This dataset is designed to understand the factors that lead a person to leave current job for HR researches too and involves using model (s) to predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision. As seen above, there are 8 features with missing values. Apply on company website AVP, Data Scientist, HR Analytics . Machine Learning Approach to predict who will move to a new job using Python! Director, Data Scientist - HR/People Analytics. Do years of experience has any effect on the desire for a job change? Goals : well personally i would agree with it. Each employee is described with various demographic features. so I started by checking for any null values to drop and as you can see I found a lot. We found substantial evidence that an employees work experience affected their decision to seek a new job. 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. There are many people who sign up. We conclude our result and give recommendation based on it. Calculating how likely their employees are to move to a new job in the near future. 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. However, according to survey it seems some candidates leave the company once trained. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Predict the probability of a candidate will work for the company In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? 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. Job Change of Data Scientists Using Raw, Encode, and PCA Data; by M Aji Pangestu; Last updated almost 2 years ago Hide Comments (-) Share Hide Toolbars 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. This Kaggle competition is designed to understand the factors that lead a person to leave their current job for HR researches too. 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. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. 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. Tags: A violin plot plays a similar role as a box and whisker plot. To the RF model, experience is the most important predictor. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. Position: Director, Data Scientist - HR/People Analytics<br>Job Classification:<br><br>Technology - Data Analytics & Management<br><br>HR Data Science Director, Chief Data Office<br><br>Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. The above bar chart gives you an idea about how many values are available there in each column. Job Posting. 2023 Data Computing Journal. (Difference in years between previous job and current job). Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). I also wanted to see how the categorical features related to the target variable. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. 3.8. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Metric Evaluation : Refresh the page, check Medium 's site status, or. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. DBS Bank Singapore, Singapore. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 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. Variable 1: Experience A not so technical look at Big Data, Solving Data Science ProblemsSeattle Airbnb Data, Healthcare Clearinghouse Companies Win by Optimizing Data Integration, Visualizing the analytics of chupacabras story production, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. 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. If nothing happens, download Xcode and try again. Target isn't included in test but the test target values data file is in hands for related tasks. March 9, 20211 minute read. OCBC Bank Singapore, Singapore. Determine the suitable metric to rate the performance from the model. Organization. Interpret model(s) such a way that illustrate which features affect candidate decision I also used the corr() function to calculate the correlation coefficient between city_development_index and target. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. How to use Python to crawl coronavirus from Worldometer. We believed this might help us understand more why an employee would seek another job. 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 . 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . 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 . Use Git or checkout with SVN using the web URL. 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. JPMorgan Chase Bank, N.A. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. We will improve the score in the next steps. On the basis of the characteristics of the employees the HR of the want to understand the factors affecting the decision of an employee for staying or leaving the current job. The city development index is a significant feature in distinguishing the target. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Does more pieces of training will reduce attrition? For any suggestions or queries, leave your comments below and follow for updates. Target isn't included in test but the test target values data file is in hands for related tasks. we have seen that experience would be a driver of job change maybe expectations are different? Not at all, I guess! The pipeline I built for prediction reflects these aspects of the dataset. It is a great approach for the first step. 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. sign in AVP, Data Scientist, HR Analytics. Learn more. Newark, DE 19713. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. NFT is an Educational Media House. Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. In 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. March 9, 2021 Use Git or checkout with SVN using the web URL. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. 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. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Github link all code found in this link. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Abdul Hamid - abdulhamidwinoto@gmail.com 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. - Build, scale and deploy holistic data science products after successful prototyping. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. I am pretty new to Knime analytics platform and have completed the self-paced basics course. In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. I do not own the dataset, which is available publicly on Kaggle. 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. Are you sure you want to create this branch? HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Work fast with our official CLI. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Share it, so that others can read it! 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! Your role. Job. Some of them are numeric features, others are category features. If nothing happens, download GitHub Desktop and try again. There was a problem preparing your codespace, please try again. Since SMOTENC used for data augmentation accepts non-label encoded data, I need to save the fit label encoders to use for decoding categories after KNN imputation. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Second, some of the features are similarly imbalanced, such as gender. More. There was a problem preparing your codespace, please try again. In addition, they want to find which variables affect candidate decisions. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. There are around 73% of people with no university enrollment. 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. These are the 4 most important features of our model. Isolating reasons that can cause an employee to leave their current company. 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. You signed in with another tab or window. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. There are more than 70% people with relevant experience. 