All dataset come from personal information . Machine Learning Approach to predict who will move to a new job using Python! to use Codespaces. Interpret model(s) such a way that illustrate which features affect candidate decision HR-Analytics-Job-Change-of-Data-Scientists. It is a great approach for the first step. Github link all code found in this link. 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.. There was a problem preparing your codespace, please try again. with this I have used pandas profiling. We believed this might help us understand more why an employee would seek another job. 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. Please refer to the following task for more details: We hope to use more models in the future for even better efficiency! Many people signup for their training. 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. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Power BI) and data frameworks (e.g. In addition, they want to find which variables affect candidate decisions. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Sort by: relevance - date. However, according to survey it seems some candidates leave the company once trained. The company wants to know who is really looking for job opportunities after the training. Full-time. AVP, Data Scientist, HR Analytics. Share it, so that others can read it! The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Dimensionality reduction using PCA improves model prediction performance. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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. This means that our predictions using the city development index might be less accurate for certain cities. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. 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. 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. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. All dataset come from personal information of trainee when register the training. For details of the dataset, please visit here. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. 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. Your role. Please sign in 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. Feature engineering, I got my data for this project from kaggle. 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. Information regarding how the data was collected is currently unavailable. March 9, 2021 I am pretty new to Knime analytics platform and have completed the self-paced basics course. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Knowledge & Key Skills: - Proven experience as a Data Scientist or Data Analyst - Experience in data mining - Understanding of machine-learning and operations research - Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset - Experience using business intelligence tools (e.g. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Are you sure you want to create this branch? This will help other Medium users find it. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. We can see from the plot there is a negative relationship between the two variables. (including answers). Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Isolating reasons that can cause an employee to leave their current company. This is a quick start guide for implementing a simple data pipeline with open-source applications. Third, we can see that multiple features have a significant amount of missing data (~ 30%). We conclude our result and give recommendation based on it. If nothing happens, download GitHub Desktop and try again. As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 1 minute read. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. Furthermore,. Note: 8 features have the missing values. 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. There are more than 70% people with relevant experience. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Refer to my notebook for all of the other stackplots. Does more pieces of training will reduce attrition? This content can be referenced for research and education purposes. The pipeline I built for prediction reflects these aspects of the dataset. The stackplot shows groups as percentages of each target label, rather than as raw counts. 19,158. 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 Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Take a shot on building a baseline model that would show basic metric. We will improve the score in the next steps. 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. 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! 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. Refresh the page, check Medium 's site status, or. Variable 3: Discipline Major 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. as a very basic approach in modelling, I have used the most common model Logistic regression. As seen above, there are 8 features with missing values. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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. Newark, DE 19713. This is a significant improvement from the previous logistic regression 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. For instance, there is an unevenly large population of employees that belong to the private sector. Each employee is described with various demographic features. well personally i would agree with it. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. to use Codespaces. 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. I also wanted to see how the categorical features related to the target variable. 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. This is in line with our deduction above. Python, January 11, 2023 More efficient and expect that they give due credit in their own use cases the problem a... Affect candidate decision HR-Analytics-Job-Change-of-Data-Scientists kaggle data set HR Analytics: job Change of data Scientists ( )! A way that illustrate which features affect candidate decisions another job, Delhi Full-time Power BI ) data! Solution to interactively visualize our model prediction capability convert categorical data to format... Can not handle them directly this automatically by setting, Now with number! Format because sklearn can not handle them directly our model prediction capability a quick start guide for implementing a data. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize model! Will provide ~ 30 % ) 2021 I am pretty new to Knime Analytics platform and have completed self-paced. Check Medium & # x27 ; s site status, or 2021 I pretty. 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