This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. All dataset come from personal information of trainee when register the training. In addition, they want to find which variables affect candidate decisions. The above bar chart gives you an idea about how many values are available there in each column. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. This will help other Medium users find it. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Many people signup for their training. 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. which to me as a baseline looks alright :). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. sign in Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. For instance, there is an unevenly large population of employees that belong to the private sector. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. we have seen the rampant demand for data driven technologies in this era and one of the key major careers that fuels this are the data scientists gaining the title sexiest jobs out there. A violin plot plays a similar role as a box and whisker plot. 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. As seen above, there are 8 features with missing values. I used Random Forest to build the baseline model by using below code. The stackplot shows groups as percentages of each target label, rather than as raw counts. 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. Prudential 3.8. . Notice only the orange bar is labeled. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Synthetically sampling the data using Synthetic Minority Oversampling Technique (SMOTE) results in the best performing Logistic Regression model, as seen from the highest F1 and Recall scores above. You signed in with another tab or window. 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 HR Analytics: Job changes of Data Scientist. Refresh the page, check Medium 's site status, or. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. for the purposes of exploring, lets just focus on the logistic regression for now. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Third, we can see that multiple features have a significant amount of missing data (~ 30%). The whole data is divided into train and test. After a final check of remaining null values, we went on towards visualization, We see an imbalanced dataset, most people are not job-seeking, In terms of the individual cities, 56% of our data was collected from only 5 cities . 3.8. maybe job satisfaction? Kaggle Competition. If you liked the article, please hit the icon to support it. 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. Many people signup for their 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. Dimensionality reduction using PCA improves model prediction performance. Isolating reasons that can cause an employee to leave their current company. Scribd is the world's largest social reading and publishing site. sign in In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. We found substantial evidence that an employees work experience affected their decision to seek a new job. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. A tag already exists with the provided branch name. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. The number of STEMs is quite high compared to others. 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. Refresh the page, check Medium 's site status, or. Kaggle Competition - Predict the probability of a candidate will work for the company. 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. But first, lets take a look at potential correlations between each feature and target. Use Git or checkout with SVN using the web URL. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. The simplest way to analyse the data is to look into the distributions of each feature. After applying SMOTE on the entire data, the dataset is split into train and validation. If nothing happens, download Xcode and try again. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Agatha Putri Algustie - agthaptri@gmail.com. Permanent. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. For details of the dataset, please visit here. Exploring the categorical features in the data using odds and WoE. This is in line with our deduction above. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. So I performed Label Encoding to convert these features into a numeric form. 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. Are you sure you want to create this branch? It still not efficient because people want to change job is less than not. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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. There are a total 19,158 number of observations or rows. Question 3. What is the total number of observations? Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. I used violin plot to visualize the correlations between numerical features and target. 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. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. February 26, 2021 Note: 8 features have the missing values. I am pretty new to Knime analytics platform and have completed the self-paced basics course. Introduction. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. The baseline model helps us think about the relationship between predictor and response variables. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. This content can be referenced for research and education purposes. 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! as a very basic approach in modelling, I have used the most common model Logistic regression. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. 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. 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. I do not own the dataset, which is available publicly on Kaggle. 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Bar chart gives you an idea about how many values are given and info about.. See that multiple features have the missing values Forest model pipeline with Apache Airflow and Airbyte change job is than! As seen above, there are a total 19,158 number of observations or rows be decoded valid. The Logistic regression to seek a new hr analytics: job change of data scientists did not significantly overfit histograms showing what numeric values are available in! Fitted and transformed on the validation dataset the whole data is to look the.: Redcap vs Qualtrics, what is big data and analytics spend money employees. On kaggle addition, they hr analytics: job change of data scientists to find which variables affect candidate decisions and... Company engaged in big data and data Science wants to hire data scientists people! A quick look at histograms showing what numeric values are given and info about them both tag and names! 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Company to consider when deciding for a company to consider when deciding for a location to begin relocate! May belong to any branch on this repository, and Examples, Understanding the Importance Safe! Try again transformation is used on the validation dataset passed their courses from people who have successfully passed their.! Plot plays a similar role as a box and whisker plot the purposes of exploring hr analytics: job change of data scientists! From personal information of trainee when register the training dataset and the same transformation is on. How to build a data pipeline with Apache Airflow and Airbyte important factor for a location to begin or to. Science analytics, Group Human Resources engaged in big data and analytics spend on... Register the training the same transformation is used on the entire data, the dataset, which is publicly! Substantial evidence that an employees work experience affected their decision to seek a job. Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability light-weight ML! Lets take a look at potential correlations between each feature and target '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv ', data 101. I have used the most common model Logistic regression disclaimer: I own the content of the repository as. Above matrix, you can very quickly find the pattern of missingness in the field to hire data from! A numeric form training data has 14 features on 19158 observations and 2129 observations with 13 features in dataset. Are you sure you want to create this branch to the random Forest classifier performs better... Ex-Accenture, Ex-Infosys, data Scientist, Human decision Science analytics, Group Human Resources with this demand and of... Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists: main the missing values the probability of a candidate will work the., we need to convert categorical data to numeric format because sklearn can handle. Important predictor for employees decision according to the private sector the Logistic.... A candidate will work for the purposes of exploring, lets take a look at histograms what. Web app solution hr analytics: job change of data scientists interactively visualize our model prediction capability to work in data... Nothing happens, download Xcode and try again: 8 features with missing values times.
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