📊 Employee Data Audit & Retention Insights

(Auditoría de Datos de Empleados y Análisis de Retención)

Start Here (Abrir Primero)

1. Final presentation (PDF): docs/employee-retention-audit-presentation.pdf
2. Key notebook (EDA + ETL + visuals): ETL/visualizaciones.ipynb
3. Clean dataset used for analysis: files/raw_data_limpio.csv

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Project Overview (Resumen del Proyecto)

This project is a structured data audit and exploratory analysis of employee and survey data for a fictional company (ABC Corporation), aimed at identifying key drivers of employee satisfaction and retention.

The work focuses on:

• Intensive Exploratory Data Analysis (EDA)
• Data cleaning and transformation (ETL)
• Insight-driven visualizations in Python (Jupyter)

The goal was not only to uncover workforce trends, but also to evaluate potential bias and data quality issues that could impact strategic HR decision-making.

This project was developed as an academic team project within the Adalab Data Analytics & AI Bootcamp.

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Objectives (Objetivos)

• Audit and clean raw HR and survey data
• Standardize and transform datasets for reliable analysis
• Identify key factors linked to employee attrition
• Visualize trends to support retention strategies
• Apply agile teamwork and version control practices

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Methodology (Metodología)

• Exploratory Data Analysis (EDA) across multiple iterations
• Data transformation and validation in Python
• Custom helper functions for dataset inspection
• Visual analysis using Matplotlib and Seaborn
• Documentation of technical decisions and assumptions

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Repository Structure (Estructura del Repositorio)

• docs/ — Technical documentation and final project presentation (PDF)
• ETL/ — Jupyter notebooks for EDA, data transformation, and visual analysis
• files/ — Raw and cleaned datasets (CSV)
• img/ — Key visualizations used in analysis
• src/ — Custom Python helper functions
• README.md — Project overview and insights

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Key Insights (Hallazgos Clave)

• Attrition is significantly higher in specific departments, particularly Sales and Human Resources.
• Certain job roles show disproportionately high turnover compared to others.
• Lower income levels are frequently associated with higher attrition rates.
• Retention challenges are not evenly distributed across the organization, highlighting targeted areas for intervention.

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Key Visualizations (Visualizaciones Clave)

Attrition Frequency by Department

(Frecuencia de rotación por departamento)

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Attrition by Job Role

(Rotación por rol de trabajo)


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Technologies Used (Tecnologías Utilizadas)

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebooks
• CSV data processing
• Git & GitHub

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Team & Credits (Equipo y Créditos)

This project was originally developed as a collaborative course project by:

• Claudia Cervantes — https://github.com/cloud9international
• Mayka Durán — https://github.com/Maykaduran
• Ona Zaragoza — https://github.com/omniaunusset
• Patricia Merchán — https://github.com/patrimerchan
• Andrea R. Virgós — https://github.com/andrearvirgos

Original team repository:
https://github.com/Maykaduran/employee-data-audit-retention-insights

This repository is a curated portfolio version maintained by Claudia Cervantes.

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Final Notes (Notas Finales)

• The analysis prioritizes data integrity and bias awareness
• Transformations were designed to preserve original data meaning
• Visual insights focus on actionable workforce trends
• The project simulates a real-world HR data audit workflow