17 jobs. Kaggle Competition. Ltd. Permanent. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Understanding whether an employee is likely to stay longer given their experience. Please refer to the following task for more details: The whole data divided to train and test . This is the violin plot for the numeric variable city_development_index (CDI) and target. All dataset come from personal information . This is a quick start guide for implementing a simple data pipeline with open-source applications. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Information related to demographics, education, experience is in hands from candidates signup and enrollment. I ended up getting a slightly better result than the last time. Machine Learning, Many people signup for their training. March 2, 2021 Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. For details of the dataset, please visit here. 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. What is a Pivot Table? Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. Question 1. Feature engineering, 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Using ROC AUC score to evaluate model performance. Senior Unit Manager BFL, Ex-Accenture, Ex-Infosys, Data Scientist, AI Engineer, MSc. Kaggle Competition - Predict the probability of a candidate will work for the company. This operation is performed feature-wise in an independent way. 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 is a significant improvement from the previous logistic regression model. Therefore if an organization want to try to keep an employee then it might be a good idea to have a balance of candidates with other disciplines along with STEM. 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. There was a problem preparing your codespace, please try again. If nothing happens, download Xcode and try again. You signed in with another tab or window. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars which to me as a baseline looks alright :). Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Human Resources. 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. In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. Introduction. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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. Features, 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 employer's company, lastnewjob: Difference in years between previous job and current job, target: 0 Not looking for job change, 1 Looking for a job change, Inspiration Python, January 11, 2023 Following models are built and evaluated. Statistics SPPU. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. 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. 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. to use Codespaces. Take a shot on building a baseline model that would show basic metric. Please HR Analytics: Job Change of Data Scientists. Heatmap shows the correlation of missingness between every 2 columns. Does the gap of years between previous job and current job affect? for the purposes of exploring, lets just focus on the logistic regression for now. Are there any missing values in the data? 1 minute read. I used Random Forest to build the baseline model by using below code. 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. Are you sure you want to create this branch? 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. 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. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. A tag already exists with the provided branch name. 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. The baseline model helps us think about the relationship between predictor and response variables. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Data Source. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. with this I have used pandas profiling. 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. Context and Content. 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. 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. - Reformulate highly technical information into concise, understandable terms for presentations. to use Codespaces. A tag already exists with the provided branch name. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Many people signup for their training. 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. 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). For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Learn more. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. I got my data for this project from kaggle. Human Resource Data Scientist jobs. 5 minute read. 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. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. The stackplot shows groups as percentages of each target label, rather than as raw counts. For another recommendation, please check Notebook. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. 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 dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Light GBM is almost 7 times faster than XGBOOST and is a much better approach when dealing with large datasets. All dataset come from personal information of trainee when register the training. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. Agatha Putri Algustie - agthaptri@gmail.com. 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. There are a total 19,158 number of observations or rows. as a very basic approach in modelling, I have used the most common model Logistic regression. Dimensionality reduction using PCA improves model prediction performance. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. to use Codespaces. In column company_size i.e be time and resource consuming if company targets all candidates only based on it observations 2129... At 372, i ran k-fold switch job and response variables crawl from! % of people with relevant experience probability increase to reduce CPH many signup! Classification models for this project is a factor with a company engaged in big data and Analytics money., https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there are 3 things that looked. Analytics platform and have completed the self-paced basics course ownership of my analysis, and full details including of! Last time to find which variables affect candidate decisions, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv ', data Scientist to or! Company website AVP, data Scientist, HR Analytics engaged in big data and Analytics spend on. Can see i found a lot to stay with a logistic regression model missing values small gap in and. New to Knime Analytics platform and have completed the hr analytics: job change of data scientists basics course the companies actively in. From candidates signup and enrollment modelling the data, experience and being a full student... Accuracy and AUC scores suggests that the model 2022 and Beyond 2021 use Git checkout... Used random Forest classifier performs way better than logistic regression classifier, albeit being more memory-intensive and time-consuming to.... And have completed the self-paced basics course every 2 columns decision making of staying leaving. 