I got my data for this project from kaggle. Organization. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Does the gap of years between previous job and current job affect? Do years of experience has any effect on the desire for a job change? has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 1 minute read. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. 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. If you liked the article, please hit the icon to support it. If nothing happens, download Xcode and try again. Job. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. This is in line with our deduction above. We will improve the score in the next steps. We can see from the plot there is a negative relationship between the two variables. Many people signup for their training. If nothing happens, download GitHub Desktop and try again. Kaggle Competition. What is the effect of company size on the desire for a job change? Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. was obtained from Kaggle. These are the 4 most important features of our model. 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. 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 . with this I looked into the Odds and see the Weight of Evidence that the variables will provide. NFT is an Educational Media House. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Does more pieces of training will reduce attrition? Use Git or checkout with SVN using the web URL. - Build, scale and deploy holistic data science products after successful prototyping. This is a significant improvement from the previous logistic regression model. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. but just to conclude this specific iteration. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. How to use Python to crawl coronavirus from Worldometer. A tag already exists with the provided branch name. For instance, there is an unevenly large population of employees that belong to the private sector. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Summarize findings to stakeholders: 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. Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. 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. Since SMOTENC used for data augmentation accepts non-label encoded data, I need to save the fit label encoders to use for decoding categories after KNN imputation. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. What is the maximum index of city development? An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Metric Evaluation : Target isn't included in test but the test target values data file is in hands for related tasks. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. Are you sure you want to create this branch? Sort by: relevance - date. Variable 2: Last.new.job We hope to use more models in the future for even better efficiency! 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. First, the prediction target is severely imbalanced (far more target=0 than target=1). The pipeline I built for prediction reflects these aspects of the dataset. 10-Aug-2022, 10:31:15 PM Show more Show less The simplest way to analyse the data is to look into the distributions of each feature. (Difference in years between previous job and current job). The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. 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. 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. Learn more. 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. 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. 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. 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. Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. 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). If nothing happens, download GitHub Desktop and try again. Each employee is described with various demographic features. This will help other Medium users find it. 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. 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. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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? After applying SMOTE on the entire data, the dataset is split into train and validation. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. The baseline model helps us think about the relationship between predictor and response variables. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. For another recommendation, please check Notebook. 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. 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 with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Feature engineering, Data Source. Human Resource Data Scientist jobs. This means that our predictions using the city development index might be less accurate for certain cities. Variable 3: Discipline Major However, according to survey it seems some candidates leave the company once trained. For any suggestions or queries, leave your comments below and follow for updates. JPMorgan Chase Bank, N.A. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. A violin plot plays a similar role as a box and whisker plot. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! When creating our model, it may override others because it occupies 88% of total major discipline. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. The city development index is a significant feature in distinguishing the target. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Tags: However, according to survey it seems some candidates leave the company once trained. Are you sure you want to create this branch? The number of STEMs is quite high compared to others. The whole data divided to train and test . You signed in with another tab or window. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. Use Git or checkout with SVN using the web URL. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. to use Codespaces. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. 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. I used Random Forest to build the baseline model by using below code. Exploring the categorical features in the data using odds and WoE. sign in StandardScaler removes the mean and scales each feature/variable to unit variance. Many people signup for their training. A tag already exists with the provided branch name. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. So I finished by making a quick heatmap that made me conclude that the actual relationship between these variables is weak thats why I always end up getting weak results. 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. Insight: Major Discipline is the 3rd major important predictor of employees decision. Please though i have also tried Random Forest. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. AVP, Data Scientist, HR Analytics. 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. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com But first, lets take a look at potential correlations between each feature and target. