HR Analysis

An end-to-end HR data analysis project built in Python, covering data cleaning, exploratory data analysis, and business insight generation across 1000+ employee records.

Overview

This project analyzes HR data to uncover patterns in employee demographics, performance, attrition, and compensation. The goal is to provide actionable insights that support data-driven HR decisions.

Sample Visualizations

Project Structure

hr-analysis/
├── data/               # Raw and processed datasets
├── plots/              # Generated visualizations
├── report/             # Final report output
├── hr_analysis.ipynb   # Main analysis notebook
├── requirements.txt
└── .gitignore

Key Analyses

• Data Cleaning — handling missing values, type casting, outlier detection
• Exploratory Data Analysis (EDA) — distributions, correlations, and group comparisons
• Attrition Analysis — identifying factors that predict employee turnover
• Department & Role Insights — performance and compensation breakdowns by department
• Business Recommendations — data-backed suggestions for HR strategy

Setup

# Clone the repo
git clone https://github.com/horridhaider/hr-analysis.git
cd hr-analysis

# Install dependencies
pip install -r requirements.txt

# Launch the notebook
jupyter notebook hr_analysis.ipynb

Requirements

• Python 3.8+
• See requirements.txt for full list of dependencies

Dataset

The dataset contains 1000+ employee records with fields including department, job role, salary, performance rating, years at company, and attrition status.

License

MIT