📈 Multiple Linear Regression Explained
PostNetwork Academy 🚀 | Machine Learning Series
📌 What is Regression?
Regression is a supervised learning technique where we predict a
continuous numerical value instead of a category.
Example: predicting house price, temperature, salary, etc.
🏠 House Price Dataset (Multiple Features)
| Area | Bedrooms | Age | Price (Lakh) |
|---|---|---|---|
| 1000 | 2 | 5 | 40 |
| 1200 | 3 | 10 | 50 |
| 1500 | 3 | 15 | 60 |
| 1800 | 4 | 20 | 70 |
| 2000 | 4 | 25 | 80 |
| 1300 | 3 | 12 | ? |
🧠 Model Equation
Multiple Linear Regression predicts output using weighted sum of inputs:
$$
y = w_1 \cdot x_1 + w_2 \cdot x_2 + w_3 \cdot x_3 + b
$$
y = w_1 \cdot x_1 + w_2 \cdot x_2 + w_3 \cdot x_3 + b
$$
- x₁ = Area
- x₂ = Bedrooms
- x₃ = Age
- y = House Price
🎯 Prediction Idea
The model learns patterns from past data and predicts:
Predicted Price ≈ 55 Lakh
🚀 Why this works?
- More area → price increases 📈
- More bedrooms → slightly higher price 🏠
- Older house → price decreases ⬇️
📚 Final Insight
Regression is not about classification (yes/no), but about
learning a mathematical function that fits data trends.
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