The IBM HR Analytics Employee Attrition & Performance dataset from the Kaggle. I have first performed Exploratory Data Analysis on the data using various libraries like pandas,seaborn,matplotlib etc.. Then I have plotted used feature selection techniques like RFE to select the features. The data is then oversampled using the SMOTE technique in order to deal with the imbalanced classes. Also the data is then scaled for better performance. Lastly I have trained many ML models from the scikit-learn library for predictive modelling and compared the performance using Precision, Recall and other metrics.

In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

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 & Performance

IBM HR Analytics Employee Attrition & Performance Analysis

IBM HR Analytics Employee Attrition & Performance

Attrition is a critical issue and pretty high in the industry these days. It’s the major problem which highlights in all the organizations. Though the term ‘ATTRITION’ is common, many would be at a loss to define what actually Attrition is, “Attrition is said to be the gradual reduction in the number of employees through retirement, resignation or death. It can also be said as Employee Turnover or Employee Defection” Whenever a well-trained and well-adapted employee leaves the organization, it creates a vacuum. So, the organization loses key skills, knowledge and business relationships. Modern managers and personnel administrators are greatly interested in reducing Attrition in the organization, in such a way that it will contribute to the maximum effectiveness, growth, and progress of the organization. Retaining employees is a critical and ongoing effort. One of the biggest challenges in having managers in the place that understands it is their responsibility to create and sustain an environment that fosters retention. Staff requires reinforcement, direction and recognition to grow and remain satisfied in their positions. Managers must recognize this and understand that establishing such fundamentals demonstrates their objectives to support nature and motivate their employees. The main objectives of this study are to know the reasons, why attrition occurs, to identify the factors which make employees dissatisfy, to know the satisfactory level of employees towards their job and working conditions and to find the areas where companiesare lagging behind.

Rice University - IBM HR Analytics Employee - Attrition & Performance Analysis

Final project for Statistical Learning

Project machine learning for school lab.

IBM HR Analytics Employee Attrition & Performance

IBM HR Analytics Employee Attrition & Performance

Predict attrition of your valuable employees

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🚀 Employee Performance Analysis using Python – Analyzing key factors like training hours, overtime, and salary to understand their impact on employee performance using Pandas, Seaborn, and NumPy. 📊✨

Analyzing IBM HR Analytics Employee Attrition & Performance using Decision Tree and Keras Deep Learning

IBM HR Analytics Employee Attrition and Performance

IBM HR Analytics Employee Attrition & Performance Project

IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction.

Challenge Kaggle || IBM HR Analytics Employee Attrition & Performance

This HR Attrition Prediction project uses a Decision Tree model to identify key factors causing employee turnover. Top drivers include Monthly Income, OverTime, and Total Working Years. The goal is to provide actionable insights for HR to boost retention and cut costs.

Project Title: IBM HR Analytics Employee Attrition & Performance, Tool: SQL, Domain: Data Analyst, Project Difficulties Level: Intermediate.

This repository contains the third project I developed during my Data Science internship at Unified Mentor.

This project focuses on analyzing employee attrition using IBM HR Analytics dataset. The goal is to uncover factors influencing attrition, perform exploratory data analysis (EDA), integrate SQL queries, and build machine learning models for prediction. A Streamlit dashboard was also developed for interactive exploration.

Attrition is a critical issue and pretty high in the industry these days. It’s the major problem which highlights in all the organizations. Though the term ‘ATTRITION’ is common, many would be at a loss to define what actually Attrition is, “Attrition is said to be the gradual reduction in the number of employees through retirement, resignation or death. It can also be said as Employee Turnover or Employee Defection” Whenever a well-trained and well-adapted employee leaves the organization, it creates a vacuum. So, the organization loses key skills, knowledge and business relationships. Modern managers and personnel administrators are greatly interested in reducing Attrition in the organization, in such a way that it will contribute to the maximum effectiveness, growth, and progress of the organization. Retaining employees is a critical and ongoing effort. One of the biggest challenges in having managers in the place that understands it is their responsibility to create and sustain an environment that fosters retention. Staff requires reinforcement, direction and recognition to grow and remain satisfied in their positions. Managers must recognize this and understand that establishing such fundamentals demonstrates their objectives to support nature and motivate their employees. The main objectives of this study are to know the reasons, why attrition occurs, to identify the factors which make employees dissatisfy, to know the satisfactory level of employees towards their job and working conditions and to find the areas where companies lagging behind

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My machine learning roject for predicting employee attrition.

Python online course

IBM HR Analytics Employee Attrition & Performance. Predict attrition of your valuable employees

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