A First Real Classifier: A Light Touch of scikit-learn

What We're Building

A classifier for the Iris dataset — the single most famous "hello world" of classification: predict which of three flower species a sample belongs to, from four simple measurements (sepal length, sepal width, petal length, petal width). It ships built directly into scikit-learn, needs zero cleaning, and is small enough to see the whole pipeline at once.

Step 1: Loading the Dataset

from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["species"] = [iris.target_names[i] for i in iris.target]
print(df.head())

iris.data is a NumPy array of the four measurements; iris.target is the numeric species label for each row (0, 1, or 2). Building a DataFrame and mapping the numbers back to real species names makes the data readable the same way every prior chapter's own data has been.

Step 2: A Quick Look — Why This Dataset Works So Well

import seaborn as sns
import matplotlib.pyplot as plt

sns.scatterplot(
    data=df, x="petal length (cm)", y="petal width (cm)",
    hue="species", palette=["#15803D", "#EAB308", "#4ade80"],
)
plt.title("Petal Measurements by Species")
plt.savefig("iris_scatter.png")

Plotting just two of the four features already shows the three species separating into clearly distinct clusters — a genuine, visible reason this particular dataset became the classic teaching example: the classes are almost perfectly separable, letting even a simple model succeed clearly enough to see what's actually happening.

Step 3: Train/Test Split

from sklearn.model_selection import train_test_split

X = df[iris.feature_names]
y = df["species"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(f"Training on {len(X_train)} rows, testing on {len(X_test)} rows.")

The full reasoning behind why a model needs to be evaluated on data it never saw during training is ml1-2's own material in full — used correctly here, not re-derived. random_state=42 just makes the split reproducible from one run to the next.

Step 4: Fitting a Simple Classifier

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)

Step 5: Evaluating — The Single Simplest Metric

accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2%}")

Step 6: Seeing the Predictions

predictions = model.predict(X_test)
results = X_test.copy()
results["actual"] = y_test.values
results["predicted"] = predictions
results["correct"] = results["actual"] == results["predicted"]

print(results[results["correct"] == False])   # the rows the model got wrong, if any

Looking directly at whichever rows the model got wrong (if any) is worth more than the single accuracy number alone — on this dataset, mistakes almost always happen between two of the three species that sit closest together in the Step 2 scatter plot, not randomly across all three, a small, concrete confirmation that the model is making genuinely sensible errors rather than arbitrary ones.

The Complete Classifier

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["species"] = [iris.target_names[i] for i in iris.target]

X = df[iris.feature_names]
y = df["species"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)

accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2%}")

What's Next

Chapter 8: Capstone — Your Own Dataset, Start to Finish — no fixed dataset this time: your own choice, taken through the complete collect-clean-explore-visualize-model pipeline this course has built one piece at a time.