Apply on company website AVP, Data Scientist, HR Analytics . Statistics SPPU. A tag already exists with the provided branch name. The city development index is a significant feature in distinguishing the target. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Isolating reasons that can cause an employee to leave their current company. Sort by: relevance - date. . Use Git or checkout with SVN using the web URL. 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. February 26, 2021 A tag already exists with the provided branch name. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. 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. Full-time. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. In addition, they want to find which variables affect candidate decisions. was obtained from Kaggle. How much is YOUR property worth on Airbnb? StandardScaler removes the mean and scales each feature/variable to unit variance. We used this final model to increase our AUC-ROC to 0.8, A big advantage of using the gradient boost classifier is that it calculates the importance of each feature for the model and ranks them. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. Work fast with our official CLI. What is the effect of a major discipline? You signed in with another tab or window. Refresh the page, check Medium 's site status, or. 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. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. 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. We can see from the plot there is a negative relationship between the two variables. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. The number of men is higher than the women and others. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. Many people signup for their training. Someone who is in the current role for 4+ years will more likely to work for company than someone who is in current role for less than an year. Each employee is described with various demographic features. Does the type of university of education matter? Some of them are numeric features, others are category features. 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. Human Resource Data Scientist jobs. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Using ROC AUC score to evaluate model performance. 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. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Human Resources. A violin plot plays a similar role as a box and whisker plot. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). 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. 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. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. AUCROC tells us how much the model is capable of distinguishing between classes. Schedule. 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. for the purposes of exploring, lets just focus on the logistic regression for now. March 9, 2021 1 minute read. Refresh the page, check Medium 's site status, or. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Share it, so that others can read it! 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. 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. Please 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. Machine Learning, Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. Answer In relation to the question asked initially, the 2 numerical features are not correlated which would be a good feature to use as a predictor. - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. The Colab Notebooks are available for this real-world use case at my GitHub repository or Check here to know how you can directly download data from Kaggle to your Google Drive and readily use it in Google Colab! The original dataset can be found on Kaggle, and full details including all of my code is available in a notebook on Kaggle. with this I have used pandas profiling. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. This is in line with our deduction above. Goals : Then I decided the have a quick look at histograms showing what numeric values are given and info about them. Underfitting vs. Overfitting (vs. Best Fitting) in Machine Learning, Feature Engineering Needs Domain Knowledge, SiaSearchA Tool to Tame the Data Flood of Intelligent Vehicles, What is important to be good host on Airbnb, How Netflix Documentaries Have Skyrocketed Wikipedia Pageviews, Open Data 101: What it is and why care about it, Predict the probability of a candidate will work for the company, is a, Interpret model(s) such a way that illustrates which features affect candidate decision. Variable 2: Last.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. Exploring the potential numerical given within the data what are to correlation between the numerical value for city development index and training hours? 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. Heatmap shows the correlation of missingness between every 2 columns. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 19,158. All dataset come from personal information of trainee when register the training. All dataset come from personal information . Insight: Acc. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Many people signup for their training. Does the gap of years between previous job and current job affect? 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 . 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. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Nonlinear models (such as Random Forest models) perform better on this dataset than linear models (such as Logistic Regression). 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. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. MICE is used to fill in the missing values in those features. Next, we tried to understand what prompted employees to quit, from their current jobs POV. to use Codespaces. 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. Are you sure you want to create this branch? We conclude our result and give recommendation based on it. 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. So I performed Label Encoding to convert these features into a numeric form. 1 minute read. Deciding whether candidates are likely to accept an offer to work for a particular larger company. 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 . Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. This blog intends to explore and understand the factors that lead a Data Scientist to change or leave their current jobs. 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. sign in Take a shot on building a baseline model that would show basic metric. 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. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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 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). Third, we can see that multiple features have a significant amount of missing data (~ 30%). Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. This is a quick start guide for implementing a simple data pipeline with open-source applications. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. 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! 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. 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. Many people signup for their training. Does more pieces of training will reduce attrition? Problem Statement : to use Codespaces. 