Explore about people who join training data science from company with their interest to change job or become data scientist in the company. First, Id like take a look at how categorical features are correlated with the target variable. maybe job satisfaction? Refer to my notebook for all of the other stackplots. sign in Target isn't included in test but the test target values data file is in hands for related tasks. Prudential 3.8. . The city development index is a significant feature in distinguishing the target. Many people signup for their training. The simplest way to analyse the data is to look into the distributions of each feature. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. 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. However, according to survey it seems some candidates leave the company once trained. I got -0.34 for the coefficient indicating a somewhat strong negative relationship, which matches the negative relationship we saw from the violin plot. This will help other Medium users find it. (Difference in years between previous job and current job). Please Agatha Putri Algustie - agthaptri@gmail.com. - Build, scale and deploy holistic data science products after successful prototyping. To know more about us, visit https://www.nerdfortech.org/. JPMorgan Chase Bank, N.A. Are you sure you want to create this branch? Schedule. February 26, 2021 Group Human Resources Divisional Office. I do not own the dataset, which is available publicly on Kaggle. 19,158. 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. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. This content can be referenced for research and education purposes. (including answers). 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. Problem Statement : Many people signup for their training. But first, lets take a look at potential correlations between each feature and target. Reduce cost and increase probability candidate to be hired can make cost per hire decrease and recruitment process more efficient. Scribd is the world's largest social reading and publishing site. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. 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. 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. Human Resources. Before this note that, the data is highly imbalanced hence first we need to balance it. Our organization plays a critical and highly visible role in delivering customer . 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. Odds shows experience / enrolled in the unversity tends to have higher odds to move, Weight of evidence shows the same experience and those enrolled in university.;[. 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. Insight: Acc. AVP, Data Scientist, HR Analytics. Each employee is described with various demographic features. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). Target isn't included in test but the test target values data file is in hands for related tasks. 1 minute read. Summarize findings to stakeholders: Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Calculating how likely their employees are to move to a new job in the near future. Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Heatmap shows the correlation of missingness between every 2 columns. This is the violin plot for the numeric variable city_development_index (CDI) and target. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. - Reformulate highly technical information into concise, understandable terms for presentations. MICE is used to fill in the missing values in those features. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Another interesting observation we made (as we can see below) was that, as the city development index for a particular city increases, a lesser number of people out of the total workforce are looking to change their job. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. This is therefore one important factor for a company to consider when deciding for a location to begin or relocate to. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time 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. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. By model(s) that uses the current credentials, demographics, and experience data, you need to predict the probability of a candidate looking for a new job or will work for the company and interpret affected factors on employee decision. Use Git or checkout with SVN using the web URL. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. with this demand and plenty of opportunities drives a greater flexibilities for those who are lucky to work in the field. The dataset has already been divided into testing and training sets. March 9, 20211 minute read. We believed this might help us understand more why an employee would seek another job. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. Variable 3: Discipline Major Context and Content. This dataset contains a typical example of class imbalance, This problem is handled using SMOTE (Synthetic Minority Oversampling Technique). 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. 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. 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! Some of them are numeric features, others are category features. 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.. Do years of experience has any effect on the desire for a job change? This article represents the basic and professional tools used for Data Science fields in 2021. 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. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Are you sure you want to create this branch? StandardScaler removes the mean and scales each feature/variable to unit variance. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! with this I have used pandas profiling. If nothing happens, download GitHub Desktop and try again. Apply on company website AVP, Data Scientist, HR Analytics . A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Take a shot on building a baseline model that would show basic metric. The above bar chart gives you an idea about how many values are available there in each column. Human Resource Data Scientist jobs. 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? Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. 17 jobs. HR-Analytics-Job-Change-of-Data-Scientists. Use Git or checkout with SVN using the web URL. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. Hadoop . for the purposes of exploring, lets just focus on the logistic regression for now. Question 1. Learn more. I also used the corr() function to calculate the correlation coefficient between city_development_index and target. 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. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. Many people signup for their training. 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. This operation is performed feature-wise in an independent way. Please The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Are there any missing values in the data? For this, Synthetic Minority Oversampling Technique (SMOTE) is used. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. How much is YOUR property worth on Airbnb? 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. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. MICE (Multiple Imputation by Chained Equations) Imputation is a multiple imputation method, it is generally better than a single imputation method like mean imputation. Your role. You signed in with another tab or window. but just to conclude this specific iteration. Our dataset shows us that over 25% of employees belonged to the private sector of employment. OCBC Bank Singapore, Singapore. 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 . 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. Feature engineering, Sort by: relevance - date. well personally i would agree with it. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. so I started by checking for any null values to drop and as you can see I found a lot. 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. After applying SMOTE on the entire data, the dataset is split into train and validation. If nothing happens, download Xcode and try again. sign in AUCROC tells us how much the model is capable of distinguishing between classes. More. Information related to demographics, education, experience is in hands from candidates signup and enrollment. Information regarding how the data was collected is currently unavailable. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Third, we can see that multiple features have a significant amount of missing data (~ 30%). Dont label encode null values, since I want to keep missing data marked as null for imputing later. Catboost can do this automatically by setting, Now with the number of iterations fixed at 372, I ran k-fold. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. Interpret model(s) such a way that illustrate which features affect candidate decision Using ROC AUC score to evaluate model performance. The source of this dataset is from Kaggle. I am pretty new to Knime analytics platform and have completed the self-paced basics course. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. This means that our predictions using the city development index might be less accurate for certain cities. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. Does more pieces of training will reduce attrition? This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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Of exploring, lets just focus on the logistic regression for now introduction... Showing what numeric values are available there in each column provided too with columns: enrollee,... Third, we can see that multiple features have a significant feature in distinguishing the target.! Some of them are numeric features, others are category features shows us that over 25 of... Hire data scientists decision to stay with a company is interested in understanding the factors that influence... Company or switch jobs how the data was collected is currently unavailable this project include data Analysis Modeling! Valid categories target is n't included in test but the test target values file! Professional tools used for data science wants to hire data scientists TASK Knime Analytics Platform and have completed self-paced.