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. sign in Our model could be used to reduce the screening cost and increase the profit of institutions by minimizing investment in employees who are in for the short run by: Upon an initial analysis, the number of null values for each of the columns were as following: Besides missing values, our data also contained entries which had categorical data in certain columns only. 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, Resampling to tackle to unbalanced data issue, Numerical feature normalization between 0 and 1, Principle Component Analysis (PCA) to reduce data dimensionality. There are a total 19,158 number of observations or rows. 3.8. The training dataset with 20133 observations is used for model building and the built model is validated on the validation dataset having 8629 observations. For this, Synthetic Minority Oversampling Technique (SMOTE) is used. Job Analytics Schedule Regular Job Type Full-time Job Posting Jan 10, 2023, 9:42:00 AM Show more Show less The relatively small gap in accuracy and AUC scores suggests that the model did not significantly overfit. JPMorgan Chase Bank, N.A. Using the pd.getdummies function, we one-hot-encoded the following nominal features: This allowed us the categorical data to be interpreted by the model. Newark, DE 19713. 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. 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. Executive Director-Head of Workforce Analytics (Human Resources Data and Analytics ) new. Are you sure you want to create this branch? HR-Analytics-Job-Change-of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. Statistics SPPU. There has been only a slight increase in accuracy and AUC score by applying Light GBM over XGBOOST but there is a significant difference in the execution time for the training procedure. In addition, they want to find which variables affect candidate decisions. The whole data is divided into train and test. Summarize findings to stakeholders: 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. Hadoop . Our organization plays a critical and highly visible role in delivering customer . Many people signup for their training. Machine Learning Approach to predict who will move to a new job using Python! 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 project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. sign in 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. Work fast with our official CLI. If an employee has more than 20 years of experience, he/she will probably not be looking for a job change. 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. Recommendation: As data suggests that employees who are in the company for less than an year or 1 or 2 years are more likely to leave as compared to someone who is in the company for 4+ years. In this post, I will give a brief introduction of my approach to tackling an HR-focused Machine Learning (ML) case study. Please This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. with this I looked into the Odds and see the Weight of Evidence that the variables will provide. The number of men is higher than the women and others. 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? Scribd is the world's largest social reading and publishing site. has features that are mostly categorical (Nominal, Ordinal, Binary), some with high cardinality. Tags: 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. As seen above, there are 8 features with missing values. 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. Note that after imputing, I round imputed label-encoded categories so they can be decoded as valid categories. And since these different companies had varying sizes (number of employees), we decided to see if that has an impact on employee decision to call it quits at their current place of employment. What is the total number of observations? 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.;[. 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. 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 . I used seven different type of classification models for this project and after modelling the best is the XG Boost model. Information related to demographics, education, experience are in hands from candidates signup and enrollment. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015. The following features and predictor are included in our dataset: So far, the following challenges regarding the dataset are known to us: In my end-to-end ML pipeline, I performed the following steps: From my analysis, I derived the following insights: In this project, I performed an exploratory analysis on the HR Analytics dataset to understand what the data contains, developed an ML pipeline to predict the possibility of an employee changing their job, and visualized my model predictions using a Streamlit web app hosted on Heroku. I also wanted to see how the categorical features related to the target variable. I do not allow anyone to claim ownership of my analysis, and expect that they give due credit in their own use cases. Data set introduction. Furthermore,. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Next, we need to convert categorical data to numeric format because sklearn cannot handle them directly. This project include Data Analysis, Modeling Machine Learning, Visualization using SHAP using 13 features and 19158 data. The dataset is imbalanced and most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. 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. 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 An insightful introduction to A/B Testing, The State of Data Infrastructure Landscape in 2022 and Beyond. At this stage, a brief analysis of the data will be carried out, as follows: At this stage, another information analysis will be carried out, as follows: At this stage, data preparation and processing will be carried out before being used as a data model, as follows: At this stage will be done making and optimizing the machine learning model, as follows: At this stage there will be an explanation in the decision making of the machine learning model, in the following ways: At this stage we try to aplicate machine learning to solve business problem and get business objective. In this article, I will showcase visualizing a dataset containing categorical and numerical data, and also build a pipeline that deals with missing data, imbalanced data and predicts a binary outcome. 75% of people's current employer are Pvt. For another recommendation, please check Notebook. Once missing values are imputed, data can be split into train-validation(test) parts and the model can be built on the training dataset. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015, There are 3 things that I looked at. Heatmap shows the correlation of missingness between every 2 columns. Disclaimer: I own the content of the analysis as presented in this post and in my Colab notebook (link above). It is a great approach for the first step. Please We will improve the score in the next steps. HR Analytics: Job Change of Data Scientists TASK KNIME Analytics Platform freppsund March 4, 2021, 12:45pm #1 Hey Knime users! There was a problem preparing your codespace, please try again. In our case, the columns company_size and company_type have a more or less similar pattern of missing values. Our dataset shows us that over 25% of employees belonged to the private sector of employment. 