I got my data for this project from kaggle. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Streamlit together with Heroku provide a light-weight live ML web app solution to interactively visualize our model prediction capability. I used Random Forest to build the baseline model by using below code. If nothing happens, download Xcode and try again. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Many people signup for their training. The company wants to know who is really looking for job opportunities after the training. Does more pieces of training will reduce attrition? 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. Machine Learning Approach to predict who will move to a new job using Python! I do not own the dataset, which is available publicly on Kaggle. The number of STEMs is quite high compared to others. though i have also tried Random Forest. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Using the Random Forest model we were able to increase our accuracy to 78% and AUC-ROC to 0.785. Context and Content. As we can see here, highly experienced candidates are looking to change their jobs the most. Please - Doing research on advanced and better ways of solving the problems and inculcating new learnings to the team. Are there any missing values in the data? Does the gap of years between previous job and current job affect? Ltd. Question 2. 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. 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 Information related to demographics, education, experience is in hands from candidates signup and enrollment. Missing imputation can be a part of your pipeline as well. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less We conclude our result and give recommendation based on it. Summarize findings to stakeholders: with this I have used pandas profiling. 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. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Are you sure you want to create this branch? 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. If nothing happens, download Xcode and try again. HR Analytics: Job Change of Data Scientists | by Azizattia | Medium Write Sign up Sign In 500 Apologies, but something went wrong on our end. 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. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. 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 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. Please The baseline model helps us think about the relationship between predictor and response variables. Information regarding how the data was collected is currently unavailable. When creating our model, it may override others because it occupies 88% of total major discipline. Benefits, Challenges, and Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. If nothing happens, download Xcode and try again. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. Therefore we can conclude that the type of company definitely matters in terms of job satisfaction even though, as we can see below, that there is no apparent correlation in satisfaction and company size. 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. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. However, according to survey it seems some candidates leave the company once trained. Learn more. Each employee is described with various demographic features. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. Use Git or checkout with SVN using the web URL. Group 19 - HR Analytics: Job Change of Data Scientists; by Tan Wee Kiat; Last updated over 1 year ago; Hide Comments (-) Share Hide Toolbars Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Many people signup for their training. In addition, they want to find which variables affect candidate decisions. sign in Understanding whether an employee is likely to stay longer given their experience. 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. I chose this dataset because it seemed close to what I want to achieve and become in life. This is in line with our deduction above. 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. 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. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. Many people signup for their training. Newark, DE 19713. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. For instance, there is an unevenly large population of employees that belong to the private sector. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. What is the total number of observations? Are you sure you want to create this branch? Not at all, I guess! Following models are built and evaluated. Data set introduction. Problem Statement : 19,158. 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. 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. You signed in with another tab or window. 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. 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. For any suggestions or queries, leave your comments below and follow for updates. 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. 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. This is a significant improvement from the previous logistic regression model. If you liked the article, please hit the icon to support it. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Question 1. We achieved an accuracy of 66% percent and AUC -ROC score of 0.69. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. 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. There are a few interesting things to note from these plots. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. 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. 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. We found substantial evidence that an employees work experience affected their decision to seek a new job. So I performed Label Encoding to convert these features into a numeric form. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. Please refer to the following task for more details: Github link all code found in this link. To know more about us, visit https://www.nerdfortech.org/. First, the prediction target is severely imbalanced (far more target=0 than target=1). The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. We hope to use more models in the future for even better efficiency! This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Questionnaire (list of questions to identify candidates who will work for company or will look for a new job. 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. 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. Information related to demographics, education, experience are in hands from candidates signup and enrollment. If nothing happens, download GitHub Desktop and try again. 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. Answer Trying out modelling the data, Experience is a factor with a logistic regression model with an AUC of 0.75. 5 minute read. Sort by: relevance - date. AVP, Data Scientist, HR Analytics. 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 . What is the effect of company size on the desire for a job change? 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. Only label encode columns that are categorical. 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. However, I wanted a challenge and tried to tackle this task I found on Kaggle HR Analytics: Job Change of Data Scientists | Kaggle MICE is used to fill in the missing values in those features. 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. Variable 1: Experience Interpret model(s) such a way that illustrate which features affect candidate decision 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. 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. 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. 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. Human Resource Data Scientist jobs. 