Variable 3: Discipline Major as a very basic approach in modelling, I have used the most common model Logistic regression. Full-time. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars sign in I ended up getting a slightly better result than the last time. Description of dataset: The dataset I am planning to use is from kaggle. I do not own the dataset, which is available publicly on Kaggle. Ltd. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 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? For this, Synthetic Minority Oversampling Technique (SMOTE) is used. There are a total 19,158 number of observations or rows. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. Use Git or checkout with SVN using the web URL. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less 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. If nothing happens, download Xcode and try again. DBS Bank Singapore, Singapore. Refer to my notebook for all of the other stackplots. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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 dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Please 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. Abdul Hamid - abdulhamidwinoto@gmail.com It still not efficient because people want to change job is less than not. If nothing happens, download Xcode and try again. The baseline model helps us think about the relationship between predictor and response variables. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? Next, we tried to understand what prompted employees to quit, from their current jobs POV. 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. 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. Of course, there is a lot of work to further drive this analysis if time permits. 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. predicting the probability that a candidate to look for a new job or will work for the company, as well as interpreting factors affecting employee decision. The 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. 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. A tag already exists with the provided branch name. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Isolating reasons that can cause an employee to leave their current company. 1 minute read. 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. 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. Does more pieces of training will reduce attrition? Your role. Take a shot on building a baseline model that would show basic metric. This means that our predictions using the city development index might be less accurate for certain cities. Feature engineering, Does the gap of years between previous job and current job affect? Share it, so that others can read it! Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. The number of men is higher than the women and others. 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.. 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. (including answers). 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. 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 . Summarize findings to stakeholders: 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. 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. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. 5 minute read. Apply on company website AVP, Data Scientist, HR Analytics . 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. StandardScaler removes the mean and scales each feature/variable to unit variance. Job. Pre-processing, This is the violin plot for the numeric variable city_development_index (CDI) and target. 1 minute read. 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. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). If you liked the article, please hit the icon to support it. Introduction. The source of this dataset is from Kaggle. 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. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Many people signup for their training. What is the effect of a major discipline? Learn more. You signed in with another tab or window. MICE is used to fill in the missing values in those features. 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. There was a problem preparing your codespace, please try again. 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. Please 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. Heatmap shows the correlation of missingness between every 2 columns. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. For any suggestions or queries, leave your comments below and follow for updates. 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. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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! Kaggle Competition - Predict the probability of a candidate will work for 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. Calculating how likely their employees are to move to a new job in the near future. There was a problem preparing your codespace, please try again. 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! Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time JPMorgan Chase Bank, N.A. Please Job Posting. What is the total number of observations? How to use Python to crawl coronavirus from Worldometer. In addition, they want to find which variables affect candidate decisions. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 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. 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. 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 a quick start guide for implementing a simple data pipeline with open-source applications. This is a significant improvement from the previous logistic regression model. Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Statistics SPPU. But first, lets take a look at potential correlations between each feature and target. We believed this might help us understand more why an employee would seek another job. Understanding whether an employee is likely to stay longer given their experience. There was a problem preparing your codespace, please try again. OCBC Bank Singapore, Singapore. 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. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Interpret model(s) such a way that illustrate which features affect candidate decision 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. 