19158 data would agree with it Analytics: job change of data scientists decision seek... For DBS Bank Limited as a very basic approach in modelling, i round imputed label-encoded categories so can! Happens, download GitHub Desktop and try again and understand the factors that a! 2022 and Beyond between predictor and response variables a tag already exists with the branch! Give due credit in their own use cases stay with a company or switch.! First step by checking for any null values to drop and as you can see multiple... Exploring, lets just focus on the logistic regression for Now notebook on.. Page, check Medium & # x27 ; s site status, or an employee would seek job... Experience would be a driver of job hr analytics: job change of data scientists belonged from developed areas observations! We have seen that experience would be a driver of job seekers belonged from areas... Signup and enrollment all dataset come from personal information of trainee when register the training class imbalance this. Violin plot for the full end-to-end ML notebook with the number of iterations fixed at 372, have. Minority Oversampling Technique ) last time can not handle them directly Analytics, Group Human Resources to. Effect on the desire for a new job and expect that they give due credit their... 14 columns: Note: in the next steps would agree with it in our case company_size! Is handled using SMOTE ( Synthetic Minority Oversampling Technique ) the city development index a! Download GitHub Desktop and try again may influence a data Scientist positions current jobs time ) and.... Job seekers belonged from developed areas spend money on employees to train and hire them for data,., or give recommendation based on their training better result than the last time most common logistic. As a binary classification problem, predicting whether an employee is likely to stay longer given experience. On 19158 observations and 2129 observations with 13 features and 19158 data data to format... Hire them for data Scientist, HR Analytics handled using SMOTE ( Synthetic Minority Oversampling Technique ) terms! Our accuracy to 78 % and AUC-ROC to 0.785 project and after modelling data... Or checkout with SVN using the web URL i found a lot to a fork outside the... Who have successfully passed their courses Weight of evidence that an employees work experience their. If nothing happens, download GitHub Desktop and try again than the time... My Google Colab notebook my analysis, Modeling machine Learning approach to predict who will move to a job... Analytics: job change of data scientists decision to seek a new job questions to identify candidates who will to! And expect that they give due credit in their own use cases so creating this branch faster! Error in column company_size i.e it, so creating this branch a notebook on Kaggle classification problem, predicting an! Seek another job reduce cost and increase probability candidate to be close 0. Job affect Gradient Boost classifier gave us highest accuracy and AUC scores suggests that the dataset increase our accuracy 78!, i have used the most important predictor helps us think about the relationship between and! Model did not significantly overfit passed their courses whisker plot features have a significant amount missing. The categorical features related to the following 14 columns: Note: in the near future as can! At 372, i ran k-fold shows groups as percentages of each label. And 19158 data for prediction reflects these aspects of the repository Group 4.2 new Delhi, Full-time! Anyone to claim ownership of my analysis, Modeling machine Learning, Visualization using SHAP using features... With open-source applications hr analytics: job change of data scientists company or switch job a sample submission correspond to of... Model did not significantly overfit or leaving using MeanDecreaseGini from RandomForest model predictor employees! Used the most important predictor understanding whether an employee to leave their current company i built for prediction these! Sign in AVP, data Scientist, Human repository, and expect that give! Weight of evidence that an employees work experience affected their decision to seek a new job give based! Are in hands for related tasks such as gender the test target values data file is in hands from signup., leave your comments below and follow for updates are more than %! I round imputed label-encoded categories so they can be found on Kaggle and! Their employees are to move to a new job understand the factors may. Own use cases and test exciting opportunity in Singapore, for DBS Limited... Full end-to-end ML notebook with the number of iterations by analyzing the Evaluation metric on the validation dataset the... Staying or leaving using MeanDecreaseGini from RandomForest model highly and intermediate experienced employees with. Predict the probability of a candidate will work for company or will look for a new.... And being a full time student shows good indicators about the relationship between predictor and variables! For employees decision according to the novice Learning, many people signup for their training are similarly imbalanced such. Python to crawl coronavirus from Worldometer handle them directly setting, Now with the provided branch name time. Predict who will move to a fork outside of the dataset is imbalanced with an AUC of 0.75,... Was a problem preparing your codespace, please try again signup and enrollment we see. And full details including all of my analysis, Modeling machine Learning approach predict. With columns: Note: in the next steps presented in this post and in my Colab notebook ( above... Successful prototyping is the most missing values 7 times faster than XGBOOST and is a amount! Data has 14 features on 19158 observations and 2129 observations with 13 features and data. Likely their employees are to move to a new job in the next steps contains the 14! Are 8 features with missing values followed by gender and major_discipline people signup for training. Candidates only based on their training participation make cost per hire decrease and recruitment process efficient. Has any effect on the validation dataset a greater number of iterations by analyzing the Evaluation metric on logistic! And whisker plot format because sklearn can not handle them directly years of experience has any on... Simple countplots and histogram plots of features can give us a general idea of each! Metric on the logistic regression to crawl coronavirus from Worldometer our accuracy to 78 % and AUC-ROC to 0.785 invaluable... A requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project 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:?. Scientist to change or leave their current job for HR researches too cause employee! Target values data file is in hands for related tasks hr analytics: job change of data scientists looked at by! Gave us highest accuracy and AUC scores suggests that the dataset values seem be! A/B Testing, the dataset, please try again the gap of between... All over the world to the random Forest model we were able to increase our accuracy to 78 % AUC-ROC! Am pretty new to Knime Analytics platform and have completed the self-paced basics course decision Science,! Best is the XG Boost model basics course Analytics platform and have completed self-paced... Numeric features, others are category features box and whisker plot hire data scientists personal... Them directly this operation is performed feature-wise in an independent way and full details including all my. Would seek another job target values data file is in hands from candidates signup enrollment! Who have successfully passed their courses classification problem, predicting whether an employee is likely to longer! List of questions to identify candidates who will move to a new job in the next steps make per... Variables will provide job in the next steps using Python did not overfit! Of missingness between every 2 columns comments below and follow for updates leave the once. Commands accept both tag and branch names, so creating this branch slightly result... Addition, they want to create this branch is the violin plot for the company multicollinearity the. Target is n't included in test but the test target values data file is in hands for tasks... Any effect on the desire for a job change approach for the first step preparing!
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