Iris Dataset Dashboard

Soumya Savarn
Roll Number: 220150031
YouTube Demo: https://www.youtube.com/watch?v=qG-8v71tYW0

Live Deployment: https://u2cv91-soumya-savarn.shinyapps.io/dashboard_iris/

This project is a data visualization dashboard created using R Shiny, showcasing the Iris dataset with interactive features for exploring the dataset and its clustering. The dashboard consists of four main pages:

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1. K-Means and Bar Plot

• Description:
  This page implements K-Means clustering on the Iris dataset. Users can select variables for the X and Y axes and view clustering results.
• Features:
  • Dropdown menus for selecting X and Y variables.
  • A scatter plot displaying the K-Means clustering results, where each point is colored according to its cluster.
  • A bar chart representing the size of each cluster.
• Backend:
  The clustering is computed using the kmeans function in R, and the results are dynamically updated with user selections.

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2. Scatter Plot

• Description:
  This page provides a simple scatter plot for exploring relationships between any two variables in the dataset.
• Features:
  • Pairwise Scatter Plot Visualisation
• Backend:
  Uses ggplot2 for creating the scatter plot, with interactivity powered by Shiny's renderPlot.

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3. Density Plot

• Description:
  This page displays density plots for analyzing the distribution of each numeric variable.
• Features:
  • Dropdown to select a variable for visualization.
  • Overlaid density curves for each species.
• Backend:
  The density plots are created using geom_density in ggplot2, grouped by species for comparison.

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4. KNN Cluster Prediction

• Description:
  This page demonstrates the prediction of species using a K-Nearest Neighbors (KNN) classifier.
• Features:
  • A pre-trained KNN model is used to classify test points.
  • Users can upload new data for prediction.
• Backend:
  The class::knn function is used for classification, and predictions are displayed in a table or plot.

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Technical Implementation

1. Framework:
   Built using R Shiny for interactivity and deployed via ShinyApps.io.
2. Libraries Used:
   • ggplot2: For data visualization.
   • dplyr: For data manipulation.
   • shiny: For building the interactive web app.
   • class: For implementing the KNN model.

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