{"id":22956912,"url":"https://github.com/caritoramos/predictive-classification-model-in-python","last_synced_at":"2025-04-02T01:30:09.985Z","repository":{"id":225228237,"uuid":"765410138","full_name":"CaritoRamos/predictive-classification-model-in-python","owner":"CaritoRamos","description":"This project applies Machine Learning classification models to predict customer acceptance of marketing offers using Python and libraries such as Pandas, NumPy, Scikit-learn, and XGBoost.","archived":false,"fork":false,"pushed_at":"2025-03-31T01:54:42.000Z","size":9101,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T02:33:48.603Z","etag":null,"topics":["classification-algorithm","data-science","decision-tree-classifier","logistic-regression","machine-learning","random-forest"],"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/CaritoRamos.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}},"created_at":"2024-02-29T21:34:17.000Z","updated_at":"2025-03-31T01:57:05.000Z","dependencies_parsed_at":"2025-02-07T18:31:14.074Z","dependency_job_id":null,"html_url":"https://github.com/CaritoRamos/predictive-classification-model-in-python","commit_stats":null,"previous_names":["anaramos2022/ds_supervised_classification_model-","caritoramos/ds_classification_model"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CaritoRamos%2Fpredictive-classification-model-in-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CaritoRamos%2Fpredictive-classification-model-in-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CaritoRamos%2Fpredictive-classification-model-in-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CaritoRamos%2Fpredictive-classification-model-in-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CaritoRamos","download_url":"https://codeload.github.com/CaritoRamos/predictive-classification-model-in-python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246738281,"owners_count":20825759,"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","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":["classification-algorithm","data-science","decision-tree-classifier","logistic-regression","machine-learning","random-forest"],"created_at":"2024-12-14T17:13:25.764Z","updated_at":"2025-04-02T01:30:09.973Z","avatar_url":"https://github.com/CaritoRamos.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 **Modelo Supervisado de Clasificación: Aceptación de Clientes a Ofertas en Campañas de Marketing**\n\nEste proyecto aplica técnicas de Machine Learning para predecir la aceptación de clientes a ofertas en campañas de marketing. \nA través del análisis predictivo, permite a las empresas anticipar el comportamiento de los clientes y optimizar sus estrategias comerciales.\n\n## 🔍 **Estructura del Proyecto**\n\n### 1️⃣ **Entendimiento de los Datos (Data Understanding)**\n- Análisis inicial del dataset.\n- Revisión de tipos de datos, valores nulos y distribución de variables.\n- Identificación de posibles sesgos o desbalance de clases.\n\n### 2️⃣ **Preparación de los Datos (Data Preparation)**\n- Manejo de valores faltantes y outliers.\n- Creación de nuevas variables relevantes.\n- Codificación de variables categóricas.\n- Normalización y escalado de variables numéricas.\n- División del dataset en entrenamiento (70%), validación (15%) y prueba (15%).\n\n### 3️⃣ **Análisis Exploratorio de Datos (EDA)**\n- Visualización de distribuciones de variables.\n- Análisis de correlaciones entre características.\n- Detección de patrones relevantes en los datos.\n\n### 4️⃣ **Modelado (Modelling)**\n- Selección de algoritmos de clasificación supervisada:\n  - Regresión logística.\n  - Random Forest.\n  - XGBoost.\n- Ajuste de hiperparámetros y validación cruzada.\n- Evaluación del desempeño con métricas como precisión, recall, F1-score y matriz de confusión.\n\n### 5️⃣ **Interpretación de Resultados y Conclusiones**\n- Comparación de modelos y selección del mejor enfoque.\n- Identificación de variables más influyentes en la predicción.\n- Recomendaciones para la aplicación del modelo en estrategias de marketing.\n\n## 🛠 **Tecnologías Utilizadas**\n- **Lenguaje**: Python  \n- **Librerías**: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, XGBoost  \n- **Herramientas**: Jupyter Notebook, Google Colab  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaritoramos%2Fpredictive-classification-model-in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcaritoramos%2Fpredictive-classification-model-in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaritoramos%2Fpredictive-classification-model-in-python/lists"}