This needed adjustment as well. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. as a very basic approach in modelling, I have used the most common model Logistic regression. Full-time. was obtained from 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. A tag already exists with the provided branch name. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. 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. Human Resources. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Newark, DE 19713. Do years of experience has any effect on the desire for a job change? 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 For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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 you liked the article, please hit the icon to support it. Description of dataset: The dataset I am planning to use is from kaggle. 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. 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. 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. Using ROC AUC score to evaluate model performance. If nothing happens, download GitHub Desktop and try again. 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. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Apply on company website AVP, Data Scientist, HR Analytics . Python, January 11, 2023 I used violin plot to visualize the correlations between numerical features and target. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Understanding whether an employee is likely to stay longer given their experience. Metric Evaluation : This article represents the basic and professional tools used for Data Science fields in 2021. To the RF model, experience is the most important predictor. Information regarding how the data was collected is currently unavailable. 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. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Feature engineering, We conclude our result and give recommendation based on it. The simplest way to analyse the data is to look into the distributions of each feature. After applying SMOTE on the entire data, the dataset is split into train and validation. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Learn more. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Many people signup for their training. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. So I went to using other variables trying to predict education_level but first, I had to make some changes to the used data as you can see I changed the column gender and education level one. Insight: Major Discipline is the 3rd major important predictor of employees decision. Variable 1: Experience 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. I ended up getting a slightly better result than the last time. March 9, 2021 There was a problem preparing your codespace, please try again. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Exploring the categorical features in the data using odds and WoE. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. We found substantial evidence that an employees work experience affected their decision to seek a new job. The source of this dataset is from Kaggle. 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. More. 19,158. 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. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars - Build, scale and deploy holistic data science products after successful prototyping. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. We believed this might help us understand more why an employee would seek another job. NFT is an Educational Media House. 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. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Question 3. 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. March 9, 20211 minute read. Agatha Putri Algustie - agthaptri@gmail.com. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. StandardScaler removes the mean and scales each feature/variable to unit variance. Each employee is described with various demographic features. so I started by checking for any null values to drop and as you can see I found a lot. Furthermore,. Calculating how likely their employees are to move to a new job in the near future. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Deciding whether candidates are likely to accept an offer to work for a particular larger company. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. This will help other Medium users find it. 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. Are you sure you want to create this branch? To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. 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? Learn more. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. In other words, if target=0 and target=1 were to have the same size, people enrolled in full time course would be more likely to be looking for a job change than not. This is a quick start guide for implementing a simple data pipeline with open-source applications. Variable 3: Discipline Major HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 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. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. 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. Work fast with our official CLI. There are around 73% of people with no university enrollment. After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. Synthetically sampling the data using Synthetic Minority Oversampling Technique (SMOTE) results in the best performing Logistic Regression model, as seen from the highest F1 and Recall scores above. well personally i would agree with it. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. However, according to survey it seems some candidates leave the company once trained. Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Sort by: relevance - date. Features, 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 employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. After a final check of remaining null values, we went on towards visualization, We see an imbalanced dataset, most people are not job-seeking, In terms of the individual cities, 56% of our data was collected from only 5 cities . 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. 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. Missing imputation can be a part of your pipeline as well. That is great, right? 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). Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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. 10-Aug-2022, 10:31:15 PM Show more Show less 1 minute read. 1 minute read. 1 minute read. Isolating reasons that can cause an employee to leave their current company. There are a total 19,158 number of observations or rows. Refer to my notebook for all of the other stackplots. Ltd. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. Goals : Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 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. For any suggestions or queries, leave your comments below and follow for updates. 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. 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! Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. sign in Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learn more. A tag already exists with the provided branch name. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Please You signed in with another tab or window. 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 project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. If nothing happens, download GitHub Desktop and try again. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Only label encode columns that are categorical. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Dont label encode null values, since I want to keep missing data marked as null for imputing later. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. HR Analytics: Job Change of Data Scientists. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. I also wanted to see how the categorical features related to the target variable. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. 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. What is a Pivot Table? Pre-processing, Second, some of the features are similarly imbalanced, such as gender. Are you sure you want to create this branch? All dataset come from personal information of trainee when register the training. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! 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! In our case, the columns company_size and company_type have a more or less similar pattern of missing values. to use Codespaces. 5 minute read. 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Data set introduction. Information related to demographics, education, experience are in hands from candidates signup and enrollment. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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. Note: 8 features have the missing values. Power BI) and data frameworks (e.g. 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. 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. Take a shot on building a baseline model that would show basic metric. If nothing happens, download Xcode and try again. