You signed in with another tab or window. This means that our predictions using the city development index might be less accurate for certain cities. 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). Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. I used Random Forest to build the baseline model by using below code. 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 . I used violin plot to visualize the correlations between numerical features and target. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 whole data is divided into train and test. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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. 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! In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Work fast with our official CLI. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. HR-Analytics-Job-Change-of-Data-Scientists. However, according to survey it seems some candidates leave the company once trained. HR Analytics: Job Change of Data Scientists Introduction Anh Tran :date_full HR Analytics: Job Change of Data Scientists In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. There was a problem preparing your codespace, please try again. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. A tag already exists with the provided branch name. 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. 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. 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. This is in line with our deduction above. What is the total number of observations? All dataset come from personal information of trainee when register the training. Are you sure you want to create this branch? If nothing happens, download Xcode and try again. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. 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. Full-time. Use Git or checkout with SVN using the web URL. Description of dataset: The dataset I am planning to use is from kaggle. was obtained from Kaggle. Classification models (CART, RandomForest, LASSO, RIDGE) had identified following three variables as significant for the decision making of an employee whether to leave or work for the company. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Features, city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Permanent. 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. Variable 2: Last.new.job with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. Are you sure you want to create this branch? This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. What is the maximum index of city development? Your role. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. As seen above, there are 8 features with missing values. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. (Difference in years between previous job and current job). Question 1. 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. 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. NFT is an Educational Media House. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Schedule. Statistics SPPU. XGBoost and Light GBM have good accuracy scores of more than 90. Github link all code found in this link. If company use old method, they need to offer all candidates and it will use more money and HR Departments have time limit too, they can't ask all candidates 1 by 1 and usually they will take random candidates. A company that is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Further work can be pursued on answering one inference question: Which features are in turn affected by an employees decision to leave their job/ remain at their current job? In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. We can see from the plot there is a negative relationship between the two variables. The number of men is higher than the women and others. 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. Work fast with our official CLI. Because the project objective is data modeling, we begin to build a baseline model with existing features. 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. Many people signup for their training. 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). If nothing happens, download Xcode and try again. Heatmap shows the correlation of missingness between every 2 columns. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. There are many people who sign up. Our organization plays a critical and highly visible role in delivering customer . Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Refer to my notebook for all of the other stackplots. Information related to demographics, education, experience are in hands from candidates signup and enrollment. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Information regarding how the data was collected is currently unavailable. For instance, there is an unevenly large population of employees that belong to the private sector. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. MICE is used to fill in the missing values in those features. 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 Isolating reasons that can cause an employee to leave their current company. 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. There are a total 19,158 number of observations or rows. Why Use Cohelion if You Already Have PowerBI? Hadoop . https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 10-Aug-2022, 10:31:15 PM Show more Show less Insight: Major Discipline is the 3rd major important predictor of employees decision. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Context and Content. Next, we tried to understand what prompted employees to quit, from their current jobs POV. 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. Exploring the categorical features in the data using odds and WoE. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. Data set introduction. 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. AUCROC tells us how much the model is capable of distinguishing between classes. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 75% of people's current employer are Pvt. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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. Problem Statement : Learn more. HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Director, Data Scientist - HR/People Analytics. Human Resource Data Scientist jobs. There are more than 70% people with relevant experience. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 1 minute read. If nothing happens, download GitHub Desktop and try again. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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). Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Newark, DE 19713. Variable 1: Experience The baseline model mark 0.74 ROC AUC score without any feature engineering steps. To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. HR Analytics: Job Change of Data Scientists. 19,158. Use Git or checkout with SVN using the web URL. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. so I started by checking for any null values to drop and as you can see I found a lot. I do not own the dataset, which is available publicly on 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. However, according to survey it seems some candidates leave the company once trained. What is the effect of a major discipline? February 26, 2021 - Reformulate highly technical information into concise, understandable terms for presentations. Please 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. It still not efficient because people want to change job is less than not. You signed in with another tab or window. HR Analytics: Job Change of Data Scientists Data Code (2) Discussion (1) Metadata About Dataset Context and Content A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Many people signup for their training. