Apply on company website AVP, Data Scientist, HR Analytics . What is the maximum index of city development? This is a quick start guide for implementing a simple data pipeline with open-source applications. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. 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. 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. Permanent. Question 2. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 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. If nothing happens, download Xcode and try again. well personally i would agree with it. All dataset come from personal information of trainee when register the training. 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. MICE is used to fill in the missing values in those features. 19,158. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. 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 Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Next, we tried to understand what prompted employees to quit, from their current jobs POV. These are the 4 most important features of our model. 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. The simplest way to analyse the data is to look into the distributions of each feature. 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. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. Human Resources. was obtained from Kaggle. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. 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 violin plot plays a similar role as a box and whisker plot. Full-time. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Context and Content. AUCROC tells us how much the model is capable of distinguishing between classes. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Prudential 3.8. . Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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. 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. If nothing happens, download GitHub Desktop and try again. Does the type of university of education matter? 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. The number of men is higher than the women and others. Heatmap shows the correlation of missingness between every 2 columns. NFT is an Educational Media House. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. What is a Pivot Table? for the purposes of exploring, lets just focus on the logistic regression for now. 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. Do years of experience has any effect on the desire for a job change? An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. There are more than 70% people with relevant experience. Scribd is the world's largest social reading and publishing site. 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. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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. Hadoop . Machine Learning, 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! This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Many people signup for their training. 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. 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 . Isolating reasons that can cause an employee to leave their current company. Let us first start with removing unnecessary columns i.e., enrollee_id as those are unique values and city as it is not much significant in this case. There are around 73% of people with no university enrollment. 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.. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time The whole data divided to train and test . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We conclude our result and give recommendation based on it. Our organization plays a critical and highly visible role in delivering customer . Pre-processing, Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle As we can see here, highly experienced candidates are looking to change their jobs the most. Work fast with our official CLI. Goals : 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. After applying SMOTE on the entire data, the dataset is split into train and validation. Exploring the categorical features in the data using odds and WoE. Understanding whether an employee is likely to stay longer given their experience. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists I chose this dataset because it seemed close to what I want to achieve and become in life. We hope to use more models in the future for even better efficiency! Learn more. To summarize our data, we created the following correlation matrix to see whether and how strongly pairs of variable were related: As we can see from this image (and many more that we observed), some of our data is imbalanced. For instance, there is an unevenly large population of employees that belong to the private sector. 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. 75% of people's current employer are Pvt. 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. Learn more. Use Git or checkout with SVN using the web URL. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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hr analytics: job change of data scientists