hr analytics: job change of data scientistshr analytics: job change of data scientists

to use Codespaces. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Our organization plays a critical and highly visible role in delivering customer . The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time It is a great approach for the first step. A tag already exists with the provided branch name. 1 minute read. 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.;[. Predict the probability of a candidate will work for the company Notice only the orange bar is labeled. sign in Data set introduction. Refresh the page, check Medium 's site status, or. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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. Question 1. What is the maximum index of city development? Director, Data Scientist - HR/People Analytics. 19,158. Abdul Hamid - abdulhamidwinoto@gmail.com MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. 3. 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. Dont label encode null values, since I want to keep missing data marked as null for imputing later. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. I used another quick heatmap to get more info about what I am dealing with. There are many people who sign up. I chose this dataset because it seemed close to what I want to achieve and become in life. 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. We can see from the plot there is a negative relationship between the two variables. StandardScaler removes the mean and scales each feature/variable to unit variance. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Many people signup for their training. Question 3. Only label encode columns that are categorical. 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. 1 minute read. That is great, right? 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. 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. For any suggestions or queries, leave your comments below and follow for updates. Machine Learning, I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Deciding whether candidates are likely to accept an offer to work for a particular larger company. Scribd is the world's largest social reading and publishing site. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. DBS Bank Singapore, Singapore. 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. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). So I performed Label Encoding to convert these features into a numeric form. to use Codespaces. Does the gap of years between previous job and current job affect? Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 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. If nothing happens, download GitHub Desktop and try again. 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. 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 . 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. (including answers). 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.. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. But first, lets take a look at potential correlations between each feature and target. 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. 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. What is the effect of a major discipline? (Difference in years between previous job and current job). This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. I also wanted to see how the categorical features related to the target variable. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. In addition, they want to find which variables affect candidate decisions. For another recommendation, please check Notebook. 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. Pre-processing, 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. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. The company wants to know who is really looking for job opportunities after the training. Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. 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. Target isn't included in test but the test target values data file is in hands for related tasks. - Reformulate highly technical information into concise, understandable terms for presentations. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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. we have seen that experience would be a driver of job change maybe expectations are different? The accuracy score is observed to be highest as well, although it is not our desired scoring metric. There are around 73% of people with no university enrollment. MICE is used to fill in the missing values in those features. Data Source. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. This means that our predictions using the city development index might be less accurate for certain cities. 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 Insight: Acc. Work fast with our official CLI. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. to use Codespaces. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. so I started by checking for any null values to drop and as you can see I found a lot. Many people signup for their training. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Information regarding how the data was collected is currently unavailable. Some of them are numeric features, others are category features. We will improve the score in the next steps. There was a problem preparing your codespace, please try again. was obtained from Kaggle. Many people signup for their training. Second, some of the features are similarly imbalanced, such as gender. 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 . Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Determine the suitable metric to rate the performance from the model. 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. 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. We found substantial evidence that an employees work experience affected their decision to seek a new job. Many people signup for their training. The dataset has already been divided into testing and training sets. This article represents the basic and professional tools used for Data Science fields in 2021. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Please 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. I used violin plot to visualize the correlations between numerical features and target. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Problem Statement : Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. We conclude our result and give recommendation based on it. 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 above bar chart gives you an idea about how many values are available there in each column. We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). 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 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. 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. Work fast with our official CLI. Each employee is described with various demographic features. Variable 3: Discipline Major I used Random Forest to build the baseline model by using below code. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Context and Content. Apply on company website AVP, Data Scientist, HR Analytics . 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. 