Python 13: Introduction to Machine Learning with Scikit-learn

🎯 Goal:

By the end of this chapter, you’ll be able to:

• Understand the basic machine learning workflow
• Train your first ML model using scikit-learn
• Make predictions and evaluate model accuracy

🧠 1. What is Machine Learning?

Machine Learning (ML) is a way to teach computers to learn patterns from data instead of being explicitly programmed.

Think of it like this:

“Here’s past data. Learn from it. Now predict the future.”

We’ll start with Supervised Learning, specifically a classification task.

📦 2. Import the Tools

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import pandas as pd

🌸 3. Load the Iris Dataset

The Iris dataset is a classic ML dataset with 3 types of flowers and 4 numeric features.

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["target"] = iris.target
print(df.head())

✂️ 4. Split Data into Train and Test

X = df.drop("target", axis=1)
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

🧠 5. Train a Decision Tree Classifier

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

🔍 6. Make Predictions

y_pred = model.predict(X_test)
print("Predicted:", y_pred)
print("Actual:   ", list(y_test))

🎯 7. Evaluate Accuracy

acc = accuracy_score(y_test, y_pred)
print(f"Model accuracy: {acc:.2f}")

🧪 8. Practice Time

Try the following:

• Replace DecisionTreeClassifier with LogisticRegression or KNeighborsClassifier
• Change the test size to 30%
• Add max_depth=2 to your tree and see how it affects accuracy
• Print a classification report (from sklearn.metrics import classification_report)

✅ Summary

• You used scikit-learn to train a basic ML model
• Split data into training and testing
• Trained, predicted, and evaluated with just a few lines
• This is the foundation of supervised learning