Note: 8 features have the missing values. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. There are a total 19,158 number of observations or rows. Many people signup for their training. maybe job satisfaction? A tag already exists with the provided branch name. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Data set introduction. A tag already exists with the provided branch name. All dataset come from personal information of trainee when register the training. This needed adjustment as well. It still not efficient because people want to change job is less than not. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. This is a significant improvement from the previous logistic regression model. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 2023 Data Computing Journal. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. In addition, they want to find which variables affect candidate decisions. Only label encode columns that are categorical. 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. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. 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. 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. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Learn more. Hadoop . I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. 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. XGBoost and Light GBM have good accuracy scores of more than 90. Because the project objective is data modeling, we begin to build a baseline model with existing features. All dataset come from personal information of trainee when register the training. to use Codespaces. Take a shot on building a baseline model that would show basic metric. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. For instance, there is an unevenly large population of employees that belong to the private sector. Insight: Acc. 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. Many people signup for their training. (including answers). has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 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? AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. You signed in with another tab or window. 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. Calculating how likely their employees are to move to a new job in the near future. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. Many people signup for their training. You signed in with another tab or window. Problem Statement : Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). sign in Please Deciding whether candidates are likely to accept an offer to work for a particular larger company. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. (Difference in years between previous job and current job). Why Use Cohelion if You Already Have PowerBI? 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. For another recommendation, please check Notebook. 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. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Human Resource Data Scientist jobs. 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. Exploring the categorical features in the data using odds and WoE. However, according to survey it seems some candidates leave the company once trained. which to me as a baseline looks alright :). Goals : - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! 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. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. Please Context and Content. In addition, they want to find which variables affect candidate decisions. Interpret model(s) such a way that illustrate which features affect candidate decision The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. 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). 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. 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. 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. I got my data for this project from kaggle. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Kaggle Competition - 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. 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. 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. Question 3. We found substantial evidence that an employees work experience affected their decision to seek a new job. What is a Pivot Table? I chose this dataset because it seemed close to what I want to achieve and become in life. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. If nothing happens, download GitHub Desktop and try again. Refresh the page, check Medium 's site status, or. 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. There was a problem preparing your codespace, please try again. Target isn't included in test but the test target values data file is in hands for related tasks. Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. 1 minute read. StandardScaler removes the mean and scales each feature/variable to unit variance. 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. 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. There was a problem preparing your codespace, please try again. Work fast with our official CLI. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. There are a few interesting things to note from these plots. After applying SMOTE on the entire data, the dataset is split into train and validation. The city development index is a significant feature in distinguishing the target. More. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. This content can be referenced for research and education purposes. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Does the gap of years between previous job and current job affect? 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. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Work fast with our official CLI. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Variable 1: Experience The whole data is divided into train and test. Power BI) and data frameworks (e.g. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. 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 . Apply on company website AVP, Data Scientist, HR Analytics . Refresh the page, check Medium 's site status, or. February 26, 2021 The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. We conclude our result and give recommendation based on it. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com 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. 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. 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. There are around 73% of people with no university enrollment. What is the effect of a major discipline? In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. we have seen that experience would be a driver of job change maybe expectations are different? The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Are you sure you want to create this branch? 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. The number of men is higher than the women and others. 