This report presents an exploratory analysis of the Iris dataset, a classic dataset widely used for machine learning and statistical modeling. The goal of this project is to demonstrate familiarity with data handling, visualization, and planning for predictive modeling. Key highlights of this report: Data Overview: Summary of the Iris dataset, including structure and basic statistics. Visual Insights: Bar charts and boxplots illustrating species distribution and feature variability. Interesting Findings: Observations on how sepal and petal measurements differ across species. Future Plans: Outline for building a Random Forest-based prediction algorithm and developing an interactive Shiny app for real-time species prediction. This document serves as a progress checkpoint and invites feedback on the proposed modeling approach and app design.

test2 plot

hw8

STAT 427, Chapter 3 Homework, Graphs of time series

Cleaning, Seeking, and Plotting Trends Using R