This repository contains two complementary approaches to analyzing the famous Iris dataset:

1. Machine Learning Classification (Python) - Predicting species using scikit-learn.
2. Statistical Analysis (R) - Hypothesis testing and exploratory data analysis (EDA).

A step-by-step workflow to classify Iris flower species (setosa, versicolor, virginica) using scikit-learn's K-Nearest Neighbors (KNN) algorithm.

• Data Exploration: Visualizing feature distributions and pairwise relationships.
• Model Training: Implementing KNN classification.
• Evaluation: Scoring model accuracy and analyzing confusion matrices.

Achieved 96% accuracy in species prediction using KNN (k=3).

A rigorous statistical examination of the Iris dataset, including:

• ANOVA to test for significant differences in sepal/petal measurements across species.
• Tukey HSD post-hoc tests to identify which species pairs differ.
• Data Visualization: Boxplots, confidence intervals, and pairwise comparison plots.

• Sepal Length: Highly significant differences between all species (ANOVA p < 0.001).
• Petal Width: Virginica petals are significantly wider than versicolor and setosa (Tukey p < 0.01).

• Machine Learning (Python): Focuses on predictive accuracy for species classification.
• Statistical Analysis (R): Explains why the model works by quantifying biological differences between species.

• Python: scikit-learn, pandas, matplotlib, Jupyter.
• R: ggplot2, rstatix, dplyr.