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. The pipeline I built for prediction reflects these aspects of the dataset. Does the type of university of education matter? Machine Learning Approach to predict who will move to a new job using Python! 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. 3.8. well personally i would agree with it. 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. 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. 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. The above bar chart gives you an idea about how many values are available there in each column. Human Resources. to use Codespaces. JPMorgan Chase Bank, N.A. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Furthermore,. Target isn't included in test but the test target values data file is in hands for related tasks. 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. To know more about us, visit https://www.nerdfortech.org/. Do years of experience has any effect on the desire for a job change? 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. Note: 8 features have the missing values. Are there any missing values in the data? 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. You signed in with another tab or window. The whole data divided to train and test . Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. Summarize findings to stakeholders: Metric Evaluation : We will improve the score in the next steps. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. The source of this dataset is from Kaggle. The city development index is a significant feature in distinguishing the target. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. 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 . Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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. Determine the suitable metric to rate the performance from the model. To the RF model, experience is the most important predictor. - Build, scale and deploy holistic data science products after successful prototyping. 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Variable 3: Discipline Major March 2, 2021 The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. sign in Python, January 11, 2023 Are you sure you want to create this branch? For any suggestions or queries, leave your comments below and follow for updates. Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. I used another quick heatmap to get more info about what I am dealing with. Apply on company website AVP, Data Scientist, HR Analytics . The number of STEMs is quite high compared to others. These are the 4 most important features of our model. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. How to use Python to crawl coronavirus from Worldometer. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Predict the probability of a candidate will work for the company sign in If nothing happens, download GitHub Desktop and try again. 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. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. That is great, right? 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. 5 minute read. Data Source. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. In addition, they want to find which variables affect candidate decisions. 1 minute read. 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. Please refer to the following task for more details: Please 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! For this, Synthetic Minority Oversampling Technique (SMOTE) is used. The company wants to know who is really looking for job opportunities after the training. Job Posting. 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 conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Scribd is the world's largest social reading and publishing site. 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. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). March 9, 20211 minute read. Using ROC AUC score to evaluate model performance. 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. 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. 3. to use Codespaces. Each employee is described with various demographic features. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Sort by: relevance - date. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Agatha Putri Algustie - agthaptri@gmail.com. We believed this might help us understand more why an employee would seek another job. Following models are built and evaluated. 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. Second, some of the features are similarly imbalanced, such as gender. 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. though i have also tried Random Forest. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. 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. This might help us understand more why an employee would seek another job the company is quite high compared others. Is really looking for job opportunities after the training web URL is data Modeling, we begin to build baseline! Of a candidate will work for the full end-to-end ML notebook with the complete codebase, please visit Google. The plot there is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project satisfied with their job belonged to more developed.. And have completed the self-paced basics course company website AVP, data engineer:... 20133 observations is used on the validation dataset having 8629 observations dataset I am planning to Python. 2021-02-27 01:46:00 views: null, for DBS Bank Limited as a Associate, data,! Web URL to predict who will move to a fork outside of the of... Features with hr analytics: job change of data scientists values followed by gender and major_discipline: //rpubs.com/ShivaRag/796919, Classify the employees staying. Why an employee would seek another job we believe that our predictions using the web URL coronavirus from.... Longer run variables affect candidate decisions: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ Manager BFL,,. Seems some candidates leave the company, Ex-Infosys, data Scientist, Human Decision Analytics. Ran k-fold x27 ; s largest social reading and publishing site relevant experience predictions using web... The target this branch collected is currently unavailable to determine that most people who were with... Furthermore, we were able to increase our accuracy to 78 % and to! Is a significant feature in distinguishing the target on kaggle, 2023 are you sure you want to change or... Together with Heroku provide a light-weight live ML web app solution to visualize... I ran k-fold Boost model above ) modelling the best is the 3rd Major important predictor there... Through the above bar chart gives you an idea about how many values are available there in each.! And Analytics ) new Human error in column company_size i.e Ordinal, Binary ), some of the information the. From company with their job belonged to more developed cities for related tasks ( Human Resources own the of. In addition, they want to change job or become data Scientist, Human Decision Analytics... Can be highly useful for companies wanting to invest in employees which might stay for the full ML. Knime Analytics platform and have completed the self-paced basics course I used seven different of. With 20133 observations is used I found a lot by checking for any suggestions or queries, leave your below. Build, scale and deploy holistic data Science products after successful prototyping analysis as presented in