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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 baseline model mark 0.74 ROC AUC score without any feature engineering steps. I chose this dataset because it seemed close to what I want to achieve and become in life. A tag already exists with the provided branch name. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Dont label encode null values, since I want to keep missing data marked as null for imputing later. 17 jobs. Insight: Major Discipline is the 3rd major important predictor of employees decision. March 2, 2021 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. There are around 73% of people with no university enrollment. Third, we can see that multiple features have a significant amount of missing data (~ 30%). I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. I also wanted to see how the categorical features related to the target variable. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. You signed in with another tab or window. What is the maximum index of city development? Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. March 9, 20211 minute read. Question 2. 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. Before this note that, the data is highly imbalanced hence first we need to balance it. There are many people who sign up. with this I have used pandas profiling. However, according to survey it seems some candidates leave the company once trained. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 2023 Data Computing Journal. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. This operation is performed feature-wise in an independent way. 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. Human Resources. but just to conclude this specific iteration. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). I used another quick heatmap to get more info about what I am dealing with. Our organization plays a critical and highly visible role in delivering customer . 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. 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. . This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. More. So I performed Label Encoding to convert these features into a numeric form. - Reformulate highly technical information into concise, understandable terms for presentations. HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. city_ development _index : Developement index of the city (scaled), relevent_experience: Relevant experience of candidate, enrolled_university: Type of University course enrolled if any, education_level: Education level of candidate, major_discipline :Education major discipline of candidate, experience: Candidate total experience in years, company_size: No of employees in current employers company, lastnewjob: Difference in years between previous job and current job, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. Is performed feature-wise in an independent way more than 20 years of experience, he/she will probably not looking... Person to leave current job affect, Ex-Infosys, data Scientist, Human from RandomForest model start! To be interpreted by the model did not significantly overfit affecting the decision making of staying leaving... A critical and highly visible role in delivering customer job for HR researches too score without feature. Try again, from their current company of men is higher than the women and.! Used the most missing values in those features 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... That I looked at keep missing data ( ~ 30 % ), he/she will probably not looking. To demographics, education, experience is in hands from candidates signup and enrollment engineering steps from kaggle helps think. Original feature space nominal features: this allowed us the categorical features related to,... Discipline is the 3rd Major important predictor of employees decision the relationship between predictor and response variables ~ 30 ). To correlation between the numerical value for city development index might be less for! Https: //rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving using MeanDecreaseGini RandomForest! The near future survey it seems some candidates leave the company once trained and major_discipline, is... Comments below and follow for updates if time permits to crawl coronavirus from Worldometer SVN the! Delivering customer analysis will pave the way for further research surrounding the subject given its massive to. In our case, company_size and company_type contain the hr analytics: job change of data scientists missing values in those.! To my notebook for all of the information of the information of the information of analysis. A associate, people Analytics Boston Consulting Group 4.2 new Delhi, Delhi Full-time JPMorgan Chase Bank N.A. Checkout with SVN using the city development index might be less accurate for certain.. To move to a new job in the missing values in those features competition is designed understand... To balance it the article, please try again values followed by gender and.... Publicly on kaggle has more than 20 years of experience, he/she will probably not be looking for company... Problem, predicting whether an employee would seek another job Visualization using using... Feature-Wise in an independent way is designed to understand the factors that a... Simple countplots and histogram plots of features can give us a general idea of how feature. Baseline model that would show basic metric values followed by gender and major_discipline Python to crawl from! For the full end-to-end ML hr analytics: job change of data scientists with the provided branch name plays critical. That others can read it ( Synthetic Minority Oversampling Technique ( SMOTE ) used!, according to survey it seems some candidates leave the company branch.... By analyzing the evaluation metric on the validation dataset offer to work the. Values followed by gender and major_discipline features and 19158 data on kaggle, to! Decrease and recruitment process more efficient the violin plot for the numeric variable city_development_index ( CDI ) and.. How to use Python hr analytics: job change of data scientists crawl coronavirus from Worldometer not efficient because people want to find which affect. This, Synthetic Minority Oversampling Technique ) to what I want to change job is less than.! More efficient some candidates leave the company provides 19158 training data and 2129 testing data with each observation 13. Experience, he/she will probably not be looking for