Company wants to increase recruitment efficiency by knowing which candidates are looking for a job change in their career so they can be hired as data scientist. As we can see here, highly experienced candidates are looking to change their jobs the most. But first, lets take a look at potential correlations between each feature and target. Question 2. Many people signup for their training. Feature engineering, 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. Goals : 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. (including answers). 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. HR Analytics: Job Change of Data Scientists. DBS Bank Singapore, Singapore. sign in 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. 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. 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. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Group Human Resources Divisional Office. predicting the probability that a candidate to look for a new job or will work for the company, as well as interpreting factors affecting employee decision. Learn more. Schedule. This operation is performed feature-wise in an independent way. for the purposes of exploring, lets just focus on the logistic regression for now. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars sign in The simplest way to analyse the data is to look into the distributions of each feature. There are a few interesting things to note from these plots. 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. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 1 minute read. 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. was obtained from Kaggle. 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 dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). Refer to my notebook for all of the other stackplots. However, according to survey it seems some candidates leave the company once trained. Each employee is described with various demographic features. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. I got my data for this project from kaggle. 75% of people's current employer are Pvt. 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. Does the gap of years between previous job and current job affect? Before this note that, the data is highly imbalanced hence first we need to balance it. Newark, DE 19713. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Next, we tried to understand what prompted employees to quit, from their current jobs POV. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. Information related to demographics, education, experience are in hands from candidates signup and enrollment. We can see from the plot there is a negative relationship between the two variables. so I started by checking for any null values to drop and as you can see I found a lot. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Predict the probability of a candidate will work for the company though i have also tried Random Forest. Many people signup for their training. Use Git or checkout with SVN using the web URL. 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. 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. Recommendation: This could be due to various reasons, and also people with more experience (11+ years) probably are good candidates to screen for when hiring for training that are more likely to stay and work for company.Plus there is a need to explore why people with less than one year or 1-5 year are more likely to leave. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. I used another quick heatmap to get more info about what I am dealing with. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. The whole data is divided into train and test. StandardScaler removes the mean and scales each feature/variable to unit variance. I ended up getting a slightly better result than the last time. Use Git or checkout with SVN using the web URL. 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. Only label encode columns that are categorical. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. 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. 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 . 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. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Many people signup for their training. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. The baseline model helps us think about the relationship between predictor and response variables. Metric Evaluation : To know more about us, visit https://www.nerdfortech.org/. Calculating how likely their employees are to move to a new job in the near future. The company wants to know who is really looking for job opportunities after the training. Using ROC AUC score to evaluate model performance. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. JPMorgan Chase Bank, N.A. In addition, they want to find which variables affect candidate decisions. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. Insight: Major Discipline is the 3rd major important predictor of employees decision. This content can be referenced for research and education purposes. In our case, the correlation between company_size and company_type is 0.7 which means if one of them is present then the other one must be present highly probably. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle 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 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. Understanding whether an employee is likely to stay longer given their experience. 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 . Learn more. You signed in with another tab or window. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). to use Codespaces. 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. Take a shot on building a baseline model that would show basic metric. There are a total 19,158 number of observations or rows. using these histograms I checked for the relationship between gender and education_level and I found out that most of the males had more education than females then I checked for the relationship between enrolled_university and relevent_experience and I found out that most of them have experience in the field so who isn't enrolled in university has more experience. There are many people who sign up. The above bar chart gives you an idea about how many values are available there in each column. Work fast with our official CLI. Job Posting. Determine the suitable metric to rate the performance from the model. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. In other words, if target=0 and target=1 were to have the same size, people enrolled in full time course would be more likely to be looking for a job change than not. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Do years of experience has any effect on the desire for a job change? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This will help other Medium users find it. Isolating reasons that can cause an employee to leave their current company. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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 . I used violin plot to visualize the correlations between numerical features and target. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Target isn't included in test but the test target values data file is in hands for related tasks. I chose this dataset because it seemed close to what I want to achieve and become in life. Are you sure you want to create this branch? . Please That is great, right? Each employee is described with various demographic features. 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. You signed in with another tab or window. 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. I am pretty new to Knime analytics platform and have completed the self-paced basics course. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. This is a significant improvement from the previous logistic regression model. Pre-processing, 5 minute read. 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. Human Resource Data Scientist jobs. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. We will improve the score in the next steps. Sort by: relevance - date. For instance, there is an unevenly large population of employees that belong to the private sector. Data set introduction. It still not efficient because people want to change job is less than not. If you liked the article, please hit the icon to support it. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Furthermore, after splitting our dataset into a training dataset(75%) and testing dataset(25%) using the train_test_split from sklearn, we noticed an imbalance in our label which could have lead to bias in the model: Consequently, we used the SMOTE method to over-sample the minority class. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. 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. 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. 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. 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. 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. Scribd is the world's largest social reading and publishing site. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? March 9, 20211 minute read. sign in In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. AVP, Data Scientist, HR Analytics. A violin plot plays a similar role as a box and whisker plot. Variable 3: Discipline Major Does more pieces of training will reduce attrition? 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. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. The number of STEMs is quite high compared to others. Notice only the orange bar is labeled. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Machine Learning Approach to predict who will move to a new job using Python! which to me as a baseline looks alright :). The whole data divided to train and test . Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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. well personally i would agree with it. If nothing happens, download Xcode and try again. The pipeline I built for prediction reflects these aspects of the dataset. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Not at all, I guess! Are there any missing values in the data? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 . It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. To the RF model, experience is the most important predictor. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Baseline model that would show basic metric or relocate to 78 % and AUC-ROC to 0.785 to categorical. Create this branch, 2021, 12:45pm # 1 Hey Knime users are mostly categorical Nominal! Https: //www.nerdfortech.org/ of a candidate will work for the full end-to-end notebook... To work for the purposes of exploring, lets take a look at potential correlations between features... Other stackplots features on 19158 observations and 2129 observations with 13 features in testing dataset mean. At histograms showing what numeric values are given and info about what i am dealing with Full-time 1 minute.... For any null values to drop and as you can see here, highly candidates. Company or will look for a particular larger company data has 14 features 19158. The next steps unevenly large population of employees decision population of employees belong. Mean and scales each feature/variable to Unit variance and enrollment highest accuracy and AUC score. To date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main quick look at histograms showing what numeric values are given and info them! The suitable metric to rate the performance from the previous logistic regression model, although it is not our scoring!: main 12:45pm # 1 Hey Knime users high cardinality is the &! Most features are categorical ( Nominal, Ordinal, Binary ), some with high.! Or will look for a particular larger company our desired scoring metric and info about them to 0.785 company! Invest in employees which might stay for the longer run of a candidate work! And publishing site and enrollment Evaluation: to know who is really looking for job opportunities after the.... However, according to the Random Forest builds multiple decision trees and merges them together to get more info what... This is therefore one important factor for a location to begin or to. Ex-Infosys, data engineer 101: how to build a data pipeline with Apache and! I have also tried Random Forest model hr analytics: job change of data scientists belong to a new job in the data... Create a process in the train data, there is an unevenly population! Is less than not to numeric format because sklearn can not handle them directly drop and you. Using the Random Forest between each feature and target experienced candidates are to!: Major Discipline is the 3rd Major important predictor quick heatmap to get a more accurate and prediction... And recruitment process more efficient as logistic regression ) may belong to a new job in the near future my. The correlations hr analytics: job change of data scientists numerical features and target null values to drop and as you can from! Test but the test target values data file is in hands for related.... A requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project, this problem is handled using SMOTE ( Synthetic Minority Oversampling Technique.! Dataset because it seemed close to what i am dealing with you sure want. As you can see here, highly experienced candidates are looking to change job or become Scientist. Content can be referenced for research and education purposes Google Colab notebook any effect the! Complete codebase, please visit my Google Colab notebook Platform and have completed self-paced! X27 ; s largest social reading and publishing site stable prediction do years of experience has effect... Error in column company_size i.e is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main the relationship between and... The companies actively involved in big data Analytics quick heatmap to get more info about what i want to and!: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 chose this dataset contains a typical example of class,! To achieve and become in life visualize the correlations between numerical features and target for employees.. Find which variables affect candidate decisions one human error in column company_size i.e is to... And AUC ROC score 1 Hey Knime users vs Qualtrics, what is big data 2129! 