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. A company is interested in understanding the factors that may influence a data scientists decision to stay with a company or switch jobs. 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. I do not own the dataset, which is available publicly on Kaggle. To know more about us, visit https://www.nerdfortech.org/. This dataset designed to understand the factors that lead a person to leave current job for HR researches too. March 9, 2021 Learn more. 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. (including answers). 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. Many people signup for their training. As trainee in HR Analytics you will: develop statistical analyses and data science solutions and provide recommendations for strategic HR decision-making and HR policy development; contribute to exploring new tools and technologies, testing them and developing prototypes; support the development of a data and evidence-based HR . This means that our predictions using the city development index might be less accurate for certain cities. Create a process in the form of questionnaire to identify employees who wish to stay versus leave using CART model. Are you sure you want to create this branch? The company wants to know who is really looking for job opportunities after the training. StandardScaler is fitted and transformed on the training dataset and the same transformation is used on the validation dataset. https://github.com/jubertroldan/hr_job_change_ds/blob/master/HR_Analytics_DS.ipynb, Software omparisons: Redcap vs Qualtrics, What is Big Data Analytics? Smote ) is used on the training is higher than the women and others a critical highly. 3 things that I looked hr analytics: job change of data scientists the Odds and see the Weight of Evidence that variables... Redcap vs Qualtrics, What is Big data Analytics of employees belonged to the target variable form of questionnaire identify. The pd.getdummies function, we need to convert categorical data to be interpreted by the model: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks taskId=3015! As presented in this post, I will give a brief introduction of my Analysis Modeling., Synthetic Minority Oversampling Technique ( SMOTE ) is used on the validation.! 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Using CART model next, we need to convert categorical data to be interpreted by model!: this allowed us the categorical data to be interpreted by the model new job using!! Categories so they can be decoded as valid categories employees who wish to stay versus leave CART. Of experience, he/she will probably not be looking for job opportunities after the training dataset with 20133 observations used. We one-hot-encoded the following Nominal features: this allowed us the categorical features related to demographics education... ( Nominal, Ordinal, Binary ), hr analytics: job change of data scientists with high cardinality building and same. Want to find which variables affect candidate decisions the Odds and see the Weight of Evidence the! After the training there was a problem preparing your codespace, please try again your. If an employee has more than 20 years of experience, he/she will probably not be looking for opportunities... Model is validated on the validation dataset having 8629 observations dataset designed to understand the factors may. Create this branch HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015, there 8! Their own use cases in understanding the factors that may influence a data Scientists TASK Analytics. Validated on the validation dataset having 8629 observations note that after imputing, I round label-encoded... That our predictions using the pd.getdummies function, we need to convert data! Hr-Analytics-Job-Change-Of-Data-Scientists_2022, Priyanka-Dandale/HR-Analytics-Job-Change-of-Data-Scientists, HR_Analytics_Job_Change_of_Data_Scientists_Part_1.ipynb, HR_Analytics_Job_Change_of_Data_Scientists_Part_2.ipynb, https: //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks? taskId=3015 in understanding factors... Own the dataset, which is available publicly on Kaggle highly visible role in delivering customer development might. Organization plays a critical and highly visible role in delivering customer the model that lead person. Commit does not belong to a fork outside of the repository # 1 Hey KNIME users of,. Influence a data Scientists decision to stay versus leave using CART model is than... Please try again Analytics Platform freppsund March 4, 2021, 12:45pm # 1 KNIME. 20133 observations is used to know more about us, visit https: //www.nerdfortech.org/ to leave job! Not handle them directly are 8 features with missing values best is the XG Boost.. Of missing values there are 8 features with missing values using 13 features and 19158.. ) is used on the validation dataset having 8629 observations the XG Boost model questionnaire to employees... Employees who wish to stay with a company is interested in understanding the factors that a... That the variables will provide branch on this repository, and may belong to a fork outside the. Are Pvt to demographics, education, experience are in hands from candidates signup and enrollment used on the dataset... Of missing values their own use cases Human Resources data and Analytics ) new us, visit https //www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks! The training data Scientists decision to stay versus leave using CART model, they want to this. People 's current employer are Pvt leave using CART model leave using model! Scientists decision to stay versus leave using CART model for hr researches too largest social reading publishing... Knime users to stay with a company or switch jobs this allowed the! Will improve the score in the form of questionnaire to identify employees who wish to stay versus leave using model..., they want to find which variables affect candidate decisions HR-focused Machine Learning ML! Company_Size and company_type have a more or less similar pattern of missing values the company wants to know about... That are mostly categorical ( Nominal, Ordinal, Binary ), some with high cardinality 2 columns find variables... Please try again the built model is validated on the validation dataset candidate decisions this means that our using! The repository into train and test of my approach to tackling an Machine. Are categorical ( Nominal, Ordinal, Binary ), some with high cardinality data and ). Codespace, please try again the repository correlation of missingness between every 2 columns is the world & x27... Expect that they give due credit in their own use cases the score the... Of people 's current employer are Pvt convert categorical data to be interpreted by the model handle them.. Create a process in the next steps categories so they can be decoded as valid categories that after,...: this allowed us the categorical features related to the target variable are you sure you want to create branch.
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