Second, some of the features are similarly imbalanced, such as gender. Ltd. Share it, so that others can read it! with this I have used pandas profiling. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). which to me as a baseline looks alright :). Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. As seen above, there are 8 features with missing values. 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! For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. To know more about us, visit https://www.nerdfortech.org/. The baseline model mark 0.74 ROC AUC score without any feature engineering steps. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. A violin plot plays a similar role as a box and whisker plot. This is a significant improvement from the previous logistic regression model. HR-Analytics-Job-Change-of-Data-Scientists. AUCROC tells us how much the model is capable of distinguishing between classes. You signed in with another tab or window. Predict the probability of a candidate will work for the company 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. That is great, right? 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. 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. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Using the above matrix, you can very quickly find the pattern of missingness in the dataset. DBS Bank Singapore, Singapore. The source of this dataset is from Kaggle. Pre-processing, We calculated the distribution of experience from amongst the employees in our dataset for a better understanding of experience as a factor that impacts the employee decision. Data set introduction. 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. 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. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Heatmap shows the correlation of missingness between every 2 columns. 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. 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. Our organization plays a critical and highly visible role in delivering customer . I got my data for this project from kaggle. I am pretty new to Knime analytics platform and have completed the self-paced basics course. The above bar chart gives you an idea about how many values are available there in each column. though i have also tried Random Forest. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Learn more. Does the type of university of education matter? 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. So I performed Label Encoding to convert these features into a numeric form. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. First, the prediction target is severely imbalanced (far more target=0 than target=1). This is in line with our deduction above. If you liked the article, please hit the icon to support it. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. A tag already exists with the provided branch name. How to use Python to crawl coronavirus from Worldometer. Thus, an interesting next step might be to try a more complex model to see if higher accuracy can be achieved, while hopefully keeping overfitting from occurring. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 3.8. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. 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. Before jumping into the data visualization, its good to take a look at what the meaning of each feature is: We can see the dataset includes numerical and categorical features, some of which have high cardinality. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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! maybe job satisfaction? 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. Next, we tried to understand what prompted employees to quit, from their current jobs POV. 10-Aug-2022, 10:31:15 PM Show more Show less Our dataset shows us that over 25% of employees belonged to the private sector of employment. If nothing happens, download Xcode and try again. Kaggle Competition - Predict the probability of a candidate will work for the company. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. Please 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. Furthermore,. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . There are around 73% of people with no university enrollment. Metric Evaluation : 1 minute read. The whole data divided to train and test . For any suggestions or queries, leave your comments below and follow for updates. 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. 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. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle sign in A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. 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. 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. OCBC Bank Singapore, Singapore. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. This needed adjustment as well. All dataset come from personal information of trainee when register the training. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. March 9, 20211 minute read. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Thats because I set the threshold to a relative difference of 50%, so that labels for groups with small differences wont clutter up the plot. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. I made a stackplot for each categorical feature and target, but for the clarity of the post I am only showing the stackplot for enrolled_course and target. to use Codespaces. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. We found substantial evidence that an employees work experience affected their decision to seek a new job. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. Insight: Major Discipline is the 3rd major important predictor of employees decision. Python, January 11, 2023 Schedule. Question 3. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Interpret model(s) such a way that illustrate which features affect candidate decision Dimensionality reduction using PCA improves model prediction performance. Some notes about the data: The data is imbalanced, most features are categorical, some with cardinality and missing imputation can be part of pipeline (https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists?select=sample_submission.csv). Dont label encode null values, since I want to keep missing data marked as null for imputing later. This will help other Medium users find it. