Logistic Regression SKLearn – Machine Learning example using Python – Part 1

In this video, we will go over a Logistic Regression example in Python using Machine Learning and the SKLearn library. This tutorial is for absolute beginners. We will cover all the steps of the machine learning process. I also explain some of the theory to help you understand Machine Learning and Logistic Regression in general.

Support the channel on Patreon:

Data Analytics Course Link:

Tutorial Overview

Video 1:
1. What is Machine Learning
2. Process of Machine Learning
3. Problem Formulation
4. Loading the Raw Data
5. Data Preprocessing
• EDA

Video 2:
5. Data Preprocessing
• Data Cleaning
• Feature Selection
6. Splitting the Raw Data
7. What is Logistic Regression Analysis

Video 3:
8. Running Logistic Regression
9. Evaluating the Model
10. Hyper Parameter Tuning
11. Final Model with Selected Parameters
12. How to use our L. Regression model

How to download and install Python through Anaconda:

Download the raw data & the Python Notebook:

Seaborn Tutorial:

Yiannis Pitsillides on Social Media:

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Book:

[YiannisPi]

How did you find this tutorial? Please let me know your thoughts!

[tai-shanlin]

This is absolutely brilliant. Thank you for the concise yet detailed walkthrough of Logistic regression. Cheers!

[martincalero]

These series of videos just saved my thesis 🤧💪

[SilentOutside]

One of the best machine learning tutorials I have ever seen. Like and subscribe...

[yanlin]

Great tutorial! Very clear explaination!

[nowandand]

This is a great tutorial and deserve a lot more views and likes. Thanks man!

[Junior]

Excellent lecture! Wish I had this when I was in Uni.
Thanks for sharing.

[junaidmalik]

You are amazing!, thanks for the efforts . really benefiting from every video u upload. feel really lucky to have found u :)

[sunmit_productions]

I am struggling with my data analysis class and your video help a lot!! Thank you very much! I have subscribed to your channel and look forward to watching more tutorials!!!

[musaargin]

Excellent Explanation. To the point. Any link for the codes ?

[sirginirgin]

Excellent Explanation. Any link for the codes for all ML videos

[Jpssue]

Great Video thanks. Did we delete the outlier or just filtered the data? What is the best practice. Thanks.

[randb]

This is an extremely helpful video! thank you

Quick question: After you train your algorithm and then optimise its hyperparameters do we then test it on our validation data and then if its performance is acceptable do we go on our testing data? If it is not acceptable do we optimise it again and then test it once again on our validation until the performance is acceptable? If this is correct, then once we get a good model, why don't we keep the hyperparameters and run our algorithm again with our chosen hyperparemeters and then remove validation data so that training data is more?

[yb]

Thank you so much, you are very talented. Keep up the good work!

[siuwaiyeung]

where can I find your link and learn from your jupiter NB ?

[livelife]

Guys how did u download the data from github ?

[uchindamiphiri]

I wonder why my pairplot is not executing still running despite doing everything right

[Cristian-bgpr]

Thank you very much! You enlighted me with a lot of stuff. Definetly, like and subscribe to you, sir!

[victordias]

I am pretty familiar with python and the concepts. As such, I could understand your thought-processing perfectly and all steps were very clear to me. However, I'm pretty sure that a person that's just diving in and trying to understand what's going on will have a hard time with this tutorial. Perhaps if you could add some pauses to your speech and, in order to not make the video very lenghty, perhaps split it, I believe you'd make yourself clearer to a even bigger audience. Cheers!