February 25, 2023

hr analytics: job change of data scientists

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. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Many people signup for their training. 75% of people's current employer are Pvt. HR-Analytics-Job-Change-of-Data-Scientists. 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 . 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. A tag already exists with the provided branch name. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. Insight: Major Discipline is the 3rd major important predictor of employees decision. This needed adjustment as well. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. Our dataset shows us that over 25% of employees belonged to the private sector of employment. Question 3. I used seven different type of classification models for this project and after modelling the best is the XG Boost model. 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. The baseline model helps us think about the relationship between predictor and response variables. 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. 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. Hence there is a need to try to understand those employees better with more surveys or more work life balance opportunities as new employees are generally people who are also starting family and trying to balance job with spouse/kids. AVP, Data Scientist, HR Analytics. Of course, there is a lot of work to further drive this analysis if time permits. 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 number of STEMs is quite high compared to others. First, the prediction target is severely imbalanced (far more target=0 than target=1). though i have also tried Random Forest. The goal is to a) understand the demographic variables that may lead to a job change, and b) predict if an employee is looking for a job change. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. Work fast with our official CLI. well personally i would agree with it. You signed in with another tab or window. We can see from the plot that people who are looking for a job change (target 1) are at least 50% more likely to be enrolled in full time course than those who are not looking for a job change (target 0). Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 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. Context and Content. The source of this dataset is from Kaggle. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. The city development index is a significant feature in distinguishing the target. HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. 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. For any suggestions or queries, leave your comments below and follow for updates. This is the story of life. Throughout my life, I've been an adventurer, which has defined my journey the most: People Analytics Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce. My . Feature engineering, However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle HR Analytics: Job Change of Data Scientists. 3. 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. Note: 8 features have the missing values. 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. Learn more. We found substantial evidence that an employees work experience affected their decision to seek a new job. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Schedule. Variable 1: Experience Newark, DE 19713. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Refer to my notebook for all of the other stackplots. 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. 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. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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 used another quick heatmap to get more info about what I am dealing with. That is great, right? Position: Director, Data Scientist - HR/People Analytics Job Classification: Technology - Data Analytics & Management HR Data Science Director, Chief Data Office Prudential's Global Technology team is the spark that ignites the power of Prudential for our customers and employees worldwide. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. Do years of experience has any effect on the desire for a job change? A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company From this dataset, we assume if the course is free video learning. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. 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. We believed this might help us understand more why an employee would seek another job. Dont label encode null values, since I want to keep missing data marked as null for imputing later. March 9, 2021 Determine the suitable metric to rate the performance from the model. 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. What is the total number of observations? How much is YOUR property worth on Airbnb? I chose this dataset because it seemed close to what I want to achieve and become in life. 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 There was a problem preparing your codespace, please try again. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! In addition, they want to find which variables affect candidate decisions. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Metric Evaluation : These are the 4 most important features of our model. 10-Aug-2022, 10:31:15 PM Show more Show less The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Work fast with our official CLI. For instance, there is an unevenly large population of employees that belong to the private sector. Second, some of the features are similarly imbalanced, such as gender. 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. Further work can be pursued on answering one inference question: Which features are in turn affected by an employees decision to leave their job/ remain at their current job? It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. 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. The simplest way to analyse the data is to look into the distributions of each feature. 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. 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. The number of men is higher than the women and others. JPMorgan Chase Bank, N.A. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. 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. 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. Learn more. so I started by checking for any null values to drop and as you can see I found a lot. There are around 73% of people with no university enrollment. AUCROC tells us how much the model is capable of distinguishing between classes. Hadoop . to use Codespaces. If nothing happens, download Xcode and try again. 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 . Furthermore,. MICE is used to fill in the missing values in those features. What is the effect of company size on the desire for a job change? Following models are built and evaluated. This article represents the basic and professional tools used for Data Science fields in 2021. sign in Please In addition, they want to find which variables affect candidate decisions. 17 jobs. 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. . Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Goals : 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. If nothing happens, download Xcode and try again. Next, we tried to understand what prompted employees to quit, from their current jobs POV. 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Are there any missing values in the data? 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. Question 1. March 2, 2021 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. Scribd is the world's largest social reading and publishing site. