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Data-science-materials-Useful-for-all-Data-science-professionals
Analytics Guide bookData-science-Interview-preparation
ibm-hr-analytics-attrition-dataset
Business Problem IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees. Attrition is a problem that impacts all businesses, irrespective of geography, industry and size of the company. Employee attrition leads to significant costs for a business, including the cost of business disruption, hiring new staff and training new staff. As such, there is great business interest in understanding the drivers of, and minimizing staff attrition. In this context, the use of classification models to predict if an employee is likely to quit could greatly increase the HR’s ability to intervene on time and remedy the situation to prevent attrition. While this model can be routinely run to identify employees who are most likely to quit, the key driver of success would be the human element of reaching out the employee, understanding the current situation of the employee and taking action to remedy controllable factors that can prevent attrition of the employee. This data set presents an employee survey from IBM, indicating if there is attrition or not. The data set contains approximately 1500 entries. Given the limited size of the data set, the model should only be expected to provide modest improvement in indentification of attrition vs a random allocation of probability of attrition. While some level of attrition in a company is inevitable, minimizing it and being prepared for the cases that cannot be helped will significantly help improve the operations of most businesses. As a future development, with a sufficiently large data set, it would be used to run a segmentation on employees, to develop certain “at risk” categories of employees. This could generate new insights for the business on what drives attrition, insights that cannot be generated by merely informational interviews with employees. Uncover the factors that lead to employee attrition and explore important questions such as ‘show me a breakdown of distance from home by job role and attrition’ or ‘compare average monthly income by education and attrition’. This is a fictional data set created by IBM data scientists. Education 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor' EnvironmentSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobInvolvement 1 'Low' 2 'Medium' 3 'High' 4 'Very High' JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' PerformanceRating 1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding' RelationshipSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High' WorkLifeBalance 1 'Bad' 2 'Good' 3 'Better' 4 'Best' IBM HR Analytics Employee Attrition & Performance Predict attrition of your valuable employees IBM HR Analytics Employee Attrition & Performance IBM HR Analytics Employee Attrition & PerformanceHigs-Bonsons-and-Background-process
Data ScienceAI-tools
AI toolsPython-Basics
Python BasicsFlight-price-prediction
Credit-EDA-Case-study
Python-Introduction
PREDICTING-CUSTOMER-CHURN-FOR-A-TELE-COM-COMPANY
PROBLEM TYPE: CLASSIFICATIONInsurance
car-evaluation-dataset
car-evaluation-data-setPima-Indians-Diabetes-Database
HR-Analytics-Job-Change-of-Data-Scientists
HR Analytics Job Change of Data ScientistsIMDB-Movie-assignment
IMDb Movie Data Visualisation AssignmentCovid-19-Case-Surveillance-Public-Use-Dataset
Covid-19 Case Surveillance Public Use DatasetEmployee-Performance-Analysis
The goals of our project are to establish key learnings relating to employee performances of INX Future Inc. Various capabilities of Machine Learning have been used to analyze the data and find the core underlying causes of employee performance. The findings of this project will help identify factors which affect performance ratings, support right course of actions to address under-performance and present recommendations to improve hiring efficiencies to INX Future Inc. The dataset provided was of very good quality and required minimal cleansing work before proceeding to data exploration. One of the challenges faced was that categorical variables were high and it was required to convert them into numerical values thereby, initially increasing the complexity.Love Open Source and this site? Check out how you can help us