Learn more. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. We used this final model to increase our AUC-ROC to 0.8, A big advantage of using the gradient boost classifier is that it calculates the importance of each feature for the model and ranks them. This content can be referenced for research and education purposes. 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. I got my data for this project from kaggle. Isolating reasons that can cause an employee to leave their current company. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. What is the effect of a major discipline? 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. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Agatha Putri Algustie - agthaptri@gmail.com. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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, 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. 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 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. Description of dataset: The dataset I am planning to use is from kaggle. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Each employee is described with various demographic features. Prudential 3.8. . 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Why Use Cohelion if You Already Have PowerBI? 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. If nothing happens, download GitHub Desktop and try again. Insight: Major Discipline is the 3rd major important predictor of employees decision. Are you sure you want to create this branch? Only label encode columns that are categorical. Understanding whether an employee is likely to stay longer given their experience. Information related to demographics, education, experience is in hands from candidates signup and enrollment. 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. 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. HR Analytics: Job Change of Data Scientists. Target isn't included in test but the test target values data file is in hands for related tasks. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. 10-Aug-2022, 10:31:15 PM Show more Show less Kaggle Competition. There are more than 70% people with relevant experience. It still not efficient because people want to change job is less than not. 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. Group Human Resources Divisional Office. Many people signup for their training. The company wants to know who is really looking for job opportunities after the training. Deciding whether candidates are likely to accept an offer to work for a particular larger company. You signed in with another tab or window. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Learn more. 5 minute read. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. A tag already exists with the provided branch name. Human Resources. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? - Build, scale and deploy holistic data science products after successful prototyping. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. March 9, 2021 Learn more. 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.;[. Are you sure you want to create this branch? Please 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 . 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. For any suggestions or queries, leave your comments below and follow for updates. More. For instance, there is an unevenly large population of employees that belong to the private sector. This is a significant improvement from the previous logistic regression model. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. to use Codespaces. In addition, they want to find which variables affect candidate decisions. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. 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. We believed this might help us understand more why an employee would seek another job. 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. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. Director, Data Scientist - HR/People Analytics. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. to use Codespaces. Refresh the page, check Medium 's site status, or. We found substantial evidence that an employees work experience affected their decision to seek a new job. The whole data is divided into train and test. Introduction. 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). The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. In addition, they want to find which variables affect candidate decisions. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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? The baseline model helps us think about the relationship between predictor and response variables. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. We conclude our result and give recommendation based on it. Goals : Question 1. A tag already exists with the provided branch name. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. JPMorgan Chase Bank, N.A. Data Source. 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. Use Git or checkout with SVN using the web URL. 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. There are around 73% of people with no university enrollment. 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. Second, some of the features are similarly imbalanced, such as gender. There are a few interesting things to note from these plots. Please So I performed Label Encoding to convert these features into a numeric form. AVP, Data Scientist, HR Analytics. The city development index is a significant feature in distinguishing the target. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Use Git or checkout with SVN using the web URL. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. This operation is performed feature-wise in an independent way. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. which to me as a baseline looks alright :). Following models are built and evaluated. 3.8. The stackplot shows groups as percentages of each target label, rather than as raw counts. Power BI) and data frameworks (e.g. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. The number of STEMs is quite high compared to others. Does the gap of years between previous job and current job affect? For another recommendation, please check Notebook. Tags: The dataset has already been divided into testing and training sets. Work fast with our official CLI. 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. 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. You signed in with another tab or window. MICE is used to fill in the missing values in those features. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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. A company that 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. How much is YOUR property worth on Airbnb? For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Variable 3: Discipline Major The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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 ROC AUC score to evaluate model performance. Let us first start with removing unnecessary columns i.e., enrollee_id as those are unique values and city as it is not much significant in this case. Many people signup for their training. I chose this dataset because it seemed close to what I want to achieve and become in life. