{"id":35066712,"url":"https://github.com/jesusgomez-data/retail-sales-data-analysis","last_synced_at":"2026-04-11T06:42:29.377Z","repository":{"id":329081141,"uuid":"1116438506","full_name":"jesusgomez-data/retail-sales-data-analysis","owner":"jesusgomez-data","description":"End-to-end retail sales data analysis project using SQL, SQLite and Python (Pandas). Includes data generation, KPIs and business insights.","archived":false,"fork":false,"pushed_at":"2025-12-17T06:57:15.000Z","size":121,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-20T20:20:21.426Z","etag":null,"topics":["data-analysis","junior-data-analyst","pandas","portfolio-project","python","retail-analysis","sql","sqlite","sqlite3"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jesusgomez-data.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-12-14T21:19:55.000Z","updated_at":"2025-12-17T06:57:18.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/jesusgomez-data/retail-sales-data-analysis","commit_stats":null,"previous_names":["jesusgomez-data/retail-sales-data-analysis"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/jesusgomez-data/retail-sales-data-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesusgomez-data%2Fretail-sales-data-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesusgomez-data%2Fretail-sales-data-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesusgomez-data%2Fretail-sales-data-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesusgomez-data%2Fretail-sales-data-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jesusgomez-data","download_url":"https://codeload.github.com/jesusgomez-data/retail-sales-data-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesusgomez-data%2Fretail-sales-data-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28078031,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-12-27T02:00:05.897Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","junior-data-analyst","pandas","portfolio-project","python","retail-analysis","sql","sqlite","sqlite3"],"created_at":"2025-12-27T11:32:14.146Z","updated_at":"2026-04-11T06:42:29.369Z","avatar_url":"https://github.com/jesusgomez-data.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🛍️ Retail Sales Data Analysis (2023)\n\n## 📌 Descripción del Proyecto\nEste proyecto presenta un análisis completo de ventas retail correspondiente al año 2023. El objetivo es transformar datos transaccionales en información accionable mediante análisis de datos, modelado y visualización interactiva orientada a negocio.\n\nEl resultado final es un dashboard en Power BI diseñado para facilitar la toma de decisiones comerciales.\n\n---\n\n## 🎯 Objetivos del Análisis\n- Analizar el rendimiento general del negocio\n- Evaluar la evolución de ingresos a lo largo del tiempo\n- Comparar el desempeño de los canales de venta\n- Identificar ciudades, productos y clientes más rentables\n- Proporcionar insights claros para la toma de decisiones\n\n---\n\n## ❓ Preguntas de Negocio\n- ¿Cuánto dinero genera el negocio?\n- ¿Cómo evolucionan los ingresos en el tiempo?\n- ¿Qué canal de venta genera más ingresos?\n- ¿Qué ciudades aportan mayor facturación?\n- ¿Qué productos son los más rentables?\n- ¿Quiénes son los clientes con mayor contribución a ingresos?\n\n---\n\n## 📊 KPIs Principales\n- Total Ingresos\n- Total Ventas\n- Ticket Promedio\n- Ingresos por Canal de Venta\n- Ingresos por Ciudad\n- Ingresos por Producto\n- Ingresos por Cliente\n\n---\n\n## 🛠️ Tecnologías Utilizadas\n- **Python** (Pandas, NumPy)\n- **SQL** (SQLite)\n- **Power BI**\n- **Git \u0026 GitHub**\n\n---\n\n## 🧱 Modelo de Datos\nEl modelo sigue un enfoque **Star Schema**, con:\n- Tabla de hechos: Ventas\n- Tablas de dimensiones: Clientes, Productos, Ciudades, Fechas y Canal de Venta\n\nEste modelo permite un análisis eficiente y escalable.\n\n---\n\n## 📈 Dashboard\nEl dashboard incluye:\n- KPIs ejecutivos\n- Evolución temporal de ingresos\n- Distribución por canal de venta\n- Rankings por ciudad, producto y cliente\n- Filtros interactivos por fecha, ciudad y canal\n\n📷 *Vista previa del dashboard disponible en el repositorio.*\n\n---\n\n## 🔍 Principales Insights\n- El canal online genera ligeramente más ingresos que la tienda física\n- Se observan picos de ventas en meses específicos del año\n- Valencia, Bilbao y Barcelona lideran la facturación\n- Un número reducido de productos y clientes concentra gran parte de los ingresos\n\n---\n\n## 📌 Conclusiones\nEl análisis demuestra un rendimiento sólido del negocio y evidencia oportunidades claras para optimizar estrategias comerciales, campañas de marketing y programas de fidelización.\n\n---\n\n## 🚀 Próximos Pasos\n- Análisis de rentabilidad por producto\n- Segmentación avanzada de clientes\n- Predicción de ventas futuras\n\n---\n\n## 👤 Autor\n**Jesús Gómez**  \nData Analyst Junior  \n📎 GitHub: https://github.com/jesusgomez-data\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesusgomez-data%2Fretail-sales-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjesusgomez-data%2Fretail-sales-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesusgomez-data%2Fretail-sales-data-analysis/lists"}