Linear Regression with Python in 60 Seconds

Learn about Linear Regression in python in this short!
#python #datascience #shorts

[@jaehuilim7467]

An excellent illustration of Overfitting

[@asdeEducation]

I hope I remember come back to this video in October

[@robmulla]

Haha. Set a reminder! Hopefully we can beat the forecast!

[@kartikgoswami]

Its November

[@nirjalkumarmahato330]

It's January

[@nirjalkumarmahato330]

It's February 🙃

[@alirezouali3119]

It’s march

[@ferrychandra9934]

Now do this without libraries, except Matplotlib and Pandas 😂

[@1_lloyd_146]

Btw, I rlly love this type of video, so make more videos of linear and nonlinear regression plsssss

[@robmulla]

Thanks for the feedback. Will do.

[@bashar9200]

Thank you for such wonderful content!! I learn a lot from your codes

[@robmulla]

Thanks for watching

[@David-hl9iv]

With Linear regression you don't necessarily need to get a line. By choosing different functions as your basis elements for the regression, you can fit any curve to the data, be it exponential or a sine curve. Linear Regression looks to minimize the distance of expected outputs at x to outputs that can be represented by the linear combinations of the functions that you are given, at that point.

[@paulantoni1934]

Yeah but then we could just go for polynomial regression.

[@spazeasterix6723]

Although correct in concept, exponentials are rarely practically used. Probably because exponentials are much more sensitive than even polynomial functions and are therefore much more susceptible to overfitting. But as I said, technically, you're implication is correct.

[@NicholasRenotte]

Rob that keyboard cam is 🔥

[@robmulla]

Thanks Nick. This was my old setup where I had an old webcam precariously attached to a mic stand. It would fall over about once an hour 😂 I need to get your opinion on cameras. Recently invested in better lighting but feel like I’m doing something wrong. Your videos are top notch.

[@NicholasRenotte]

@@robmulla LOL i've been there, the ol' jerry rigged camera setup 😂. Elgato Multi-Mount arms are a lifesaver!!

[@txsw]

Love these!! I’ve seen two and they are excellent ideas and useful

[@robmulla]

Thanks for the feedback. My first attempts at making shorts. It’s really hard to keep it under a minute.

[@broxxad]

Ey, you should only expect logarithmic regression with your awesome contents.

[@robmulla]

Haha. Thanks!

[@Ligress]

Exponential

[@h44r96]

Well said 👍

[@broxxad]

@@Ligress logarithmic sounds more realistic.

[@Ligress]

@@broxxad Fine, by the way Do you work in Data feild

[@Kashi_Mochibean]

I just found this randomly. But it's the most wonderfully-subtly-obvious and nerdiest way I've ever been asked for views and subscriptions. So I'm hitting the button

[@boimchartis]

Nice video bro👍👍

[@robmulla]

Glad you liked it

[@thespicycabbage]

you should be using time series

[@yunuskaratepe]

I don't understand the idea behind the keyboard cam but cool video :)

[@CaribouDataScience]

You misspelled Ggplot2 😅

[@robmulla]

No, I think I got it right. 😉

[@halkkoi1538]

Wow you are so amazing bro

[@robmulla]

Thanks Halk!

[@kadourkadouri3505]

don't you think a nonlinear least squares model would better fit this data ?

[@robmulla]

Great question. It may be!

[@eron1633]

I'm kinda new in python and programming,and which interface did you use in this video?)

[@robmulla]

I'm using jupyter lab. I have an intro video that would be perfect for you here: kzbin.info/www/bejne/a6HJYZKYpbOVodk

[@satyamARAS]

Reading regression analysis books 😅 .

[@1_lloyd_146]

Good work, but im curious what is day_count, is it the total of days or sth? Also, I rll want to have the dataset of this if it doesnt bother you

[@robmulla]

Thanks. I don’t have this dataset public but I do have another video on exploratory data analysis where I go over a similar dataset which I make public. Check that one out. I think day count is just a increasing number since the day the channel started.

[@1_lloyd_146]

@@robmulla Tysm! That’s super helpful

[@giraffa-analytics]

Linear regression is a bad choice for this kind of data. This is a time series problem. cumulative sums introduce autocorrelation between datapoints, which violates the assumption of iid residuals of linear regression. Code ok, reasoning not.

[@hasijasanskar]

October -> September :)

[@viewsfromthechris7810]

Looks like you got way more than the prediction lol!

[@robmulla]

Yep :D

[@gustavomaia2252]

Now you have learned that the skicit learn developers know how to do a linear regression 😊

[@bhargavapothakamuri4218]

I am getting value error when I run on my 2D CSV, with 2 columns “date” and “value”, during model.fit(x,y)

[@shivayshakti6575]

👍

[@robmulla]

🙌

[@jensenholm2361]

Wow that’s the most linear dataset I’ve ever seen

[@robmulla]

Not any more :D

[@Belawithnoalcohol]

This is October, so how is it so far?

[@SayoojGarrix]

83k. So regression miserably failed 😂

[@akankshashrivastava100]

Do you teach the same?

[@user-kp9si1km6g]

How to write gta 6 on python, just import gta6 lib

[@robmulla]

Pretty much. Import and add 1.

[@lukas8385]

How to linear regression: step 1 import linear regression -.-

[@robmulla]

Yep! You’ve got the hang of it!

[@gokayfem]

did you get 100k views?

[@robmulla]

Yes I did! As of today (Oct 20) I'm at 387k views. Thanks for watching.

[@primary4075]

Nah.... You forgot normality test

[@bharathraj221]

Filenotfounderror😢