Data Science 🐍 Interpolation

The video tutorial covers data fitting techniques, focusing on regression and interpolation. It explains methods like 1D and 2D interpolation, with a detailed look at cubic splines. Activities demonstrate interpolation and optimization using splines, including a temperature control lab exercise. The tutorial highlights the differences between interpolation and regression, emphasizing practical applications in prediction and optimization.

What is the primary purpose of using regression in data analysis?

Explain the difference between interpolation and extrapolation.

What is the role of the interp 1D function in data interpolation?

What are cubic splines and how do they differ from linear interpolation?

Describe the significance of continuous derivatives in cubic splines.

In what scenarios would you prefer using a cubic spline over a linear interpolation?

What are the potential dangers of extrapolating data beyond the interpolation range?

How can splines be beneficial in optimization problems?

How does the choice of interpolation method affect the prediction accuracy?

What is the impact of noisy data on regression and interpolation methods?