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HR Analytics with SQL & Python

This project explores employee attrition patterns using a mix of PostgreSQL for querying and Python for analysis & visualization.
It is designed as a portfolio project — showing end-to-end data handling, from raw CSV → database → SQL analysis → Python stats → machine learning → reproducible deliverables.

📂 Project Structure

HR-Analytics-SQL-Python/
├─ queries/
│    ├─ basic_checks.sql
│    ├─ overall_attrition.sql
│ 	 ├─ dept_attrition.sql
│ 	 ├─ salary_analysis.sql
│ 	 ├─ jobrole_attrition.sql
│ 	 ├─ tenure_attrition.sql
│ 	 ├─ overtime_attrition.sql
│ 	 └─ age_gender_attrition.sql
│ 	 
├─ notebook/
│    ├─ hr_analysis.ipnyb
│    ├─ eda_summary.ipnyb
│    └─ figures/
│    		├─univariate/
│    		│ 	├─ age_distribution.png
│    		│ 	├─ attribution_balance.png
│    		│ 	├─ department_counts.png
│    		│ 	├─ monthly_income_distribution.png
│    		│ 	└─ overtime_counts.png
│    		│ 	
│    		├─bivariate/
│    		│ 	├─ attrition_vs_age.png
│   		│ 	├─ attrition_vs_dept.png
│    		│ 	├─ attrition_vs_gender.png
│    		│ 	├─ attrition_vs_jobrole.png
│    		│ 	├─ attrition_vs_maritalstatus.png
│    		│ 	├─ attrition_vs_monthlyincome.png
│    		│ 	├─ attrition_vs_overtime.png
│    		│ 	└─ attrition_vs_tenure.png
│    		│ 	
│    		└──eda_summary/
│    			├─ age_tenure_analysis.png
│    			├─ attrition.png
│    			├─ attrition_gender_maritalstatus.png
│    			├─ dept_and_jobroles.png
│    			├─ monthlyincome_vs_attrition.png
│    			└─ overtime_impact.png
├─ data/
│    └─ ibm_hr.csv
│ 
├─ results/
│    ├─ overall_attrition.csv
│    ├─ dept_attrition.csv
│    ├─ salary_analysis.csv
│    ├─ jobrole_attrition.csv
│    ├─ tenure_attrition.csv
│    ├─ overtime_attrition.csv
│    └─ age_gender_attrition.csv
│
├─ LICENSE	
├─ requirements.txt
└─ README.md # Project Overview (this file)

🎯 Objectives

  • Understand employee attrition drivers using SQL queries
  • Perform exploratory data analysis (EDA) with Python
  • Run statistical checks (e.g., overtime vs attrition significance)
  • Build a basic predictive model (logistic regression)
  • Deliver visuals, CSV outputs, and notebooks in a clean repo

🛠️ Tools Used

  • Database: PostgreSQL
  • Querying: SQL
  • Analysis: Python (Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, Scikit-learn)
  • Version Control: Git + GitHub

📊 Key Findings

  • Overall attrition: ~16% of employees
  • Departmental differences: R&D had higher attrition vs HR & Sales
  • Overtime: Employees working overtime are significantly more likely to leave
  • Job roles: Sales reps & lab technicians showed higher attrition
  • Income effect: Lower monthly income strongly correlates with attrition
  • Predictive model: Logistic regression flagged Overtime, MonthlyIncome, JobRole, and Age as the strongest predictors.

📊 Key Findings (Simple Version)

  • About 1 in 6 employees left the company during the observed period.
  • Working overtime is one of the biggest factors linked to leaving.
  • Lower salary employees tend to leave more often than higher salary ones.
  • Certain job roles and departments (like Sales and Lab Techs) have higher turnover.
  • Younger employees are more likely to quit compared to older ones.

📌 Deliverables

  • Reproducible SQL queries → queries/
  • Query outputs → results/
  • Python notebooks with EDA, stats, and model → notebook/
  • Figures for visualization → notebook/figures/
  • README.md (project overview)

🚀 How to Run

  1. Clone this repo

    git clone https://github.com/ajx7/HR-Analytics-SQL-Python.git
cd HR-Analytics-SQL-Python

  2. Install requirements

    pip install -r requirements.txt

  3. Set up PostgreSQL DB

    • Create a database
    • Import data/ibm_hr.csv

  4. Run SQL scripts in queries/

  5. Open notebooks in notebook/ for Python analysis

📖 Dataset Source

IBM HR Analytics Employee Attrition & Performance dataset (publicly available on Kaggle): IBM HR Analytics Dataset – Kaggle

⚖️ License

This project is licensed under the MIT License — free to use with attribution.

About

This project was built as part of a portfolio refresh, showing end-to-end data analysis and reproducibility skills.