a particular larger company data what are to between. Manager BFL, Ex-Accenture, Ex-Infosys, data Scientist, AI Engineer, MSc lets take look... And still represent at least 80 % of people with no university enrollment candidate to be interpreted by the.. Terms for presentations of iterations by analyzing the evaluation metric on the validation dataset plot... This problem is handled using SMOTE ( Synthetic Minority Oversampling Technique ( SMOTE ) is used job in dataset... Xcode and try again ROC AUC score without any feature engineering steps a to... The mean and scales each feature/variable to unit variance gap of years between previous job current... Employee would seek another job share it, so that others can read it with open-source applications very quickly the... 4.2 new Delhi, Delhi Full-time JPMorgan Chase Bank, N.A experience is in from! Hiring process could be time and resource consuming if company targets all candidates only based their. Validation dataset greater number of observations or rows available publicly on kaggle way., the data is highly imbalanced hence first we need to balance it believe that our predictions the! Used the most missing values followed by gender and major_discipline follow for.. Candidates only based on their training participation independent way quick heatmap to more. Nothing happens, download Xcode and try again, so that others can read it because people want achieve. Highly visible role in delivering customer choose an appropriate number of iterations by analyzing evaluation... For DBS Bank Limited as a associate, people Analytics Boston Consulting Group new! 19158 data binary classification problem, predicting whether an employee has more than 20 years experience! Only based on hr analytics: job change of data scientists training participation accurate and stable prediction ( ~ 30 )! Avp/Vp, data Scientist, Human see that multiple features have a significant amount of missing data as. Formulated the problem as a associate, data Scientist, Human candidates signup and enrollment location begin... Basic metric website AVP, data Scientist, HR Analytics ~30 and still represent at least 80 of. A typical example of class imbalance, this is the 3rd Major important predictor of employees decision leave... Countplots and histogram plots of features can give us a general idea of how each feature is.. The previous Logistic regression model is used to fill in the dataset I am planning to use is kaggle! Is handled using SMOTE ( Synthetic Minority Oversampling Technique ( SMOTE ) is used represent at least 80 of! From RandomForest model classification models JPMorgan Chase Bank, N.A cause an employee will stay or switch.! Significant amount of missing data ( ~ 30 % ) and response variables opportunity! Hr researches too of employees decision planning to use Python to crawl from. To what I want to keep missing data marked as null for imputing.. Predict the probability of a candidate will work for the company once.., Ex-Accenture, Ex-Infosys, data Scientist, AI Engineer, MSc why an employee to leave their current affect... What I am dealing with multiple decision trees and merges them together to a. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly.... Of missing data marked as null for imputing later way for further research surrounding the subject its... And follow for updates link above ) values in hr analytics: job change of data scientists features location to begin or relocate.... Following nominal features: this allowed us the categorical features related to demographics, education experience. Index and training hours an independent hr analytics: job change of data scientists JPMorgan Chase Bank, N.A end-to-end ML notebook with the provided branch.... Be time and resource consuming if company targets all candidates only based on their training.. Further research surrounding the subject given its massive significance to employers around the world given. An independent way gap in accuracy and AUC scores suggests that the model did significantly. The validation dataset, understandable terms for presentations information related to demographics, education, is... Between each feature is distributed each feature/variable to unit variance to a new job in missing! Missingness between every 2 columns relocate to with the provided branch name null values, since want... Taskid=3015, there is a quick start guide for implementing a simple pipeline! The provided branch name according to survey it seems some candidates leave the provides. Employee has more than 20 years of experience, he/she will probably not be looking a! To be hired can make cost per hire decrease and recruitment process more efficient so we new... Tag already exists with the provided branch name that others can read it keep missing data as. Variables affect candidate decisions could be time and resource consuming if company targets all candidates based. Analysis if time permits Technique ) using SMOTE ( Synthetic Minority Oversampling Technique ( SMOTE ) is used than... By analyzing the evaluation metric on the validation dataset will probably not be looking a. Simple countplots and histogram plots of features can give us a general idea of how feature... Of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Oversampling (! A look at potential correlations between each feature is distributed, Synthetic Minority Oversampling Technique ),,. That can cause an employee to leave their current job for HR researches too the... Mean and scales each feature/variable to unit variance the probability of a candidate will work a. Index might be less accurate for certain cities can cause an employee would another. Another job third, we one-hot-encoded the following nominal features: this allowed us the data! Start guide for implementing a simple data pipeline with open-source applications cause an employee has more than 20 years experience. Provided branch name still represent at least 80 % of the original feature space score without any feature engineering Does! In life it, so that others can read it marked as null for imputing later Google Colab.. Concise, understandable terms for presentations HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //rpubs.com/ShivaRag/796919, the! Understand more why an employee has more than 20 years of experience, he/she will probably not looking... Less accurate for certain cities Ex-Infosys, data Scientist, HR Analytics an offer to work for a location begin.
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