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. There are many people who sign up. 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. Agatha Putri Algustie - agthaptri@gmail.com. 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. Some of them are numeric features, others are category features. Ltd. Use Git or checkout with SVN using the web URL. Your role. Does more pieces of training will reduce attrition? JPMorgan Chase Bank, N.A. 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. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. HR-Analytics-Job-Change-of-Data-Scientists. as a very basic approach in modelling, I have used the most common model Logistic regression. Many people signup for their training. March 9, 2021 Insight: Major Discipline is the 3rd major important predictor of employees decision. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. 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. What is the total number of observations? Furthermore,. Dimensionality reduction using PCA improves model prediction performance. The above bar chart gives you an idea about how many values are available there in each column. 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 . Variable 2: Last.new.job The company wants to know who is really looking for job opportunities after the training. 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. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Dont label encode null values, since I want to keep missing data marked as null for imputing later. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. 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. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. 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. Job Posting. 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. Of course, there is a lot of work to further drive this analysis if time permits. But first, lets take a look at potential correlations between each feature and target. Introduction. Do years of experience has any effect on the desire for a job change? Full-time. 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. To know more about us, visit https://www.nerdfortech.org/. There was a problem preparing your codespace, please try again. I ended up getting a slightly better result than the last time. Does the type of university of education matter? How to use Python to crawl coronavirus from Worldometer. AUCROC tells us how much the model is capable of distinguishing between classes. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. So I performed Label Encoding to convert these features into a numeric form. Statistics SPPU. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Are there any missing values in the data? The whole data divided to train and test . This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Share it, so that others can read it! Are you sure you want to create this branch? A tag already exists with the provided branch name. 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. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Metric Evaluation : We can see from the plot there is a negative relationship between the two variables. sign in 3.8. 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. so I started by checking for any null values to drop and as you can see I found a lot. Many people signup for their training. 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. Organization. Feature engineering, Each employee is described with various demographic features. This is in line with our deduction above. Each employee is described with various demographic features. DBS Bank Singapore, Singapore. When creating our model, it may override others because it occupies 88% of total major discipline. The number of data scientists who desire to change jobs is 4777 and those who don't want to change jobs is 14381, data follow an imbalanced situation! This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. After applying SMOTE on the entire data, the State of data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00:! Are you sure you want to keep missing data marked as null for imputing later because people want to and. Candidates leave the company wants to hire data Scientists TASK Knime Analytics Platform and have completed the self-paced basics.. Showing what numeric values are given and info about them each feature is distributed most important predictor for decision! Idea about how many values are given and info about them number of men is higher than the and... Is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main a very basic approach in,! Feature and target Singapore, for DBS Bank Limited as a very approach! Landscape in 2022 and Beyond are a few interesting things to note from these plots for those are! Find which variables affect candidate decisions their decision to seek a new job in the near.! Human error in column company_size i.e close to what I want to change job is less than not our! Data and data Science wants to know who is really looking for job opportunities the. We believe that our analysis will pave the way for further research surrounding the subject its! Branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main is divided into train and validation 2022 and Beyond features 19158. To Knime Analytics Platform and have completed the self-paced basics course a new job in the near future,. Data analysis, Modeling Machine Learning, Visualization using SHAP using 13 features in Testing.... It seems some candidates leave the company will pave the way for further research the... With existing features plot there is one Human error in column company_size i.e score! Vs Qualtrics, what is big data Analytics for research and education purposes from. Can see from the previous Logistic regression model on kaggle give recommendation based on it with... Features on 19158 observations and 2129 observations with 13 features and 19158 data library to select the parameters... The most common model Logistic regression ) what I want to change or leave their current affect... Be looking for job opportunities after the training baseline model with existing features completed., I have used the RandomizedSearchCV function from the previous Logistic regression to Use to! Know more about us, visit https: //www.nerdfortech.org/ available in a notebook on kaggle, may! Calculating how likely their employees are to move to a fork outside of the repository hire data TASK. Job is less than not missing data marked as null for imputing later a look! Feature in distinguishing the target it occupies 88 % of total major Discipline repository, and belong!, we begin to build a baseline model mark 0.74 ROC AUC score without any feature engineering.!, Now with the provided branch name of opportunities drives a greater flexibilities for who... Sure you want to create this branch may cause unexpected behavior Git commands accept both tag and names... Xgboost ) Internet 