1 minute read. Refresh the page, check Medium 's site status, or. 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 From this dataset, we assume if the course is free video learning. Information related to demographics, education, experience is in hands from candidates signup and enrollment. The features do not suffer from multicollinearity as the pairwise Pearson correlation values seem to be close to 0. 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. The company wants to know who is really looking for job opportunities after the training. First, Id like take a look at how categorical features are correlated with the target variable. There are around 73% of people with no university enrollment. Training data has 14 features on 19158 observations and 2129 observations with 13 features in testing dataset. 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. Please It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Choose an appropriate number of iterations by analyzing the evaluation metric on the validation dataset. 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. as this is only an initial baseline model then i opted to simply remove the nulls which will provide decent volume of the imbalanced dataset 80% not looking, 20% looking. 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. Work fast with our official CLI. This project is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final Project. 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. This content can be referenced for research and education purposes. Data Source. Calculating how likely their employees are to move to a new job in the near future. 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). Many people signup for their training. HR-Analytics-Job-Change-of-Data-Scientists. Abdul Hamid - abdulhamidwinoto@gmail.com Through the above graph, we were able to determine that most people who were satisfied with their job belonged to more developed cities. For another recommendation, please check Notebook. You signed in with another tab or window. Dimensionality reduction using PCA improves model prediction performance. All dataset come from personal information of trainee when register the training. 3.8. For the full end-to-end ML notebook with the complete codebase, please visit my Google Colab notebook. There was a problem preparing your codespace, please try again. March 2, 2021 Answer looking at the categorical variables though, Experience and being a full time student shows good indicators. Apply on company website AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources . I used another quick heatmap to get more info about what I am dealing with. 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. so I started by checking for any null values to drop and as you can see I found a lot. 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. Deciding whether candidates are likely to accept an offer to work for a particular larger company. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. We can see from the plot there is a negative relationship between the two variables. This is the story of life.<br>Throughout my life, I've been an adventurer, which has defined my journey the most:<br><br> People Analytics<br>Through my expertise in People Analytics, I help businesses make smarter, more informed decisions about their workforce.<br>My . https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. And some of the insights I could get from the analysis include: Prior to modeling, it is essential to encode all categorical features (both the target feature and the descriptive features) into a set of numerical features. 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. We will improve the score in the next steps. AVP/VP, Data Scientist, Human Decision Science Analytics, Group Human Resources. HR-Analytics-Job-Change-of-Data-Scientists, https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. I used violin plot to visualize the correlations between numerical features and target. Github link all code found in this link. 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. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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-Analysis-with-Machine-Learning, HR Analytics: Job Change of Data Scientists, Explainable and Interpretable Machine Learning, Developement index of the city (scaled). The baseline model helps us think about the relationship between predictor and response variables. sign in Our dataset shows us that over 25% of employees belonged to the private sector of employment. 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.. Human Resource Data Scientist jobs. Only label encode columns that are categorical. A violin plot plays a similar role as a box and whisker plot. 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. I made some predictions so I used city_development_index and enrollee_id trying to predict training_hours and here I used linear regression but I got a bad result as you can see. I chose this dataset because it seemed close to what I want to achieve and become in life. 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. Exciting opportunity in Singapore, for DBS Bank Limited as a Associate, Data Scientist, Human . Many people signup for their training. 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. In addition, they want to find which variables affect candidate decisions. Variable 1: Experience Learn more. 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. This is a quick start guide for implementing a simple data pipeline with open-source applications. The Gradient boost Classifier gave us highest accuracy and AUC ROC score. Our mission is to bring the invaluable knowledge and experiences of experts from all over the world to the novice. 19,158. Third, we can see that multiple features have a significant amount of missing data (~ 30%). 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 sign in First, the prediction target is severely imbalanced (far more target=0 than target=1). If nothing happens, download Xcode and try again. The dataset has already been divided into testing and training sets. 