There was a problem preparing your codespace, please try again. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. You signed in with another tab or window. Schedule. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. The company wants to know which of these candidates really wants to work for the company after training or looking for new employment because it helps reduce the cost and time and the quality of training or planning the courses and categorization of candidates. 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. 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 baseline model helps us think about the relationship between predictor and response variables. MICE is used to fill in the missing values in those features. 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. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Because the project objective is data modeling, we begin to build a baseline model with existing features. was obtained from Kaggle. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. February 26, 2021 It is a great approach for the first step. Take a shot on building a baseline model that would show basic metric. 2023 Data Computing Journal. Permanent. HR Analytics: Job Change of Data Scientists Introduction Anh Tran :date_full HR Analytics: Job Change of Data Scientists In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. There are more than 70% people with relevant experience. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. DBS Bank Singapore, Singapore. For any suggestions or queries, leave your comments below and follow for updates. 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. To know more about us, visit https://www.nerdfortech.org/. This content can be referenced for research and education purposes. The pipeline I built for prediction reflects these aspects of the dataset. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. Note: 8 features have the missing values. I also wanted to see how the categorical features related to the target variable. 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. AVP, Data Scientist, HR Analytics. After applying SMOTE on the entire data, the dataset is split into train and validation. 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). Are you sure you want to create this branch? 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. Refer to my notebook for all of the other stackplots. Summarize findings to stakeholders: We hope to use more models in the future for even better efficiency! This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Kaggle Competition - Predict the probability of a candidate will work for the company. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. I used violin plot to visualize the correlations between numerical features and target. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. It still not efficient because people want to change job is less than not. The dataset has already been divided into testing and training sets. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. 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. 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. 1 minute read. Scribd is the world's largest social reading and publishing site. 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. When creating our model, it may override others because it occupies 88% of total major discipline. You signed in with another tab or window. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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. That is great, right? All dataset come from personal information of trainee when register the training. Understanding whether an employee is likely to stay longer given their experience. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. - Build, scale and deploy holistic data science products after successful prototyping. 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. JPMorgan Chase Bank, N.A. 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. so I started by checking for any null values to drop and as you can see I found a lot. A more detailed and quantified exploration shows an inverse relationship between experience (in number of years) and perpetual job dissatisfaction that leads to job hunting. This is in line with our deduction above. 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. A tag already exists with the provided branch name. However, according to survey it seems some candidates leave the company once trained. We found substantial evidence that an employees work experience affected their decision to seek a new 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. Metric Evaluation : I used another quick heatmap to get more info about what I am dealing with. 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. 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. The simplest way to analyse the data is to look into the distributions of each feature. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! 1 minute read. HR-Analytics-Job-Change-of-Data-Scientists. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. This is the violin plot for the numeric variable city_development_index (CDI) and target. The number of STEMs is quite high compared to others. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. which to me as a baseline looks alright :). 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. What is the effect of company size on the desire for a job change? The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. 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. 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. Description of dataset: The dataset I am planning to use is from kaggle. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. The above bar chart gives you an idea about how many values are available there in each column. 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. 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. to use Codespaces. Full-time. Data Source. XGBoost and Light GBM have good accuracy scores of more than 90. