HR-Employee-Attrition-SQL

HR Employee Attrition Analysis Project using SQL

Project Overview

This project is a complete end-to-end HR Employee Attrition analysis using MySQL. The dataset contains detailed employee information, and this project demonstrates skills in:

• Database design and schema creation
• Data loading and validation
• Data cleaning and preprocessing
• Basic, intermediate, and advanced analytical queries

The goal of this project is to showcase SQL expertise and analytical thinking suitable for entry-level data analyst roles.

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Dataset

• Source file: hr_employee_attrition.csv
• File format: CSV (UTF-8)
• Contains columns such as Age, Department, JobRole, MonthlyIncome, Attrition, OverTime, YearsAtCompany, and many others.

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Project Structure

HR-Employee-Attrition-SQL/
│
├── dataset/
│   └── hr_employee_attrition.csv
│
├── schema/
│   └── create_schema.sql
│
├── data_loading/
│   ├── load_data.sql          # Documentation of CSV import
│   └── validate_data.sql      # Validation queries post-import
│
├── cleaning/
│   └── data_cleaning.sql      # Data cleaning & preprocessing queries
│
├── analysis/
│   ├── basic_queries.sql      # Basic analysis queries
│   ├── intermediate_queries.sql # Intermediate analysis queries
│   └── advanced_queries.sql   # Advanced analysis queries
│
└── README.md                 # Project documentation

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Step 1: Database Creation

• Database: hr_analytics
• Table: employee_attrition
• Primary Key: Employee_id (AUTO_INCREMENT)
• Columns include all HR features such as Age, Attrition, Department, JobRole, MonthlyIncome, OverTime, YearsAtCompany, etc.
• Schema stored in: schema/create_schema.sql

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Step 2: Data Loading

• CSV file imported using MySQL Workbench Table Data Import Wizard

• Header row included, Employee_id auto-generated

• Post-import validation queries ensure:

  • Correct row count
  • Auto-increment integrity
  • No NULL or invalid ENUM values

• Files:

  • data_loading/load_data.sql (documentation)
  • data_loading/validate_data.sql (validation queries)

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Step 3: Data Cleaning & Preprocessing

• Detect duplicates excluding Employee_id
• Check NULL values across columns
• Standardize categorical data (Attrition, Gender, OverTime, BusinessTravel, MaritalStatus, Department)
• Validate numeric ranges (Age, YearsAtCompany, MonthlyIncome, PercentSalaryHike)
• Logical consistency checks (e.g., YearsInCurrentRole ≤ YearsAtCompany)
• Derived columns: age groups, income bands, attrition flags
• File: cleaning/data_cleaning.sql

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Step 4: Analysis Queries

Basic Queries

• Total employees
• Attrition counts & percentages
• Department-wise counts
• Average income by job role
• Distinct job roles
• File: analysis/basic_queries.sql

Intermediate Queries

• Attrition rate by department
• Top 5 job roles with highest attrition
• Average income by Attrition status
• Employees with >10 years who left
• Average age by job level
• File: analysis/intermediate_queries.sql

Advanced Queries

• Department ranking by attrition rate
• Overtime correlation with attrition
• High-risk employee segments based on multiple factors
• File: analysis/advanced_queries.sql

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Tools & Technologies

• MySQL 8.x
• MySQL Workbench
• GitHub (for version control and project publishing)
• Dataset: HR Employee Attrition CSV

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Key Skills Demonstrated

• Database design & schema creation
• Data loading, validation, and cleaning
• Advanced SQL queries: aggregates, window functions, conditional logic
• Analytical thinking for HR attrition insights
• Professional GitHub project structure

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How to Use This Project

1. Clone the repository from GitHub.
2. Use schema/create_schema.sql to create the database and table.
3. Load the CSV using MySQL Workbench as documented in data_loading/load_data.sql.
4. Run data_loading/validate_data.sql to verify successful import.
5. Execute queries from cleaning/data_cleaning.sql for data quality checks.
6. Perform analyses using analysis/*.sql files.

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Author: S Mohan