I have performed a comprehensive Employee Performance and Attrition Analysis using Python in Jupyter Notebook, applying statistical analysis, data visualization, and exploratory data analysis (EDA) to uncover key insights related to employee behavior, performance, and retention.

The project includes:

Data Processing & Analysis

Data loading, inspection, and cleaning
Summary statistics and distribution analysis
Handling categorical and numerical variables
Use of histograms and count plots for understanding patterns

Key Business Questions Answered

Mean and standard deviation of employee hourly rate
Salary hike trends for employees with 10+ years of experience
Distribution and quartiles of total work experience
Correlation between experience and performance rating
Relationship between job satisfaction and environment satisfaction
Average years with current manager by job role
Comparison of satisfaction levels between employees who left vs stayed
Impact of promotions on attrition
Effect of number of companies worked on performance
Influence of business travel on attrition
Relationship between age, experience, and performance
Department-wise attrition analysis

Techniques Used

Pandas for data manipulation
Matplotlib & Seaborn for visualization
Correlation analysis
Descriptive statistics
HR analytics and business insights

This project is useful for HR teams, people analytics professionals, business analysts, and organizations looking to understand workforce performance, engagement, and retention patterns.

HR Analytics: Employee Performance Analysis in Python

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