I use simple words and real-life examples they can relate to.

For example, to explain a Random Forest model, I would say — 'Imagine you want to decide which movie to watch, so you ask 10 friends. Each one gives you a suggestion based on their own taste. Then, you pick the movie that most friends suggested. That’s how a Random Forest works — it asks many small models (like your friends), and then chooses the answer most of them agree on.'

This way, I don’t use heavy technical terms, but still help them understand what the model is doing and why it’s useful

1. Random Forest

"Imagine you want to decide which movie to watch, so you ask 10 friends. Each one gives a suggestion based on their own taste. Then, you go with the movie that most friends suggest. That’s how a Random Forest works — it asks many small models (like your friends), and then picks the answer that most of them agree on."

🧠 2. Neural Network

"Think of a neural network like how our brain works. Let’s say you're recognizing faces. First, your eyes see the shape. Then your brain breaks it down — eyes, nose, mouth — and finally says, ‘Hey, that’s your friend John!’ A neural network works in layers. Each layer understands part of the data, and together they make a final decision — like recognizing an object, voice, or even handwriting."

🧠 3. Support Vector Machine (SVM)

"Imagine you have two types of fruits — apples and oranges — placed on a table. Now you want to draw a straight line between them so that apples are on one side and oranges on the other. SVM finds the best possible line (or even curve) that separates them as clearly as possible. It tries to keep the maximum distance between that line and the nearest fruit from each type. That way, it can classify new fruits easily based on which side they fall on."e...