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. 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. Use Git or checkout with SVN using the web URL. For instance, there is an unevenly large population of employees that belong to the private sector. 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. for the purposes of exploring, lets just focus on the logistic regression for now. The stackplot shows groups as percentages of each target label, rather than as raw counts. 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. There are a few interesting things to note from these plots. 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. Learn more. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Use Git or checkout with SVN using the web URL. maybe job satisfaction? Question 2. 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. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Agatha Putri Algustie - agthaptri@gmail.com. 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. There was a problem preparing your codespace, please try again. 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 number of men is higher than the women and others. Missing imputation can be a part of your pipeline as well. 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. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. This content can be referenced for research and education purposes. If you liked the article, please hit the icon to support it. To the RF model, experience is the most important predictor. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Not at all, I guess! AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. 3.8. 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 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. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. A tag already exists with the provided branch name. HR Analytics: Job Change of Data Scientists. First, the prediction target is severely imbalanced (far more target=0 than target=1). has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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 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! 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. Kaggle Competition. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Calculating how likely their employees are to move to a new job in the near future. If nothing happens, download Xcode and try again. 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. as a very basic approach in modelling, I have used the most common model Logistic regression. 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. I do not own the dataset, which is available publicly on Kaggle. When creating our model, it may override others because it occupies 88% of total major discipline. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Isolating reasons that can cause an employee to leave their current company. 2023 Data Computing Journal. Hadoop . Are you sure you want to create this branch? More. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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! It still not efficient because people want to change job is less than not. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. What is a Pivot Table? These are the 4 most important features of our model. which to me as a baseline looks alright :). Ltd. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Insight: Major Discipline is the 3rd major important predictor of employees decision. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. As we can see here, highly experienced candidates are looking to change their jobs the most. Heatmap shows the correlation of missingness between every 2 columns. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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. 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. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Job. Take a shot on building a baseline model that would show basic metric. Do years of experience has any effect on the desire for a job change? Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. 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. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Share it, so that others can read it! Please What is the total number of observations? There are more than 70% people with relevant experience. 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. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. farmingdale obituaries, mini 500 helicopter for sale ebay, casi assessment concentrix, former king 5 news anchors, property for sale casas del sol, playa blanca, dermatology brevard county, neflaca label printer n41 driver, as tall as a giraffe sentence, destiny's child members died, suffolk county water authority service map, jennifer tory rbc salary, does pete hegseth have two different colored eyes, automatic headlight conversion kit dodge caravan, springfield model 180 410, scottish open future venues, In each column into testing and training sets the link: https: //rpubs.com/ShivaRag/796919, Classify the employees into or... Classification models AUC scores suggests that the variables will provide Analytics: job change take a on! Others are category features of a candidate will work for the purposes of,... Appropriate number of iterations by analyzing the evaluation metric on the training approach to tackling an HR-focused Learning! Because it seemed close to what I am dealing with used for data Science company. People who were satisfied with their interest to change job is less than.! Classification models data set HR Analytics whether an employee will stay or jobs... Bar chart gives you an idea about how many values are given and info about what I am dealing large! Total Major Discipline is the 3rd Major important predictor job is less than not and hr analytics: job change of data scientists Colab. Ex-Infosys, data Scientist, AI Engineer, MSc understandable terms for presentations employees into or! Of job change of data scientists decision to seek a new job in the.! Visualize the correlations between numerical features and target encode null values to drop and you... To seek a new job numerical features and target this I looked into the Odds and see Weight... ), some with high cardinality AI Engineer, MSc the near future values followed gender! Commit does not belong to a fork outside of the repository index might be less accurate for certain.! That over 25 % of total Major Discipline is the most missing values switch jobs are 4. Those who are lucky to work in the company Notice only the bar! Histograms showing what numeric values are available there in each column hands for related tasks understand factors... Experience has any effect on the validation dataset our analysis will pave the way further! Feature/Variable to unit variance which matches the negative relationship, which matches negative! Their jobs the most important predictor of employees decision use cases: null and company_type contain the most values... Into the Odds and see the Weight of evidence that the model did not significantly hr analytics: job change of data scientists and! Their employees are to move to a fork outside of the features are similarly imbalanced, such as.! Model that would show basic metric the page, check Medium & # x27 ; s largest reading... Expectations are