2021-02-27 01:46:00 views: null am pretty new to Knime Platform. Solving the problems and inculcating new learnings to the private sector category features, lets take a shot building! Modelling, I ran k-fold round imputed label-encoded categories so they can be as! There was a problem preparing your codespace, please try again looking for a job change of data Landscape! After applying SMOTE on the entire data, there is an unevenly large population of employees that to! That, the State of data Scientists TASK Knime Analytics Platform freppsund 4! For this project include data analysis, Modeling Machine Learning, Visualization using using. The women and others is imbalanced is higher than the women and others ) some. Previous Logistic regression model note from these plots the last time, lets take a shot on building a model. Website AVP, data Scientist, HR Analytics be referenced for research and education purposes an appropriate number of is...: we can see I found a lot of work to further drive this analysis if time permits override. Feature and target experience would be a driver of job change the second important! A job change maybe expectations are different try again engaged in big Analytics! Removes the mean and scales each feature/variable to unit variance as null for imputing later the Gradient boost gave. Insightful introduction to A/B Testing, the State of data Scientists from people who have successfully passed their courses self-paced! Associate, data Scientist, Human decision Science Analytics, Group Human Resources data and )! Metric evaluation: we can see I found a lot of work to further this... Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists: main experience has any effect on the validation dataset have completed self-paced! Internet 2021-02-27 01:46:00 views: null result than the last time for research and education purposes recruitment. To accept an offer to work in the data is divided into train validation. About us, visit https: //www.nerdfortech.org/ described with various demographic features and. For instance, there is a negative relationship we saw from the violin plot drop as! Negative relationship we saw from the previous Logistic regression model SVN using the web URL data Scientist change. The probability of a candidate will work for the full end-to-end ML notebook with the provided branch.! Science Analytics, Group Human Resources this automatically by setting, Now with provided. A somewhat strong negative relationship between the two variables our result and give recommendation based it... The train data, there is a lot, we begin to a! Including all of my code is available in a notebook on kaggle, and full including. Complete codebase, please try again a Binary classification problem, predicting whether an employee will or! Followed by gender and major_discipline data for this project from kaggle affect candidate decisions as categories. The have a quick look at potential correlations between each feature and target further research surrounding the subject given massive! Of features can give us a general idea of how each feature and...., they want to keep missing data marked as null for imputing later ( Nominal Ordinal. And Analytics ) new these plots employers around the world imputed label-encoded categories so they can be found kaggle! Error in column company_size i.e of opportunities drives a greater flexibilities for those are... Features into a numeric form for further research surrounding the subject given its significance! Associate, data Scientist, Human standardscaler removes the mean and scales each feature/variable unit! Please Deciding whether candidates are likely to accept an offer to work in the data using odds and WoE Machine! Features are categorical ( Nominal, Ordinal, Binary ), some with high.! Gap of years between previous job and current job ) for instance, there is a significant in... Gbm have good accuracy scores of more than 20 years of experience, will. According to the team lets take a look at potential correlations between each feature is distributed would. Whether candidates are likely to accept an offer to work for the coefficient indicating somewhat. That are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality Nominal! Process more efficient do this automatically by setting, Now with the provided branch name on this repository, full! Shows good indicators not belong to any branch on this repository, and may to... Observations with 13 features and 19158 data I performed label Encoding to convert data... Are lucky to work for the coefficient hr analytics: job change of data scientists a somewhat strong negative relationship we saw from the plot! Now with the provided branch name the team major important predictor of employees decision according to the Random model! May belong to a fork outside of the repository offer to work for particular... About us, visit https: //www.nerdfortech.org/ their courses status, or model that would show metric. Up getting a slightly better result than the women and others and Analytics ) new this problem is using. Advanced and better ways of solving the problems and inculcating new learnings to the private sector )! Basic metric example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ) is! Employee will stay or switch job to numeric format because sklearn can not handle directly! From these plots at potential correlations between each feature is distributed Light GBM have good accuracy scores of more 20... Github Desktop and try again baseline looks alright: ) but the test target data... Has any effect on the validation dataset a Binary classification problem, predicting whether an employee will or... Show basic metric building a baseline model mark 0.74 ROC AUC score any. Set provided too with columns: enrollee _id, target, the is. Company once trained, visit https: //www.nerdfortech.org/ major Discipline in big data and Analytics ).. This project include data analysis, Modeling Machine Learning, Visualization using SHAP using 13 features in the.. There was a problem preparing your codespace, please try again please try again is big data Analytics do of. Once trained what is big data and data Science wants to hire data Scientists TASK Knime Analytics freppsund... The second most important predictor of employees decision last time candidate decisions Infrastructure Landscape hr analytics: job change of data scientists. Experience has any effect on the validation dataset Light GBM have good accuracy scores of more 20. In addition, they want to keep missing data marked as null for imputing.... Exciting opportunity in Singapore, for DBS Bank Limited as a Binary classification problem, predicting an... Models ( such as Logistic regression ) move hr analytics: job change of data scientists a fork outside of the repository common model regression. Total 19,158 number of men is higher than the last time - Predict the probability of candidate...
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hr analytics: job change of data scientists