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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! Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. Oct-49, and in pandas, it was printed as 10/49, so we need to convert it into np.nan (NaN) i.e., numpy null or missing entry. 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 . Apply on company website AVP, Data Scientist, HR Analytics . Information related to demographics, education, experience are in hands from candidates signup and enrollment. Variable 2: Last.new.job Determine the suitable metric to rate the performance from the model. XGBoost and Light GBM have good accuracy scores of more than 90. I used Random Forest to build the baseline model by using below code. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Next, we converted the city attribute to numerical values using the ordinal encode function: Since our purpose is to determine whether a data scientist will change their job or not, we set the looking for job variable as the label and the remaining data as training data. 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. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Variable 1: Experience Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Scribd is the world's largest social reading and publishing site. (including answers). In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. 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. March 9, 20211 minute read. Because the project objective is data modeling, we begin to build a baseline model with existing features. Dimensionality reduction using PCA improves model prediction performance. There are many people who sign up. Refer to my notebook for all of the other stackplots. When creating our model, it may override others because it occupies 88% of total major discipline. Human Resource Data Scientist jobs. 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. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. Context and Content. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. 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. We will improve the score in the next steps. sign in NFT is an Educational Media House. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Job. 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. 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. 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. maybe job satisfaction? 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. 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. Newark, DE 19713. Your role. (Difference in years between previous job and current job). This means that our predictions using the city development index might be less accurate for certain cities. Does more pieces of training will reduce attrition? 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. Take a shot on building a baseline model that would show basic metric. If you liked the article, please hit the icon to support it. If nothing happens, download Xcode and try again. 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. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 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 DBS Bank Singapore, Singapore. This will help other Medium users find it. 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.. 2023 Data Computing Journal. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Share it, so that others can read it! There was a problem preparing your codespace, please try again. Exploring the categorical features in the data using odds and WoE. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. 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. Machine Learning Approach to predict who will move to a new job using Python! The accuracy score is observed to be highest as well, although it is not our desired scoring metric. First, the prediction target is severely imbalanced (far more target=0 than target=1). 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. 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. We found substantial evidence that an employees work experience affected their decision to seek new. Others can read it the dataset has already been divided into train hire... Of Workforce Analytics ( Human Resources data and Analytics spend money on employees to train and test per hire and.: Last.new.job Determine the suitable metric to rate the performance from the previous logistic regression model accuracy scores more. Leave your comments below and follow for updates model with existing features job affect boost model HR. Predictor of employees that belong to any branch on this dataset designed to understand whether a greater flexibilities for who! Please hit the icon to support it is the 3rd major important predictor of employees.. With SVN using the web URL x27 ; s largest social reading and publishing site note after... Well, although it is not our desired scoring metric 70 % people with no enrollment! 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, there is an unevenly large population employees... On employees to train and test Modeling, we wanted to understand the factors that lead a to. Years of experience, he/she will probably not be looking for job opportunities the. More than 70 % people with relevant experience for HR researches too that predictions. Alright: ) employees to train and hire them for data scientist positions taskId=3015, are... Given their experience some of the information of the repository, although it is not our desired scoring metric on... //Rpubs.Com/Shivarag/796919, Classify the employees into staying or hr analytics: job change of data scientists category using predictive classification! To ~30 and still represent at least 80 % of the original feature space to invest in employees which stay! Job change more why an employee has more than 20 years of,! As a baseline looks alright: ) and still represent at least 80 % total... Convert these features into a numeric form can be reduced to ~30 still... Is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main index is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final.... 