Work fast with our official CLI. 5 minute read. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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 project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. so I started by checking for any null values to drop and as you can see I found a lot. All dataset come from personal information of trainee when register the training. 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. This is the violin plot for the numeric variable city_development_index (CDI) and target. 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.;[. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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. Many people signup for their training. though i have also tried Random Forest. StandardScaler removes the mean and scales each feature/variable to unit variance. Next, we tried to understand what prompted employees to quit, from their current jobs POV. 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 . Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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. Use Git or checkout with SVN using the web URL. 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. There was a problem preparing your codespace, please try again. There was a problem preparing your codespace, please try again. 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). Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . After applying SMOTE on the entire data, the dataset is split into train and validation. Some of them are numeric features, others are category features. Are you sure you want to create this branch? Metric Evaluation : Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less I do not own the dataset, which is available publicly on Kaggle. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Data Source. Github link all code found in this link. 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 In our case, the columns company_size and company_type have a more or less similar pattern of missing values. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Hadoop . The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Data set introduction. Please Human Resource Data Scientist jobs. We conclude our result and give recommendation based on it. 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. 3.8. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Missing imputation can be a part of your pipeline as well. Many people signup for their training. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Not at all, I guess! I used violin plot to visualize the correlations between numerical features and target. MICE is used to fill in the missing values in those features. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. OCBC Bank Singapore, Singapore. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Information related to demographics, education, experience are in hands from candidates signup and enrollment. I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. You signed in with another tab or window. 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. Variable 3: Discipline Major has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. There are around 73% of people with no university enrollment. In addition, they want to find which variables affect candidate decisions. Agatha Putri Algustie - agthaptri@gmail.com. You signed in with another tab or window. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. For details of the dataset, please visit here. Exploring the categorical features in the data using odds and WoE. 75% of people's current employer are Pvt. 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. There are a total 19,158 number of observations or rows. However, according to survey it seems some candidates leave the company once trained. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. If nothing happens, download GitHub Desktop and try again. HR-Analytics-Job-Change-of-Data-Scientists. A tag already exists with the provided branch name. 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? Then I decided the have a quick look at histograms showing what numeric values are given and info about them. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. 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 . AVP, Data Scientist, HR Analytics. Company wants to increase recruitment efficiency by knowing which candidates are looking for a job change in their career so they can be hired as data scientist. 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. 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. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model.
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