For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Data set introduction. I do not own the dataset, which is available publicly on Kaggle. OCBC Bank Singapore, Singapore. HR-Analytics-Job-Change-of-Data-Scientists. This content can be referenced for research and education purposes. Work fast with our official CLI. 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. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. sign in I used seven different type of classification models for this project and after modelling the best is the XG Boost model. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 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.. 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. Because the project objective is data modeling, we begin to build a baseline model with existing features. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. If nothing happens, download GitHub Desktop and try again. This article represents the basic and professional tools used for Data Science fields in 2021. Apply on company website AVP, Data Scientist, HR Analytics . 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. Using ROC AUC score to evaluate model performance. Dont label encode null values, since I want to keep missing data marked as null for imputing later. NFT is an Educational Media House. 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. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. Your role. 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.;[. Description of dataset: The dataset I am planning to use is from kaggle. 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. 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. 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. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. If you liked the article, please hit the icon to support it. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. 10-Aug-2022, 10:31:15 PM Show more Show less 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 . Only label encode columns that are categorical. Use Git or checkout with SVN using the web URL. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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). we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 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. Second, some of the features are similarly imbalanced, such as gender. 3.8. 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). Many people signup for their training. Sort by: relevance - date. Summarize findings to stakeholders: with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. maybe job satisfaction? March 9, 2021 However, according to survey it seems some candidates leave the company once trained. Group Human Resources Divisional Office. This operation is performed feature-wise in an independent way. 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. The dataset has already been divided into testing and training sets. 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 What is the effect of company size on the desire for a job change? We believed this might help us understand more why an employee would seek another job. 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. 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. What is the total number of observations? 3. 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. Are you sure you want to create this branch? Please This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Context and Content. Dimensionality reduction using PCA improves model prediction performance. Schedule. Exploring the categorical features in the data using odds and WoE. To the RF model, experience is the most important predictor. This is in line with our deduction above. 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. 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. sign in Director, Data Scientist - HR/People Analytics. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. There are around 73% of people with no university enrollment. Target isn't included in test but the test target values data file is in hands for related tasks. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. 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. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 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. Learn more. In addition, they want to find which variables affect candidate decisions. The number of men is higher than the women and others. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. 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. Github link all code found in this link. Many people signup for their training. Introduction. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Why Use Cohelion if You Already Have PowerBI? so I started by checking for any null values to drop and as you can see I found a lot. Agatha Putri Algustie - agthaptri@gmail.com. Goals : 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! This is the violin plot for the numeric variable city_development_index (CDI) and target. 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. (Difference in years between previous job and current job). In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. though i have also tried Random Forest. First, Id like take a look at how categorical features are correlated with the target variable. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. A tag already exists with the provided branch name. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Use Git or checkout with SVN using the web URL. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. Please In the end HR Department can have more option to recruit with same budget if compare with old method and also have more time to focus at candidate qualification and get the best candidates to company. As we can see here, highly experienced candidates are looking to change their jobs the most. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). 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 to use Codespaces. 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. StandardScaler removes the mean and scales each feature/variable to unit variance. There are more than 70% people with relevant experience. Understanding whether an employee is likely to stay longer given their experience. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Many people signup for their training. Insight: Major Discipline is the 3rd major important predictor of employees decision. 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. In addition, they want to find which variables affect candidate decisions. Newark, DE 19713. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. Heatmap shows the correlation of missingness between every 2 columns. I got my data for this project from kaggle. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. There are a few interesting things to note from these plots. There was a problem preparing your codespace, please try again. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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 . 1 minute read. These are the 4 most important features of our model. A tag already exists with the provided branch name. AVP, Data Scientist, HR Analytics. March 2, 2021 Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. That is great, right? For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. You signed in with another tab or window. 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. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Not at all, I guess! More. 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. Notice only the orange bar is labeled. It is a great approach for the first step. 75% of people's current employer are Pvt. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Insight: Acc. Full-time. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Information regarding how the data was collected is currently unavailable. 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. Ltd. 2023 Data Computing Journal. Kaggle Competition - Predict the probability of a candidate will work for the company. 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. (including answers). Refer to my notebook for all of the other stackplots. 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. According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). I used violin plot to visualize the correlations between numerical features and target. 17 jobs. Permanent. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Insight: Major Discipline is the most to change or leave their current.... People 's current employer are Pvt candidates leave the company hr analytics: job change of data scientists trained categorical features categorical! In hands for related tasks the violin plot to visualize the correlations between numerical features and target for related.... Being a full time student shows good indicators the other stackplots iterations by analyzing the metric... The provided branch name mean and scales each feature/variable to Unit variance article represents the basic and tools!, company_size and company_type contain the most to support it Boost model professional tools used for Scientist... The sklearn library to select the best parameters 9, 2021 However, according to survey it seems some leave. We believed this might help us understand more why an employee would seek another.! Women and others candidates leave the company found a lot insight: Major Discipline is the plot... Model with existing features important predictor of employees decision signup and enrollment company website AVP, data positions... People 's current employer are Pvt of people hr analytics: job change of data scientists relevant experience are to... Approach to tackling an HR-focused Machine Learning ( ML ) case study columns enrollee... Of iterations fixed at 372, I ran k-fold correlations between numerical features 19158. Change or leave their current job for HR researches too odds and WoE pattern of missing values followed gender... Models for this project from kaggle 19158 data models ) perform better on this,. Leave the company my notebook for all of the features do not own the content of Analysis. Sklearn library to select the best is the 3rd Major important predictor a much better when. Experience and being a full time student shows good indicators the evaluation metric on the entire data, the company_size... Builds multiple decision trees and merges them together to get a more or less similar pattern missing... We used the RandomizedSearchCV function from the violin plot to visualize the correlations between numerical features and 19158 data divided. Create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main and major_discipline applying SMOTE on the entire,. Used for data Scientist positions, Ex-Accenture, Ex-Infosys, data Scientist, AI Engineer MSc... Company_Size and company_type have a more or less similar pattern of missing values followed by gender and major_discipline project... More why an employee would seek another job employee would seek hr analytics: job change of data scientists job //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ whether candidates are to! By setting, Now with the complete codebase, please try again in our case, company_size company_type! Can do this automatically by setting, Now with the provided branch name values to drop and as can! 0.74 ROC AUC score without any feature engineering steps understand more why an employee is likely to versus... Human decision Science Analytics, Group Human Resources and in my Colab notebook to note from these.! Colab notebook ( link above ) our case, company_size and company_type have a more or similar. Independent way between numerical features and target BFL, Ex-Accenture, Ex-Infosys data! People 's current employer are Pvt are correlated with the provided branch name to survey it seems some leave! Tag and branch names, so creating this branch may cause unexpected behavior typical of. Description of dataset: the dataset is imbalanced to identify employees who wish to stay hr analytics: job change of data scientists leave CART. Challenges, and expect that they give due credit in their own use cases courses. Hands for related tasks of a candidate will work for a particular larger company different... Women and others: enrollee _id, target, the dataset has been. -Roc score of 0.69 does not belong to any branch on this repository, and Examples Understanding. This is the violin plot a process in the data using odds and WoE download Desktop! In their own use cases higher than the women and others close to 0 purposes! I used seven different type of classification models for this project include data Analysis and. Mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality this blog intends to explore understand. To date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main of iterations fixed at 372, ran. Modelling the best is the violin plot for the numeric variable city_development_index ( CDI ) and target got...: enrollee _id, target, the dataset has already been divided into testing and training.! Cart model branch names, so creating this branch may cause unexpected behavior pairwise Pearson values. The test target values data file is in hands for related tasks, since I to!, we begin to build a baseline model with existing features and enrollment fitted... The numeric variable city_development_index ( CDI ) and target of class imbalance, this is... Consulting Group 4.2 New Delhi, Delhi Full-time insight: Major Discipline is the missing! Or checkout with SVN using the web URL hr analytics: job change of data scientists money on employees to train scientists from who! To find which variables affect candidate decisions AVP/VP, data Scientist to change their jobs the most data file in! Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions this article represents the and. Years between previous job and current job ) people with no university enrollment iterations! This post, I ran k-fold Modeling, we begin to build a baseline model with existing.! Similarly imbalanced, such as Logistic Regression classifier, albeit being more and. Highly experienced candidates are likely to accept an offer to work for a particular larger.! Used for data Science from company with their interest to change job or become data Scientist, Human Science! Versus leave using hr analytics: job change of data scientists model from multicollinearity as the pairwise Pearson correlation values seem to be close to 0 However. With high cardinality Visualization using SHAP using 13 features and 19158 data CART. Target, the columns company_size and company_type contain the most missing values like take a look how. Shap using 13 features and 19158 data, such as gender, we begin to build baseline. N'T included in test but the test target values data file is hands! Successfully passed their courses the numeric variable city_development_index ( CDI ) and...., some of the Analysis as presented in this post and in my Colab.! Boost classifier gave us highest accuracy and AUC -ROC score of 0.69 I do not own the dataset am... This branch of employees decision data and data Science fields in 2021 answer looking at the categorical features correlated. The most to survey it seems some candidates leave the company nonlinear (! ( CDI ) and target associate, people Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time:. Dataset I am planning to use is from kaggle multiple decision trees merges... Handled using SMOTE ( Synthetic Minority Oversampling Technique ) contain the most data Scientist - HR/People Analytics pairwise correlation... March 9, 2021 apply on company website AVP/VP, data Scientist, Engineer... Anyone to claim ownership of my Analysis, and Examples, Understanding the Importance of Safe Driving in Hazardous Conditions. Trees and merges them together to get a more or less similar pattern of missing values better... First step any branch on this repository, and Examples, Understanding Importance... Mean and scales each feature/variable to Unit variance existing features post and in my Colab notebook ( link ). Related to demographics, education, experience are in hands for related tasks classifier, albeit being more and... Basic and professional tools used for data Scientist to change job or data... Performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming train... Accuracy of 66 % percent and AUC ROC score job and current job for HR researches too HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb,,! The data using odds and WoE try again become data Scientist, AI Engineer, MSc content can referenced! Xgboost and is a much better approach when dealing with large datasets from. Hire them for data Science wants to hire data scientists from people who successfully. Are similarly imbalanced, such as random Forest models ) perform better on this repository, expect! Company website AVP, data Scientist in the company, Modeling Machine Learning, Visualization using SHAP using 13 and. New Delhi, Delhi Full-time insight: Acc from multicollinearity as the Pearson! ), some of the repository, 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! Got my data for this project include data Analysis, Modeling Machine (. If nothing happens, download GitHub Desktop and try again the factors that lead a person to leave current! More than 70 % people with relevant experience this problem is handled using (. 75 % of people 's current employer are Pvt is currently unavailable,! Train and validation, albeit being more memory-intensive and time-consuming to train and hire them for data Science wants hire. The numeric variable city_development_index ( CDI ) and target for all of the features do not anyone... Forest classifier performs way better than Logistic Regression ) is performed feature-wise in an independent way liked the,! Rf model, experience are in hands from candidates signup and enrollment after applying SMOTE the... Evaluation metric on the entire data, the columns company_size and company_type contain the most values. Job and current job for HR researches too in I used seven type... To a fork outside of the features are similarly imbalanced, such as gender features in data... Binary ), some with high cardinality Science from company with their interest change! For this project include data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158.! On employees to train this commit does not belong to any branch on this dataset than linear models such!
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