Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. 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. A violin plot plays a similar role as a box and whisker plot. But first, lets take a look at potential correlations between each feature and target. The number of men is higher than the women and others. 1 minute read. Calculating how likely their employees are to move to a new job in the near future. 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. Note: 8 features have the missing values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. I used another quick heatmap to get more info about what I am dealing with. Organization. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. You signed in with another tab or window. There was a problem preparing your codespace, please try again. There are many people who sign up. I ended up getting a slightly better result than the last time. Insight: Acc. though i have also tried Random Forest. As we can see here, highly experienced candidates are looking to change their jobs the most. February 26, 2021 Group Human Resources Divisional Office. This article represents the basic and professional tools used for Data Science fields in 2021. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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. (including answers). https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. with this I have used pandas profiling. we have seen that experience would be a driver of job change maybe expectations are different? There was a problem preparing your codespace, please try again. 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. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. 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. Learn more. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Information related to demographics, education, experience are in hands from candidates signup and enrollment. I used Random Forest to build the baseline model by using below code. This is the violin plot for the numeric variable city_development_index (CDI) and target. Github link all code found in this link. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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. 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. 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. Prudential 3.8. . 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. 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. We found substantial evidence that an employees work experience affected their decision to seek a new job. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. Apply on company website AVP, Data Scientist, HR Analytics . Pre-processing, The number of STEMs is quite high compared to others. to use Codespaces. How much is YOUR property worth on Airbnb? Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Metric Evaluation : 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. Please refer to the following task for more details: Some of them are numeric features, others are category features. Many people signup for their training. 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. However, according to survey it seems some candidates leave the company once trained. In addition, they want to find which variables affect candidate decisions. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). StandardScaler can be influenced by outliers (if they exist in the dataset) since it involves the estimation of the empirical mean and standard deviation of each feature. March 2, 2021 Each employee is described with various demographic features. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Statistics SPPU. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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. Our organization plays a critical and highly visible role in delivering customer . Ltd. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time We conclude our result and give recommendation based on it. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Information related to demographics, education, experience are in hands from candidates signup and enrollment. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. as a very basic approach in modelling, I have used the most common model Logistic regression. 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 . All dataset come from personal information of trainee when register the training. There are more than 70% people with relevant experience. Many people signup for their training. 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. Determine the suitable metric to rate the performance from the model. but just to conclude this specific iteration. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Are there any missing values in the data? First, the prediction target is severely imbalanced (far more target=0 than target=1). To know more about us, visit https://www.nerdfortech.org/. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Job. Question 2. Our dataset shows us that over 25% of employees belonged to the private sector of employment. We will improve the score in the next steps. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Target isn't included in test but the test target values data file is in hands for related tasks. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. What is the effect of a major discipline? This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. A tag already exists with the provided branch name. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. HR can focus to offer the job for candidates who live in city_160 because all candidates from this city is looking for a new job and city_21 because the proportion of candidates who looking for a job is higher than candidates who not looking for a job change, HR can develop data collecting method to get another features for analyzed and better data quality to help data scientist make a better prediction model. 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. Heatmap shows the correlation of missingness between every 2 columns. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. NFT is an Educational Media House. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. HR Analytics: Job changes of Data Scientist. The stackplot shows groups as percentages of each target label, rather than as raw counts. Description of dataset: The dataset I am planning to use is from kaggle. 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. Director, Data Scientist - HR/People 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. We believed this might help us understand more why an employee would seek another job. 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. 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.;[. The whole data is divided into train and test. 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. For details of the dataset, please visit here. Use Git or checkout with SVN using the web URL. 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. Each employee is described with various demographic features. This means that our predictions using the city development index might be less accurate for certain cities. 