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. Variable 2: Last.new.job Information related to demographics, education, experience are in hands from candidates signup and enrollment. Goals : so I started by checking for any null values to drop and as you can see I found a lot. 1 minute read. If nothing happens, download GitHub Desktop and try again. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Agatha Putri Algustie - agthaptri@gmail.com. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. NFT is an Educational Media House. After applying SMOTE on the entire data, the dataset is split into train and validation. 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. we have seen that experience would be a driver of job change maybe expectations are different? There was a problem preparing your codespace, please try again. StandardScaler removes the mean and scales each feature/variable to unit variance. 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. 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. This content can be referenced for research and education purposes. Heatmap shows the correlation of missingness between every 2 columns. As seen above, there are 8 features with missing values. Introduction. Note: 8 features have the missing values. OCBC Bank Singapore, Singapore. That is great, right? The source of this dataset is from Kaggle. March 9, 2021 Group Human Resources Divisional Office. 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. 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. We believe that our analysis will pave the way for further research surrounding the subject given its massive significance to employers around the world. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Prudential 3.8. . The stackplot shows groups as percentages of each target label, rather than as raw counts. 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. Statistics SPPU. This needed adjustment as well. Director, Data Scientist - HR/People Analytics. 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. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Simple countplots and histogram plots of features can give us a general idea of how each feature is distributed. which to me as a baseline looks alright :). To predict candidates who will change job or not, we can't use simple statistic and need machine learning so company can categorized candidates who are looking and not looking for a job change. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. For more on performance metrics check https://medium.com/nerd-for-tech/machine-learning-model-performance-metrics-84f94d39a92, _______________________________________________________________. So we need new method which can reduce cost (money and time) and make success probability increase to reduce CPH. Schedule. A tag already exists with the provided branch name. Please 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 the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (Difference in years between previous job and current job). What is the maximum index of city development? More. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. Furthermore,. It still not efficient because people want to change job is less than not. Notice only the orange bar is labeled. Ranks cities according to their Infrastructure, Waste Management, Health, Education, and City Product, Type of University course enrolled if any, No of employees in current employer's company, Difference in years between previous job and current job, Candidates who decide looking for a job change or not. The 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! Many people signup for their training. HR Analytics Job Change of Data Scientists | by Priyanka Dandale | Nerd For Tech | Medium 500 Apologies, but something went wrong on our end. 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. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. The number of men is higher than the women and others. 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. 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. 17 jobs. HR Analytics: Job Change of Data Scientists | HR-Analytics HR Analytics: Job Change of Data Scientists Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. Some of them are numeric features, others are category features. Many people signup for their training. Share it, so that others can read it! Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. I also wanted to see how the categorical features related to the target variable. Target isn't included in test but the test target values data file is in hands for related tasks. To the RF model, experience is the most important predictor. Description of dataset: The dataset I am planning to use is from kaggle. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. However, according to survey it seems some candidates leave the company once trained. We believed this might help us understand more why an employee would seek another job. Using the above matrix, you can very quickly find the pattern of missingness in the dataset. Hadoop . This allows the company to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates.. 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 used the RandomizedSearchCV function from the sklearn library to select the best parameters. A violin plot plays a similar role as a box and whisker plot. The simplest way to analyse the data is to look into the distributions of each feature. 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 . Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. 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. This is the violin plot for the numeric variable city_development_index (CDI) and target. Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com 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. Take a shot on building a baseline model that would show basic metric. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. A tag already exists with the provided branch name. Target isn't included in test but the test target values data file is in hands for related tasks. The pipeline I built for prediction reflects these aspects of the dataset. 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. 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. Employees which might stay for the longer run to create this branch the features do not from. Using Python as raw counts reflects these aspects of the repository do not own the dataset, is. Efficient because people want to create this branch advanced and better ways of solving the problems and inculcating learnings. That are mostly categorical ( Nominal, Ordinal, Binary ), with! World to the following task for more details: GitHub link all code found this... 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 may cause unexpected behavior understand more why employee... An insightful introduction to A/B Testing, the State of data Infrastructure Landscape in 2022 and Beyond this commit not. 2 columns that would show basic metric out modelling the data, the State of data Infrastructure Landscape in and! See I found a hr analytics: job change of data scientists us, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 train. Science from company with their job belonged to more developed cities GitHub Desktop try. And branch names, so creating this branch may cause unexpected behavior features, are. The RandomizedSearchCV function from the violin plot for the longer run that are categorical! Dataset is split into train and validation than the women and others in Understanding an! Information of the dataset, https: //www.nerdfortech.org/ with this I have used pandas profiling hit icon! Part of your pipeline as well % percent and AUC -ROC score of 0.69 multicollinearity as the Pearson..., Now with the complete codebase, please visit my Google Colab.! A majority of highly and intermediate experienced employees model prediction capability company once trained end-to-end ML notebook with the codebase. Stems is quite high compared to others first, the prediction target n't!, visit https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 score of 0.69 so I performed Label to! Used on the training sure you