Iris Dataset Classification Overview
Table of Contents
Description
The Iris dataset is a classic and widely used dataset for classification problems. It contains measurements of iris flowers from three different species: Iris setosa, Iris versicolor, and Iris virginica. The goal is to classify the species based on four features:
Sepal length
Sepal width
Petal length
Petal width
This dataset is often used to demonstrate the machine learning workflow using Data Science tools.
Prerequisites
- Basic Python
- NumPy, Pandas, Matplotlib, Seaborn
- Scikit-learn basics
Examples
Here's a simple example of a data science task using Python:
import warnings
warnings.filterwarnings("ignore")
# Import necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix
# Load the iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
# Convert to DataFrame
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['target'] = iris.target
df['species'] = df['target'].apply(lambda x: iris.target_names[x]) # Add species names
# Preview data
print(df.head())
# Visualize pairplot
sns.pairplot(df, hue='species')
plt.show()
# Feature matrix and target
X = df[iris.feature_names]
y = df['target']
# Train-Test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Feature scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Train a KNN classifier
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train_scaled, y_train)
# Predictions and evaluation
y_pred = model.predict(X_test_scaled)
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))
🧠 SelectKBest selects top 'k' features based on a scoring function like chi2 or f_classif.
Real-World Applications
Education
Teaching classification techniques
Biology
Botanical species classification
ML Demos
Demonstrating model tuning and evaluation
Where topic Is Applied
- Supervised Machine Learning
- Model Evaluation
- Feature Engineering & Scaling
Resources
Data Science topic PDF
Harvard Data Science Course
Free online course from Harvard covering data science foundations
Interview Questions
➤ It’s a labeled dataset containing 150 iris flower records with 4 features and 3 classes.
➤ It's clean, small, and ideal for learning basic classification and model evaluation techniques.
➤ KNN, Logistic Regression, Decision Trees, SVM, etc.
➤ Iris-setosa, Iris-versicolor, and Iris-virginica.
➤ Use cross-validation, regularization, or simpler models like KNN with optimal neighbors.