Linear Regressions with StatsModels
Vložit
- čas přidán 3. 07. 2024
- In this video I'll show how to create Linear Regressions with StatsModels. We'll explore the awesome datasets provided by StatsModels. I'll also be showing how to work through 2 examples using a combination of NumPy, Pandas, Matplotlib, StatsModels and Seaborn.
We'll explore whether a professors rating has anything to do with how attractive they are. Also we'll explore how to get bigger tips.
Code is Here : github.com/derekbanas/StatsMo...
I will be covering TensorFlow in detail over the next few weeks.
MY UDEMY COURSES ARE 87.5% OFF TIL June 28th ($9.99)
www.udemy.com/
➡️ Python Data Science Series for $9.99 : Highest Rated & Largest Python Udemy Course + 56 Hrs + 200 Videos + Data Science bit.ly/Master_Python_34
➡️ New C++ Programming Bootcamp Series for $9.99 : Over 23 Hrs + 53 Videos + Quizzes + Graded Assignments + New Videos Every Month bit.ly/C_Course_34
I Live Code Here : / derekbanas
Probability in One Video : • Probability for Data S...
Statistics in One Video : • Statistics for Data Sc...
NumPy in One Video : • NumPy Tutorial : Numpy...
Pandas in One Video : • Pandas Tutorial : Pand...
Matplotlib in One Video : • Matplotlib Tutorial : ...
Seaborn in One Video : • Seaborn Tutorial : Sea...
Plotly in One Video : • Plotly Tutorial 2023
Datasets Mentioned : vincentarelbundock.github.io/...
Free Math Quizzes : bit.ly/LAQuizzes
#Python #DataScience #StatsModels
The most in demand skills in the world right now are in Data Science & Machine Learning! I will teach you everything imaginable about Data Science & Machine Learning including all the math, libraries, algorithms so that rather then playing with it on an elementary level you will Master it! The journey will be hard, but those willing to put in the time and brain power will be rewarded in the end.
Like the channel? Consider becoming a Patreon and get access to exclusive videos! All Patreons who contribute $1 or more get a FREE coupon code to my Python Programming Bootcamp Series!!!
Check it out here: / derekbanas
THANK YOU TO MY PATREON SUPPORTERS LIKE :
shapeganic.com/app/store (Calorie Counter & Weight Tracking App)
ckcoder.com
vsolutions.be
lumarycodes
github.com/metabake
greedygammon.com
mrjak318
MY UDEMY COURSES ARE 87.5% OFF TIL April 26th ($9.99)
www.udemy.com/
➡ Python Data Science Series for $9.99 : Highest Rated & Largest Python Udemy Course + 55 Hrs + 200 Videos + Data Science bit.ly/Master_Python_29
➡ New C++ Programming Bootcamp Series for $9.99 : Over 23 Hrs + 53 Videos + Quizzes + Graded Assignments + New Videos Every Month bit.ly/C_Course_29
this is gold, i dont think anyone makes as simple tutorials as you
Derek, the vast amount of knowledge you have is impressive!
Thank you :) It is just what I do for a living. Anyone could learn this stuff if I did
Dear Derek, thanks so much for this video.
I'm happy you liked it :) I'll do my best to start covering machine learning with TensorFlow next week
easy to follow along and good content as well
You shouldn't have substituted the std error value of 0.262 for the coefficient of x1, but rather the eval coefficient of 0.2687 because that is what our independent variable is. Let me know if there is something wrong with what I have said.
I'm also interested in a reply to this. I immediately caught this too but I want to make sure I'm not misinterpreting something.
Thank you Derek!
You're awesome !!
Is there any non-linear regression tutorial?
Thanks Derek , but I really wait for calculus and linear algebra in one video
I'll see what I can do. Linear algebra is very doable. Calculus would be a very long video
can you use this for multivariate linear regression?
Inventory analysis please
It was very helpful to watch, just one comment: Usually y is the dependent variable, so your variable names in the beginning are the wrong way around. In this case it does not matter because it was only one independent variable, but technically speaking you tried to estimate the effect of evaluation results on beauty there The same later on (total_bill got explained by tip), which is why I imagine it didnt work with binary variables.
can you please make tutorial a bout scapy in python
can someone tell me why I am adding a column of 1 to X?
Greetings, Derek Banas
Thank you for checking out my video :)
un grand merci (:
C'est un plaisir pour moi d'aider
Thanks for this intro to linear regression. I think "sex" and "smoker" didn't work because of their "categorical" nature as opposed to other "numerical" predictors.
Yes that is most definitely true. I could have gotten them to work, but I thought it didn't matter that much
@@derekbanas from real life examples - it does not matter what model are you choosing if you are an expert you can combine enough compexity and at least moderate results. Or if you are black boxing you do not care what model is.
There is no such rule that catrgorical variables will not be predictive. In general education, wealth, family status, age, gender, Credit score, location are preferable
Sooo... "Expressions" or "Regressions" now?
Regressions, sorry if I said expressions at some point?
@@derekbanas Psshht, the title of the video says "Expressions" 🤫
That's funny :D I'll fix it
Exclusiveshiv is me
Thank you so much for stopping by my livestream :) Normally I don't have technical difficulties. I greatly appreciate it!