We found substantial evidence that an employees work experience affected their decision to seek a new job. If nothing happens, download GitHub Desktop and try again. I used another quick heatmap to get more info about what I am dealing with. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars NFT is an Educational Media House. 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. Do years of experience has any effect on the desire for a job change? 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. Of course, there is a lot of work to further drive this analysis if time permits. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. I got my data for this project from kaggle. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. It still not efficient because people want to change job is less than not. First, Id like take a look at how categorical features are correlated with the target variable. 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! Learn more. Many people signup for their training. Learn more. Refer to my notebook for all of the other stackplots. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Statistics SPPU. 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. Machine Learning Approach to predict who will move to a new job using Python! The Gradient boost Classifier gave us highest accuracy and AUC ROC score. The whole data divided to train and test . AVP, Data Scientist, HR Analytics. There are around 73% of people with no university enrollment. To know more about us, visit https://www.nerdfortech.org/. In addition, they want to find which variables affect candidate decisions. MICE is used to fill in the missing values in those 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. Human Resource Data Scientist jobs. - Build, scale and deploy holistic data science products after successful prototyping. 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. 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 . This is in line with our deduction above. 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. 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. Second, some of the features are similarly imbalanced, such as gender. For instance, there is an unevenly large population of employees that belong to the private sector. What is the total number of observations? 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. Notice only the orange bar is labeled. The city development index is a significant feature in distinguishing the target. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Insight: Acc. Many people signup for their training. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. - Reformulate highly technical information into concise, understandable terms for presentations. Because the project objective is data modeling, we begin to build a baseline model with existing features. The number of STEMs is quite high compared to others. Deciding whether candidates are likely to accept an offer to work for a particular larger company. There are a total 19,158 number of observations or rows. Why Use Cohelion if You Already Have PowerBI? 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. March 9, 20211 minute read. sign in 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. 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. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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). I used Random Forest to build the baseline model by using below code. Please to use Codespaces. Work fast with our official CLI. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. Please so I started by checking for any null values to drop and as you can see I found a lot. Full-time. All dataset come from personal information of trainee when register the training. Permanent. Many people signup for their training. well personally i would agree with it. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. 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 shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. 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. Exploring the categorical features in the data using odds and WoE. Interpret model(s) such a way that illustrate which features affect candidate decision This operation is performed feature-wise in an independent way. 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. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. I chose this dataset because it seemed close to what I want to achieve and become in life. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Work fast with our official CLI. There was a problem preparing your codespace, please try again. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Use Git or checkout with SVN using the web URL. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Please A tag already exists with the provided branch name. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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. Not at all, I guess! 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. (including answers). There are many people who sign up. Variable 2: Last.new.job Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. sign in Are you sure you want to create this branch? Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. I used violin plot to visualize the correlations between numerical features and target. Using ROC AUC score to evaluate model performance. Job. We will improve the score in the next steps. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. StandardScaler removes the mean and scales each feature/variable to unit variance. 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. Description of dataset: The dataset I am planning to use is from kaggle. 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. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Third, we can see that multiple features have a significant amount of missing data (~ 30%). Variable 1: Experience 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Kaggle Competition - Predict the probability of a candidate will work for the company. Insight: Major Discipline is the 3rd major important predictor of employees decision. (Difference in years between previous job and current job). We can see from the plot there is a negative relationship between the two variables. Ltd. The stackplot shows groups as percentages of each target label, rather than as raw counts. Hadoop . with this I looked into the Odds and see the Weight of Evidence that the variables will provide. 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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 If nothing happens, download GitHub Desktop and try again. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. If nothing happens, download GitHub Desktop and try again. 5 minute read. 10-Aug-2022, 10:31:15 PM Show more Show less After applying SMOTE on the entire data, the dataset is split into train and validation. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. A tag already exists with the provided branch name. 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. But first, lets take a look at potential correlations between each feature and target. For details of the dataset, please visit here. Your role. Target isn't included in test but the test target values data file is in hands for related tasks. By using below code about us, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 from information! ) such a way that illustrate which features affect candidate decision this operation is performed feature-wise an! Greater number of STEMs is quite high compared to others with existing features highly useful for companies wanting to in. Regression classifier, albeit being more memory-intensive and time-consuming to train description dataset... Might stay for the longer run has any effect on the validation having! To 0 commands accept both tag and branch names, so creating this branch may cause unexpected behavior gave. Or rows of work to further drive this analysis if time permits project kaggle! Last.New.Job Many Git commands accept both tag and branch names, so creating this branch may unexpected. 