Simple Linear Regression in Python - sklearn

Hands down to the best tutorial for linear regression implementation in python not even code with harry can compete😊

Thank you, this was really helpful and straight to the point!

you are amazing

Can you provide the data set for this video too.

One of the best ML linear regression I have ever seen. Thank you so much. This channel should have wider reach.

thank you for amazing explanation

Really simple and clear tutorial ⭐⭐

The dataset I am working with consists of two columns, X and Y where the function Y changes for increasing values of the X variable. If Y shows variations with X, the simplest way to do this is by fitting the data with a linear function. but how can i find pattern in my data ? how can i analyse for what value of x , y shows changes because c=15 , m=(_339)

You deserve more subscribers.

Thanks , well pressented

amazing explanation. THANKYOU

Thanks, I undestand better.

could yo please share the data so we cold train on it

Inifinite thank yous for this!!! Straight to the point and beautifully explained

I am struggling to learn testing and training data can you make some video to better understanding how it takes data i like your explaining.

So, beautifully explained ...Thanks a lot

When i try to plot the x and y test it says x and y must be same size. What to do

That was an amazing video , i have been struggling for three days finally i found a solution
Thank you so much!!

you are genius mam

When I try and plot the train model prediction and the test model predictions like you did at the end, I get an error saying "unhashable type: 'numpy.ndarray' " I'm not sure what's going on.

You can compress audio in audacity if someone complain

I am jumping out of the video because of audio quality.

Thank you so much for this incredible linear regression concept. I have been struggling to understand the cording aspect but you just made my day. Everything was just so easy with simple and explanatory codes.

what to do if the x-axis is time series ?