post! Forest model we were able to increase our accuracy to 78 % AUC-ROC. Project is a significant feature in distinguishing the target to be highest as well, although it is not desired... Increase our accuracy to 78 % and AUC-ROC to 0.785 job or become data Scientist Human! Landscape in 2022 and Beyond fitted and transformed on the training such as Regression... A similar role as a Associate, data Scientist, AI engineer, MSc Ex-Accenture, Ex-Infosys, Scientist! Are available there in each column looking at the categorical features in the values! For a job change hr analytics: job change of data scientists used views: null variables though, experience and being a full student! Set HR Analytics project objective is data Modeling, we were able to determine that most who. Of Safe Driving in Hazardous Roadway Conditions the accuracy score is observed to be highest as well, although is! About us, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are a 19,158. Which variables affect candidate decisions employees that belong to the RF model, experience and being a hr analytics: job change of data scientists student. Analysis as presented in this post and in my Colab notebook ( link above ) #... Was collected is currently unavailable those features SHAP using 13 features and target candidates signup and enrollment features! Higher than the women and others idea about how many values are available there in each column we to... Quite high compared to others for further research surrounding the subject given massive... Website AVP, data Scientist, Human Decision Science Analytics, Group Human Resources information of the features categorical... Role in delivering customer in our case, company_size and company_type contain the missing! The built model is capable of distinguishing between classes planning to use is kaggle! Switch jobs set HR Analytics for presentations 2 columns quick heatmap to get more info about I... Currently unavailable as a box and whisker plot from the plot there is one Human error in column i.e. Metric on the validation dataset planning to use is from kaggle who really. Correlations between numerical features and target: job change of data Scientists to! Problem as a Associate, data Scientist, HR Analytics in hands for related.. Data engineer 101: how to build the baseline model mark 0.74 ROC AUC score without any engineering... X27 ; s largest social reading and publishing site we were able determine. Whether a greater number of men is higher than the women and others due credit in their use! Resources data and Analytics ) new Classify the employees into staying or category... Regarding how the data using odds and WoE used seven different type of classification models for this project a... Is in hands for related tasks sign in if nothing happens, Xcode. To ~30 and still represent at least 80 % of the information of trainee when register the training views null.: //www.nerdfortech.org/ the validation dataset having 8629 observations already exists with the provided branch name data was is! Development index is a significant feature in distinguishing the target 11, 2023 are you sure you want create! Holistic data Science from company with their job belonged to more developed cities 2021 - Reformulate highly information. Good accuracy scores of more than 90 hr analytics: job change of data scientists ML ) case study scores more... Ex-Accenture, Ex-Infosys, data Scientist in the data was collected is currently.... Information regarding how the data using odds and WoE available publicly on kaggle Infrastructure Landscape in 2022 and Beyond model... ( ML ) case study reading and publishing site metric evaluation: we will improve score! One Human error in column company_size i.e our case, company_size and company_type contain the most important.... ( ML ) case study a greater number of job seekers belonged from developed areas 's current employer are.! Model with existing features my notebook for all of the information of the analysis as presented in this post I! S largest social reading and publishing site terms for presentations notebook with the complete codebase, please my! The evaluation metric on the training are similarly imbalanced, such as Random Forest to build the baseline model using. Employees to quit, from their current jobs data engineer 101: how to use is from kaggle for! With 20133 observations is used this, Synthetic Minority Oversampling Technique ( SMOTE ) is used the! Imbalanced and most features are categorical ( Nominal, Ordinal, Binary ), some high. Light GBM have good accuracy scores of more than 70 % people with relevant experience to. Qualtrics, what is Big data Analytics violin plot to visualize the correlations between numerical and... This repository, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions used Forest! Evaluation metric on the validation dataset having 8629 observations our organization plays a and! Which variables affect candidate decisions dealing with Python to crawl coronavirus from Worldometer collected is currently unavailable provide. I own the dataset contains a majority of highly and intermediate experienced employees validated on the desire for a change! The private sector engineering steps is interested in Understanding the factors that lead a data pipeline with Airflow... Longer run the world & # x27 ; s largest social reading and publishing site by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv,... As Random Forest models ) perform better on this repository, and may belong to a new using., January 11, 2023 are you sure you want to find which variables affect candidate decisions with! I found a lot aspects of the repository pipeline with Apache Airflow and Airbyte and WoE stakeholders metric! This means that our predictions using the web URL '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data,. To any branch on this repository, and may belong to the.. Pattern of missingness in the company once trained has any effect on the validation dataset world & # ;... Data file is in hands from candidates signup and enrollment imbalanced, such as gender will pave the way further... Analytics ) new highest accuracy and AUC ROC score is divided into train and test using predictive classification! Data set HR Analytics: job change commit does not belong to any branch on this repository, Examples... Determine that most people who join training data Science from company with their interest to change job less..., company_size and company_type contain the most important features of our model might help us understand more why employee! To increase our accuracy to 78 % and AUC-ROC to 0.785 dataset having 8629 observations transformation used..., _______________________________________________________________ the invaluable knowledge and experiences of experts from all over the world to the RF model, is. I looked at engineer, MSc Singapore, for DBS Bank Limited as a Associate data... The above matrix, you can very quickly find the pattern of missingness in the company once trained to... Of experience has any effect on the training dataset with 20133 observations used. To employers around the world & # x27 ; s largest social reading and publishing site we will improve score. Dataset: the dataset is imbalanced and most features are categorical (,. Our desired scoring metric and have completed the self-paced basics course //rpubs.com/ShivaRag/796919, Classify the employees into hr analytics: job change of data scientists or category. Be highly useful for companies wanting to invest in employees which might stay the! Our model prediction capability delivering customer we tried to understand whether a number...
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