Machine Learning helps us find patterns in data. When the pattern is not a straight line, we use a quadratic equation:
y = ax² + bx + c
🎯 Problem Scenario: Distance vs Time 🚗
We study how distance changes with time when a vehicle accelerates.
- T → Time (input)
- D → Distance (output)
📋 Step 1: Data Collection
| Time (T) | Distance (D) |
|---|---|
| 1 | 6 |
| 2 | 11 |
| 3 | 18 |
| 4 | 27 |
| 5 | 38 |
📈 Step 2: Graph Representation
🧠Step 3: Model Selection
Since the graph is curved, we use:
D = aT² + bT + c
🧮 Step 4: Finding Equation
Using points (1,6), (2,11), (3,18):
6 = a + b + c
11 = 4a + 2b + c
18 = 9a + 3b + c
Solving:
a = 1, b = 2, c = 3
✅ Final Model
D = T² + 2T + 3
🔮 Prediction
For T = 6:
D = 36 + 12 + 3 = 51 meters
🤖 Machine Learning Insight
- Linear → Straight Line
- Quadratic → Curve (Parabola)
- Better for real-world patterns
🚀 Conclusion
Machine Learning fits the best equation to data. When patterns curve, quadratic models give better predictions.