Ever wondered how Gmail knows which emails are spam and which ones are legit? That’s classification in action. While regression predicts how much, classification predicts what kind. It’s the difference between estimating a house price and deciding whether that house is residential or commercial.

What Is Classification?
Classification is a branch of supervised learning where the goal is to assign labels to data based on its features. It’s like teaching a model to recognize patterns and sort things into categories.
Unlike regression, which deals with continuous outputs, classification handles discrete categories.
You train the model on labelled data, it learns the mapping, and then it can classify new, unseen data with confidence.

Common Algorithms in Classification.
Here are some of the go-to algorithms in classification :

1. Logistic Regression – Simple, interpretable, great for binary classification
2. K-Nearest Neighbors (KNN) – Classifies based on similarity to nearby data points
3. Decision Trees – Rule-based and easy to visualize
4. Random Forest – Combines multiple trees for better accuracy
5. Support Vector Machines (SVM) – Finds the optimal boundary between classes
6. Naive Bayes – Probabilistic model, ideal for text and spam filtering Each algorithm has its strengths depending on your dataset’s size.

What I learnt.
Here are my top three insights from diving into classification:

1. Feature selection is everything.
2. Class imbalance is sneaky.
3. Evaluation metrics matter.

Its Disadvantages.
No model is perfect, and classification has its pitfalls:

1. Overfitting – Especially with complex models that memorize instead of generalize.
2. Bias in data – If your training data is biased, your predictions will be too.
3. Interpretability – Some models are hard to explain.

Why It Matters
Classification powers decision-making across industries—from diagnostics to fraud detection. But it’s not just about algorithms. It’s about understanding the data, the context, and the impact of your predictions.