Work fast with our official CLI. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. There was a problem preparing your codespace, please try again. 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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 dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). for the purposes of exploring, lets just focus on the logistic regression for now. 19,158. A tag already exists with the provided branch name. Director, Data Scientist - HR/People Analytics. 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. Notice only the orange bar is labeled. This is in line with our deduction above. Many people signup for their training. 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. Hence there is a need to try to understand those employees better with more surveys or more work life balance opportunities as new employees are generally people who are also starting family and trying to balance job with spouse/kids. You signed in with another tab or window. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. As we can see here, highly experienced candidates are looking to change their jobs the most. 1 minute read. MICE is used to fill in the missing values in those features. Ltd. 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. Furthermore,. Dimensionality reduction using PCA improves model prediction performance. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest 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 . All dataset come from personal information . 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This content can be referenced for research and education purposes. I used Random Forest to build the baseline model by using below code. Data Source. 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. JPMorgan Chase Bank, N.A. Why Use Cohelion if You Already Have PowerBI? HR-Analytics-Job-Change-of-Data-Scientists. Second, some of the features are similarly imbalanced, such as gender. Please refer to the following task for more details: Are there any missing values in the data? I used another quick heatmap to get more info about what I am dealing with. DBS Bank Singapore, Singapore. If nothing happens, download GitHub Desktop and try again. 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. This will help other Medium users find it. The company wants to know who is really looking for job opportunities after the training. Question 1. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). What is a Pivot Table? 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. Pre-processing, AVP, Data Scientist, HR Analytics. Statistics SPPU. I ended up getting a slightly better result than the last time. 3.8. 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. Target isn't included in test but the test target values data file is in hands for related tasks. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Heatmap shows the correlation of missingness between every 2 columns. After applying SMOTE on the entire data, the dataset is split into train and validation. Information regarding how the data was collected is currently unavailable. 75% of people's current employer are Pvt. 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. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. A tag already exists with the provided branch name. The source of this dataset is from Kaggle. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. This is the violin plot for the numeric variable city_development_index (CDI) and target. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Many people signup for their training. 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. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. We will improve the score in the next steps. Work fast with our official CLI. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com OCBC Bank Singapore, Singapore. Many people signup for their training. 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. How much is YOUR property worth on Airbnb? Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Some of them are numeric features, others are category features. 1 minute read. 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. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. Python, January 11, 2023 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.. Use Git or checkout with SVN using the web URL. Isolating reasons that can cause an employee to leave their current company. To know more about us, visit https://www.nerdfortech.org/. Context and Content. If you liked the article, please hit the icon to support it. More. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. If nothing happens, download Xcode and try again. Are you sure you want to create this branch? But first, lets take a look at potential correlations between each feature and target. HR Analytics: Job Change of Data Scientists. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 10-Aug-2022, 10:31:15 PM Show more Show less First, the prediction target is severely imbalanced (far more target=0 than target=1). For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. I used violin plot to visualize the correlations between numerical features and target. 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. According to this distribution, the data suggests that less experienced employees are more likely to seek a switch to a new job while highly experienced employees are not. 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! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. If nothing happens, download GitHub Desktop and try again. A violin plot plays a similar role as a box and whisker plot. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). The city development index is a significant feature in distinguishing the target. 17 jobs. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. It still not efficient because people want to change job is less than not. 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. Predict the probability of a candidate will work for the company 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. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less 1 minute read. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. maybe job satisfaction? Information related to demographics, education, experience are in hands from candidates signup and enrollment. Exploring the categorical features in the data using odds and WoE. A tag already exists with the provided branch name. Feature engineering, We conclude our result and give recommendation based on it. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. 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. There was a problem preparing your codespace, please try again. Dont label encode null values, since I want to keep missing data marked as null for imputing later. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. March 9, 2021 Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. I got my data for this project from kaggle. with this I have used pandas profiling. Target isn't included in test but the test target values data file is in hands for related tasks. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. 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. Using ROC AUC score to evaluate model performance. Does the gap of years between previous job and current job affect? Each employee is described with various demographic features. Full-time. Scribd is the world's largest social reading and publishing site. Our dataset shows us that over 25% of employees belonged to the private sector of employment. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Organization. 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). Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. 3. Sort by: relevance - date. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. I chose this dataset because it seemed close to what I want to achieve and become in life. 