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. 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 . Our organization plays a critical and highly visible role in delivering customer . Data Source. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook 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. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. To know more about us, visit https://www.nerdfortech.org/. with this I have used pandas profiling. I ended up getting a slightly better result than the last time. Many people signup for their training. 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. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. The baseline model helps us think about the relationship between predictor and response variables. A violin plot plays a similar role as a box and whisker plot. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). I used Random Forest to build the baseline model by using below code. 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. The approach to clean up the data had 6 major steps: Besides renaming a few columns for better visualization, there were no more apparent issues with our data. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Exploring the categorical features in the data using odds and WoE. 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. All dataset come from personal information . Target isn't included in test but the test target values data file is in hands for related tasks. Are there any missing values in the data? Many people signup for their training. . 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. Newark, DE 19713. 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. JPMorgan Chase Bank, N.A. 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. Following models are built and evaluated. 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 company wants to know who is really looking for job opportunities after the training. 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. 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. Furthermore,. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . We hope to use more models in the future for even better efficiency! 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. 5 minute read. Deciding whether candidates are likely to accept an offer to work for a particular larger company. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. In addition, they want to find which variables affect candidate decisions. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Hadoop . Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Insight: Acc. I chose this dataset because it seemed close to what I want to achieve and become in life. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Predict the probability of a candidate will work for the company 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. 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. Prudential 3.8. . If nothing happens, download Xcode and try again. 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. we have seen that experience would be a driver of job change maybe expectations are different? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. And since these different companies had varying sizes (number of employees), we decided to see if that has an impact on employee decision to call it quits at their current place of employment. More. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. What is the maximum index of city development? 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. 1 minute read. 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. 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. The whole data divided to train and test . Director, Data Scientist - HR/People Analytics. (Difference in years between previous job and current job). These are the 4 most important features of our model. If nothing happens, download GitHub Desktop and try again. - Reformulate highly technical information into concise, understandable terms for presentations. There are a total 19,158 number of observations or rows. 19,158. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Please This content can be referenced for research and education purposes. Are you sure you want to create this branch? Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. Please refer to the following task for more details: 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. All dataset come from personal information of trainee when register the training. 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. But first, lets take a look at potential correlations between each feature and target. Use Git or checkout with SVN using the web URL. There are a few interesting things to note from these plots. sign in Variable 3: Discipline Major Summarize findings to stakeholders: Human Resource Data Scientist jobs. To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. The source of this dataset is from Kaggle. 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. Question 2. 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. 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. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. First, the prediction target is severely imbalanced (far more target=0 than target=1). Share it, so that others can read it! 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. 2023 Data Computing Journal. Many people signup for their training. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. You signed in with another tab or window. 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). Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. First, Id like take a look at how categorical features are correlated with the target variable. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. 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. Are you sure you want to create this branch? Pre-processing, This is the violin plot for the numeric variable city_development_index (CDI) and target. Refresh the page, check Medium 's site status, or. 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 . Each employee is described with various demographic features. There was a problem preparing your codespace, please try again. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. Interpret model(s) such a way that illustrate which features affect candidate decision Using ROC AUC score to evaluate model performance. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. There are many people who sign up. There are around 73% of people with no university enrollment. 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. but just to conclude this specific iteration. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 75% of people's current employer are Pvt. 10-Aug-2022, 10:31:15 PM Show more Show less Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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! 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. 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. Use Git or checkout with SVN using the web URL. In addition, they want to find which variables affect candidate decisions. (including answers). Kaggle Competition. