{"id":23298873,"url":"https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny","last_synced_at":"2025-04-06T20:28:43.624Z","repository":{"id":205645299,"uuid":"479150910","full_name":"smdlabtech/cy_ranaviz_ml_with_shiny","owner":"smdlabtech","description":"🌎Datamart Analysis with Machine 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📊 Datamart Analysis with Machine Learning (ML)  \n[![GitHub](https://img.shields.io/badge/GitHub-repository-blue?style=flat\u0026logo=github\u0026logoColor=white)](https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny)\n[![Shiny](https://img.shields.io/badge/Built_with-R_Shiny-blue?style=flat\u0026logo=r\u0026logoColor=white)](https://shiny.rstudio.com/)\n[![Machine Learning](https://img.shields.io/badge/Machine_Learning-Powered-green?style=flat\u0026logo=python\u0026logoColor=white)](https://scikit-learn.org/)\n\n\u003cp align=\"left\"\u003e \n    \u003cimg width=\"150\" height=\"150\" src=\"assets/img/logo_shiny.png\" align=\"left\"\u003e\n\u003c/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\n\n## 🔗 Links  \n- 📈 **Application** : [Visual Analytics for ML](https://smd-lab-tech.shinyapps.io/Shiny_Dataviz/)  \n- 📕 **Report** : [Case Study Report](./_docs/rprt_ana_donnee_avancees_22-1.pdf)  \n\n\n## 📌 Summary  \nDevelopment of a predictive model for the **\"display\"** variable using Machine Learning techniques by transforming all continuous variables into categorical for modeling.\n\n### 1️⃣ Data Presentation  \n📌 **Descriptive analysis** of qualitative and quantitative variables, and their transformation for analysis.\n\n### 2️⃣ Multiple Component Analysis (MCA)  \n📉 Use of **MCA** to reduce data dimensionality, identify principal components, and interpret results.\n\n### 3️⃣ Modeling  \n- **Decision Tree**: Classification with specific parameters and a **confusion matrix** to assess performance.  \n- **Random Forest**: Application of **random forest**, parameter tuning, and classification results.  \n- **Logistic Regression**: Prediction using logistic regression, including **error rates** and accuracy metrics.\n\n### 4️⃣ Model Comparison  \n📊 Comparative analysis of three machine learning models: **Decision Tree, Random Forest, and Logistic Regression**.\n\n### 5️⃣ Model Performance (Best Model Analysis)  \n📏 Evaluation of model performance based on **precision** and **sensitivity**.\n\n\n🚀 **Let's make data-driven decisions!**  \n\n---\n\u003e [@smdlabtech](https://github.com/smdlabtech)  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmdlabtech%2Fcy_ranaviz_ml_with_shiny","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmdlabtech%2Fcy_ranaviz_ml_with_shiny","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmdlabtech%2Fcy_ranaviz_ml_with_shiny/lists"}