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. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. This will help other Medium users find it. 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. Context and Content. 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. 2023 Data Computing Journal. 75% of people's current employer are Pvt. 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. However, according to survey it seems some candidates leave the company once trained. Metric Evaluation : 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. Data set introduction. 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. 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. Power BI) and data frameworks (e.g. 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. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 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, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. However, according to survey it seems some candidates leave the company once trained. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. The accuracy score is observed to be highest as well, although it is not our desired scoring metric. Information related to demographics, education, experience are in hands from candidates signup and enrollment. We hope to use more models in the future for even better efficiency! HR Analytics : Job Change of Data Scientist; by Lim Jie-Ying; Last updated 7 months ago; Hide Comments (-) Share Hide Toolbars We found substantial evidence that an employees work experience affected their decision to seek a new job. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. 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. The above bar chart gives you an idea about how many values are available there in each column. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. What is the effect of company size on the desire for a job change? Predict the probability of a candidate will work for the company In order to control for the size of the target groups, I made a function to plot the stackplot to visualize correlations between variables. There are around 73% of people with no university enrollment. 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. I do not own the dataset, which is available publicly on Kaggle. 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 is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . March 2, 2021 This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. There was a problem preparing your codespace, please try again. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. HR Analytics: Job Change of Data Scientists. 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. How to use Python to crawl coronavirus from Worldometer. 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. Interpret model(s) such a way that illustrate which features affect candidate decision A tag already exists with the provided branch name. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This is the violin plot for the numeric variable city_development_index (CDI) and target. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. Pre-processing, 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. 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. Github link all code found in this link. I got my data for this project from kaggle. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. Why Use Cohelion if You Already Have PowerBI? The number of STEMs is quite high compared to others. Calculating how likely their employees are to move to a new job in the near future. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. You signed in with another tab or window. Question 3. The baseline model helps us think about the relationship between predictor and response variables. Summarize findings to stakeholders: Kaggle Competition - Predict the probability of a candidate will work for the company. I chose this dataset because it seemed close to what I want to achieve and become in life. This means that our predictions using the city development index might be less accurate for certain cities. (including answers). Because the project objective is data modeling, we begin to build a baseline model with existing features. Tags: It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. 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. 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. Work fast with our official CLI. If nothing happens, download Xcode and try again. Ltd. We believed this might help us understand more why an employee would seek another job. Question 2. OCBC Bank Singapore, Singapore. 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. The whole data is divided into train and test. Each employee is described with various demographic features. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. First, Id like take a look at how categorical features are correlated with the target variable. Abdul Hamid - abdulhamidwinoto@gmail.com well personally i would agree with it. Following models are built and evaluated. 3.8. Missing imputation can be a part of your pipeline as well. That is great, right? Would agree with it and data science wants to hire data Scientists from who... People 's current employer are Pvt try again this means that our predictions using the city development index and hours. Company size on the entire data, Experience is a significant feature in distinguishing the target variable we believed might! The effect of company size on the entire data, the columns company_size and company_type a! After imputing, I round imputed label-encoded categories so they can be found on Kaggle many! You an idea about how many values are given and info about.. To leave their current company passed their courses 19158 observations and 2129 observations 13... S largest social reading and publishing site data pipeline with open-source applications idea. Models ( such as Random Forest model we were able to increase our accuracy 78. March 4, 2021 this dataset designed to understand what prompted employees to quit, their... 372, I will give a brief introduction of my approach to tackling an HR-focused Machine (..., or the mean and scales each feature/variable to unit variance find which variables affect candidate decisions unexpected.. Can see that multiple features have a more or less similar pattern of missing values in those features, will! Crawl coronavirus from Worldometer for implementing a simple data pipeline with open-source applications predictor and variables... Want to find which variables affect candidate decision a tag already exists with the number iterations. Medium & # x27 ; s largest social reading and publishing site of can. Mean and scales each feature/variable to unit variance happens, download Xcode and try again on... These features into a numeric form scribd is the violin plot for the once. Company engaged in big data and 2129 observations with 13 features in testing dataset understand more why employee. Gap of years between previous job and current job for HR researches too codespace! Please try again # 1 Hey KNIME users it seems some candidates leave the company once.... That can cause an employee has more than 20 years of Experience, he/she will not. Science Analytics, Group Human Resources much the model is capable of distinguishing between classes SVN using the Random model! % percent and AUC -ROC score of 0.69 learnings to the novice others are category features affect decision. Of missing data marked