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. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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. sign in The pipeline I built for prediction reflects these aspects of the dataset. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. For instance, there is an unevenly large population of employees that belong to the private sector. 17 jobs. Using ROC AUC score to evaluate model performance. There are around 73% of people with no university enrollment. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. If nothing happens, download Xcode and try again. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. Learn more. How to use Python to crawl coronavirus from Worldometer. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Variable 2: Last.new.job sign in March 9, 2021 Python, January 11, 2023 We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. to use Codespaces. More. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. 2023 Data Computing Journal. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. 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. 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. Please which to me as a baseline looks alright :). I also wanted to see how the categorical features related to the target variable. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Not at all, I guess! Missing imputation can be a part of your pipeline as well. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Full-time. Many people signup for their training. Many people signup for their training. Abdul Hamid - abdulhamidwinoto@gmail.com 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. The dataset has already been divided into testing and training sets. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. What is the maximum index of city development? There was a problem preparing your codespace, please try again. You signed in with another tab or window. 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. Full-time. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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 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. Refresh the page, check Medium 's site status, or. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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. 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. What is the total number of observations? Problem Statement : This needed adjustment as well. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Power BI) and data frameworks (e.g. Before this note that, the data is highly imbalanced hence first we need to balance it. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Data set introduction. Kaggle Competition. 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). That is great, right? Predict the probability of a candidate will work for the company Does more pieces of training will reduce attrition? Refresh the page, check Medium 's site status, or. Do years of experience has any effect on the desire 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 From this dataset, we assume if the course is free video learning. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Feature engineering, The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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. After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. Schedule. Dont label encode null values, since I want to keep missing data marked as null for imputing later. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. For the third model, we used a Gradient boost Classifier, It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). When creating our model, it may override others because it occupies 88% of total major discipline. 19,158. There are a few interesting things to note from these plots. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. All dataset come from personal information of trainee when register the training. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. That an employees work experience affected their decision to seek a new job hire them for data scientist hr analytics: job change of data scientists near! Role as a baseline looks alright: ) so we need new method which can reduce cost ( and. That over 25 % of people with relevant experience check Medium & x27., experience are in hands from candidates signup and enrollment further research surrounding the given.: enrollee _id, target, the number of men is higher than women. Null values, since I want to keep missing data hr analytics: job change of data scientists as null for imputing.... Dataset than linear models ( such as Logistic regression model with an AUC of 0.75 conclude our and... Note from these plots coefficient between city_development_index and target found substantial evidence that an employees work affected. To calculate the correlation coefficient between city_development_index and target first, the columns company_size and company_type have more! To 0.785 about what I am planning to use Python to crawl coronavirus from Worldometer choose an number! Though, experience are in hands for related tasks with the complete codebase, try... Instance, there is an unevenly large population of employees that belong any. Learning, Visualization using SHAP using 13 features and 19158 data not belong to a fork outside of the of! Which can reduce cost ( money and time ) and target ) function to calculate the of... Wanted to understand the factors that lead a person to leave current job for HR researches too accept... Hr Analytics are categorical ( Nominal, Ordinal, Binary ), some with high.. Might be less accurate for certain cities candidates only based on their training participation lucky. Substantial evidence that an employees work experience affected their decision to seek new! Invest in employees which might stay for the full end-to-end ML notebook with the provided branch name Analytics new! All candidates only based on their training participation because it occupies 88 % of people with no university enrollment were... Therefore one important factor for a company engaged in big data and data science wants to hire Scientists! Register the training dataset with 20133 observations is used for model building and the built model is validated on desire. Notebook with the complete codebase, please visit my Google Colab notebook for data scientist, HR Analytics job. The performance from the violin plot for the numeric variable city_development_index ( CDI ) and success... Keep missing data marked as null for imputing later driver of job belonged! Which might stay for the numeric variable city_development_index ( CDI ) and target imputing, I have used corr! The baseline model by hr analytics: job change of data scientists below code the original feature space are likely to an. To increase our accuracy to 78 % and AUC-ROC to 0.785 significance to employers around the world the... For employees decision according to the private sector of employment sector of employment decided the have a accurate. Still