Employee Performance Analytics Hr

# Employee Performance Analytics HR Skill

> Skill by [ara.so](https://ara.so) — Data Skills collection.

## Overview

Employee Performance Analytics is a Python and SQL-based HR analytics tool that transforms employee data into actionable insights. It uses SQLite for KPI aggregation and pandas/matplotlib for visualization, generating departmental performance reports, efficiency metrics, and workload analysis.

The project provides an end-to-end analytics pipeline: data loading → SQL feature engineering → Python analysis → visualization exports.

## Installation

```bash
# Clone the repository
git clone https://github.com/AmirhosseinHonardoust/Employee-Performance-Analytics.git
cd Employee-Performance-Analytics

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
```

### Dependencies

```txt
pandas>=2.0.0
matplotlib>=3.7.0
seaborn>=0.12.0
sqlite3  # Built-in with Python
numpy>=1.24.0
```

## Project Structure

```
employee-performance-analytics/
├── data/
│   └── employees.csv          # Raw employee data
├── src/
│   ├── create_db.py          # CSV to SQLite loader
│   ├── queries.sql           # SQL KPI queries
│   ├── analyze_performance.py # Main analysis script
│   └── utils.py              # Helper functions
└── outputs/
    ├── department_kpis.csv
    ├── performance_summary.csv
    └── charts/               # Generated visualizations
```

## Data Schema

The project expects employee data with these columns:

| Column | Type | Description |
|--------|------|-------------|
| `employee_id` | int | Unique identifier |
| `name` | string | Employee name |
| `department` | string | Department (Engineering, Sales, etc.) |
| `role` | string | Job title |
| `date` | date | Record date (YYYY-MM-DD) |
| `tasks_completed` | int | Daily tasks completed |
| `hours_worked` | float | Hours worked |
| `rating` | float | Performance rating (1-5) |
| `projects` | int | Active projects |
| `absences` | int | 1 if absent, 0 otherwise |

## Key Commands

### 1. Load Data into SQLite

```bash
python src/create_db.py --csv data/employees.csv --db hr.db
```

**Options:**
- `--csv`: Path to input CSV file
- `--db`: Output SQLite database path
- `--table`: Table name (default: `employees`)

### 2. Run Performance Analysis

```bash
python src/analyze_performance.py --db hr.db --sql src/queries.sql --outdir outputs
```

**Options:**
- `--db`: Path to SQLite database
- `--sql`: Path to SQL queries file
- `--outdir`: Output directory for CSV reports and charts

## Core SQL Queries

The `queries.sql` file contains three main analytical views:

### Department KPIs

```sql
-- Department-level performance metrics
CREATE VIEW IF NOT EXISTS department_kpis AS
SELECT
    department,
    COUNT(DISTINCT employee_id) AS employee_count,
    AVG(rating) AS avg_rating,
    SUM(tasks_completed) AS total_tasks,
    SUM(hours_worked) AS total_hours,
    ROUND(AVG(CAST(absences AS FLOAT)), 2) AS absence_rate,
    ROUND(SUM(tasks_completed) * 1.0 / SUM(hours_worked), 2) AS tasks_per_hour
FROM employees
GROUP BY department
ORDER BY avg_rating DESC;
```

### Employee Summary

```sql
-- Individual employee performance aggregation
CREATE VIEW IF NOT EXISTS employee_summary AS
SELECT
    employee_id,
    name,
    department,
    role,
    SUM(tasks_completed) AS total_tasks,
    SUM(hours_worked) AS total_hours,
    AVG(rating) AS avg_rating,
    COUNT(DISTINCT projects) AS pro