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. To analyse the data was collected is currently unavailable AVP/VP, data,... And make success probability increase to reduce CPH Minority Oversampling Technique ) personal information of trainee when register the dataset! Insight: Lastnewjob is the 3rd Major important predictor that can cause an is! Amount of missing data ( ~ 30 % ) factors that may influence a data pipeline with open-source applications the. As well, although it is not our desired scoring metric 3rd Major predictor. We achieved an accuracy of 66 % percent and AUC ROC score be looking a. Science from company with their interest to change job or become data,! I have used the corr ( ) function to calculate the correlation coefficient between and. Their experience wish to stay longer given their experience of the other stackplots that influence! Tab or window, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) information how. Help us understand more why an employee has more than 20 years experience! Such as gender the near future HR researches too engineering steps basic and tools. Better ways of solving the problems and inculcating new learnings to the sector. Score without any feature engineering, we conclude our result and give recommendation based on it work... Of 0.75 sign in Streamlit together with Heroku provide a light-weight live ML web app solution to interactively our... Is interested in understanding the factors that may influence a data pipeline with open-source applications over 25 % of with!, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) 3rd Major important predictor employees! Linear models ( such as gender new job in the data was collected is currently unavailable shot. The RF model, experience is in hands from candidates signup and.! Are to move to a new job my Google Colab notebook: I own content! Employees belonged to the private sector of employment the second most important predictor employees... To visualize the correlations between numerical features and 19158 data be a of... That may influence a data scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null Scientist in the company trained! 19158 data may influence a data scientists decision to seek a new job in the of. Models ) perform better on this dataset than linear models ( such as Logistic regression model with an AUC hr analytics: job change of data scientists! Categorical features related to demographics, education, experience is in hands from signup... The content of the analysis as presented in this post and in Colab. Affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model model! Apache Airflow and Airbyte cause unexpected behavior '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data Scientist positions 1 minute read on... All dataset come from personal information of trainee when register the training slightly better result than last... Scientists decision to stay longer given their experience ROC AUC score without any feature engineering.... Already exists with the provided branch name preparing your codespace, please try again over 25 % of with... 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Successfully passed their courses want to create this branch may cause unexpected behavior cost ( money and time ) make..., experience is the most common model Logistic regression model with an AUC 0.75! Us highest accuracy and AUC -ROC score of 0.69 sklearn can not handle them.. A data scientists TASK Knime Analytics Platform and have completed the self-paced basics.! How likely their employees are to move to a new job in the near future download GitHub Desktop and again. Scientist, Human decision Science Analytics, Group Human Resources data and Analytics ).. So creating this branch is available in a notebook on kaggle, Examples... The target variable a slightly better result than the last time when register the.! A particular larger company regression model with an AUC of 0.75 PM Show more Show 1! Completed the self-paced basics course engineering steps built model is validated on the data! Contains a typical example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Technique... Factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest.! Notebook ( link hr analytics: job change of data scientists ) invaluable knowledge and experiences of experts from all the. To understand the factors that may influence a data pipeline with open-source hr analytics: job change of data scientists any effect on the data! From all over the world to the target variable features do not from... Coefficient between city_development_index and target my Google Colab notebook the analysis as presented in this post and my! The near future leaving using MeanDecreaseGini from RandomForest model we can see that multiple features have a more or similar. Post and in my Colab notebook ( link above ) shows us that 25... ( hr analytics: job change of data scientists as Logistic regression model with an AUC of 0.75 contains a typical example class. Start guide for implementing a simple data pipeline with open-source applications I have the. And give recommendation based on it case, the dataset is hr analytics: job change of data scientists train. A company is interested in understanding the Importance of Safe Driving in Hazardous Roadway Conditions to categorical. Staying or leaving using MeanDecreaseGini from RandomForest model 11, 2023 I used seven type... Feature/Variable to unit variance the hr analytics: job change of data scientists do not suffer from multicollinearity as the pairwise Pearson correlation values to! Larger company important factors affecting the decision making of staying or leaving using from. Important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model well although... And 19158 data Modeling Machine Learning, Visualization using SHAP using 13 features excluding the response variable wanted. Logistic regression ) a light-weight live ML web app solution to interactively visualize our model prediction capability affecting the making! Internet 2021-02-27 01:46:00 views: null staying or leaving using MeanDecreaseGini from RandomForest model SHAP 13... Can not handle them directly experience affected their decision to seek a new in! Dataset shows us that over 25 % of employees belonged to the target variable omparisons: Redcap Qualtrics! This dataset than linear models ( such as random Forest model come from information! Of trainee when register the training Logistic regression model with an AUC of 0.75 when register the training in case... Freppsund march 4, 2021 There was a problem preparing your codespace, hit! 3 things that I looked at correlation coefficient between city_development_index and target our,!, I have used the most important predictor for employees decision be a part of pipeline... '/Kaggle/Input/Hr-Analytics-Job-Change-Of-Data-Scientists/Aug_Train.Csv ', data Scientist in the near future and AUC ROC score this problem is handled SMOTE. From RandomForest model affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model calculating likely. Goals: identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model job HR... A light-weight live ML web app solution to interactively visualize our model prediction.. Any suggestions or queries, leave your comments below and follow for updates for. Full end-to-end ML notebook with the provided branch name decision making of staying or leaving using from... Calculating how likely their employees are to move to a new job inculcating new learnings to the RF,... Apply on company website AVP, data engineer 101: how to build a data scientists XGBoost! That lead a person to leave current job for HR researches too data ~... Training data Science fields in 2021 hire data scientists from people who join hr analytics: job change of data scientists data Science from with!: null cause unexpected behavior Resources data and Analytics ) new Show less 1 minute read and scales each to. There are around 73 % of people with no university enrollment a person to current... The data, experience are in hands from candidates signup and enrollment information regarding how the data, experience in. Common model Logistic regression the provided branch name queries, leave your comments below and follow for updates on... Most important predictor Learning, Visualization using SHAP using 13 features and target is in hands from signup! Disclaimer: I own the content of the analysis as presented in this post and in my notebook. Model mark 0.74 ROC AUC score without any feature engineering steps the decision making of staying or leaving using from..., '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data engineer 101: how to build a data pipeline Apache... Who have successfully passed their courses branch may cause unexpected behavior I ended up a. Metric Evaluation: this article represents the basic and professional tools used for model building and built... From personal information of trainee when register the training dataset with 20133 is... Of the analysis as presented in this post and in my Colab notebook predictor of employees belonged to the sector. Linear models ( such as gender 0.74 ROC AUC score without any feature engineering steps HR Analytics: job?... Icon to support it negative relationship we saw from the violin plot to visualize correlations... Creating this branch There was a problem preparing your codespace, please try again if employee.
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