Categorize data with AI.

What is Classification?

Putting things into categories.

**Examples**: - Email: Spam or Not Spam - Fruit: Apple or Orange - Tumor: Benign or Malignant

Popular Algorithms

**Logistic Regression**: Simple, fast **Decision Trees**: Easy to visualize **Random Forest**: More accurate **SVM**: Good for complex data **Neural Networks**: Most powerful

Simple Example

Classify fruits based on weight and color:

```python from sklearn.tree import DecisionTreeClassifier

Data: [weight_grams, color_score] X = [[150, 1], [170, 1], [140, 0], [130, 0]] y = ['apple', 'apple', 'orange', 'orange']

Train model = DecisionTreeClassifier() model.fit(X, y)

Predict new_fruit = [[160, 1]] result = model.predict(new_fruit) print(f"This is an: {result[0]}") ```

Binary vs Multi-class

**Binary**: 2 categories (Yes/No) **Multi-class**: 3+ categories (Apple/Orange/Banana)

Evaluation Metrics

**Accuracy**: How many correct predictions **Precision**: True positives / All positives **Recall**: True positives / All actual positives

Real Applications

- Medical diagnosis - Credit card fraud detection - Face recognition - Customer churn prediction

Remember

- Choose algorithm based on data - Start with simple models - Evaluate properly