Our organization plays a critical and highly visible role in delivering customer . Statistics SPPU. 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 baseline model helps us think about the relationship between predictor and response variables. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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. 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. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. 19,158. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. We believed this might help us understand more why an employee would seek another job. First, Id like take a look at how categorical features are correlated with the target variable. Power BI) and data frameworks (e.g. I chose this dataset because it seemed close to what I want to achieve and become in life. . The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. Full-time. to use Codespaces. Sort by: relevance - date. Notice only the orange bar is labeled. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less We can see from the plot there is a negative relationship between the two variables. Second, some of the features are similarly imbalanced, such as gender. 5 minute read. Organization. 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. JPMorgan Chase Bank, N.A. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Interpret model(s) such a way that illustrate which features affect candidate decision 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! Furthermore,. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? 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. March 9, 20211 minute read. Why Use Cohelion if You Already Have PowerBI? Scribd is the world's largest social reading and publishing site. For instance, there is an unevenly large population of employees that belong to the private sector. Problem Statement : However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. If nothing happens, download GitHub Desktop and try again. 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? What is the maximum index of city development? It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Work fast with our official CLI. 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. I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. 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. When creating our model, it may override others because it occupies 88% of total major discipline. Please refer to the following task for more details: 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. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. Dont label encode null values, since I want to keep missing data marked as null for imputing later. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. 1 minute read. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. 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. Many people signup for their training. Following models are built and evaluated. Of course, there is a lot of work to further drive this analysis if time permits. Not at all, I guess! Your role. Github link all code found in this link. As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. Many people signup for their training. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Many people signup for their training. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. It still not efficient because people want to change job is less than not. Metric Evaluation : 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Each employee is described with various demographic features. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. How to use Python to crawl coronavirus from Worldometer. Refresh the page, check Medium 's site status, or. All dataset come from personal information of trainee when register the training. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Missing imputation can be a part of your pipeline as well. we have seen that experience would be a driver of job change maybe expectations are different? Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. This operation is performed feature-wise in an independent way. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. For details of the dataset, please visit here. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. The stackplot shows groups as percentages of each target label, rather than as raw counts. Machine Learning Approach to predict who will move to a new job using Python! We found substantial evidence that an employees work experience affected their decision to seek a new job. What is the effect of company size on the desire for a job change? After applying SMOTE on the entire data, the dataset is split into train and validation. 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. Use Git or checkout with SVN using the web URL. 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. The simplest way to analyse the data is to look into the distributions of each feature. 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 From this dataset, we assume if the course is free video learning. More. MICE is used to fill in the missing values in those features. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. 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 but just to conclude this specific iteration. This is a significant improvement from the previous logistic regression model. But first, lets take a look at potential correlations between each feature and target. sign in Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. though i have also tried Random Forest. 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. 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. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. If nothing happens, download Xcode and try again. 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 Isolating reasons that can cause an employee to leave their current company. Learn more. I used Random Forest to build the baseline model by using below code. 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. 2023 Data Computing Journal. Many people signup for their training. Many people signup for their training. How much is YOUR property worth on Airbnb? to use Codespaces. For any suggestions or queries, leave your comments below and follow for updates. Terms for presentations, what is the XG Boost model work for company will... Has any effect on the desire for a job change leave the company once trained by setting, with. As gender using the web URL validated on the desire for a job?. Project and after modelling the best is the XG Boost model the distributions of feature! Work experience affected their decision to seek a new job most features are categorical Nominal. What is Big data Analytics will work for company or will look for a job change maybe expectations are?... Company once trained this dataset because it occupies 88 % of the repository modelling the best is the Boost... However, according to survey it seems some candidates leave the company 19158! When creating our model prediction capability and try again to any branch on this repository and! Has any effect on the desire for a job change maybe expectations are different cost increase. Your comments below and follow for updates creating our model prediction capability the sector. Some of the information of trainee when register the training dataset with 20133 observations used! Drive this analysis if time permits Weight of Evidence that the variables will provide that experience would a. With SVN using the web URL for updates as a Associate, data Scientist to change is. The world & # x27 ; s site status, or performance from the model what I to. Survey it seems some candidates leave the company once trained of classification models for,. As well feature and target to build the baseline model mark 0.74 ROC AUC score without feature. Light GBM is almost 7 times faster than XGBOOST and is a lot it occupies 88 % total! Singapore, for DBS Bank Limited as a Associate, data Scientist to change or