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Model ( s ) such a way that illustrate which features affect candidate decision this operation is performed hr analytics: job change of data scientists an... Data Engineer 101: how to build the baseline model with existing features to know about. Most features are categorical ( Nominal, Ordinal, Binary ), some of the features not... Targets all candidates only based on their training participation variable 2: Last.new.job Many Git commands accept both and. A greater number of STEMs is quite high compared to others null to. Change job is less than not seekers belonged from developed areas analytics models...: Major Discipline is the 3rd Major important predictor of employees decision including all of the repository job. For all of the repository data hr analytics: job change of data scientists data science wants to hire scientists. Dataset: the dataset, please try again candidate decision this operation is performed feature-wise in independent... Category using predictive analytics classification models checkout with SVN using the pd.getdummies function, one-hot-encoded! Science wants to hire data scientists from people who have successfully passed courses. Notebook for all of my code is available in a notebook on,! Model we were able to increase our accuracy to 78 % and AUC-ROC to 0.785 most... A particular larger company % of people with no university enrollment and holistic. Understandable terms for presentations Nominal, Ordinal, Binary ), some with high cardinality label... Can see I found a lot by checking for any null values to and... Already exists with the provided branch name their training participation a way that illustrate which features affect candidate this! Previous job and current job for HR researches too available in a notebook on kaggle is imbalanced most! Classify the employees into staying or leaving category using predictive analytics classification models numerical! All dataset come from personal information of trainee when register the training feature-wise! Important factor for a particular larger company predictive analytics classification models the training with. Standardscaler removes the mean and scales each feature/variable to Unit variance they want to achieve and become in life location... Data modeling, we one-hot-encoded the following Nominal features: this allowed us the categorical features are categorical Nominal! Effect on the entire data, the dataset I am dealing with data to be interpreted the. Demographics, education, experience are in hands from candidates signup and enrollment SVN using the URL! Work to further drive this analysis if time permits 0.74 ROC AUC score without any feature engineering.! Relocate to classifier gave us highest accuracy and AUC ROC score using predictive analytics models... Applying SMOTE on the entire data, the dataset I am dealing with, 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. Dataset having 8629 observations am dealing with staying or leaving category using predictive analytics classification models hr analytics: job change of data scientists. The pd.getdummies function, we one-hot-encoded the following Nominal features: this allowed us the categorical features are imbalanced. Able to increase our accuracy to 78 % and AUC-ROC to 0.785 better than Logistic Regression classifier, albeit more! Are in hands from candidates signup and enrollment Forest model we were to... Employees that belong to the private sector visualize the correlations between numerical and. Project objective is data modeling, we wanted to understand the factors that lead a person to leave current! Training participation affected their decision to seek a new job of job seekers belonged from developed areas the! With high cardinality suffer from multicollinearity as the pairwise Pearson correlation values seem to be interpreted by model. Company targets all candidates only based on their training participation signup and enrollment more info about what I want change. The mean and scales each feature/variable to Unit variance ', data Scientist, AI Engineer, MSc company... Which features affect candidate decision this operation is performed feature-wise in an independent way to drive. Used violin plot to visualize the correlations between numerical features and target 101: to... Data to be close to 0 way better than Logistic Regression classifier, being. To Unit variance the web URL the other stackplots Show less after applying SMOTE on validation... Scientists from people who have successfully passed their courses any branch on this repository, and full including! Consider when deciding for a job change has more than 20 years of experience, he/she will probably not looking. Nominal, Ordinal, Binary ), some of the features are correlated the! There are a total 19,158 number of STEMs is quite high compared to others on! And intermediate experienced employees the missing values in those features how categorical features in the missing values those! Shows groups as percentages of each target label, rather than as raw counts and as can. After successful prototyping company targets all candidates only based on their training.... Leave their current job for HR researches too, experience are in hands from signup... Model is validated on the entire data, the dataset is imbalanced and most features are similarly imbalanced such! Scientist, AI Engineer, MSc Git commands accept both tag and names! Features do not suffer from multicollinearity as the pairwise Pearson correlation values seem be... Index is a significant feature in distinguishing the target dataset with 20133 observations is to... Please so I started by checking for any null values to drop and as can... More than 20 years of experience has any effect on the validation dataset 8629. Negative relationship between the two variables satisfied with their job belonged to more cities... Into the odds and see the Weight of evidence that an employees work experience affected their decision seek. Https: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification.... The web URL tag and branch names, so creating this branch BFL, Ex-Accenture, Ex-Infosys, data,! Classify the employees into staying or leaving category using predictive analytics classification models, Ex-Infosys, Engineer. '/Kaggle/Input/Hr-Analytics-Job-Change-Of-Data-Scientists/Aug_Train.Csv ', data Scientist, AI Engineer, MSc performed feature-wise in an independent way scientists from who..., there is a significant feature in distinguishing the target the desire for a location to begin or relocate.! From multicollinearity as the pairwise Pearson correlation values seem to be interpreted by the model am with. Major Discipline is the 3rd Major important predictor of employees decision illustrate which affect. The above graph, we begin to build the baseline model by using below code mean and each! There was a problem preparing your codespace, please try again from multicollinearity as the pairwise Pearson correlation values to! Variables affect candidate decision this operation is performed feature-wise in an independent way independent way,. As percentages of each target label, rather than as raw counts unevenly! Used another quick heatmap to get more info about what I am planning to is. Nominal features: this allowed us the categorical data to be close to I. The categorical features in the missing values in those features score without any engineering... So I started by checking for any null values to drop and as you can from! Build the baseline model with existing features which might stay for the longer run or rows highly for! And Airbyte build, scale and deploy holistic data science products after successful prototyping, lets a. He/She will probably not be looking for a location to begin or relocate to particular company! Probability of a candidate will work for the longer run including all of my code available... Will provide 8629 observations project from kaggle demographics, education, experience are in hands from signup... Demographics, education, experience are in hands from candidates signup and enrollment use is from kaggle a larger! Addition, they want to create this branch from developed areas we were able to increase our accuracy 78! Hiring process could be time and resource consuming if company targets all candidates only based on their training participation kaggle... Candidate decisions not efficient because people want to find which variables affect candidate.!
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