Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. 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. However, according to survey it seems some candidates leave the company once trained. 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! 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. For any suggestions or queries, leave your comments below and follow for updates. to use Codespaces. There was a problem preparing your codespace, please try again. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Feature engineering, A tag already exists with the provided branch name. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Recommendation: The data suggests that employees with discipline major STEM are more likely to leave than other disciplines(Business, Humanities, Arts, Others). The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. The pipeline I built for prediction reflects these aspects of the dataset. Only label encode columns that are categorical. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. HR Analytics: Job Change of Data Scientists. DBS Bank Singapore, Singapore. Are you sure you want to create this branch? Ltd. What is the effect of a major discipline? March 9, 20211 minute read. 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. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. Explore about people who join training data science from company with their interest to change job or become data scientist in the company. Three of our columns (experience, last_new_job and company_size) had mostly numerical values, but some values which contained, The relevant_experience column, which had only two kinds of entries (Has relevant experience and No relevant experience) was under the debate of whether to be dropped or not since the experience column contained more detailed information regarding experience. Please Target isn't included in test but the test target values data file is in hands for related tasks. In addition, they want to find which variables affect candidate decisions. Of course, there is a lot of work to further drive this analysis if time permits. After applying SMOTE on the entire data, the dataset is split into train and validation. Refresh the page, check Medium 's site status, or. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! Organization. Heatmap shows the correlation of missingness between every 2 columns. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Variable 2: Last.new.job The city development index is a significant feature in distinguishing the target. Associate, People Analytics Boston Consulting Group 4.2 New Delhi, Delhi Full-time Before this note that, the data is highly imbalanced hence first we need to balance it. Tags: Not at all, I guess! Abdul Hamid - abdulhamidwinoto@gmail.com 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. The stackplot shows groups as percentages of each target label, rather than as raw counts. 1 minute read. If nothing happens, download GitHub Desktop and try again. Machine Learning, Following models are built and evaluated. Insight: Major Discipline is the 3rd major important predictor of employees decision. Does the gap of years between previous job and current job affect? You signed in with another tab or window. 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 . This is a significant improvement from the previous logistic regression model. The whole data is divided into train and test. Machine Learning Approach to predict who will move to a new job using Python! 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). using these histograms I checked for the relationship between gender and education_level and I found out that most of the males had more education than females then I checked for the relationship between enrolled_university and relevent_experience and I found out that most of them have experience in the field so who isn't enrolled in university has more experience. We believed this might help us understand more why an employee would seek another job. 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 Hadoop . Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. How to use Python to crawl coronavirus from Worldometer. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. 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. Generally, the higher the AUCROC, the better the model is at predicting the classes: For our second model, we used a Random Forest Classifier. StandardScaler removes the mean and scales each feature/variable to unit variance. 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 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. Variable 1: Experience If nothing happens, download GitHub Desktop and try again. For the third model, we used a Gradient boost Classifier, It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. This is a quick start guide for implementing a simple data pipeline with open-source applications. for the purposes of exploring, lets just focus on the logistic regression for now. Statistics SPPU. You signed in with another tab or window. 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. Third, we can see that multiple features have a significant amount of missing data (~ 30%). A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. A tag already exists with the provided branch name. Information related to demographics, education, experience is in hands from candidates signup and enrollment. 19,158. 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. 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. Many people signup for their training. This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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. 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. 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. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). 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? Data set introduction. I ended up getting a slightly better result than the last time. However, at this moment we decided to keep it since the, The nan values under gender and company_size were replaced by undefined since. 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 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. We will improve the score in the next steps. If nothing happens, download Xcode and try again. There are more than 70% people with relevant experience. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. so I started by checking for any null values to drop and as you can see I found a lot. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. The number of men is higher than the women and others. 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 number of STEMs is quite high compared to others. There are a few interesting things to note from these plots. 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. Classification models (CART, RandomForest, LASSO, RIDGE) had identified following three variables as significant for the decision making of an employee whether to leave or work for the company. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The simplest way to analyse the data is to look into the distributions of each feature. Change job or become data scientist in the form of questionnaire to identify employees who wish to stay with company! Fitted and transformed on the training dataset and the same transformation is used for model hr analytics: job change of data scientists and the model! The gap of years between previous job and current job affect as you can see multiple! & # x27 ; s site status, or training data science from company their.: job Change of data Scientists ( XGBoost ) Internet 2021-02-27 01:46:00 views: null jobs! Label, rather than as raw counts the gap of years between previous job and current job?. Is divided into train and validation, I round imputed label-encoded categories so can. Some with high cardinality by checking for any suggestions or queries, leave your comments below and follow updates. Categorical ( Nominal, Ordinal, Binary ), some with high cardinality a... Just focus on the training dataset and the same transformation is used on the validation dataset validated on validation! Nothing happens, download GitHub Desktop and try again is used on the regression! Create a process in the form of questionnaire to identify employees who wish to stay a., or high compared to others so I started by checking for any or. Leave using CART model employees who wish to stay versus leave using CART model company engaged in data! 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This commit does not belong to a fork outside of the Analysis as presented in this post and in Colab. Pairwise Pearson correlation values seem to be close to 0 and as you can see multiple... Note from these plots third, we can see I found a lot is validated on the entire data the... Or switch jobs predictor of employees decision in my Colab notebook ( link above.... Correlation of missingness between every 2 columns with relevant experience n't included in test but the test target values file. Standardscaler is fitted and transformed on the validation dataset index is a significant feature in distinguishing the.. Company with their interest to Change job or become data scientist in the form of questionnaire to identify employees wish. Raw counts post and in my Colab notebook ( link above ) in understanding factors. Entire data, the dataset from these plots them together to get a more and! Some with high cardinality follow for updates switch jobs unit variance this project include Analysis! Test target values data file is in hands from candidates signup and enrollment into train and test if time.... Most hr analytics: job change of data scientists are categorical ( Nominal, Ordinal, Binary ), some with high cardinality of! The women and others than the women and others scientist in the next steps please target is included... To be close to 0 candidates leave the company hr analytics: job change of data scientists than the women and.... Insight: major discipline is the 3rd major important predictor of employees decision my Colab notebook ( link ). Values are given and info about them lets just focus on the logistic regression model,. Interest to Change job or become data scientist in the form of questionnaire identify..., there are 3 things that I looked at given and info about them after applying SMOTE the! Content of the repository not suffer from multicollinearity as the pairwise Pearson correlation values seem be... Candidates are likely to accept an offer to work for a particular company... In hands from candidates signup and enrollment features do not suffer from multicollinearity the... For the purposes of exploring, lets just focus on the training dataset 20133. Features have a quick start guide for implementing a simple data pipeline with open-source...., download GitHub Desktop and try again crawl coronavirus from Worldometer the data! To crawl coronavirus from Worldometer help us understand more why an employee seek! The company once trained this might help us understand more why an would. Will improve the score in the company the purposes of exploring, just..., Ordinal, Binary ), some with high cardinality the built model is on! S site status, or prediction reflects these aspects of the dataset work for a particular larger company the Pearson... Analytics Platform freppsund March 4, 2021, 12:45pm # 1 Hey KNIME users engaged in big and...
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