Day 1: Regression — The Art of Prediction
Source: Dev.to
What is Regression?
Regression means:
Using past information to predict a number in the future.
Examples
| Question | Type |
|---|---|
| Predict house price | Regression |
| Predict salary | Regression |
| Predict temperature | Regression |
| Predict pass/fail | ❌ Not regression |
👉 If the output is a NUMBER → it’s regression
Why Do We Need Regression?
Humans:
- Guess roughly
- Forget patterns
- Get biased
Machines:
- Remember all data
- See patterns clearly
- Give consistent predictions
So we let the machine learn from past data and predict for us.
Input & Output
Think of regression like a juice machine.
| Part | ML Term |
|---|---|
| Fruits you put in | Input / Features |
| Juice you get | Output / Target |
Example
- Inputs: House size, number of rooms, location
- Output: House price
Regression learns: “If inputs look like this → output is usually that”.
Regression vs Classification
| Regression | Classification |
|---|---|
| Predicts numbers | Predicts labels |
| Salary = ₹50,000 | Spam / Not Spam |
| House price | Yes / No |
| Temperature | Pass / Fail |
📌 Interview rule: If output is continuous → Regression
Real‑Life Use Cases
| Field | Regression Use |
|---|---|
| Finance | Loan amount prediction |
| Healthcare | Recovery time |
| Real estate | House prices |
| E‑commerce | Demand forecasting |
| Weather | Rainfall amount |
Regression is everywhere, quietly working.
Supervised Learning
Imagine a child learning maths. The teacher:
- Shows a question
- Shows the correct answer
- Corrects mistakes
The child learns: “When I see this kind of question, the answer is usually this.”
That’s supervised learning.
Applying This to Regression
In regression, the machine is the child.
- Inputs: house size, rooms, location
- Correct output: actual house price
The model learns: “When these inputs appear together, this is the price.”
It is called supervised learning because the model is not guessing blindly; we already know the right answers and “supervise” the learning by correcting it.
Simple rule to remember: If the data already has correct answers → it’s supervised learning.
Tiny Real‑Life Analogy
| Situation | Learning Type |
|---|---|
| Teacher checks homework | Supervised |
| Child learns alone by trial | Unsupervised |
Regression = teacher checking homework.
Regression is a supervised learning algorithm because the model learns from labeled data where the correct output is already known.
Tiny Intuition Practice
Think about your phone price.
- Inputs: RAM, storage, brand
- Output: Price
Your brain already does regression. ML just does it faster and better.
3‑Line Takeaway (Remember This)
- Regression predicts numbers, not labels.
- It learns patterns from past data.
- You already use regression in daily life.
What’s Coming Next
Now that we know what regression is, the next question is:
“How does a machine actually learn the best prediction?”
That’s where Linear Regression comes in.
👉 Day 2: How a Straight Line Learns From Data