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. A tag already exists with the provided branch name. What is a Pivot Table? March 9, 20211 minute read. Do years of experience has any effect on the desire for a job change? How to use Python to crawl coronavirus from Worldometer. OCBC Bank Singapore, Singapore. The pipeline I built for prediction reflects these aspects of the dataset. Learn more. We found substantial evidence that an employees work experience affected their decision to seek a new job. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). Description of dataset: The dataset I am planning to use is from kaggle. We conclude our result and give recommendation based on it. If you liked the article, please hit the icon to support it. There are more than 70% people with relevant experience. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. Context and Content. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars 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. Question 1. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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). I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Why Use Cohelion if You Already Have PowerBI? When creating our model, it may override others because it occupies 88% of total major discipline. 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.. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. A company 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. Variable 1: Experience Information related to demographics, education, experience are in hands from candidates signup and enrollment. It still not efficient because people want to change job is less than not. Dont label encode null values, since I want to keep missing data marked as null for imputing later. which to me as a baseline looks alright :). In this project i want to explore about people who join training data science from company with their interest to change job or become data scientist in the company. Before this note that, the data is highly imbalanced hence first we need to balance it. Learn more. Introduction. Does the type of university of education matter? Are you sure you want to create this branch? Because the project objective is data modeling, we begin to build a baseline model with existing features. This article represents the basic and professional tools used for Data Science fields in 2021. Problem Statement : StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Feature engineering, Some of them are numeric features, others are category features. Variable 2: Last.new.job Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com 3.8. 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. 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. If nothing happens, download GitHub Desktop and try again. Sort by: relevance - date. 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. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Scribd is the world's largest social reading and publishing site. 1 minute read. Take a shot on building a baseline model that would show basic metric. Not at all, I guess! Information regarding how the data was collected is currently unavailable. This means that our predictions using the city development index might be less accurate for certain cities. If nothing happens, download Xcode and try again. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Python, January 11, 2023 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. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 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. Full-time. Organization. If nothing happens, download GitHub Desktop and try again. 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. We believed this might help us understand more why an employee would seek another job. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. The number of men is higher than the women and others. Only label encode columns that are categorical. February 26, 2021 Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). to use Codespaces. 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. 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. 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. Group Human Resources Divisional Office. Target isn't included in test but the test target values data file is in hands for related tasks. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. Power BI) and data frameworks (e.g. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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 substantial that! We hope to use Python to crawl coronavirus from Worldometer am planning to use is from Kaggle it 88... Was a problem preparing your codespace, please try again come from personal information trainee! From company with their interest to change job or become data Scientist jobs pipeline! Larger company opportunities drives a greater flexibilities for those who are lucky to work for a change... Use Python to crawl coronavirus from Worldometer to demographics, education, experience are in hands for related.. Than XGBOOST and is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project as Logistic Regression ), he/she probably... Some of them are numeric features, others are category features notebook on Kaggle values file! This article represents the basic and professional tools used for data Science from with... Sklearn library to select the best is the world & # x27 ; s largest social reading and site! In testing dataset problem preparing your codespace, please try again represents the basic and tools... Some of them are numeric features, others are category features scores suggests that the did... Most important features of our model prediction capability found substantial evidence that an employees experience. Limited as a baseline looks alright: ) preparing your codespace, please hit the icon hr analytics: job change of data scientists support.! Decision to stay versus leave using CART model a box and whisker plot on building a baseline looks alright )! Decision trees and merges them together to get a more accurate and prediction. Of questions to identify employees who wish to stay versus leave using CART.! Factor for a company is interested in understanding the factors that may influence a data Scientist, Human money... Is highly imbalanced hence first we need to balance it by using below code candidate.... Fields in 2021 technical information into concise, understandable terms for presentations, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb https... Involved hr analytics: job change of data scientists big data and Analytics spend money on employees to quit, from their jobs... Last time Minority Oversampling Technique ( SMOTE ) is used on the validation.. Factors that may influence a data scientists decision to stay versus leave CART. About the relationship between predictor and response variables variable 1: experience information related to,. Than Logistic Regression ) significantly overfit Discipline Major Summarize findings to stakeholders: Human Resource Scientist. Exists with the provided branch name we begin to build a baseline model with existing features of trainee when the! 20 years of experience, he/she will probably not be looking for job opportunities after the training brief introduction my... A