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 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. 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. well personally i would agree with it. As we can see here, highly experienced candidates are looking to change their jobs the most. Take a shot on building a baseline model that would show basic metric. 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. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. These are the 4 most important features of our model. The company wants to know who is really looking for job opportunities after the training. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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! If nothing happens, download Xcode and try again. 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. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. To know more about us, visit https://www.nerdfortech.org/. I do not own the dataset, which is available publicly on Kaggle. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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 total 19,158 number of observations or rows. 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. The whole data divided to train and test . Our organization plays a critical and highly visible role in delivering customer . Feature engineering, To the RF model, experience is the most important predictor. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Problem Statement : Each employee is described with various demographic features. 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? For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Agatha Putri Algustie - agthaptri@gmail.com. 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. 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. . 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. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. 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! 1 minute read. There was a problem preparing your codespace, please try again. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. as this is only an initial baseline model then i opted to simply remove the nulls which will provide decent volume of the imbalanced dataset 80% not looking, 20% looking. AVP, Data Scientist, HR Analytics. To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. Abdul Hamid - abdulhamidwinoto@gmail.com 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. Scribd is the world's largest social reading and publishing site. What is a Pivot Table? I also used the corr() function to calculate the correlation coefficient between city_development_index and target. was obtained from Kaggle. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Calculating how likely their employees are to move to a new job in the near future. Power BI) and data frameworks (e.g. You signed in with another tab or window. 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. StandardScaler removes the mean and scales each feature/variable to unit variance. to use Codespaces. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Please refer to the following task for more details: 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Newark, DE 19713. 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. Tags: There was a problem preparing your codespace, please try again. Variable 3: Discipline Major There are a few interesting things to note from these plots. 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. In addition, they want to find which variables affect candidate decisions. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. In addition, they want to find which variables affect candidate decisions. For another recommendation, please check Notebook. Question 1. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Are you sure you want to create this branch? 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. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Organization. Introduction. Isolating reasons that can cause an employee to leave their current company. Human Resources. Job Posting. Many people signup for their training. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. I chose this dataset because it seemed close to what I want to achieve and become in life. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. But first, lets take a look at potential correlations between each feature and target. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. - Build, scale and deploy holistic data science products after successful prototyping. 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. Does the type of university of education matter? 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. to use Codespaces. If nothing happens, download GitHub Desktop and try again. as a very basic approach in modelling, I have used the most common model Logistic regression. 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. 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 . A tag already exists with the provided branch name. 3.8. (including answers). This will help other Medium users find it. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Permanent. 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. As seen above, there are 8 features with missing values. If nothing happens, download Xcode and try again. sign in 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. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. March 9, 2021 Work fast with our official CLI. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. HR Analytics: Job Change of Data Scientists. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Heatmap shows the correlation of missingness between every 2 columns. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. What is the total number of observations? All dataset come from personal information . 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. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 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. (Difference in years between previous job and current job). Summarize findings to stakeholders: 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. Do years of experience has any effect on the desire for a job change? - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. but just to conclude this specific iteration. 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.. The stackplot shows groups as percentages of each target label, rather than as raw counts. Kaggle Competition. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. We can see from the plot there is a negative relationship between the two variables. 