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. . Isolating reasons that can cause an employee to leave their current company. Use Git or checkout with SVN using the web URL. 10-Aug-2022, 10:31:15 PM Show more Show less To know more about us, visit https://www.nerdfortech.org/. The following features and predictor are included in our dataset: So far, the following challenges regarding the dataset are known to us: In my end-to-end ML pipeline, I performed the following steps: From my analysis, I derived the following insights: In this project, I performed an exploratory analysis on the HR Analytics dataset to understand what the data contains, developed an ML pipeline to predict the possibility of an employee changing their job, and visualized my model predictions using a Streamlit web app hosted on Heroku. 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. 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. 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. Note: 8 features have the missing values. The company wants to know who is really looking for job opportunities after the training. On the basis of the characteristics of the employees the HR of the want to understand the factors affecting the decision of an employee for staying or leaving the current job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. A tag already exists with the provided branch name. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Context and Content. 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. StandardScaler removes the mean and scales each feature/variable to unit variance. This operation is performed feature-wise in an independent way. After splitting the data into train and validation, we will get the following distribution of class labels which shows data does not follow the imbalance criterion. Notice only the orange bar is labeled. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. DBS Bank Singapore, Singapore. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. 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 Does the type of university of education matter? All dataset come from personal information . I also wanted to see how the categorical features related to the target variable. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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. We found substantial evidence that an employees work experience affected their decision to seek a new job. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. For details of the dataset, please visit here. 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. Not at all, I guess! It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Interpret model(s) such a way that illustrate which features affect candidate decision Variable 3: Discipline Major Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. 2023 Data Computing Journal. 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 . (Difference in years between previous job and current job). which to me as a baseline looks alright :). Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Are you sure you want to create this branch? What is the effect of a major discipline? 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. The simplest way to analyse the data is to look into the distributions of each feature. I used another quick heatmap to get more info about what I am dealing with. 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. 1 minute read. 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. 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. The pipeline I built for prediction reflects these aspects of the dataset. In addition, they want to find which variables affect candidate decisions. Permanent. That is great, right? A violin plot plays a similar role as a box and whisker plot. for the purposes of exploring, lets just focus on the logistic regression for now. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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? Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. XGBoost and Light GBM have good accuracy scores of more than 90. Machine Learning, February 26, 2021 to use Codespaces. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. Are you sure you want to create this branch? There are around 73% of people with no university enrollment. 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. I used violin plot to visualize the correlations between numerical features and target. To the RF model, experience is the most important predictor. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Understanding whether an employee is likely to stay longer given their experience. was obtained from Kaggle. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. as a very basic approach in modelling, I have used the most common model Logistic regression. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. Question 2. This is in line with our deduction above. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. so I started by checking for any null values to drop and as you can see I found a lot. The number of STEMs is quite high compared to others. These are the 4 most important features of our model. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com Deciding whether candidates are likely to accept an offer to work for a particular larger company. Information regarding how the data was collected is currently unavailable. Data set introduction. Machine Learning Approach to predict who will move to a new job using Python! 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. 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 .
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