https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny

🌎Datamart Analysis with Machine Learning
https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny

data-analysis data-science dataviz machine-learning ml r rstudio shiny

Last synced: 8 days ago
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🌎Datamart Analysis with Machine Learning

• Host: GitHub
• URL: https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny
• Owner: smdlabtech
• Created: 2022-04-07T21:16:18.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2025-02-09T21:36:29.000Z (11 days ago)
• Last Synced: 2025-02-09T22:26:49.203Z (11 days ago)
• Topics: data-analysis, data-science, dataviz, machine-learning, ml, r, rstudio, shiny
• Language: R
• Homepage:
• Size: 9.77 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 📊 Datamart Analysis with Machine Learning (ML)  

[![GitHub](https://img.shields.io/badge/GitHub-repository-blue?style=flat&logo=github&logoColor=white)](https://github.com/smdlabtech/cy_ranaviz_ml_with_shiny)

[![Shiny](https://img.shields.io/badge/Built_with-R_Shiny-blue?style=flat&logo=r&logoColor=white)](https://shiny.rstudio.com/)

[![Machine Learning](https://img.shields.io/badge/Machine_Learning-Powered-green?style=flat&logo=python&logoColor=white)](https://scikit-learn.org/)

 

    












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## 🔗 Links  

- 📈 **Application** : [Visual Analytics for ML](https://smd-lab-tech.shinyapps.io/Shiny_Dataviz/)  

- 📕 **Report** : [Case Study Report](./_docs/rprt_ana_donnee_avancees_22-1.pdf)  

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## 📌 Summary  

Development of a predictive model for the **"display"** variable using Machine Learning techniques by transforming all continuous variables into categorical for modeling.

### 1️⃣ Data Presentation  

📌 **Descriptive analysis** of qualitative and quantitative variables, and their transformation for analysis.

### 2️⃣ Multiple Component Analysis (MCA)  

📉 Use of **MCA** to reduce data dimensionality, identify principal components, and interpret results.

### 3️⃣ Modeling  

- **Decision Tree**: Classification with specific parameters and a **confusion matrix** to assess performance.  

- **Random Forest**: Application of **random forest**, parameter tuning, and classification results.  

- **Logistic Regression**: Prediction using logistic regression, including **error rates** and accuracy metrics.

### 4️⃣ Model Comparison  

📊 Comparative analysis of three machine learning models: **Decision Tree, Random Forest, and Logistic Regression**.

### 5️⃣ Model Performance (Best Model Analysis)  

📏 Evaluation of model performance based on **precision** and **sensitivity**.

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🚀 **Let's make data-driven decisions!**  

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> [@smdlabtech](https://github.com/smdlabtech)