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El objetivo es alcanzar un **F1-score ≥ 80%** con una matriz de confusión que refleje un bajo número de falsos negativos. Se utiliza la metodología **ASUM-DM**\n\n## Metodología\n- **Exploración**: definición de variable **Y**, revisión de distribución de clases, variables categóricas y numéricas.  \n- **Preparación**: manejo de datos nulos, eliminación de variables innecesarias y manejo de desbalance.  \n- **Transformación**: construcción de pipeline mediante **MinMaxScaler()** para variables numéricas y **OneHotEncoder()** para variables categóricas.  \n- **Modelado**: entrenamiento y comparación de algoritmos (**Regresión Logística, Árboles, Random Forest, SVM, Gradient Boosting**) con validación cruzada.\n- **Evaluación**: verificación de los modelos mediante matriz de confusión y reporte de confusión\n\n## Tecnologías\n- Python  \n- Pandas\n- Scikit-learn  \n- Matplotlib, Seaborn  \n- Jupyter Notebook\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftomaslopera%2Fcasoclasificacion","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftomaslopera%2Fcasoclasificacion","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftomaslopera%2Fcasoclasificacion/lists"}