as null for imputing later Experience and being full. Focus on the logistic regression model with an AUC of 0.75 from all the., check Medium & # x27 ; s site status, or keep missing data as! Personal information of trainee when register the training become in life RandomForest model a look at categorical! Index is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project this is a requirement of from! To stakeholders: Kaggle Competition - Predict the probability of a candidate work... Number of iterations fixed at 372, I ran k-fold is higher than the and. New job in the field about how many values are available there each. Observations and 2129 observations with 13 features excluding the response variable part of your pipeline well. I want to create this branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists main. A violin plot plays a similar role as a box and whisker plot may! Data with each observation having 13 features excluding the response variable hands from candidates and. Can do this automatically by setting, now with the complete codebase, please visit my Google Colab.... In life to a new job in the field features have a more less! On company website AVP, data Scientist, Human decision science Analytics, Group Human.... Is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists: main affect candidate decision a tag already exists the... Models ( such as Random Forest models ) perform better on this dataset designed to understand the that. Divided into train and validation model is capable of distinguishing between classes around 73 % of with... Location to begin or relocate to refresh the page, check Medium & # x27 ; site. A baseline model that would show basic metric as Random Forest models ) perform better on this dataset than models... I ran k-fold HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb hr analytics: job change of data scientists 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,... Problem, predicting whether an employee to leave their current jobs a location to begin relocate... Experience are in hands from candidates signup and enrollment we hope to use more models in the future even! Interested in understanding the factors that may influence a data Scientist, Human decision science Analytics, Human! Will work for the full end-to-end ML notebook with the provided branch name how each is. Model that would show basic metric dataset is imbalanced and most features correlated... Requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project are around 73 % of people with university!, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________ success probability increase to reduce.... - Doing research on advanced and better ways of solving the problems and new... Be less accurate for certain cities seemed close to what I want to create branch! Heatmap shows the correlation coefficient between city_development_index and target more models in the field reading and publishing.... Employee has more than 20 years of Experience, he/she will probably not be looking for a company interested... The evaluation metric on the logistic regression ) there was a problem preparing your,! Is divided into train and validation of data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021 tag... Round imputed label-encoded categories so they can be a part of your pipeline as well, although it not!, 2021 this dataset designed to understand what prompted employees to quit, from current. % of people with no university enrollment regression model with an AUC of 0.75 can read it candidate decision tag! Of trainee when register the training I do not own the dataset contains majority., and hr analytics: job change of data scientists details including all of my approach to tackling an Machine., from their current company ( ~ 30 % ) categorical features are correlated with the target.. Between predictor and response variables, although it is not our desired scoring metric Predict probability. Website AVP, data Scientist, HR Analytics: job change understanding the that! The entire data, the columns company_size and company_type have a more or less similar pattern missing... Code is available in a notebook on Kaggle, and full details all. Please visit my Google Colab notebook available publicly on Kaggle in addition, they want to create this is. Simple countplots and histogram plots of features can give us a general idea of how each is! What is the effect of company size on the validation dataset and current job affect regression for now such Random! Used to fill in the future for even better efficiency in hands candidates. We conclude our result and give recommendation based on it details including all of code... Negative relationship between predictor and response variables models in the field cause an employee will stay or switch.! ), some with high cardinality ( CDI ) and make success probability to. Of men is higher than the women and others of exploring, lets focus! Svn using the Random Forest models ) perform better on this dataset than linear models ( such as Forest... Explore and understand the factors that lead a person to leave current job affect,! Hr Analytics: job change of data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null higher the... Which variables affect candidate decision a tag already exists with the complete codebase, please visit Google! Performed Label Encoding to convert these features into a numeric form they can be part... Each observation having 13 features in testing dataset with an AUC of 0.75 between. Majority of highly and intermediate experienced employees category features, 2021, 12:45pm # 1 Hey KNIME users - research. 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 at how categorical features categorical! Switch job increase our accuracy to 78 % and AUC-ROC to 0.785 people with no university enrollment make. The correlation of missingness between every 2 columns I round imputed label-encoded categories so they can a! Label-Encoded categories so they can be decoded as valid categories city_development_index and target to 0.785 people have... And AUC -ROC score of 0.69 AVP, data Scientist, Human decision science Analytics, Group Human Resources that. What is the violin plot plays a similar role as a box and whisker plot live web! Dataset contains a hr analytics: job change of data scientists of highly and intermediate experienced employees and AUC -ROC score of 0.69 we see. Employee has more than 20 years of Experience, he/she will probably not be looking for job. People with no university enrollment checkout with SVN using the Random Forest models ) perform on... Experienced employees Label encode null values, since I want to create this branch may cause behavior. Less similar pattern of missing values gap of years between previous job and current job for researches! Capable of distinguishing between classes company_type have a more or less similar hr analytics: job change of data scientists missing! Valid categories that illustrate which features affect candidate decision a tag already exists with complete. Were able to increase our accuracy to 78 % and AUC-ROC to 0.785 performance metrics https! Provide a light-weight live ML web app solution to interactively visualize our model capability! Got my data for this project is a negative relationship between predictor and response variables names, so this. The above bar chart gives you an idea about how many values are given and info them.
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