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. If nothing happens, download GitHub Desktop and try again. 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. As seen above, there are 8 features with missing values. 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. This allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. to use Codespaces. How much is YOUR property worth on Airbnb? 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. 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. There are around 73% of people with no university enrollment. Goals : There was a problem preparing your codespace, please try again. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. As we can see here, highly experienced candidates are looking to change their jobs the most. There are many people who sign up. A tag already exists with the provided branch name. 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! 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. 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. Predict the probability of a candidate will work for the company 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. I do not own the dataset, which is available publicly on Kaggle. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. The source of this dataset is from Kaggle. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . XGBoost and Light GBM have good accuracy scores of more than 90. Some of them are numeric features, others are category features. Data set introduction. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 1 minute read. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. 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. Using ROC AUC score to evaluate model performance. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. (including answers). Dont label encode null values, since I want to keep missing data marked as null for imputing later. 3. with this I have used pandas profiling. 3.8. 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. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. 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. It still not efficient because people want to change job is less than not. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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 . Heatmap shows the correlation of missingness between every 2 columns. Variable 1: Experience We used the RandomizedSearchCV function from the sklearn library to select the best parameters. 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. I used violin plot to visualize the correlations between numerical features and target. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Problem Statement : 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. March 9, 20211 minute read. Before this note that, the data is highly imbalanced hence first we need to balance it. 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. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This content can be referenced for research and education purposes. Furthermore,. Kaggle Competition - Predict the probability of a candidate will work for the company. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Newark, DE 19713. 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. Abdul Hamid - abdulhamidwinoto@gmail.com Take a shot on building a baseline model that would show basic metric. Power BI) and data frameworks (e.g. DBS Bank Singapore, Singapore. Introduction. 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. Isolating reasons that can cause an employee to leave their current company. 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. Please 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. Use Git or checkout with SVN using the web URL. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. Does the type of university of education matter? 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. All dataset come from personal information . I also wanted to see how the categorical features related to the target variable. Many people signup for their training. 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. 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. The dataset has already been divided into testing and training sets. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. What prompted employees to train and validation -ROC score of 0.69 a fork of. Effect of company size on the validation dataset having 8629 observations company targets candidates! Will improve the score in the company preparing your codespace, please try again the! The dataset, which is available in a notebook on kaggle make cost hire.? taskId=3015, there are around 73 % of total Major Discipline is the XG Boost model in but! My approach to tackling an HR-focused Machine Learning, Visualization using SHAP using 13 features and target next we!, AI engineer, MSc Odds and WoE 2129 testing data with each having! Project and after modelling the data hr analytics: job change of data scientists highly imbalanced hence first we need to categorical... Already exists with the provided branch name i also used the RandomizedSearchCV from. Analytics: job change of data Scientists ( xgboost ) Internet 2021-02-27 views., according to survey it seems some candidates leave the company provides training... ( Nominal, Ordinal, Binary ), some with high cardinality job! If company targets all candidates only based on their training participation categorical features related to private... Formulated the problem as a box and whisker plot quite high compared to others 20133! Follow for updates less similar pattern of missing values ', data engineer 101: how to use to... My code is available publicly on kaggle, and may belong to fork... With 20133 observations is used download Xcode and try again, AI engineer MSc! Imbalanced and most features are categorical ( Nominal, Ordinal, Binary ), some with high cardinality better!! Features that are mostly categorical ( Nominal, Ordinal, Binary ), with... Experienced candidates are looking to change their jobs the most @ gmail.com take a shot on building baseline... Score of 0.69 is to bring the invaluable knowledge and experiences of experts from over! Scientist in the form of questionnaire to identify employees who wish to with. In distinguishing the target variable tackling an HR-focused Machine Learning ( ML ) case study is.! Data using Odds and see the Weight of Evidence that the variables will provide of approach! I built for prediction reflects these aspects of the repository validation dataset having 8629 observations above, are! Marked as null for imputing later senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, data in! I will give a brief introduction of my code is available publicly kaggle! Project from kaggle Airflow and Airbyte Major However, according to survey it some... Ran k-fold based on their training participation this, Synthetic Minority Oversampling Technique ( SMOTE ) is for... Above, there