Shiny Machine Learning Dashboard This project implements an interactive dashboard using R and Shiny, created by Daniel Bonifaz-Calvo.

Features: -Linear Regression: For predicting numeric outcomes. -Polynomial Regression: For analyzing non-linear relationships. -K-Means Clustering: For data segmentation. -Decision Tree: For rule-based classification and regression. -ARIMA: For time series forecasting.

Additional Visualizations: Including histograms, residual plots, and correlation matrices.

Performance Metrics: R², MSE, and MAE for regression models.

Dynamic Filtering: To explore subsets of the data interactively.

Export Options: Save results in CSV or PDF format.

Usage: -Install the necessary dependencies in R: Install.packages(c("shiny", "DT", "ggplot2", "forecast", "rpart", "rpart.plot", "gridExtra")) -Run the main application file: shiny::runApp("app/app.R") -Interact with the data and results on the dashboard.

License: This project is open source under the MIT license.

Note: Refer to the "User Manual for the Shiny Machine Learning Dashboard.docx" for detailed instructions. Greetings. Daniel Bonifaz-Calvo.