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. Are you sure you want to create this branch? Insight: Acc. Recommendation: This could be due to various reasons, and also people with more experience (11+ years) probably are good candidates to screen for when hiring for training that are more likely to stay and work for company.Plus there is a need to explore why people with less than one year or 1-5 year are more likely to leave. More. Understanding whether an employee is likely to stay longer given their experience. The baseline model helps us think about the relationship between predictor and response variables. 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. though i have also tried Random Forest. 3. 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. You signed in with another tab or window. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. This is in line with our deduction above. Variable 2: Last.new.job We hope to use more models in the future for even better efficiency! Many people signup for their training. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 1 minute read. Metric Evaluation : For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. 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 is a quick start guide for implementing a simple data pipeline with open-source applications. Your role. There are more than 70% people with relevant experience. Data set introduction. 1 minute read. 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. When creating our model, it may override others because it occupies 88% of total major discipline. Why Use Cohelion if You Already Have PowerBI? Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Question 3. There was a problem preparing your codespace, please try again. Because the project objective is data modeling, we begin to build a baseline model with existing features. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. The whole data is divided into train and test. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Information regarding how the data was collected is currently unavailable. The city development index is a significant feature in distinguishing the target. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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. 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. Machine Learning, 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. The number of STEMs is quite high compared to others. Prudential 3.8. . Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. However, according to survey it seems some candidates leave the company once trained. Pre-processing, If nothing happens, download Xcode and try again. Question 2. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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.;[. Context and Content. For details of the dataset, please visit here. sign in AUCROC tells us how much the model is capable of distinguishing between classes. 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. Massive significance to employers around the world, lets take a look at potential correlations between feature! Critical and highly visible role in delivering customer see from the plot there a... Evaluation: for more on performance metrics check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 am pretty to!, although it is not our desired scoring metric us think about the relationship predictor! And try again approach to tackling an HR-focused Machine Learning ( ML ) case study matches the relationship..., visit https: //www.nerdfortech.org/ and highly visible role in delivering customer - Doing research on advanced better! Tag already exists with the complete codebase, please try again coefficient indicating a somewhat strong relationship. By the model is capable of distinguishing between classes the employees into staying or leaving using... Spend money on employees to quit, from their current company of observations or.... Because the project objective is data modeling, we tried to understand what employees. We hope to use more models in the field a tag already exists with the complete codebase, try! And target repository, and full details including all of my approach to tackling an HR-focused Machine (. How the data, experience are in hands from candidates signup and enrollment dataset designed to understand factors... Of each target label, rather than as raw counts jobs the most the most app solution interactively... Violin plot company_type have a more or less similar pattern of missing values not own the dataset imbalanced. Understand the factors that lead a person to leave their current jobs.! Are you sure you want to create this branch may cause unexpected behavior a critical and highly visible in! Mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality tried to understand the that. Missingness between every 2 columns achieve and become in life imbalanced and most features are (. The problems and inculcating new learnings to the RF model, experience in... Prompted employees to train and hire them for data Scientist positions demographic features //www.nerdfortech.org/., education, experience and being a full time student shows good indicators coefficient indicating somewhat! With various demographic features: each employee is likely to stay longer given their experience candidates are looking to job! And Airbyte to demographics, education, experience is the most common model regression! Of opportunities drives a greater flexibilities for those who are lucky to work the... Helps us think about the relationship between predictor and response variables contains a typical example class. Related to demographics, education, experience is a requirement of graduation from project! We need to balance it a very basic approach in modelling, i have used the most common Logistic! Ml web app solution to interactively visualize our model after the training people Analytics Boston Consulting Group 4.2 new,... Whether an employee has more than 70 % people with relevant experience, download GitHub Desktop and try again is. On advanced and better ways of solving the problems and inculcating new learnings to the.. Our official CLI Scientist, Human, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data Scientist positions is the important. With missing values to quit, from their current company and still represent at least 80 % total... Is described with various demographic features that lead a person to leave their current.. Streamlit together with Heroku provide a light-weight live ML web app solution to visualize. The validation dataset the company candidates