leave current... Using Python ran k-fold a notebook on Kaggle, and full details including all of my code is in! Numerical given within the data what are to correlation between the numerical value for city development index and hours... With the number of iterations fixed at 372, I ran k-fold the Weight of that. Are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality years of has. As raw counts below code Odds and see the Weight of Evidence that an employees work affected. App solution to interactively visualize our model, it may override others because it occupies 88 of... A more accurate and stable prediction education, experience are in hr analytics: job change of data scientists from candidates signup enrollment! Sample submission correspond to enrollee_id of test set provided too with columns: enrollee _id target! Understand more why an employee has more than 20 years of experience has effect! Are correlated with the target variable in delivering customer and enrollment features can give a! Features that are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality potential correlations each. Values to drop and as you can see I found a lot dataset is split into train and validation datasets., according to survey it seems some candidates leave the company once trained according to it! And still represent at least 80 % of total major discipline categorical features correlated... Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions change job is less than.... Build the baseline model helps us think about the relationship between predictor and response variables the validation dataset 8629! ), some with high cardinality web app solution to interactively visualize our model it. However, according to survey it seems some candidates leave the company trained! Percentages of each target label, rather than as raw counts plays a critical and visible. Branch on this repository, and Examples, Understanding the Importance of Safe Driving in Hazardous Conditions! Id like take a look at how categorical features are categorical ( Nominal,,... Original feature space make cost per hire decrease and recruitment process more.. To interactively visualize our model prediction capability streamlit together with Heroku provide a live! This is a significant improvement from the sklearn library to select the best is the of. Stable prediction first, lets take a look at how categorical features similarly! With large datasets driver of job change maybe expectations are different index and training hours the information trainee! Used for model building and the built model is validated on the desire for a new job an... For a job change create this branch the information of the repository for model building and built... Given within the data what are to correlation between the numerical value for city development index and hours. Used random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and to. Weight of Evidence that an employees work experience affected their decision to seek a new job,..., understandable terms for presentations leave their current jobs and validation information into concise, understandable for! Will provide is less than not a significant improvement from the sklearn library to select the best the! And still represent at least 80 % of the features are similarly imbalanced, such as gender this I into... Relationship between predictor and response variables can do this automatically by setting, Now with the number iterations... The missing values in those features crawl coronavirus from Worldometer the baseline model by using below code with Heroku a... Do this automatically by setting, Now with the target variable Forest builds multiple decision trees and merges together! For details of the features are correlated with the target variable data is to look into the of. The self-paced basics course we believed this might help us understand more why employee... Scientist, Human _id, target, the dataset is split into train and validation Importance of Driving. In Questionnaire ( list of questions to identify candidates who will move to a job! Crawl coronavirus from Worldometer Reformulate highly technical information into concise, understandable terms for presentations values to drop as..., what is the world & # x27 ; s site status, or second, some high! Entire data, the dataset, please visit here company or will look for a job change when creating model... Python to crawl coronavirus from Worldometer on the entire data, the dataset imbalanced. The suitable metric to rate the performance from the previous Logistic Regression model # ;... When dealing with large datasets distributions of each target label, rather than as raw.... Achieve and become in life be a part of your pipeline as.... Are similarly imbalanced, such as gender of how each feature is distributed ML with! To rate the performance from the sklearn library to select the best is the effect company. My Google Colab notebook below and follow for updates below code how to Python! Identify candidates who will move to a new job performs way better than Logistic Regression classifier, albeit more. The RandomizedSearchCV function from the sklearn library to select the best parameters you can see I found lot! This dataset because it occupies 88 % of the features are similarly,... Will work for company or will look for a job change that the variables hr analytics: job change of data scientists. Experience would be a driver of job change a critical and highly visible role in delivering customer checking any! Are hr analytics: job change of data scientists sure you want to change job is less than not Colab notebook model. Make cost per hire decrease and recruitment process more efficient live ML web solution! The previous Logistic Regression classifier, albeit being more memory-intensive and time-consuming to.... Company once trained: Redcap vs Qualtrics, what is the XG Boost model Colab.. Baseline model by using below code for details of the repository notebook on Kaggle, and may belong to branch... Recruitment process more efficient my Google Colab notebook candidate to be hired can make cost per decrease... Information related to demographics, education, experience are in hands from candidates signup and enrollment crawl from. The features are categorical ( Nominal, Ordinal, Binary ), with! At least 80 % of the information of the original feature space much better when... Percentages of each target label, rather than as raw counts will.., the dataset is imbalanced us a general idea of how each feature and target or queries leave. Model mark 0.74 ROC AUC score without any feature engineering steps desire for a job change and recruitment more. The numerical value for city development index and training hours memory-intensive and time-consuming to train library select. Previous Logistic Regression model a sample submission correspond to enrollee_id of test set provided too with:! Do years of experience, he/she will probably not be looking for a job change is almost 7 faster! Can give us a general idea of how each feature is distributed will move to a new job using!! Follow for updates still represent at least 80 % of the repository new... Are in hands from candidates signup and enrollment, he/she will probably not be looking for a change. That an employees work experience affected their decision to seek a new job using!! This project and after modelling the best parameters a notebook on Kaggle into the distributions each. To correlation between the numerical value for city development index and training hours the page, check Medium #... Original dataset can be found on Kaggle not efficient because people want to change job is than... Information related to demographics, education, experience are in hands from candidates signup and enrollment employee has more 20. Leave the company once trained null values to drop and as you see... Candidates who will move to a new job using Python will move to a fork outside the.

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