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TripleTen.  \nEl objetivo fue aplicar habilidades integradas en **Python, análisis exploratorio de datos y visualización**, trabajando con datasets reales para responder preguntas de negocio.  \n\n**English:**  \nThis project corresponds to **Sprint 6 (Module 1 Project)** from the Data Analysis Bootcamp at TripleTen.  \nThe goal was to apply integrated skills in **Python, exploratory data analysis, and visualization**, working with real datasets to answer business questions.  \n\n---\n\n## 🎯 Objetivos / Objectives\n- Limpieza y preparación de datos.  \n- Análisis exploratorio (EDA).  \n- Creación de métricas descriptivas.  \n- Visualización de resultados para facilitar la toma de decisiones.  \n\n---\n\n## 🛠️ Tecnologías / Tools\n- Python (Pandas, NumPy, Matplotlib, Seaborn)  \n- Jupyter Notebook  \n\n---\n\n## 📊 Resultados / Results\n**Español:**  \n- Se identificaron patrones clave en los datos (ejemplo: distribución de usuarios, outliers en consumo).  \n- Se generaron métricas descriptivas que permiten comprender mejor el comportamiento de los datos.  \n- Se aplicaron visualizaciones efectivas para comunicar los hallazgos.  \n\n**English:**  \n- Key patterns in the dataset were identified (e.g., user distribution, consumption outliers).  \n- Descriptive metrics were generated to better understand data behavior.  \n- Effective visualizations were applied to communicate findings.  \n\n---\n\n## 📑 Feedback del Revisor / Reviewer Feedback\n**Español:**  \n- ✅ Buen uso de librerías de análisis y visualización.  \n- ⚠️ Se recomendó optimizar algunos fragmentos de código para mayor eficiencia y claridad.  \n- ✔️ Se aplicaron correcciones y mejoras en el notebook final.  \n\n**English:**  \n- ✅ Good use of analysis and visualization libraries.  \n- ⚠️ Suggested to optimize parts of the code for better efficiency and readability.  \n- ✔️ Corrections and improvements were applied in the final notebook.  \n\n---\n\n✍️ *Autor / Author:* Lai Yi Peña 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