Linear Regression in Python - Full Project for Beginners
ฝัง
- เผยแพร่เมื่อ 21 ก.ค. 2024
- Welcome to this comprehensive "Linear Regression with Python Tutorial" for beginners! In this video, we will cover the basics of linear regression, a fundamental concept in data science and machine learning. With this tutorial, you will learn how to perform data analysis using linear regression in Python. This tutorial is perfect for anyone who is new to the field of data science and machine learning and wants to get started with a real-world data science project.
We will start by introducing the concepts of statistical learning and statistics, and then move on to applying these concepts in Python to perform linear regression. You will learn how to use Python libraries such as NumPy and Pandas for data preprocessing, and then use scikit-learn for building and evaluating a linear regression model.
By the end of this tutorial, you will have a solid understanding of linear regression and how to apply it in Python for your own data science projects. So, if you're ready to dive into the world of data science and machine learning, make sure to watch this tutorial till the end. Don't forget to like and subscribe for more tutorials like this!
👉 Check out the code: github.com/alejandro-ao/py-ec...
👉 Dataset: www.kaggle.com/datasets/kolaw...
Errata
30:30 Intersection with the Y axis*
------------------------------------------------------------------------------------------------------------------------------------------------
Timestamps:
00:00 Intro
02:10 Importing the dataset
08:14 Exploratory Data Analysis
11:31 Pairplot of all numerical variables
14:34 Quick Linear Regression Explanation
20:10 Split the data using Scikit-Learn
27:05 Train a model using Scikit-Learn
29:08 Interpreting the Coefficients
33:09 Create Predictions
35:20 Graphical Evaluation of the Predictions
37:40 Analytical Evaluation of the Errors
42:19 Residual Analysis
48:37 Outro
Amazing! Please keep doing these beginner friendly projects, theyre incredibly useful
It is very helpful. Keep up with the great work!
Great video! Here's a quick tip: consider using distplot to display Yhat and Y_test in the same axis to evaluate your model's performance.
This is really awesome, it helped me to understand linear regression concept , love the way you teach through projects.
I have watched endless Regression videos. I graduated with a math degree a couple years ago, and my programming skills are beginner level. I've done Datacamp stuff, youtube guided projects, you name it. This is by far the most concise, easy to follow, welcoming, calming video I have ever watched explaining how to do a regression project. I appreciate the quick definitions along the way, that help the audience including myself, not feel stupid lol. And explaining every small step really helps. Also the big picture explanation of how leverage this code and automate it, was helpful. Very few if any, ever talks about that step. I'm trying to get crack into the tech world and land a data job. Just this one video has truly given me the confidence, to not feel overwhelmed and that its obtainable. Sorry for the lengthy response haha Please keep making videos man, you rock!!
So wholesome! Thanks for helping me find the best video.
You're really underrated dude, this video was very well made especially for beginners interested in the field. Thank you so much!
thank you!!
Thank you for this lesson, Alejandro: it helped me tremendously. Have a nice day and great holidays.
thank you! i'm glad to hear it helped. i hope you had great holidays :)
Dude your explanation is far more better than anyone else and simple to understand i do not comment actually but i can say that u explain really good.
i really appreciate it! glad it was helpful :)
You are great brother.
this is such a good beginner-friendly tutorial! thank u sm
hey there! it makes me very happy to hear that! keep it up ;)
the video I wanted to find..amazing content..keep working great things will happen.
i appreciate it!
I'm a math major with a minor in CS. I'm looking to break into data science. Your video was very informative and easy to follow. Thank you!
you are literally in the best position to start off in the world of AI. enjoy the ride!
very helpful, thank you
looking forward to another END TO END video but for more intermediate level!
Amazing video ! for me as a beginner, that was very helpfull, thank you
Great to hear! Keep it up :)
Wonderful video! I was able to follow along happily until the scatterplot command, for which I got the error, "TypeError: scatterplot() takes from 0 to 1 positional arguments but 2 were given". Had to change the command to sns.scatterplot(x=predictions, y=y_test). Everything else was perfect, and I learned a lot - THANK YOU!!!
i got at that point too, thanks for the fix :))
Amazing video i learned a lot from you thank you for the job
Glad to hear it!
It is very helpful brother, Keep up good work!
Thank you, I will!
big thanks for this video -- i learned a lot!!
Very welcome!
After watching a lot of videos for Linear Regression your video is by far THE BEST!!! thanks. Just one thing couldn't use scatterplot got a 'scatterplot() takes from 0 to 1 positional arguments but 2 were given' error.
yeah, I suggest you use the following instead :
plt.scatter(predictions, y_test, alpha=0.5)
it does the same thing, hope it helps
Put x= and y=. and it works
Great work ! Im waiting for others ML algorithms.
thanks!! i’ll try to make more of this 💪
Hey. Nice video. There's just a thing I'm not sure about. Shouldn't normality, homoscedasticity and tests regarding residuals be done over y_train - predict, where predict is based on the training set? You verified the normality assumption on residuals of y_test - "predict_test". Which one is it?
please make video on lasso,ridge,svm,knn,gradient boost etc..
Really thanks it is very helpful
hey there, i'm glad it was helpful! keep it up :)
The coefficient does not determine the most important. The magnitude of each variable varies in the formula. A small coefficient applied to a variable with more magnitude can be the most variance on the model
Can you tell me from where I can get this csv file ?
u are the best
you are