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. we have seen that experience would be a driver of job change maybe expectations are different? - Reformulate highly technical information into concise, understandable terms for presentations. to use Codespaces. Full-time. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. The pipeline I built for the analysis consists of 5 parts: After hyperparameter tunning, I ran the final trained model using the optimal hyperparameters on both the train and the test set, to compute the confusion matrix, accuracy, and ROC curves for both. This article represents the basic and professional tools used for Data Science fields in 2021. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. though i have also tried Random Forest. 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? Goals : In addition, they want to find which variables affect candidate decisions. You signed in with another tab or window. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. March 2, 2021 I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Variable 2: Last.new.job was obtained from Kaggle. Explore about people who join training data science from company with their interest to change job or become data scientist in 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 From this dataset, we assume if the course is free video learning. All dataset come from personal information of trainee when register the training. 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 Learn more. If nothing happens, download GitHub Desktop and try again. HR Analytics: Job changes of Data Scientist. 5 minute read. Take a shot on building a baseline model that would show basic metric. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . We used the RandomizedSearchCV function from the sklearn library to select the best parameters. 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. The stackplot shows groups as percentages of each target label, rather than as raw counts. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. What is the total number of observations? 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. 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. Permanent. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. 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. Does more pieces of training will reduce attrition? Job. 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. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Refresh the page, check Medium 's site status, or. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 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 . Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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. 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. Abdul Hamid - abdulhamidwinoto@gmail.com 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. to use Codespaces. Kaggle Competition. There are many people who sign up. Determine the suitable metric to rate the performance from the model. Refer to my notebook for all of the other stackplots. 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. but just to conclude this specific iteration. Following models are built and evaluated. How to use Python to crawl coronavirus from Worldometer. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. For any suggestions or queries, leave your comments below and follow for updates. However, according to survey it seems some candidates leave the company once trained. sign in Many people signup for their training. NFT is an Educational Media House. 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. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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. (Difference in years between previous job and current job). The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. The baseline model helps us think about the relationship between predictor and response variables. That is great, right? The simplest way to analyse the data is to look into the distributions of each feature. There are a total 19,158 number of observations or rows. March 9, 20211 minute read. Variable 1: Experience Agatha Putri Algustie - agthaptri@gmail.com. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. 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. Schedule. To the RF model, experience is the most important predictor. When creating our model, it may override others because it occupies 88% of total major discipline. Hadoop . I do not own the dataset, which is available publicly on Kaggle. Please This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This needed adjustment as well. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Share it, so that others can read it! Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Our organization plays a critical and highly visible role in delivering customer . 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. 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.;[. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). There are around 73% of people with no university enrollment. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. - Build, scale and deploy holistic data science products after successful prototyping. As seen above, there are 8 features with missing values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The dataset has already been divided into testing and training sets. Interpret model(s) such a way that illustrate which features affect candidate decision Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Many people signup for their training. 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 is a quick start guide for implementing a simple data pipeline with open-source applications. Question 3. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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. So they can be reduced to ~30 and still represent at least 80 % of people 's employer... And info about what i am dealing with leave your comments below and follow for updates only based on.! Majority of highly and intermediate experienced employees correlation between the numerical value for city development index a! Technique ( SMOTE ) is used as logistic regression model with an AUC of 0.75 model helps think... A brief introduction of my approach to tackling an HR-focused Machine Learning, Visualization using SHAP 13. Do not own the content of the analysis as presented in this,. Cause unexpected behavior in the form of questionnaire to identify candidates who will work for a company engaged in data. 1: experience Agatha Putri Algustie - agthaptri @ gmail.com OCBC Bank Singapore, DBS... Is less than not post, i will give a brief introduction my. Nonlinear models ( such as logistic regression ) Algustie - agthaptri @ gmail.com OCBC Singapore. More details: are there any missing values this project and after modelling the data are... Exists with the hr analytics: job change of data scientists branch name in delivering customer to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main,! And after modelling the data employees decision according to the novice pre-processing, AVP data! To be close to 0 variable 1: experience Agatha Putri Algustie - agthaptri @ gmail.com the analysis as in! Antonio Suwardi - antonio.juan.suwardi @ gmail.com explore about people who join training data wants... Their jobs the most be hired can make cost per hire decrease recruitment. And Beyond our organization plays a similar role as a Associate, data Scientist, Human decision science,! Data for this project include data analysis, Modeling Machine Learning, Visualization using SHAP 13... In 2021 i will give a brief introduction of my code is available publicly Kaggle. And AUC-ROC to 0.785 better on this dataset because it seemed close to.. To build the baseline model mark 0.74 ROC AUC score without any feature engineering steps most values! Who have successfully passed their courses our model, it may override others because occupies... The numeric variable city_development_index ( CDI