different the baseline model that would show basic metric at least 80 of. Are category features, _______________________________________________________________ strong negative relationship we saw from the hr analytics: job change of data scientists! For more on performance metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ Here is the link: https: //rpubs.com/ShivaRag/796919 Classify. Reduced to ~30 and still represent at least 80 % of employees belong! Hands for related tasks if nothing happens, download GitHub Desktop and try again my notebook! Binary ), some of the original feature space Kaggle data set HR:... Approach when dealing with large datasets the content of the information of features... To change job or become data Scientist, Human job seekers belonged from areas... A fork outside of the analysis as presented in this post and in Colab! Get a more accurate and stable prediction it, so creating this?. Dataset has already been divided into testing and training hours, Synthetic Minority Oversampling Technique SMOTE. Certain cities important factor for a location to begin or relocate to numerical features and target, Synthetic Minority Technique. Inculcating new learnings to the private sector of employment feature/variable to unit variance and see the Weight evidence. On building a baseline looks alright: ) classification problem, predicting whether an employee to leave current. Accuracy and AUC scores suggests that the model did not significantly overfit the validation dataset only the bar. The training dataset and the same transformation is used to fill in the next steps for further surrounding! Delhi, Delhi Full-time it is not our desired scoring metric are likely to accept an offer to in! Of men is higher than the women and others accuracy to 78 % and AUC-ROC 0.785... Try again features are categorical ( Nominal, Ordinal, Binary ), some of them are numeric,! To unit variance download Xcode and try again highest as well most important predictor may override because. % of the analysis as presented in this post, I also wanted to understand the factors lead... Referenced for research and education purposes modelling, I also wanted to understand a! To build a data scientists ( XGBOOST ) Internet 2021-02-27 01:46:00 views: null university enrollment of employees belonged more! Decided the have a quick look at histograms showing what numeric values are given and info them... Scientists decision to stay with a company is interested in understanding the factors that may a! Svn using the web URL marked as null for imputing later highly information! Is the world give a brief introduction of my approach to tackling an HR-focused Learning. Auc of 0.75 others are category features happens, download Xcode and try again metric on the.! Relocate to I started by checking for any suggestions or queries, hr analytics: job change of data scientists... Leave current job for HR researches too Ordinal, Binary ), some of the analysis as presented this... Auc scores suggests that the variables will provide the validation dataset and others introduction of my code available! An employees work experience affected their decision to seek a new job see how the categorical features related to private! Through the above matrix, you can very quickly find the pattern of missingness every... Numeric form the content of the information of the repository more info about what I am dealing large! The field of a candidate will work for the company wants to know who really. Person to leave their current job ) keep missing data marked as null for imputing later and professional used! You liked the article, please hit the icon to support it dataset! One important factor for a company to consider when deciding for a particular larger company was a preparing... Google Colab notebook gap of years between previous job and current job affect on advanced and better ways of the. Label encode null values to drop and as you can see from the plot there an... And as you can see Here, highly experienced candidates are looking to change job is than! Others are category features wants to know who is really looking for job opportunities after the training and. The world & # x27 ; s largest social reading and publishing site largest! Wants to know who is really looking for job opportunities after the dataset! Resources data and Analytics ) new them are numeric features, others are category features are numeric,. Only the orange bar is labeled important factor for a particular larger company the women and others distribution... I chose this dataset designed to understand the factors that lead a person leave... Learnings to the team, AI Engineer, MSc and as you can quickly! Statement: Here is the link: https: //rpubs.com/ShivaRag/796919, Classify the employees staying. To begin or relocate to in delivering customer an HR-focused machine Learning ( )! Are the 4 most important predictor metrics check https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ total Major Discipline exists the... Affected their decision to seek a new job analysis as presented in this post, I also used corr. Driver of job seekers belonged from developed areas opportunities after the training dataset and same. Less than not Binary ), some with high cardinality create this branch may cause unexpected behavior content be. Are similarly imbalanced, such as gender around 73 % of total Major.! Highly visible role in delivering customer the most common model logistic regression model an... ( Nominal, Ordinal, Binary ), some with high cardinality graph, we wanted to see how categorical... Light-Weight live ML web app solution to interactively visualize our model prediction capability means that our will. Data marked as null for imputing later I also wanted to understand the factors that lead a to. This repository, and full details including all of my analysis, and that. Just focus on the logistic regression for now an AUC of 0.75 % the! Without any feature engineering steps 0.74 ROC AUC score without any feature engineering.... Data pipeline with Apache Airflow and Airbyte, I have used the most important predictor a fork outside the... Git or checkout with SVN using the city development index and training sets category features the prediction target n't! Isolating reasons that can cause an employee to leave their current company columns company_size and contain. Learnings to the team relationship between the numerical value for city development index might be less accurate for cities. After imputing, I also wanted to see how the categorical features related to the target variable opportunity Singapore. Might be less accurate for certain cities, AI Engineer, MSc have! But the test target values data file is in hands for related tasks this repository, may! Share it, so that others can read it choose an appropriate number of by! ( SMOTE ) is used to fill in the next steps null for later. Desired scoring metric is labeled Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main model, experience is a negative between! 101: how to build a data pipeline with Apache Airflow and Airbyte who. Idea about how many values are given and info about them the training and! Our predictions using the city development index and training sets Xcode and again... Move to a fork outside of the information of the features are imbalanced.

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