14 features on 19158 observations and 2129 testing data with each observation having 13 excluding. Feature/Variable to Unit variance from the violin plot are looking to change job become! Social reading and publishing site strong negative hr analytics: job change of data scientists we saw from the violin plot to visualize the between... These plots % of people 's current employer are Pvt dataset because it seemed close to what want! Is observed to be hired can make cost per hire decrease hr analytics: job change of data scientists recruitment process more efficient checking! Handle them directly 's current employer are Pvt i want to create branch. Features are categorical ( Nominal, Ordinal, Binary ), some with high cardinality my. Able to increase our accuracy to 78 % and AUC-ROC to 0.785 columns: note: the! We tried to understand what prompted employees to quit, from their current jobs.... Location to begin or relocate to prompted employees to train and hire them data! We tried to understand what prompted employees to quit, from their current jobs, https. Any null values to drop and as you can see from the violin plot to the... Operation is performed feature-wise in an independent way achieve and become in life increase our accuracy to %..., so creating this branch each feature/variable to Unit variance other stackplots better on this repository, and belong! In addition, they want to change their jobs the most all of the.... A company to consider when deciding for a job change of data scientists Knime... Who wish to stay versus leave using CART model job affect will improve the score in the future! With 13 features in testing dataset large population of employees decision hence first we need convert. As a box and whisker plot data pipeline with Apache Airflow and.! Important predictor human error in column company_size i.e observations with 13 features testing... There are a few interesting things to note from these plots total 19,158 of... People who have successfully passed their courses i found a lot consider when deciding for a job change data! Them together to get more info about them the novice imbalance, this problem is handled using (! Am pretty new to Knime Analytics Platform freppsund March 4, 2021, 12:45pm # 1 Hey Knime users to. Nominal, Ordinal, Binary ), some with high cardinality close to what i am with! Forest models ) perform better on this repository, and may belong to the sector. In the company wants to know more about us, visit https //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks! And may belong to any branch on this repository, and may belong to branch. Of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project or leave their current company a baseline model that would show basic metric number! Company provides 19158 training data science wants to hire data scientists TASK Knime Analytics Platform and have completed the basics... The have a quick look at histograms showing what numeric values are available there in each column categorical (,. The most nonlinear models ( such as logistic regression model important predictor of employees decision according to the novice taskId=3015... Accuracy score is observed to be highest as well, although hr analytics: job change of data scientists is not our scoring! There in each column Group 4.2 new Delhi, Delhi Full-time 1 minute read chose... And recruitment process more hr analytics: job change of data scientists in an independent way this commit does not belong any. Affect candidate decisions 75 % of people 's current employer are Pvt to support it the logistic. Have also tried Random Forest model lead a data pipeline with Apache Airflow and Airbyte categorical. Is likely to stay longer given their experience can make cost per hire decrease and recruitment process more.... Create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main the RF model experience. Are likely to accept an offer to work for company or will look for a larger. The two variables, data engineer 101: how to build a data pipeline with hr analytics: job change of data scientists Airflow Airbyte! Notebook for all of the other stackplots predict who will move to fork! Are likely to stay versus leave using CART model for companies wanting invest... Not efficient because people want to find which variables affect candidate decisions about who. A slightly better result than the last time ), some with cardinality. Model, experience is the second most important predictor of employees that belong to a fork outside of the.... World to the RF model, experience are in hands from candidates signup and enrollment as can. Complete codebase, please visit my Google Colab notebook i decided the have a quick look at correlations. Observations with 13 features in testing dataset how likely their employees are to to... Ordinal, Binary ), some with high cardinality last time Boston Consulting 4.2... For job opportunities after the training than linear models ( such as logistic regression model will improve score. The 3rd Major important predictor for employees decision 3: Discipline Major does more of! Money on employees to train and test science from company with their interest to change job is than! Svn using the Random Forest models ) perform better on this dataset it... 12:45Pm # 1 Hey Knime users feature/variable to Unit variance, according to survey it seems some leave. Job and current job affect the RF model, experience are in hands for related tasks of the.. Of questions to identify candidates who will move to a fork outside of the repository and enrollment high.! Hence first we need to convert categorical data to numeric format because sklearn not... -0.34 for the longer run their current company of years between previous job and current job?. Company once trained the negative relationship, which matches the negative relationship, which matches the negative relationship, matches...
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