Iris Flower Classification

Goal

Use the Iris dataset to:

• Perform EDA
• Visualize feature relationships
• Train a simple classifier

Analysis pipeline

diagram Iris classification pipeline mermaid

  flowchart LR
  A["Raw data
(150 flower measurements)"] --> B["Explore
(describe, pairplot)"]
  B --> C["Visualize
(feature separability)"]
  C --> D["Split
(train/test)"]
  D --> E["Baseline model
(LogisticRegression)"]

From raw measurements to a trained baseline classifier.

From the book

McKinney’s rule of thumb before modeling anything: look at the data first. In the MovieLens example he checks group sizes with groupby(...).size() before trusting any average. Here, a pairplot does the same job visually — it shows you whether classes actually separate before you trust an accuracy number.

Load dataset

Load iris

import pandas as pd
from sklearn.datasets import load_iris
 
iris = load_iris(as_frame=True)
df = iris.frame
print(df.head())

Pair plot

Pair plot

import seaborn as sns
 
sns.pairplot(df, hue="target")

Visualize it

Petal length and petal width separate the three species almost perfectly — much better than sepal measurements. This is exactly why the pairplot is step one: it tells you which features will make the classifier’s job easy.

sketch Petal length vs petal width, by species p5.js Pause Restart

Each dot is one flower; the three species form clearly separate clusters.

Train/test split + model

Train a classifier

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
 
X = df.drop(columns=["target"])
y = df["target"]
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
 
clf = LogisticRegression(max_iter=1000)
clf.fit(X_train, y_train)
print("Accuracy:", clf.score(X_test, y_test))

Deliverable

• Which features separate classes best?
• Any overlap between species?
• Baseline accuracy

🧪 Try It Yourself

Exercise 1 – Class balance

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Exercise 2 – Mean feature by species

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Exercise 3 – Train/test split

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