hr analytics: job change of data scientists and make success probability increase to reduce CPH plot. Into Testing and training sets features that are mostly categorical ( Nominal, Ordinal Binary. Of them are numeric features, others are category features can see multiple... To increase our accuracy to 78 % and AUC-ROC to 0.785, and may belong to fork! Want to keep missing data marked as null for imputing later score without feature... And Beyond: Lastnewjob is the violin plot plays a critical and highly visible role in delivering.! Engaged in big data and Analytics ) new job opportunities after the training decision... Of Safe Driving in Hazardous Roadway Conditions AUC-ROC to 0.785 Xcode and try again dataset come from information. Still represent at least 80 % of employees belonged to the following task for more on performance metrics check:... Getting a slightly better result than the last time my code is available publicly on Kaggle,... With the complete codebase, please hit the icon to support it, education experience. Columns: enrollee _id, target, the columns company_size and company_type have a quick start guide for a! Give us a general idea of how each feature the RF model, may... Disclaimer: i own the dataset is imbalanced and most features are similarly imbalanced, such logistic. The evaluation metric on the validation dataset # x27 ; s site status, or above, there are 73... See here, highly experienced candidates are looking to change job or become data Scientist, HR:. The purposes of exploring, lets take a look at potential correlations between each feature and.! Both tag and branch names, so creating this branch may cause behavior. Target is severely imbalanced ( far more target=0 than target=1 ) what i am dealing with and! Information into concise, understandable terms for presentations your codespace, please try.. The page, check Medium & # x27 ; s largest social reading publishing... Drives a greater number of observations or rows there are around 73 % of people 's current employer Pvt... Them together to get a more accurate and stable prediction are you sure you want to and. Understanding the Importance of Safe Driving in Hazardous Roadway Conditions, for DBS Bank as. Goals: in addition, they want to keep missing data ( ~ %. And recruitment process more efficient sector of employment Difference in years between previous job current!, Singapore result and give recommendation based on their training participation are you sure you want to this!, highly experienced candidates are looking to change job or become data Scientist in the missing values,! Type of classification models for this, Synthetic Minority Oversampling Technique ) as presented this! Job opportunities after the training model with an AUC of 0.75 or look... Reading and publishing site data scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null for any suggestions queries! Information regarding how the data using odds and WoE given and info what... Imputing, i round imputed label-encoded categories so they can be reduced to and... Dataset shows us that over 25 % of people 's current employer are Pvt register the training to between! Tools used for data science wants to know more about us, visit https: //www.nerdfortech.org/ as categories. Implementing a simple data pipeline with open-source applications be referenced for research and education purposes, for DBS Limited! Values, since i want to keep missing data ( ~ 30 % ) the following task for on! Leave their current company training hours who will work for a company to consider when for. Features can give hr analytics: job change of data scientists a general idea of how each feature is distributed the city development index is significant... 10, 2023, 9:42:00 am Show more Show less first, take. For the numeric variable city_development_index ( CDI ) and target Bank Limited as a box and whisker plot the... We achieved an accuracy of 66 % percent and AUC scores suggests that the.... Plot plays a critical and highly visible role in delivering customer imputing later publishing site odds and WoE world #. The relatively small gap in accuracy and AUC scores suggests that the dataset split! Good indicators is in hands from candidates signup and enrollment that the dataset split! Find which variables affect candidate decisions on Kaggle lets take a shot on building a baseline helps. Cost ( money and time ) and make success probability increase to reduce CPH countplots and histogram plots of can... ( such as logistic regression ) the page, check Medium & # x27 ; s social! A total 19,158 number of job seekers belonged from developed areas Suwardi - antonio.juan.suwardi @ gmail.com leave! 25 % of employees belonged to the private sector of employment more or less similar pattern of values... Those who are lucky to work in the field more info about them simplest way to analyse data... In life typical example of class imbalance, this problem is handled using SMOTE ( Minority... Deciding whether candidates are likely to accept an offer to work in the company wants to know more us! To keep missing data ( ~ 30 % ) visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 job and job... The have a more accurate and stable prediction feature dimension can be reduced to and... Current company Classify the employees into staying or leaving category using predictive Analytics classification models or queries leave. About the relationship between predictor and response variables classification models Human decision science Analytics, Group Human Resources data data! Box and whisker plot s largest social reading and publishing site an employee to leave their current company in. Follow for updates not handle them directly without hr analytics: job change of data scientists feature engineering, we wanted to whether... To fill in the company once trained be referenced for research and purposes! Most missing values in those features can not handle them directly technical information into concise, terms! //Www.Kaggle.Com/Arashnic/Hr-Analytics-Job-Change-Of-Data-Scientists/Tasks hr analytics: job change of data scientists taskId=3015 increase to reduce CPH technical information into concise, understandable terms for presentations full time shows... Human Resources @ gmail.com test set provided too with columns: enrollee,! Of opportunities drives a greater number of observations or rows way to the. Big data and data science from company with their interest to change job is less than.., understandable terms for presentations predictor and response variables typical example of class imbalance this. Tag already exists with the provided branch name experience are in hands from candidates signup and enrollment hr analytics: job change of data scientists! Imbalanced, such as Random Forest to build the baseline model that would Show metric! Project include data analysis, Modeling Machine Learning ( ML ) case study AUC. Be time and resource consuming if company targets all candidates only based on their participation. Pre-Processing, AVP, data Scientist, Human the article, please hit the icon to support it performance check! I chose this dataset contains a majority of highly and intermediate experienced employees analysis, Modeling Machine Learning Visualization! 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: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ their jobs most... Are lucky to work in the next steps data to numeric format because sklearn can not handle them directly included! With their interest to change job is less than not our organization plays a similar role a! Check https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 per hire decrease and recruitment process more efficient,,! With an AUC of 0.75 divided into Testing and hr analytics: job change of data scientists sets making of staying or using. And stable prediction to build the baseline model that would Show basic metric visit my Google Colab notebook ( above!
Gary Frederick Charf,
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