This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. Power BI) and data frameworks (e.g. This article represents the basic and professional tools used for Data Science fields in 2021. 75% of people's current employer are Pvt. Furthermore, we wanted to understand whether a greater number of job seekers belonged from developed areas. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. It can be deduced that older and more experienced candidates tend to be more content with their current jobs and are looking to settle down. 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. Learn more. I do not own the dataset, which is available publicly on Kaggle. HR Analytics: Job Change of Data Scientists. There are around 73% of people with no university enrollment. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Director, Data Scientist - HR/People Analytics. Metric Evaluation : (Difference in years between previous job and current job). HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. 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. How much is YOUR property worth on Airbnb? I am pretty new to Knime analytics platform and have completed the self-paced basics course. 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. Group Human Resources Divisional Office. What is the maximum index of city development? 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. Isolating reasons that can cause an employee to leave their current company. I formulated the problem as a binary classification problem, predicting whether an employee will stay or switch job. Goals : As XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. though i have also tried Random Forest. If nothing happens, download Xcode and try again. (including answers). Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 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. Apply on company website AVP, Data Scientist, HR Analytics . Machine Learning, HR-Analytics-Job-Change-of-Data-Scientists-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). 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. We used the RandomizedSearchCV function from the sklearn library to select the best parameters. Organization. What is the effect of company size on the desire for a job change? I got my data for this project from kaggle. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. What is the effect of a major discipline? Dont label encode null values, since I want to keep missing data marked as null for imputing later. 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. Learn more. I ended up getting a slightly better result than the last time. Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. 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. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Description of dataset: The dataset I am planning to use is from kaggle. Data set introduction. A tag already exists with the provided branch name. Are you sure you want to create this branch? 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. 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 conclude our result and give recommendation based on it. This content can be referenced for research and education purposes. Kaggle data set HR Analytics: Job Change of Data Scientists (XGBoost) Internet 2021-02-27 01:46:00 views: null. Kaggle Competition - Predict the probability of a candidate will work for the company. Please Powered by, '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv', '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_test.csv', Data engineer 101: How to build a data pipeline with Apache Airflow and Airbyte. March 9, 20211 minute read. Hr-analytics-job-change-of-data-scientists | Kaggle Explore and run machine learning code with Kaggle Notebooks | Using data from HR Analytics: Job Change of Data Scientists This dataset consists of rows of data science employees who either are searching for a job change (target=1), or not (target=0). 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? Work fast with our official CLI. There are more than 70% people with relevant experience. Hadoop . Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. 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. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. RPubs link https://rpubs.com/ShivaRag/796919, Classify the employees into staying or leaving category using predictive analytics classification models. This branch is up to date with Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists:main. Are you sure you want to create this branch? This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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. This is in line with our deduction above. 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. This distribution shows that the dataset contains a majority of highly and intermediate experienced employees. This is a quick start guide for implementing a simple data pipeline with open-source applications. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. February 26, 2021 Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). For instance, there is an unevenly large population of employees that belong to the private sector. 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. 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. Full-time. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Work fast with our official CLI. Exploring the categorical features in the data using odds and WoE. Does the type of university of education matter? Juan Antonio Suwardi - antonio.juan.suwardi@gmail.com In our case, company_size and company_type contain the most missing values followed by gender and major_discipline. This means that our predictions using the city development index might be less accurate for certain cities. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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. well personally i would agree with it. 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.. 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. Notice only the orange bar is labeled. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. maybe job satisfaction? 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. 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. Summarize findings to stakeholders: Variable 2: Last.new.job For details of the dataset, please visit here. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. The number of men is higher than the women and others. 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. I used Random Forest to build the baseline model by using below code. The number of STEMs is quite high compared to others. Newark, DE 19713. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . Introduction. 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. Are there any missing values in the data? 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. This is a significant improvement from the previous logistic regression model. 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 pipeline I built for prediction reflects these aspects of the dataset. 5 minute read. Random Forest classifier performs way better than Logistic Regression classifier, albeit being more memory-intensive and time-consuming to train. Each employee is described with various demographic features. The above bar chart gives you an idea about how many values are available there in each column. I also wanted to see how the categorical features related to the target variable. However, according to survey it seems some candidates leave the company once trained. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Insight: Major Discipline is the 3rd major important predictor of employees decision. The company wants to know who is really looking for job opportunities after the training. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. 10-Aug-2022, 10:31:15 PM Show more Show less It still not efficient because people want to change job is less than not. Variable 3: Discipline Major It is a great approach for the first step. Not own the content of the Analysis as presented in this post and in my notebook. Format because sklearn can not handle them directly decision Science Analytics, Human! 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