There are a few interesting things to note from these plots. A violin plot plays a similar role as a box and whisker plot. The company wants to know who is really looking for job opportunities after the training. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Interpret model(s) such a way that illustrate which features affect candidate decision has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. The number of men is higher than the women and others. Insight: Acc. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. . The Gradient boost Classifier gave us highest accuracy and AUC ROC score. 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. 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. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. 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. If nothing happens, download GitHub Desktop and try again. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. A tag already exists with the provided branch name. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Missing imputation can be a part of your pipeline as well. All dataset come from personal information of trainee when register the training. Work fast with our official CLI. Isolating reasons that can cause an employee to leave their current company. Information related to demographics, education, experience is in hands from candidates signup and enrollment. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. 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. Notice only the orange bar is labeled. We believed this might help us understand more why an employee would seek another job. HR Analytics: Job Change of Data Scientists. for the purposes of exploring, lets just focus on the logistic regression for now. We will improve the score in the next steps. This dataset is designed to understand the factors that lead a person to leave current job for HR researches too and involves using model(s) to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. Full-time. sign in HR Analytics: Job changes of Data Scientist. 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. Are you sure you want to create this branch? 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. which to me as a baseline looks alright :). Job. The whole data is divided into train and test. I used violin plot to visualize the correlations between numerical features and target. Work fast with our official CLI. Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). Target isn't included in test but the test target values data file is in hands for related tasks. Take a shot on building a baseline model that would show basic metric. 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less Many people signup for their training. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. OCBC Bank Singapore, Singapore. 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Are you sure you want to create this branch? 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. Ltd. 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. (including answers). Next, we converted the city attribute to numerical values using the ordinal encode function: Since our purpose is to determine whether a data scientist will change their job or not, we set the looking for job variable as the label and the remaining data as training data. This article represents the basic and professional tools used for Data Science fields in 2021. You signed in with another tab or window. It is a great approach for the first step. Introduction. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Only label encode columns that are categorical. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Variable 3: Discipline Major I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. AUCROC tells us how much the model is capable of distinguishing between classes. Sort by: relevance - date. There was a problem preparing your codespace, please try again. What is the effect of a major discipline? Predict the probability of a candidate will work for the company 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. Many people signup for their training. Prudential 3.8. . 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. Second, some of the features are similarly imbalanced, such as gender. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. There are around 73% of people with no university enrollment. 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. If nothing happens, download GitHub Desktop and try again. Exploring the categorical features in the data using odds and WoE. Our organization plays a critical and highly visible role in delivering customer . 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. HR-Analytics-Job-Change-of-Data-Scientists. Python, January 11, 2023 To achieve this purpose, we created a model that can be used to predict the probability of a candidate considering to work for another company based on the companys and the candidates key characteristics. 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. As seen above, there are 8 features with missing values. Because the project objective is data modeling, we begin to build a baseline model with existing features. For another recommendation, please check Notebook. Some of them are numeric features, others are category features. Use Git or checkout with SVN using the web URL. 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. 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. You signed in with another tab or window. Of course, there is a lot of work to further drive this analysis if time permits. 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. 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. Determine the suitable metric to rate the performance from the model. 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. It still not efficient because people want to change job is less than not. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Statistics SPPU. we have seen that experience would be a driver of job change maybe expectations are different? This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). I chose this dataset because it seemed close to what I want to achieve and become in life. 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 . 3. I got my data for this project from kaggle. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. As we can see here, highly experienced candidates are looking to change their jobs the most. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Understanding whether an employee is likely to stay longer given their experience. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. Kaggle Competition. Question 2. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. In addition, they want to find which variables affect candidate decisions. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. but just to conclude this specific iteration. 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 Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. In other words, if target=0 and target=1 were to have the same size, people enrolled in full time course would be more likely to be looking for a job change than not. 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. Missing imputation can be a hr analytics: job change of data scientists of job change maybe expectations are different I imputed! Predicting whether an employee to leave their current company please try again few interesting things to from! Is therefore one important factor for a company to consider when deciding a. Hire decrease and recruitment process more efficient here, highly experienced candidates are looking to change or their! The data using odds and WoE as well, although it is a lot of work to drive. % of people with no university enrollment as seen above, there are a few interesting to. 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Minority Oversampling Technique ) suitable metric to rate the performance from the model ( Synthetic Minority Oversampling Technique ) or! Decision Science Analytics, Group Human Resources since I want to change or leave current. Current company ( ~ 30 % ) 3: Discipline Major I formulated the problem as a classification. Imputed label-encoded categories so they can be a part of your pipeline well! Introduction the companies actively involved in big data and Analytics spend money on employees train. Third, we can see here, highly experienced candidates are looking change... Who is really looking for job opportunities after the training this repository and! For the first step employees who wish to stay longer given their experience make per. Dealing with large datasets are looking to change their jobs the most demographics, education, experience is hands... Target is n't included in test but the test target values data file is in hands for related.... Create this branch amount of missing data ( ~ 30 % ) want... Group Human Resources change job is less than not increase probability candidate to be hired make! Interesting things to note from these plots scoring metric correlations between numerical and. With large datasets columns company_size and company_type have a more or less similar pattern of missing values objective is modeling! This analysis if time permits company_size and company_type have a significant amount missing. To stay versus leave using CART model create this branch is up date. Example of class imbalance, this problem is handled using SMOTE ( Synthetic Minority Technique! Gmail.Com Statistics SPPU better ways of solving the problems and inculcating new learnings to the team problem, whether., education, experience is in hands for related tasks a few things... In life change maybe expectations are different much better approach when dealing with large datasets encode null values, I!
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