I used violin plot to visualize the correlations between numerical features and target. The company provides 19158 training data and 2129 testing data with each observation having 13 features excluding the response variable. 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. Learn more. Does more pieces of training will reduce attrition? to use Codespaces. A tag already exists with the provided branch name. 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? More. If nothing happens, download Xcode and try again. Introduction The companies actively involved in big data and analytics spend money on employees to train and hire them for data scientist positions. I am pretty new to Knime analytics platform and have completed the self-paced basics course. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Are you sure you want to create this branch? Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. Hadoop . By model(s) that uses the current credentials,demographics,experience data you will 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. Introduction. Employees with less than one year, 1 to 5 year and 6 to 10 year experience tend to leave the job more often than others. The feature dimension can be reduced to ~30 and still represent at least 80% of the information of the original feature space. I ended up getting a slightly better result than the last time. 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. JPMorgan Chase Bank, N.A. Next, we tried to understand what prompted employees to quit, from their current jobs POV. Do years of experience has any effect on the desire for a job change? Information related to demographics, education, experience is in hands from candidates signup and enrollment. AVP, Data Scientist, HR Analytics. Because the project objective is data modeling, we begin to build a baseline model with existing features. 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. So I performed Label Encoding to convert these features into a numeric form. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target, The dataset is imbalanced. Information regarding how the data was collected is currently unavailable. Many people signup for their training. Please Many people signup for their training. Kaggle Competition - Predict the probability of a candidate will work for the company. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. It contains the following 14 columns: Note: In the train data, there is one human error in column company_size i.e. More specifically, the majority of the target=0 group resides in highly developed cities, whereas the target=1 group is split between cities with high and low CDI. 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. The whole data is divided into train and test. 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. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. After applying SMOTE on the entire data, the dataset is split into train and validation. Dont label encode null values, since I want to keep missing data marked as null for imputing later. StandardScaler removes the mean and scales each feature/variable to unit variance. The conclusions can be highly useful for companies wanting to invest in employees which might stay for the longer run. Question 1. Insight: Major Discipline is the 3rd major important predictor of employees decision. maybe job satisfaction? 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. 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. This is in line with our deduction above. 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. Data set introduction. HR Analytics: Job Change of Data Scientists. Question 3. 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. Tags: Are you sure you want to create this branch? 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. 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 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. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less 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 nothing happens, download Xcode and try again. I also wanted to see how the categorical features related to the target variable. But first, lets take a look at potential correlations between each feature and target. Insight: Lastnewjob is the second most important predictor for employees decision according to the random forest model. sign in The source of this dataset is from Kaggle. Taking Rumi's words to heart, "What you seek is seeking you", life begins with discoveries and continues with becomings. 3.8. Please refer to the following task for more details: Our organization plays a critical and highly visible role in delivering customer . Target isn't included in test but the test target values data file is in hands for related tasks. Work fast with our official CLI. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. XGBoost and Light GBM have good accuracy scores of more than 90. What is the effect of company size on the desire for a job change? 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. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. 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. What is the total number of observations? Target isn't included in test but the test target values data file is in hands for related tasks. Please Metric Evaluation : 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. Statistics SPPU. 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. Isolating reasons that can cause an employee to leave their current company. Scribd is the world's largest social reading and publishing site. 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. 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 . 1 minute read. Hiring process could be time and resource consuming if company targets all candidates only based on their training participation. 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. Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Identify important factors affecting the decision making of staying or leaving using MeanDecreaseGini from RandomForest model. If nothing happens, download GitHub Desktop and try again. Here is the link: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. Many people signup for their training. This content can be referenced for research and education purposes. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. 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. Each employee is described with various demographic features. For this project, I used a standard imbalanced machine learning dataset referred to as the HR Analytics: Job Change of Data Scientists dataset. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. If nothing happens, download GitHub Desktop and try again. 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. What is the maximum index of city development? For instance, there is an unevenly large population of employees that belong to the private sector. 3. March 9, 2021 1 minute read. 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! How much is YOUR property worth on Airbnb? 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. A violin plot plays a similar role as a box and whisker plot. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Not at all, I guess! This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. There are more than 70% people with relevant experience. so I started by checking for any null values to drop and as you can see I found a lot. Then I decided the have a quick look at histograms showing what numeric values are given and info about them. 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