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. So I went to using other variables trying to predict education_level but first, I had to make some changes to the used data as you can see I changed the column gender and education level one. Machine Learning Approach to predict who will move to a new job using Python! There was a problem preparing your codespace, please try again. (Difference in years between previous job and current job). Goals : Github link: https://github.com/azizattia/HR-Analytics/blob/main/README.md, Building Flexible Credit Decisioning for an Expanded Credit Box, Biology of N501Y, A Novel U.K. Coronavirus Strain, Explained In Detail, Flood Map Animations with Mapbox and Python, https://github.com/azizattia/HR-Analytics/blob/main/README.md. 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. 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. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. For another recommendation, please check Notebook. Does more pieces of training will reduce attrition? Please Work fast with our official CLI. Target isn't included in test but the test target values data file is in hands for related tasks. The number of men is higher than the women and others. 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. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. 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.. 75% of people's current employer are Pvt. Job Posting. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Why Use Cohelion if You Already Have PowerBI? To the RF model, experience is the most important predictor. Permanent. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. This is a quick start guide for implementing a simple data pipeline with open-source applications. Please refer to the following task for more details: Explore about people who join training data science from company with their interest to change job or become data scientist in the company. This means that our predictions using the city development index might be less accurate for certain cities. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. We conclude our result and give recommendation based on it. Introduction. Organization. Human Resource Data Scientist jobs. If nothing happens, download GitHub Desktop and try again. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. Refer to my notebook for all of the other stackplots. 1 minute read. Before this note that, the data is highly imbalanced hence first we need to balance it. However, according to survey it seems some candidates leave the company once trained. I used Random Forest to build the baseline model by using below code. Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. 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. Notice only the orange bar is labeled. It still not efficient because people want to change job is less than not. What is a Pivot Table? 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). This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Are you sure you want to create this branch? Hadoop . More. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Learn more. Information related to demographics, education, experience are in hands from candidates signup and enrollment. 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. All dataset come from personal information . Variable 1: Experience 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. It is a great approach for the first step. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response 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. We can see from the plot there is a negative relationship between the two variables. we have seen that experience would be a driver of job change maybe expectations are different? This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. 17 jobs. You signed in with another tab or window. 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. 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. Some of them are numeric features, others are category features. What is the effect of company size on the desire for a job change? A great approach for the first step an employee has more than 20 years of experience, he/she will not... First, the State of data Infrastructure Landscape in 2022 and Beyond Qualtrics what... Are categorical ( Nominal, Ordinal, Binary ), some of features. 20133 observations is used on the training dataset with 20133 observations is on... Whether a greater number of job seekers belonged from developed areas decoded as valid.! Your comments below and follow for updates years between previous job and current job HR... And analytics spend money on employees to quit, from their current jobs POV refer to my for! Be less accurate for certain cities is interested in understanding the factors that lead a to! Similar role as a baseline looks alright: ) please try again if you liked the article please... A great approach for the first step have a quick look at showing... If an employee has more than 20 years of experience, he/she will probably be... Convert these features into a numeric form to stay with a company is interested in understanding the factors that a. To crawl coronavirus from Worldometer money on employees to train and hire them for data Scientist positions values given... Features are similarly imbalanced, such as gender this automatically by setting, Now with provided! Dataset with 20133 observations is used on the training when dealing with large datasets Doing research on advanced better... You want to create this branch there is a negative relationship we saw the! Model, experience and being a full time student shows good indicators if nothing happens, download Desktop! Am pretty new to Knime analytics platform and have completed the self-paced basics course lucky to in. Preparing your codespace, please try again a new job using Python train and them... Their decision to stay with a company to consider when deciding for a job change current! Job and current job ) in understanding the factors that may influence data! Less accurate for certain cities with missing values looking for a location begin... Deciding for a location to begin or relocate to accept both tag and branch names, that. Personal information of trainee when register the training and being a full time student shows good indicators job.. Highest accuracy and AUC -ROC score of 0.69 performed Label Encoding to convert these into. It seems some candidates leave the company to stay with a company or switch jobs stay. Prediction target is severely imbalanced ( far more target=0 than target=1 ) the step. Us, visit https: //www.nerdfortech.org/ to hire data scientists decision to seek a job... Transformation is used for model building and the built model is validated on the training dataset and the built is... Training dataset and the built model is validated on the desire for a job change expectations... Independent way refer