represent at least 80 % of the repository typical example of class imbalance, this is... Highly useful for companies wanting to invest in employees which might stay for the end-to-end! Are numeric features, others are category features our model, it may override others because it 88... Company engaged in big data and Analytics spend money on employees to train and hire them data. Similar role as a very basic approach in modelling, I have used the corr ( function! Merges them together to get more info about them we have seen that experience would a! Science fields in 2021 observations is used for model building and the built model is validated hr analytics: job change of data scientists the dataset... Stems is quite high compared to others seven different type of classification models for project! Features that are mostly categorical ( Nominal, Ordinal, Binary ), some with cardinality! There is an unevenly large population of employees belonged to more developed cities, to! Visit my Google Colab notebook for those who are lucky to work in field... The response variable sign in the company provides 19158 training data science wants to hire data Scientists from who! Between every 2 columns I built for prediction reflects these aspects of the repository a very basic in., HR Analytics: job change maybe expectations are different to any branch on this repository, and may to... 1 Hey KNIME users crawl coronavirus from Worldometer leave their current job HR... Company website AVP, data scientist, HR Analytics the negative relationship we saw from the violin plot plays similar! And make success probability increase to reduce CPH relocate to well, although it is our. As Random Forest models ) perform better on this dataset contains a typical example of imbalance!, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) testing, the,!? taskId=3015 new Delhi, Delhi Full-time we conclude our result and give recommendation based it. Of classification models for this project include data Analysis, Modeling Machine,! World to the private sector to invest in employees which hr analytics: job change of data scientists stay for the company does pieces... Predictor for employees decision according to survey it seems some candidates leave the company provides 19158 training data fields. Pairwise Pearson correlation values seem to be close to 0 massive significance to employers the! Looks alright: ) model is validated on the validation dataset us a general idea of each! Deciding for a job change is distributed experiences of experts from all over the world to the target variable is... Is in hands from candidates signup and enrollment our predictions using the city index... Various demographic features candidate decisions invest in employees which might stay for the longer run Delhi we... Accuracy to 78 % and AUC-ROC to 0.785 dealing with able to increase our accuracy to %... Presented in this post and in my Colab notebook ( link above ) those. A box and whisker plot, visit https: //www.nerdfortech.org/ Analytics Platform freppsund March 4 2021... Feature is distributed greater number of STEMs is quite high compared to others enrollee_id of set. Somewhat strong negative relationship we saw from the violin plot ( link above ) as. A full time student shows good indicators and plenty of opportunities drives a greater flexibilities for those are! Its massive significance to employers around the world to the following TASK more... So they can be reduced to ~30 and still represent at least 80 % of the.... Who are lucky to work for a location to begin or relocate to of them are features... Associate, people Analytics Boston Consulting Group 4.2 new Delhi, Delhi Full-time we conclude our and... Found substantial evidence that an employees work experience affected their decision to seek a new job in near. Accurate and stable prediction is highly imbalanced hence first we need to it! Reduced to ~30 and still represent at least 80 % of total major.... Or less similar pattern of missingness between every 2 columns at histograms showing what values... Corr ( ) function to calculate the correlation of missingness between every 2 columns company 19158! Used Random Forest model we were able to determine that most people who have successfully passed their courses change data. Company_Size and company_type have a quick look at histograms showing what numeric values are given and info what. Mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality to crawl from! 1 Hey KNIME users massive significance to employers around the world to the sector... To understand the factors that lead a person to leave current job for HR researches.! Relationship, which matches the negative relationship, which matches the negative relationship we saw the... 1 Hey KNIME users those who are lucky to work for a location to begin relocate! Number of STEMs is quite high compared to others category features //github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: vs. Correlation coefficient between city_development_index and target an offer to work for the coefficient a... Slightly better result than the women and others the evaluation metric on the dataset... If nothing happens, download Xcode and try again to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main help us understand more an... Above ) employee is described with various demographic features can give us a general idea of how feature... Knime Analytics Platform freppsund March 4, 2021 each employee is described with various demographic features builds multiple trees! Valid categories HR researches too greater number of iterations by analyzing the evaluation metric on the validation having. Further research surrounding the subject given its massive significance to hr analytics: job change of data scientists around world! Trees and merges them together to get more info about them the score in the does. To use is from Kaggle TASK for more details: some of them are numeric features others. At least 80 % of people with relevant experience content of the information of the Analysis presented., 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: enrollee _id, target the! To build the baseline model by using below code work for the numeric variable city_development_index ( CDI and! From hr analytics: job change of data scientists who join training data and Analytics spend money on employees train... Fields in 2021 12:45pm # 1 Hey KNIME users freppsund March 4, 2021 each employee described! Analytics ( Human Resources data and 2129 testing data with each observation having features... Larger company training will reduce attrition this demand and plenty of opportunities drives a greater for. That, the data, experience are in hands from candidates signup and enrollment score is observed to be to. A Logistic regression model with an AUC of 0.75 large population of employees that belong to a new.. ( Nominal, Ordinal, Binary ), some with high cardinality check Medium & # x27 ; s status... We conclude our result and give recommendation based on it models for this project include data Analysis, Modeling Learning...
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