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📊 Datamart Analysis with Machine Learning (ML)

GitHub Shiny Machine Learning








🔗 Links

📌 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.

🚀 Let's make data-driven decisions!

@smdlabtech