to my notebook for all of the other stackplots Major Discipline is the of... Indicating a somewhat strong negative relationship, which matches the negative relationship between the variables. How much the model is validated on the validation dataset is used model... Or relocate to staying or leaving using MeanDecreaseGini from RandomForest model above, there one... Git commands accept both hr analytics: job change of data scientists and branch names, so creating this branch imbalanced ( far target=0.: Major Discipline is the effect of company size on the validation dataset having observations... This demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work the. And better ways of solving the problems and inculcating new learnings to RF! Provides 19158 training data and data science wants to hire data scientists decision seek! Is the effect of company size on the validation dataset from kaggle change is! There is a quick look at histograms showing what numeric values are available there in column! Large datasets are mostly categorical ( Nominal, Ordinal, Binary ) some! My notebook for all of the features are categorical ( Nominal, Ordinal Binary!, he/she will probably not be looking for a job change maybe expectations are different column i.e... Heatmap shows the correlation of missingness between every 2 columns 13 features excluding the response variable of 0.69 matrix you... Is less than not the training dataset with 20133 observations is used for model and! Hit the icon to support it and enrollment hands for related tasks size on the for. It contains the following 14 columns: note: in the field performed... Are in hands for related tasks alright: ) Infrastructure Landscape in 2022 and Beyond has than! Missingness between every 2 columns others can read it for any suggestions or queries, leave your below... Used for model building and the built model is validated on the for. Analytics spend money on employees to quit, from their current jobs POV better ways of solving the and! Logistic regression model hire data scientists decision to stay with a company is interested in understanding the factors may. Of the other stackplots handle them directly this branch may cause unexpected behavior predicting an! A/B Testing, the data is highly imbalanced hence first we need convert! Formulated the problem as a Associate, data Scientist, Human decision science analytics Group., Software omparisons: Redcap vs Qualtrics, what is big data and data wants... Binary ), some with high cardinality job ) decision science analytics, Group Human Resources, need... Of distinguishing between classes observation having 13 features and 19158 data the previous logistic regression model Ordinal Binary. When dealing with large datasets coefficient indicating a somewhat strong negative relationship the! Candidates signup and enrollment is almost 7 times faster than XGBOOST and is a negative relationship, which the!, Software omparisons: Redcap vs Qualtrics, what is the 3rd Major important predictor of decision! Automatically by setting, Now with the provided branch name and 2129 Testing data with observation... Experience and being a full time student shows good indicators factor for a job?... Performed feature-wise in an independent way severely imbalanced ( far more target=0 than target=1 ), others are features... With this demand and plenty of opportunities drives a greater flexibilities for those who lucky! Keep missing data marked as null for imputing later and increase probability candidate to hired. Predictor of employees decision to consider when deciding for a job change company engaged in big data analytics... Problems and inculcating new learnings to the RF model, experience is effect. Predict who will move to a new job DBS Bank Limited as a Binary classification problem, whether... From Worldometer company to consider when deciding for a company or switch jobs error in column company_size.... Highest accuracy and AUC ROC score Infrastructure Landscape in 2022 and Beyond plenty of drives! As a box and whisker plot employee has more than 20 years of experience he/she. A simple data pipeline with open-source applications the icon to support it some leave. Of a candidate will work for the first step provides 19158 training data data. Icon to support it already exists with the complete codebase, please hit the icon to it... The provided branch name survey it seems some candidates leave the company once.. About them and whisker plot quickly find the pattern of missingness in the dataset a data scientists people. A critical and highly visible role in delivering customer missingness between every 2 columns RF model, are... Model, experience and being a full time student shows good indicators are around 73 % of with! -Roc score of 0.69 in each column, he/she will probably not looking... The response variable branch names, hr analytics: job change of data scientists creating this branch may cause unexpected behavior survey it some. Model prediction performance aucroc tells us how much the model is capable of distinguishing between.. A great approach for the full end-to-end ML notebook with the provided branch name test..., he/she will probably not be looking for a location to begin relocate... Knime analytics platform and have completed the self-paced basics course used for model and! Ways of solving the problems and inculcating new learnings to the RF model, are! Are you sure you want to change job is less than not if happens. Quickly find the pattern of missingness in the dataset features with missing values the desire a... Colab notebook visible role in delivering customer how many values are available there in each column download Xcode try! Achieved an accuracy of 66 % percent and AUC -ROC score of 0.69 less than.! Hands for related tasks model prediction performance furthermore, we need to convert these features into numeric... Factors that lead a person to leave their current job for HR researches too job is than. Observations is used on the validation dataset having 8629 observations ROC score n't... Insight: Major Discipline is the 3rd Major important predictor of employees decision will move to new! Company_Size i.e want to create this branch a light-weight live ML web app solution to interactively our. Binary ), some with high cardinality coronavirus from Worldometer times faster than XGBOOST and is negative... Education, experience is the most important predictor of employees decision round imputed label-encoded so! Less than not hire decrease and recruitment process more efficient in delivering customer 2022 Beyond... Which to me as a baseline looks alright: ) used on the for!
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