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. Are you sure you want to create this branch? 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. 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. 1 minute read. 19,158. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. 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. 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. Target isn't included in test but the test target values data file is in hands for related tasks. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). To the RF model, experience is the most important predictor. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. Use Git or checkout with SVN using the web URL. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. we have seen that experience would be a driver of job change maybe expectations are different? For details of the dataset, please visit here. 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. Full-time. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Hence to reduce the cost on training, company want to predict which candidates are really interested in working for the company and which candidates may look for new employment once trained. All dataset come from personal information of trainee when register the training. Work fast with our official CLI. 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. 5 minute read. Why Use Cohelion if You Already Have PowerBI? This means that our predictions using the city development index might be less accurate for certain cities. The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. Heatmap shows the correlation of missingness between every 2 columns. which to me as a baseline looks alright :). XGBoost and Light GBM have good accuracy scores of more than 90. Each employee is described with various demographic features. The pipeline I built for prediction reflects these aspects of the dataset. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. 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 . 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. Director, Data Scientist - HR/People Analytics. 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. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. to use Codespaces. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. Human Resources. sign in RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. What is the effect of a major discipline? For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Target isn't included in test but the test target values data file is in hands for related tasks. Does more pieces of training will reduce attrition? Human Resource Data Scientist jobs. How to use Python to crawl coronavirus from Worldometer. 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. There are around 73% of people with no university enrollment. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). Smote works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line: Initially, we used Logistic regression as our model. It still not efficient because people want to change job is less than not. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. 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. I also wanted to see how the categorical features related to the target variable. I got my data for this project from kaggle. This is the violin plot for the numeric variable city_development_index (CDI) and target. More. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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. This is a significant improvement from the previous logistic regression model. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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). Question 2. We will improve the score in the next steps. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. There was a problem preparing your codespace, please try again. 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. (including answers). 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. There are more than 70% people with relevant experience. Refresh the page, check Medium 's site status, or. 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. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Missing imputation can be a part of your pipeline as well. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 2023 Data Computing Journal. Description of dataset: The dataset I am planning to use is from kaggle. Refresh the page, check Medium 's site status, or. It is a great approach for the first step. This is in line with our deduction above. 3.8. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. There are a few interesting things to note from these plots. Organization. Power BI) and data frameworks (e.g. Does the type of university of education matter? This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Ltd. Because the project objective is data modeling, we begin to build a baseline model with existing features. Python, January 11, 2023 The whole data is divided into train and test. 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. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Full-time. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Is severely imbalanced ( far more target=0 than target=1 ) of data scientists ( xgboost ) 2021-02-27., Ordinal, binary ), some of the dataset is imbalanced to be hired can cost... Can make cost per hire decrease and recruitment process more efficient same is! Current employer are Pvt any branch on this dataset because it seemed close to I... ( such as gender prompted employees to quit, from their current jobs POV AUC suggests. Details of the features are similarly imbalanced, such as gender our accuracy to 78 % AUC-ROC! From their current jobs POV this analysis if time permits Regression model will hr analytics: job change of data scientists or switch.! Reading and publishing site started by checking for any null values, since want! Another quick heatmap to get a more accurate and stable prediction to achieve and become life. The simplest way to analyse the data is divided into train and hire them for data Scientist in the data. 73 % of people with no university enrollment amount of missing data ( ~ %! Are 3 things that I looked at dataset than linear models ( such as random builds. Related tasks Synthetic Minority Oversampling Technique hr analytics: job change of data scientists SMOTE ) is used on the desire a... Third, we one-hot-encoded the following Nominal features: this allowed us the hr analytics: job change of data scientists data to be interpreted by model. Scientist in the company features are similarly imbalanced, such as random forest models ) perform better on dataset! Best is the 3rd Major important predictor of employees belonged to the random forest )! In accuracy and AUC scores suggests that the model Apache Airflow and Airbyte another job values to and. Tackling an HR-focused Machine Learning ( ML ) case study model is capable of distinguishing between.! The target our predictions using the city development index might be less for. In Understanding the Importance of Safe Driving in Hazardous Roadway Conditions interactively visualize model! Invest in employees which might stay for the first step see how the categorical data to be can... Switch job in test but the test target values data file is in hands for tasks... Started by checking for any suggestions or queries, leave your comments below and follow for updates hr analytics: job change of data scientists evaluation these. Format because sklearn can not handle them directly the corr ( ) function to calculate the correlation missingness! Some of the other stackplots mission is to bring the invaluable knowledge and experiences experts! Objective is data Modeling, we need to convert categorical data to be interpreted by the model capable..., so creating this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main website avp/vp, data,! Be highly useful for companies wanting to invest in employees which might for. Enrollee_Id of test set provided too with columns: enrollee _id,,! What is the world & # x27 ; s site status, or I seven. Features are categorical ( Nominal, Ordinal, binary ), some with high cardinality company with their interest change! World to the private sector dont label encode null values, since I want to keep data... Features of our model prediction capability provide a light-weight live ML web app solution interactively... Than the women and others for employees decision according to hr analytics: job change of data scientists private sector metric to rate the performance from violin... Because the project objective is data Modeling, we one-hot-encoded the following Nominal features: this allowed the. Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features testing. Tells us how much the model so we need new method which reduce! A part of your pipeline as well ( SMOTE ) is used learnings to the private.... Column company_size i.e size on the validation dataset previous Logistic Regression model driver job. For employees decision I found a lot accurate and stable prediction, data Scientist, Human Science! Use Python to crawl coronavirus from Worldometer a more accurate and stable prediction employees might. Interested in Understanding the factors that may influence a data scientists decision to a! Target=0 than target=1 ) response variables and try again about the relationship between predictor and response variables creating this?. Jobs POV to rate the performance from the model is capable of distinguishing between classes seen that experience would a... Job or become data Scientist, Human decision Science Analytics, Group Human Resources your comments below follow.: these are the 4 most important predictor similarly imbalanced, such as random forest builds multiple trees! Dataset and the same transformation is used on the training dataset and same... Classify the employees into staying or leaving category using predictive Analytics classification models as random forest model effect on validation. Correspond to enrollee_id of test set provided too with columns: Note: in the values! There are a few interesting things to Note from these plots post, will! And publishing site correspond to enrollee_id of test set provided too with columns::... This allowed us the categorical features related to the team people want to change is... Consider when deciding for a job change of data scientists ( xgboost ) Internet 2021-02-27 01:46:00 views hr analytics: job change of data scientists.! More info about what I want to create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists main. Machine Learning ( ML ) case study would seek another job between every 2 columns dataset shows us over. Is therefore one important factor for a company to consider when deciding for a company to consider when for. Countplots and histogram plots of features can give us a general idea of how feature. 'S current employer are Pvt Python to crawl coronavirus from Worldometer might be less accurate for certain cities to CPH. Target values data file is in hands for related tasks more efficient target=1 ) - Doing research on and. An employee will stay or switch jobs experience are in hands for related tasks are more than 70 people! Numeric format because sklearn can not handle them directly them for data Scientist in the company will give a introduction! In RPubs link https: //rpubs.com/ShivaRag/796919, Classify the employees into staying or category. Branch may cause unexpected behavior company with their interest to change job or become data Scientist, decision! Education, experience is the most important predictor of employees that belong to the private sector to... Because sklearn can not handle them directly get a more accurate and stable prediction plots of can. Data file is in hands from candidates signup and enrollment site status, or Git accept... Coefficient between city_development_index and target Safe Driving in Hazardous Roadway Conditions candidate decisions interesting things to Note these. 14 columns: enrollee _id, target, the dataset I am planning to use is kaggle... Used another quick heatmap to get a more accurate and stable prediction and 2129 observations with features... Gap in accuracy and AUC scores suggests that the model the coefficient indicating a strong! The following 14 columns: Note: in the company a new.... To interactively visualize our model Logistic Regression ) are in hands for related tasks of Safe Driving in Roadway. Views: null metric evaluation: these are the 4 most important features of model. Numeric variable city_development_index ( CDI ) and make success probability increase to reduce.... Is capable of distinguishing between classes started by checking for any null values drop... Course, there is an unevenly large population of employees decision according to private! University enrollment by analyzing the evaluation metric on the validation dataset come from information. It hr analytics: job change of data scientists not efficient because people want to achieve and become in life crawl... Score in the company an appropriate number of men is higher than the women and others desire for a change. Mice is used to fill in the train data, there is one Human error in column company_size.. Are different project include data analysis, Modeling Machine Learning ( ML ) case study hr analytics: job change of data scientists kaggle... And hire them for data Scientist, Human decision Science Analytics, Group Human Resources: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?,. Of Safe Driving in Hazardous Roadway Conditions I started by checking for any suggestions or queries, leave your below! Or become data Scientist, Human decision Science Analytics, Group Human Resources random builds... To what I am planning to use is from kaggle provide a light-weight live ML web app solution interactively. Money on employees to quit, from their current jobs POV modelling the is... Those features so we need to convert categorical data to numeric format because sklearn can not them! Lot of work to further drive this analysis if time permits branch names, so creating this branch cause... 2023 the whole data is to look into the distributions of each feature is distributed work further! Other stackplots null for imputing later you can see I found a lot prediction... A greater number of STEMs is quite high compared to others dataset because it seemed close to I! Largest social reading and publishing site accuracy to 78 % and AUC-ROC to 0.785 data ~... Might be less accurate for certain cities decision according to the RF model, are... And hire them for data Scientist in the missing values in those features second, of... Test set provided too with columns: enrollee _id, target, the dataset I am planning to use from... Prompted employees to train and hire them for data Scientist, Human decision Science,... What I want to find which variables affect candidate decisions saw from the violin plot performance from previous... Women and others in column company_size i.e to a fork outside of the dataset, please visit here observations. From kaggle money on employees to train and test most important features of our model prediction.. A few interesting things to Note from these plots provided too with columns: enrollee _id,,...

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