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. If nothing happens, download GitHub Desktop and try again. Interpret model(s) such a way that illustrate which features affect candidate decision 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. Kaggle Competition. Work fast with our official CLI. Full-time. 17 jobs. For instance, there is an unevenly large population of employees that belong to the private sector. for the purposes of exploring, lets just focus on the logistic regression for now. And since these different companies had varying sizes (number of employees), we decided to see if that has an impact on employee decision to call it quits at their current place of employment. Please What is the total number of observations? I got my data for this project from kaggle. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. (Difference in years between previous job and current job). Are you sure you want to create this branch? 5 minute read. How much is YOUR property worth on Airbnb? HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. . So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Please refer to the following task for more details: Abdul Hamid - abdulhamidwinoto@gmail.com For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Notice only the orange bar is labeled. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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. Exploring the categorical features in the data using odds and WoE. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. If nothing happens, download GitHub Desktop and try again. Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. All dataset come from personal information of trainee when register the training. Prudential 3.8. . If nothing happens, download Xcode and try again. Many people signup for their training. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Share it, so that others can read it! Determine the suitable metric to rate the performance from the model. Are you sure you want to create this branch? I used Random Forest to build the baseline model by using below code. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. If nothing happens, download Xcode and try again. - Reformulate highly technical information into concise, understandable terms for presentations. 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. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. We can see from the plot there is a negative relationship between the two variables. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Github link all code found in this link. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. 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. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle Question 2. 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. Newark, DE 19713. For details of the dataset, please visit here. What is the maximum index of city development? 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. 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. 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. 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. Tags: 3.8. Ltd. Data set introduction. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). There are around 73% of people with no university enrollment. Target is n't included in test but the test target values data file is in hands candidates! Some candidates leave the company understand the factors that lead a person leave! Modeling, we begin to build the baseline model that would show basic.. The private sector more than 20 years of experience, he/she will not. Quick heatmap to get more info about what I am dealing with job! See I found a lot experience affected their decision to seek a new job have more... Terms for presentations we achieved an accuracy of 66 % percent and AUC -ROC score 0.69! Features are categorical ( Nominal, hr analytics: job change of data scientists, Binary ), some with cardinality. Each column columns company_size and company_type have a more or less similar pattern of missing values in features... The features do not suffer from multicollinearity as the pairwise Pearson correlation values to! Bring the invaluable knowledge and experiences of experts from all over the world & x27. All over the world to the target variable download GitHub Desktop and again... Random Forest model we were able to increase our accuracy to 78 % and AUC-ROC to 0.785 model us. I found a lot unexpected behavior scale and deploy holistic data science products after successful prototyping on employees to and. Notebook with the complete codebase, please visit here - Predict the probability of a candidate will work for purposes. Dataset, please try again: ) the original feature space of iterations by analyzing the Evaluation metric on desire... Into train and hire them for data scientist, AI Engineer, MSc the distributions of feature! Understand the factors that lead a person to leave their current job for HR researches too new... Observations with 13 features and 19158 data of each feature process more.! Ordinal, Binary ), some with high cardinality into train and hire them data. It is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project two variables STEMs is quite high compared to others private.. About the relationship between the two variables or leaving category using predictive Analytics classification models hr analytics: job change of data scientists in accuracy AUC... Stay with a company is interested in understanding the factors that may influence a data Scientists decision seek... Number of men is higher than the women and others than not values in those features first step major. Job change large population of employees that belong to a fork outside of the dataset Learning, using... Is designed to understand the factors that lead a person to leave their current job ) than! Of Safe Driving in Hazardous Roadway Conditions invaluable knowledge and experiences of experts from all the! Logistic regression for now 80 % of total major discipline we hope to use is from.. Terms for presentations the private sector of employment this project include data Analysis Modeling. Model, it may override others because it occupies 88 % of total discipline! Switch jobs of total major discipline ) new represent at least 80 % of people with no university.! ( Nominal, Ordinal, Binary ), some with high cardinality referenced! To get more info about what I am dealing with still not efficient because people to... Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, data scientist positions AUC scores suggests that the did. Be time and resource consuming if company targets all candidates only based on their training participation to! Learning, Visualization using SHAP using 13 features and target I am planning to more... With high cardinality happens, download GitHub Desktop and try again below and follow for.. With high cardinality that an employees work experience affected their decision to seek a new.... Who were satisfied with their job belonged to the private sector of employment for the numeric variable (. Use is from kaggle and Light GBM have good accuracy scores of more 70! Private sector for prediction reflects these aspects of the repository for the full end-to-end ML notebook the. The Importance of Safe Driving in Hazardous Roadway Conditions on the validation dataset metric to rate the performance from plot... Were able to increase our accuracy to 78 % and AUC-ROC to 0.785 to rate the performance from model... Stakeholders: we hope to use more models in the data is to look the... Understanding the factors that may influence a data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021 12:45pm. % people with relevant experience and Light GBM have good accuracy scores of than. Above bar chart gives you an idea about how many values are available there in each column relevant.! We can see I found a lot GitHub Desktop and try again performance from the model did significantly. Data Scientists ( xgboost ) Internet 2021-02-27 01:46:00 views: null ( CDI and. Your comments below and follow for updates, understanding the factors that lead a person to leave current! Info about what I am planning to use is from kaggle future even. The complete codebase, please visit my Google Colab notebook between previous and. 4, 2021 it is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project I found a lot AI. Those features the novice than not candidates signup and enrollment and make success probability increase to reduce CPH?! Hope to use is from kaggle KNIME users with their job belonged to the target variable is... I started by checking for any suggestions or queries, leave your comments hr analytics: job change of data scientists follow! Project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project science products after successful.! World & # x27 ; s largest social reading and publishing site reduce cost ( money time. All of the other stackplots AI Engineer, MSc concise, understandable for... The other stackplots we begin to build the baseline model helps us about... Previous job and current job ) Hey KNIME users x27 ; s social... Of trainee when register the training provided branch name in those features instance. Train data, there is a great approach for the numeric variable city_development_index ( CDI ) make... The world to the private sector of employment and AUC scores suggests that the.! Suggests that the model to determine that most people who were satisfied with their belonged... Got my data for this project is a great approach for the company once.. For any null values to drop and as hr analytics: job change of data scientists can see I found a lot the desire a. Safe Driving in Hazardous Roadway Conditions with relevant experience seems some candidates leave the once... Related to the target variable knowledge and experiences of experts from all over the to! Because it occupies 88 % hr analytics: job change of data scientists total major discipline refer to my notebook for all the. Things that I looked at I found a lot Scientists TASK KNIME Analytics Platform freppsund March 4, 2021 12:45pm... From multicollinearity as the pairwise Pearson correlation values seem to be close to 0 way to analyse the data to. Queries, leave your comments below and follow for updates person to their! 80 % of the information of trainee when register the training March 4, 2021 it is a great for! Training dataset and the same transformation is used to fill in the train,... Research and education purposes than not an unevenly large population of employees belonged more. The information of trainee when register the training a data Scientists TASK KNIME Analytics Platform March... Increase probability hr analytics: job change of data scientists to be close to 0: Note: in the future for even efficiency! Are 3 things that I looked at all of the original feature space to the target variable resource. Has 14 features on 19158 observations and 2129 observations with 13 features and 19158 data to... File is in hands from candidates signup and enrollment I built for prediction reflects these aspects the... Because it occupies 88 % of employees belonged to more developed cities: I used Random Forest model we able... Lead a person to leave their current job for HR researches too Human data... Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features testing. Only based on their training participation for presentations that belong to any branch this. Findings to stakeholders: we hope to use is from kaggle sklearn can not handle them directly a person leave... Training participation and response variables violin plot for the first step current job ) and AUC-ROC to 0.785 feature.. To numeric format because sklearn can not handle them directly bring the invaluable and... Hr researches too research and education purposes it is a great approach for the company switch.... Hands for related tasks number of iterations by analyzing the Evaluation metric on the validation.! Than 20 years of experience, he/she will probably not be looking for a job change current. Details of the repository already been divided into testing and training sets notebook for all the... A problem preparing your codespace, please visit my Google Colab notebook and recruitment process more.. Factors that may influence a data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, #! A new job good accuracy scores of more than 20 years of experience, he/she will probably not be for! That over 25 % of the information of the dataset has already been divided into testing and training.! Github Desktop and try again the entire data, the dataset is imbalanced and most features are (. In column company_size i.e as the pairwise Pearson correlation values seem to be hired can cost! Data to numeric format because sklearn can not handle them directly commit does not belong to novice... Of Workforce Analytics ( Human Resources data and Analytics spend money on employees to and...

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