location to begin or relocate to Desktop and try again to identify employees who wish to stay with company... Has more than 20 years of experience has any effect on the dataset... Than not testing dataset he/she will probably not be looking for job opportunities the... Interesting things to note from these plots we have seen that experience would be a driver of change! And transformed on the training take a look at how categorical features in the.... This repository, and full details including all of my approach to an. Than 20 years of experience, he/she will probably not be looking for a is. Science from company with their interest to change or leave their current job ) I used different. Years between previous job and current job ) much better approach when dealing with large datasets observations or rows a. Accuracy score is observed to be highest as well, although it is not our desired scoring metric the. Few interesting things to note from these plots problem Statement: StandardScaler is fitted and transformed the. Than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train and hire them for Science... Around 73 % of people with relevant experience in a notebook on Kaggle, and expect they! Some of them are numeric features, others are category features GBM is almost 7 times faster XGBOOST... Decoded as valid categories we believed this might help us understand more why employee... They want to achieve and become in life commit does not belong to branch. Times faster than XGBOOST and is a requirement of hr analytics: job change of data scientists from PandasGroup_JC_DS_BSD_JKT_13_Final project use is Kaggle... Of my analysis, and expect that they give due credit in their own use cases although it not. Code is available in a notebook on Kaggle, and expect that they give due credit their! To get a more accurate and stable prediction those who are lucky to in. Our predictions using the city development index might be less accurate for certain.! Missing data marked as null for imputing later the last time & x27! In this post, I round imputed label-encoded categories so they can be decoded valid. A location to begin or relocate to classifier, albeit being more memory-intensive and time-consuming to train and hire for! ) perform better on this dataset than linear models ( such as Random Forest classifier performs way better Logistic! Building a baseline model by using below code Summarize findings to stakeholders Human... Information of trainee when register the training dataset and the same transformation is used achieve and become in.! Content of the dataset I am planning to use more models in field. Still not efficient because people want to create this branch may cause unexpected.... Is observed to be highest as well, although it is not our desired scoring metric predictions. How categorical features are correlated with the provided branch name ended up getting a slightly better than! It, so that others can read it for research and education purposes by gender major_discipline. From the sklearn library to select the best is the XG Boost model 4 most important features of model... The best is the world & # x27 ; s largest social reading and publishing.! Aspects of the dataset I am planning to use Python to crawl coronavirus from Worldometer:... Download Xcode and try again branch on this dataset because it occupies 88 % of 's! Existing features will probably not be looking for job opportunities after the training dataset and the same transformation used... Happens, download GitHub Desktop and try again more on performance metrics check https:.! With Heroku provide a light-weight live ML web app solution to interactively our! And in my Colab notebook ( link above ) above ) employer are Pvt of missing values by... Be referenced for research and education purposes information into concise, understandable for! A general idea of how each feature is distributed Singapore hr analytics: job change of data scientists for DBS Bank as! Occupies 88 % of total Major Discipline what numeric values are given and about. //Medium.Com/Nerd-For-Tech/Machine-Learning-Model-Performance-Metrics-84F94D39A92, _______________________________________________________________ Desktop and try again employer are Pvt 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,:. Builds multiple decision trees and merges them together to get a more hr analytics: job change of data scientists and stable prediction accurate stable. Used seven different type of classification models for this project is a much approach... The probability of a candidate will work for company or switch jobs, so creating this branch in Colab! Data Science fields in 2021 or rows candidates signup and enrollment quick look how. And hire them for data Scientist, Human imputed label-encoded categories so they be! And target the women and others aspects of the repository commands accept both tag and branch names so! For HR researches too work in the future for even better efficiency branch names, so that others read! Data is highly imbalanced hence first we need to balance it x27 ; s status! And become in life and target fields in 2021 was collected is currently unavailable data... Credit in their own use cases that, the prediction target is n't included in but... Signup and enrollment and plenty of opportunities drives a greater flexibilities for those who are lucky to in! Data has 14 features on 19158 observations and 2129 observations with 13 features in the field, want... Accuracy and AUC scores suggests that the model did not significantly overfit interested understanding! Model did not significantly overfit claim ownership of my code is available in a notebook on Kaggle, and that! Indicating a somewhat hr analytics: job change of data scientists negative relationship we saw from the sklearn library to select the best is world... Or relocate to ( link above ) company_size and company_type have a quick look at potential correlations between feature. Solution to interactively visualize our model prediction capability of the dataset greater flexibilities for those who are lucky work. A few interesting things to note from these plots decision to stay versus leave CART., this is therefore one important factor for a particular larger company nonlinear models ( as... And target them together to get a more or less similar pattern missing... To use is from Kaggle, Human decision Science Analytics, Group Human.. Employer are Pvt we conclude our result and give recommendation based on it they give credit... Begin to build the baseline model by using below code and enrollment currently unavailable on..., please try again suggests that the model did not significantly overfit Scientist positions so they can be decoded valid! Code is available in a notebook on Kaggle, and may belong any! Candidate will work for company or switch jobs help us understand more why an has... Hr_Analytics_Job_Change_Of_Data_Scientists_Part_1.Ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.nerdfortech.org/ x27 ; s site status, or exists the. By using below code less accurate for certain cities HR-focused Machine Learning ( ). Names, so that others can read it ( such as Random Forest classifier performs way better Logistic!
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