Python 🐍 Nonlinear Regression Curve Fit

The Scipy curve_fit function determines four unknown coefficients to minimize the difference between predicted and measured heart rate. Pandas is used to import and view the data. A Matplotlib plot is used to see how the initial guess values and the final optimized parameter values compare for generating predictions. An R-squared value is calculated to show how well the predictions and measurements align.

[alibastami]

Just the fact that you provide this invaluable content for free, when others are selling low quality courses for a lot of money, proves how a great human being you are.
You are a true Master.

[QanarIsDelirious]

thanks for the help this was a long long journey it took me like 3-5 hours to translate my german function name in its actual english equivalent since the direct translation is miles miles away :D and finaly i got a model that should help me solve my enzyme activity measures

[gomezest]

This is really good content! Thank you very much for sharing your knowledge. Im still blown away by the quality and how you explained the material

[stevethach]

Hi! Great video!

A couple of questions:

1) What if you had multiple people's heart rate? Is it possible to create a curve that would fit all of them?
2) What if we do not have the BPM equation?

Thank you!

[DFS]

thank u X 10^10 . U r a great professional and teacher !

[tonycardinal]

Thank you so much for posting this. Ques: If there is a high covariance between the parameters why is this bad? thanx!

[arcesarino]

Perfect! Thank you so much for the great video.

[asifraj]

Very helpful and valuable too. Thank you.

[NawtieBoy]

6:26 whats the formula really that you have used in that bpm fn ? i didnt understand that part. usually what I know is that therea re various non-linear methods that one can choose for non-linear reg types such as polynomial fits, lograthmic fits and so on . correct me if I am wrong pls

[SuperCamProductions]

This was exactly what I needed, thank you!

[senolkurt]

Thanks for the great tutorial. Since in real life we have only the data, how can we find the best non-linear equation that fits to our data?

[badinhbk]

Thank you very much.

Your channel is very helpful for me

:)

[erenyeager]

very good tutorial, however my doubt is what if you have a matrix of parameters? how do you make the objective function. eg: Ax = b.
I need to find A. how do you frame the problem?

[KR-uyor]

So for non linear regression we have to assume/guess which curve equation will fit the data and unlike linear regression in PYTHON which predicts itself??

[obinnaizima]

Hi, this was very helpful. However, if you had a dataset that comprises of multiple exponentially decaying curves, how would this work out? I can relatively do this for a single exponential decay curve but unsure on how to deal with multiple exp. decay curves.

[agustingambaretto]

Thanks for the demo, very useful for a beginner like me! managed to do something similar following the steps. I wanted to ask something. My formula in stead of having a single variable ( time), y have 3 input variables, i have to compute p1[i], p2[i], p3[i] to be able to calculate the y value. I manage to adapt my code to that and plot it, but i got stucked in the curve_fit. In the curve_fit uses only 1 xdata if i am correct. What variation can i use? I hope you know, thanks !

[arushijain]

Super! So easily explained, thank you.

[benedictodhiambo]

How did you determine the values of the constants

[guerreirodaluzgmailcom]

Hi Mr., What criteria does it take to choose the values for the initial conjecture of the parameters?
I will be grateful for the answer

[KarthikeyanMmmm]

is there iany way to solve equations in a python list
with adition constainsts in form of equations e.g. a+bX=90, a*X^2+b*X=100,

like in regression we have a model, if we substitute data points in it we will get many equations of model parameters along with it we add additional constaraints

please help me