hr analytics: job change of data scientists

Variable 3: Discipline Major These are the 4 most important features of our model. As we can see here, highly experienced candidates are looking to change their jobs the most. There are a total 19,158 number of observations or rows. For any suggestions or queries, leave your comments below and follow for updates. 10-Aug-2022, 10:31:15 PM Show more Show less The pipeline I built for prediction reflects these aspects of the dataset. for the purposes of exploring, lets just focus on the logistic regression for now. Some of them are numeric features, others are category features. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. was obtained from Kaggle. 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). The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. All dataset come from personal information . 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. Question 2. Human Resources. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. More specifically, the majority of the target=0 group resides in highly developed cities, whereas the target=1 group is split between cities with high and low CDI. The 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! Power BI) and data frameworks (e.g. I used Random Forest to build the baseline model by using below code. 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. 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. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. The simplest way to analyse the data is to look into the distributions of each feature. 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. There has been only a slight increase in accuracy and AUC score by applying Light GBM over XGBOOST but there is a significant difference in the execution time for the training procedure. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. Are you sure you want to create this branch? Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 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 This is a significant improvement from the previous logistic regression model. 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. February 26, 2021 It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. 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. Information regarding how the data was collected is currently unavailable. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Note: 8 features have the missing values. For details of the dataset, please visit here. We will improve the score in the next steps. Insight: Major Discipline is the 3rd major important predictor of employees decision. Work fast with our official CLI. Full-time. Does the type of university of education matter? The Gradient boost Classifier gave us highest accuracy and AUC ROC score. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? When creating our model, it may override others because it occupies 88% of total major discipline. 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? Isolating reasons that can cause an employee to leave their current company. 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. More. Director, Data Scientist - HR/People Analytics. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com You signed in with another tab or window. Work fast with our official CLI. A tag already exists with the provided branch name. 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. JPMorgan Chase Bank, N.A. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. 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. A tag already exists with the provided branch name. How to use Python to crawl coronavirus from Worldometer. Use Git or checkout with SVN using the web URL. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. sign in Our organization plays a critical and highly visible role in delivering customer . sign in There are more than 70% people with relevant experience. So I performed Label Encoding to convert these features into a numeric form. Group Human Resources Divisional Office. 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. 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. 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. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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. Calculating how likely their employees are to move to a new job in the near future. 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. We believed this might help us understand more why an employee would seek another job. The stackplot shows groups as percentages of each target label, rather than as raw counts. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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. Each employee is described with various demographic features. Work fast with our official CLI. Question 1. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. Please Variable 2: Last.new.job Heatmap shows the correlation of missingness between every 2 columns. Metric Evaluation : All dataset come from personal information of trainee when register the training. It still not efficient because people want to change job is less than not. Because the project objective is data modeling, we begin to build a baseline model with existing features. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). I chose this dataset because it seemed close to what I want to achieve and become in life. Please In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. 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. well personally i would agree with it. I got my data for this project from kaggle. There was a problem preparing your codespace, please try again. sign in Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. (Difference in years between previous job and current job). 2023 Data Computing Journal. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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 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 dataset has already been divided into testing and training sets. 5 minute read. 75% of people's current employer are Pvt. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. You signed in with another tab or window. Next, we tried to understand what prompted employees to quit, from their current jobs POV. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 3. 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). This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Refer to my notebook for all of the other stackplots. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 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. 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. What is the maximum index of city development? 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. The city development index is a significant feature in distinguishing the target. Refresh the page, check Medium 's site status, or. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. AVP, Data Scientist, HR Analytics. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). DBS Bank Singapore, Singapore. What is the effect of company size on the desire for a job change? We found substantial evidence that an employees work experience affected their decision to seek a new job. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Use Git or checkout with SVN using the web URL. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. Target isn't included in test but the test target values data file is in hands for related tasks. Please Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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.;[. The whole data is divided into train and test. Of course, there is a lot of work to further drive this analysis if time permits. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0).

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