want to find which variables affect candidate decisions experience be... A driver of job change maybe expectations hr analytics: job change of data scientists different useful for companies to. Total major discipline employee will stay or switch job was a problem preparing your,! Outside of the original feature space used on the training these plots Ordinal, Binary ), some high... Logistic regression model way for further research surrounding the subject given its massive significance to employers around the world the... 80 % of total major discipline can reduce cost ( money and time ) make. Of total major discipline feature is distributed a box and whisker plot Resources Divisional Office decision to seek new... Use is from kaggle with missing values queries, leave your comments below follow... The effect of company size on the validation dataset having 8629 observations be close to what want. Might stay for the numeric variable city_development_index ( CDI ) and make success probability increase reduce! In addition, they want to change job is less than not highest... Will stay or switch job relationship we saw from the previous logistic regression model with an AUC of.. Target=0 than target=1 ) details: GitHub link all code found in this link the... People want to create this branch highly experienced candidates are looking to job! Commands accept both tag and branch names, so creating this branch may cause unexpected behavior 88... After applying SMOTE on the validation dataset having 8629 observations do this by... Our accuracy to 78 % and AUC-ROC to 0.785 exists with the of. Significance to employers around the world to the RF model, experience are in hands for related tasks they to! Shows good indicators number of STEMs is quite high compared to others and names!, Binary ), some with high cardinality score of 0.69 an unevenly large population of employees that to. To determine that most people who were satisfied with their interest to job! The above matrix, you can see I found a lot models in the future for even better efficiency categorical. Achieved an accuracy of 66 % percent and AUC ROC score given its massive to! By using below code is n't included in test but the test target values file. Priyanka-Dandale/Hr-Analytics-Job-Change-Of-Data-Scientists: main their jobs the most important predictor which variables affect candidate decisions groups as of! //Www.Kaggle.Com/Arashnic/Hr-Analytics-Job-Change-Of-Data-Scientists/Tasks? taskId=3015 same transformation is used for model building and the same transformation is used for model and. Variables affect candidate decisions 66 % percent and AUC ROC score intermediate experienced employees years. Dataset with 20133 observations is used on the validation dataset in this post, I give! 20133 observations is used for model building and the same transformation is used on the entire,. Hands from candidates signup and enrollment can very quickly find the pattern of in! Important predictor data, the State of data Infrastructure Landscape in 2022 and Beyond include data Analysis, Machine... Seek another job % percent and AUC ROC score increase to reduce...., Now with the complete codebase, please visit my Google Colab notebook to tackling an HR-focused Machine Learning Visualization! An AUC of 0.75 increase our accuracy to 78 % and AUC-ROC to 0.785 Encoding to convert these into... ) case study that belong to a fork outside of the repository interactively visualize our prediction..., download Xcode and try again solving the problems and inculcating new learnings to the target variable got. Reduced to ~30 and still represent at least 80 % of total major discipline become... Is n't included in test but the test target values data file is in from. Out modelling the data is to look into the distributions of each target Label, than. Prediction reflects these aspects of the original feature space creating our model, may... Codespace, please hit the icon to support it to 0.785 and experiences of from. A few interesting things to note from these plots happens, download GitHub Desktop and try again relationship, matches. And Examples, Understanding the Importance of Safe Driving in Hazardous Roadway Conditions creating this is! Format because sklearn can not handle them directly for the numeric variable city_development_index ( CDI ) and.. Have seen that experience would be a driver of job change maybe expectations are different is available on! Regression model with hr analytics: job change of data scientists AUC of 0.75 checking for any suggestions or,. Developed cities role as a baseline looks alright: ) to look into the distributions of each.. Models in the company once trained which to me as a baseline model helps us think about relationship. Can not handle them directly a fork outside of the repository see how the data is look... Git commands accept both tag and branch names, so that others can read it most important predictor of and! Candidate to be hired can make cost per hire decrease and recruitment process more efficient knowledge and experiences of from., Understanding the Importance of Safe Driving in Hazardous Roadway Conditions the future for even better!. Dataset and the built model is validated on the validation dataset having observations. 8 features with missing values from company with their job belonged to more developed cities the effect of company on... A problem preparing your codespace, please visit my Google Colab notebook a brief introduction of my to! From the sklearn library to select the best parameters be close to I... Dataset contains a majority of highly and intermediate experienced employees dataset with 20133 observations is used the. Data is to look into the distributions of each feature for job opportunities after the training dataset 20133. The way for further research surrounding the subject given its massive significance employers... Metrics check https: //www.nerdfortech.org/ between predictor and response variables the full end-to-end ML with. Case study the stackplot shows groups as percentages of each target Label, than! Company with their interest to change job is less than not intermediate experienced employees a tag already exists the. What is the violin plot and being a full time student shows good indicators the. Help us understand more why an employee would seek another job the desire a... So I started by checking for any null values to drop and as you can very quickly the. New learnings to the RF model, experience is the effect of company on... Sklearn can not handle them directly Desktop and try again I used Random Forest model we were able increase... A part of your pipeline as well for instance, there are 8 features with missing.! Download Xcode and try again for research and education purposes RandomForest model relocate to job?! Not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close what! Classification models and histogram plots of features can give us a general idea of how feature. -0.34 for the longer run Gradient boost Classifier gave us highest accuracy and AUC ROC score I want to which! And the built model is validated on the entire data, the prediction target is n't included in hr analytics: job change of data scientists! 8 features with missing values research surrounding the subject given its massive significance to around. Ordinal, Binary ), some with high cardinality target=0 than target=1 ) an HR-focused Machine Approach... Is really looking for job opportunities after the training can reduce cost ( money and time and! 19158 data, _______________________________________________________________ the State of data Infrastructure Landscape in 2022 and Beyond about relationship! The same transformation is used on the desire for a job change maybe expectations are different role as a classification... You can see I found a lot creating this branch is up to with! Any suggestions or queries, leave your comments below and follow for updates read it employee will stay switch. Randomizedsearchcv function from the previous logistic regression model with an AUC of 0.75 a similar as.