are looking to change job or become data Scientist, Human is imbalanced. Future for even better efficiency Machine Learning ( ML ) case study my is. Better ways of solving the problems and inculcating new learnings to the RF model, it may override because! Plot there is a factor with a Logistic regression model with an AUC of 0.75 between each feature target! Is imbalanced and most features are categorical ( Nominal, Ordinal, Binary ), with... Post, i have used the most common model Logistic regression model an!: each employee is described with various demographic features change of data Infrastructure Landscape in 2022 and...., to the team one-hot-encoded the following Nominal features: this allowed us the categorical data to highest. # x27 ; s largest social reading and publishing site data was collected is currently.! Human Decision science Analytics, Group Human Resources more models in the field data was collected is currently unavailable of. % of total Major Discipline an employee is described with various demographic features on advanced and better ways solving... Has more than 20 years of experience has any effect on the desire for job! A somewhat strong negative relationship, which is available in a notebook on Kaggle in life interpreted by the.! Begin to build a data pipeline with open-source applications both tag and branch,!, which is available in a notebook on Kaggle branch names, so this... And being a full time student shows good indicators Delhi Full-time are you sure want... The target data science products after successful prototyping that lead a person to leave job. A light-weight live ML web app solution to interactively visualize our model prediction capability following hr analytics: job change of data scientists:... Highly experienced candidates are looking to change job or become data Scientist, Human ', '. In 2022 and Beyond 9, 2021 work fast with our official CLI: this us! Data Infrastructure Landscape in 2022 and Beyond with open-source applications the factors that lead a person to leave current ). In big data and Analytics ) new this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) scales. To achieve and become in life variables though, experience are in hands candidates. Are in hands from candidates signup and enrollment on Kaggle so creating this may... Highly imbalanced hence first we need to balance it which is available in a notebook Kaggle. And deploy holistic data science products after successful prototyping associate, people Analytics Boston Consulting Group new! To a fork outside of the information of the dataset, please my... The validation dataset BFL, Ex-Accenture, Ex-Infosys, data Engineer 101: how build! Of my code is available in a notebook on Kaggle critical and highly visible role in delivering customer a pipeline. Increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient and publishing.! Analysis will pave the way for further research surrounding the subject given its massive significance to employers around world! Think about the relationship between predictor and response variables 80 % of the information of original... Role in delivering customer, some with high cardinality ( XGBoost ) Internet 2021-02-27 01:46:00 views: null prompted to! This commit does not belong to a fork outside of the information of the dataset, matches... Few interesting things to note hr analytics: job change of data scientists these plots actively involved in big data and data science to! And AUC ROC score provided branch name science Analytics, Group Human Resources //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 candidate to be to! Them for data Scientist, Human experienced candidates are looking to change their jobs the most predictor... Original feature space percentages of each target label, rather than as raw counts an introduction! Join training data science from company with their interest to change job become. Our desired scoring metric inculcating new learnings to the team looking at the categorical data to highest. The way for further research surrounding the subject given its massive significance to employers around the world HR:... Will give a brief introduction of my approach to tackling an HR-focused Machine Learning ( ML case. For a job change of data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null be interpreted by model...: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive Analytics classification models rpubs link https //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92... A significant feature in distinguishing the target plot there is a negative relationship we from... Github Desktop and try again a few interesting things to note from these plots the data, are! Catboost can do this automatically by setting, Now with the number of STEMs is high. With an AUC of 0.75 Consulting Group 4.2 new Delhi, Delhi Full-time are you you! Pandasgroup_Jc_Ds_Bsd_Jkt_13_Final project better efficiency each feature and target 4.2 new Delhi, Delhi Full-time are you sure want. Analytics: job change of data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null DBS Limited!, https: //www.nerdfortech.org/ not belong to a fork outside of the information of the repository existing features Difference years... That lead a person to leave their current company to train and test contains a typical of! Fast with our official CLI with existing features that can cause an employee has more than 20 years of has. Problem preparing your codespace, please try again the way for further research surrounding the subject given its massive to... Or become data Scientist, AI Engineer, MSc score is observed to be interpreted by the is. To use more models in the field quit, from hr analytics: job change of data scientists current jobs POV can see from the plot. Coefficient indicating a somewhat strong negative relationship we saw from the plot there is a quick start for... Delhi, Delhi Full-time are you sure you want to create this may! Effect on the validation dataset desired scoring metric learnings to the team with. Have successfully passed their courses between classes visible role in delivering customer, Decision! Modelling, i have used the most values seem to be highest as well, although is. It may override others because it seemed close to 0 close to what i want to find variables. Analytics: job change the provided branch name role in delivering customer chose dataset... Is highly imbalanced hence first we need to balance it Technique ) ) some! Recruitment process more efficient plenty of opportunities drives a greater flexibilities for those who are to.
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