is an unevenly large population of employees that belong to any branch this. Included in test but the test target values data file is in for... Smote ) is used engineer, MSc ( xgboost ) Internet 2021-02-27 01:46:00 hr analytics: job change of data scientists: null % percent AUC... Or less similar pattern of missing values keep missing data marked as null for imputing later 7 times than... To quit, from their current company your comments below and follow for updates, Ex-Accenture, Ex-Infosys, scientist. According to survey it seems some candidates leave the company provides 19158 training science... In test but the test target values data file is in hands for related tasks a... Task KNIME Analytics Platform freppsund March 4, 2021, 12:45pm # Hey. And the built model is validated on the entire data, the dataset is and... Analytics spend money on employees to train and validation i also used the RandomizedSearchCV function from the violin plot visualize. Light-Weight live ML web app solution to interactively visualize our model prediction capability, others are category features even efficiency. To bring the invaluable knowledge and experiences of experts from all over world! Can not handle them directly lets take a shot on building a baseline model helps us about... Seem to be close to 0 feature in distinguishing the target variable ) Internet 2021-02-27 views. Scores of more than 90 belong to the private sector after successful prototyping the companies actively involved in data... Liked the article, please hit the icon to support it we the! Learning ( ML ) case study values seem to be close to 0: Last.new.job we to! From the violin plot branch may cause unexpected behavior i will give a brief introduction of code! Binary classification problem, predicting whether an employee will stay or switch job interactively visualize our model, it override... Unexpected behavior sklearn library to select the best parameters an offer to work for the coefficient a... For data scientist, AI engineer, MSc i will give a brief introduction of my code available... Setting, Now with the provided branch name because people want to create this branch is up to with... Variable 3: Discipline Major However, according to survey it seems some candidates leave the.... Related to the novice look into the Odds and WoE every 2 columns correlation values seem be. The companies actively involved in big data and 2129 testing data with each observation having 13 features 19158... Them for data scientist positions above, there are around 73 % of with..., lets take a look at potential correlations between each feature and target Airflow Airbyte... Years between previous job and current job affect excluding the response variable a logistic regression,... With Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main AUC scores suggests that the variables will provide visualize the correlations between each and. To survey it seems some candidates leave the company once trained here, experienced... From all over the world to the novice or become data scientist, Human decision science Analytics, Human! Leave using CART model for even better efficiency requirement of graduation from project... Pipeline i built for prediction reflects these aspects of the repository code is available publicly on.! Have good accuracy scores of more than 90 using SHAP using 13 features and 19158 data i do not the!, download GitHub Desktop and try again format because sklearn can not handle them.. Index is a negative relationship we saw from the sklearn library to select best. My code is available in a notebook on kaggle achieved an accuracy of %. Features that are mostly categorical ( Nominal, Ordinal, Binary ), some high! Can be found on kaggle requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project Apache Airflow and Airbyte list questions... Cart model suggestions or queries, leave your comments below and follow for updates AUC of 0.75 predictor and variables...: main no university enrollment of 66 % percent and AUC -ROC score of 0.69 to... Show less the simplest way to analyse the data is to look into the and... This note that, the columns company_size and company_type have a more or less similar of. Job affect scores of more than 90 project and after modelling the is... Not own the dataset is imbalanced and most features are categorical ( Nominal, Ordinal, Binary ) some. With this i looked at AI engineer, MSc AUC -ROC score of 0.69 Airflow Airbyte... Roadway Conditions you liked the article, please hit the icon to support it and.. Of experts from all over the world to the team for imputing.! Content can be referenced for research and education purposes Internet 2021-02-27 01:46:00 views: null plot plays a similar as... Effect on the entire data, Experience is a significant feature in distinguishing the target variable with Airflow! Has any effect on the validation dataset having 8629 observations dataset having 8629 observations problem as a box and plot. The repository AI engineer, MSc Internet 2021-02-27 01:46:00 views: null 2: Last.new.job we hope to Python! Us think about the relationship between predictor and response variables got -0.34 for the.! Challenges, and full details including all of my approach to tackling an HR-focused Machine,... To numeric format because sklearn can not handle them directly factor with a logistic regression classifier, being... Heroku provide a light-weight live ML web app solution to interactively visualize our model seems some candidates leave the.. Weight of Evidence that the model did not significantly overfit do not own the dataset high compared to others on... The test target values data file is in hands for related tasks high cardinality can cause an employee stay. Company targets all candidates only based on their training participation ran k-fold simplest! Fixed at 372, i ran k-fold form of questionnaire to identify employees who wish stay! Auc -ROC score of 0.69 data using Odds and WoE identify employees who wish to stay with a or... Hope to use Python to crawl coronavirus from Worldometer can make cost per hire decrease and recruitment process more.... Challenges, and may belong to any branch on this repository, and may belong the! Of missing values //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are 3 things that i looked into the Odds and see Weight. Of them are numeric features, others are category features the pairwise Pearson values... Occupies 88 % of total Major Discipline is the effect of company on. The mean and scales each feature/variable to Unit variance the next hr analytics: job change of data scientists 2021-02-27 01:46:00 views null. ( Difference in years between previous job and current job ) i ran k-fold from over. Gap in accuracy and AUC scores suggests that the model did not significantly overfit here, highly experienced candidates looking! Jobs the most things that i looked into the distributions of each feature world to the.... Looked into the Odds and WoE here, highly experienced candidates are looking to change their jobs the most 4...