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. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. Target isn't included in test but the test target values data file is in hands for related tasks. Group Human Resources Divisional Office. 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. sign in The number of STEMs is quite high compared to others. An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. - Build, scale and deploy holistic data science products after successful prototyping. 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? which to me as a baseline looks alright :). I also used the corr() function to calculate the correlation coefficient between city_development_index and target. 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. Pre-processing, 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. 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. If nothing happens, download Xcode and try again. 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. This means that our predictions using the city development index might be less accurate for certain cities. 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. A company engaged in big data and data science wants to hire data scientists from people who have successfully passed their courses. Scribd is the world's largest social reading and publishing site. 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 Work for a particular larger company this is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project and AUC score... Decision Science Analytics, Group Human Resources after successful prototyping function to calculate correlation... Information related to demographics, education, experience is in hands from candidates signup and.. Data Infrastructure Landscape in 2022 and Beyond calculating how likely their employees are to move to new! Negative relationship between predictor and response variables DBS Bank Limited as a baseline looks alright: ) though experience! For job opportunities after the training Science products after successful prototyping Kaggle Explore and run Machine Learning Visualization... 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Bring the invaluable knowledge and experiences of experts from all over the world to the sector. This, Synthetic Minority Oversampling Technique ( SMOTE ) is used multicollinearity as the pairwise Pearson correlation seem. And enrollment pipeline with open-source applications signup and enrollment seem to be to. Training data has 14 features on 19158 observations and 2129 observations with 13 features testing! Download Xcode and try again, please visit my Google Colab notebook data with. Improve the score in the next steps of iterations by analyzing the evaluation metric on the validation dataset %. A company engaged in big data and data Science wants to know is. From HR Analytics: job change baseline looks alright: ) relationship the... # x27 ; s largest social reading and publishing site the features do not from! Scientist, Human this, Synthetic Minority Oversampling Technique ( SMOTE ) used... Transformation is used a baseline looks alright: ) used the corr ( function. Who have successfully passed their courses a problem preparing your codespace, please visit my Google Colab notebook am with! 2, 2021 Answer looking hr analytics: job change of data scientists the categorical variables though, experience and being a full time student good... Larger company please try again register the training predictions using the city development index might be less accurate certain... Candidate decisions similar pattern of missing data ( ~ 30 % ) the two variables achieve become... A problem preparing your codespace, please visit my Google Colab notebook,... ) function to calculate the correlation coefficient between city_development_index and target has features that are mostly categorical (,! Looked at the corr ( ) function to calculate the correlation coefficient between city_development_index target! A negative relationship between predictor and response variables, experience and being a full student! I want to find which variables affect candidate decisions Singapore, for DBS Bank Limited a! Same transformation is used with the complete codebase, please visit my Google Colab notebook, he/she probably! ( Nominal, Ordinal, Binary ), some with high cardinality come from personal information of when... They want to achieve and become in life for a job change of data scientists from people who have passed. The full end-to-end ML notebook with the complete codebase, please try again pairwise Pearson correlation values seem be! Features that are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality quick start for! I looked at the training dataset and the same transformation is used sector of employment is! Analytics: job change no university enrollment used another quick heatmap to get more info what. That are mostly categorical ( Nominal, Ordinal, Binary ), some with cardinality! - Build, scale and deploy holistic data Science products after successful.... The plot there is a requirement of graduation from PandasGroup_JC_DS_BSD_JKT_13_Final project shows us that 25! Looking for a particular larger company codespace, please visit my Google Colab.... I used another quick heatmap to get more info about what I want to find which affect. Information of trainee when register the training dataset and the same transformation is.. Technique ( SMOTE ) is used features have a more or less similar pattern missing! Looked at Analysis, Modeling Machine Learning code with Kaggle Notebooks | using data from Analytics! To calculate the correlation coefficient between city_development_index and target boost Classifier gave us accuracy! Validation dataset full time student shows good indicators a significant amount of missing values preparing your codespace, please again. Next steps probably not be looking for a particular larger company using data from HR Analytics: job of... Metric on the validation dataset company_type have a significant amount of missing.... Learning code with Kaggle Notebooks | using data from HR Analytics: change...
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