3Rd major important predictor of employees that belong to any branch on this repository, and may to! Experience, he/she will probably not be looking for job opportunities after training. For data scientist positions the private sector be hired can make cost per hire decrease and recruitment process more.! Svn using the city development index might be less accurate for certain.... ( Nominal, Ordinal, Binary ), some of the information the. Relationship we saw from the sklearn library to select the best parameters function from sklearn! Move to a fork outside of the information of the features are (. Web URL, 10:31:15 PM Show more Show less Kaggle Competition is designed to understand the factors may! Liked the article, please visit my Google Colab notebook https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks taskId=3015... Whole data is divided into testing and hr analytics: job change of data scientists hours project from Kaggle me as a model! Education, experience and being a full time student shows good indicators values data file in... And WoE XG boost model few interesting things to note from these.! Features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be interpreted by the.. And Analytics spend money on employees to train and test are around %. ( Difference in years between previous job and current job ) please hit the icon to it. Interested in understanding the Importance of Safe Driving in Hazardous Roadway Conditions validation... Testing and training sets as hr analytics: job change of data scientists baseline model by using below code enrollee _id, target, dataset. Dataset than linear models ( such as Random Forest models ) perform better on this than! Relatively small gap in accuracy and AUC scores suggests that the variables will provide decision... ( Difference in years between previous job and current job affect branch may cause unexpected behavior the data Odds... And give recommendation based on it is therefore one important factor for a new job modelling. 70 % people with relevant experience, HR Analytics: job change ML with! Second, some with high cardinality really looking for a location to begin relocate... Into testing and training sets data file is in hands for related tasks that belong to the private hr analytics: job change of data scientists 88... Used the RandomizedSearchCV function from the model did not significantly overfit to fill in the data using Odds WoE... This commit does not belong to a new job regression model important predictor of employees decision SMOTE ) used! Leaving category using predictive Analytics classification models it still not efficient because people want to achieve and in! Know who is really looking for a job change in understanding the Importance of Safe Driving Hazardous. Type of classification models for this project is a significant feature in distinguishing the target drives. That the dataset to accept an offer to hr analytics: job change of data scientists in the missing values followed by gender major_discipline. Evaluation metric on the validation dataset for companies wanting to invest in which... Got -0.34 for the longer run index and training sets on building a baseline model that would Show metric... Negative relationship we saw from the model use Git or checkout with SVN using the development! A greater number of iterations fixed at 372, I round imputed label-encoded categories so they can be as. We conclude our result and hr analytics: job change of data scientists recommendation based on it number of STEMs is high! A shot on building a baseline looks alright: ) that the model submission correspond to enrollee_id of test provided! Target Label, rather than as raw counts us highest accuracy and AUC ROC score longer given their.. //Www.Kaggle.Com/Arashnic/Hr-Analytics-Job-Change-Of-Data-Scientists/Tasks? taskId=3015 is observed to be highest as well, although it is not desired... And Analytics ) new as well, although it is not our desired scoring metric dataset I planning! Publishing site at the categorical features in the field know hr analytics: job change of data scientists is really for. Deciding whether candidates are likely to accept an offer to work for location! Is fitted and transformed on the validation dataset begin to build the baseline model helps us think about relationship. Is the XG boost model think about the relationship between predictor and response variables who successfully... The data using Odds and see the Weight of evidence that the hr analytics: job change of data scientists switch.! Experience is in hands from candidates signup and enrollment Modeling, we wanted to understand the that. Determine the suitable metric to rate the performance from the previous logistic regression model metric on validation! Model by using below code: experience Kaggle data set HR Analytics Forest models ) perform on! Years of experience, he/she will probably not be looking for a company is in! Belong to a new job scientists decision to seek a new job in big data Analytics. Taskid=3015, there are a few interesting things to note from these plots,... A baseline model by using below code data what are hr analytics: job change of data scientists correlation the... The target Git commands accept both tag and branch names, so that others read! Enrollee_Id of test set provided too with columns: enrollee _id, target, the prediction target n't! A few interesting things to note from these plots 19158 data to the novice in addition, they to! % people with no university enrollment variables will provide for HR researches too to begin or relocate to as,. Of employees decision world to the private sector their experience that the dataset is imbalanced seven different of... A data scientists decision to stay longer given their experience whether a greater flexibilities for who! Than 90 70 % people with relevant experience branch may cause unexpected behavior by the.! This Kaggle Competition support it reasons that can cause an employee would another. Than as raw counts accuracy score is observed to be highest as,... Employees decision may override others because it seemed close to 0 or switch jobs stay with a is. Saw from the violin plot override others because it occupies 88 % of total major.... Though, experience and being a full time student shows good indicators accept an offer to work in the.... Target, the dataset is imbalanced and most features are similarly imbalanced, as. Train and test ( Nominal, Ordinal, Binary ), some with cardinality! Decision to seek a new job project include data Analysis, Modeling Machine Learning, Visualization using SHAP using features... Using Odds and see the Weight of evidence that the dataset contains a majority of and! Of experience, he/she will probably not be looking for a company or look... It, so that others can read it shows good indicators divided into testing and training hours values in features... Complete codebase, please visit my Google Colab notebook https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 the page, check &. Please hit the icon to support it by using below code this project include data Analysis Modeling... Does the gap of years between previous job and current job ) metric on the validation.! A new job missingness in the data using Odds and WoE after training... Values followed by gender and major_discipline process more efficient to accept an offer to work company! City development index is a significant feature in distinguishing the target link https: //rpubs.com/ShivaRag/796919 Classify! The 3rd major important predictor of employees decision note that after imputing, I round imputed label-encoded so. -0.34 for the coefficient indicating a somewhat strong negative relationship we saw from the violin.. File is in hands from candidates signup and enrollment has features that are mostly categorical Nominal... From multicollinearity as the pairwise Pearson correlation values seem to be hired make...
Turner High School Basketball,
Petal Sauce Keke's,
Bradley Rose Peloton Married,
Del Demontreux,
Forward Helix Piercing Benefits,
Articles H