Logistic Regression | Logistic Regression in Python | Machine Learning Algorithms | Simplilearn

This Logistic Regression video will help you understand how a Logistic Regression algorithm works in Machine Learning. You will learn what is Supervised Learning, what is a classification problem, and the maths behind Logistic Regression. In the end, you will see a demo on how to predict the number present in an image using Logistic Regression in Python.

Below topics are covered in this Logistic Regression Tutorial:
0:00 - 01:52 Introduction
01:52 - 05:35 What is Supervised Learning?
05:35 - 06:10 What is Classification?
06:10 - 15:49 What is Logistic Regression?
15:49 - 16:58 Comparing Linear and Logistic regression
16:58 - 18:40 Logistic regression applications
18:40 - 38:17 Use case - Predicting the number in an image

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What is Logistics Regression?
Logistic regression is a classification algorithm that is used to predict a binary outcome based on a set of independent variables. Logistic regression uses a logistic function to estimate the probability of a target variable belonging to a particular class or category. It assumes that the dependent variable is binary or dichotomous and there should be no or very little multicollinearity between the predictor variables. Logistic regression

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[loririggins]

You made Logistic Regression so fun! Thank you! I did not want to take Predictive Modeling, but you inspired me so much! I gained so much knowledge in 1 hour. An amazing video. Thank you.

[navinaws]

This is simplest explanation I found for logistic regression after viewing more than 15 videos ... Thank you Mr Mohan, really appreciate it

[rajamoorthy]

Excellent way of explaining Logistic Regression. The explanation was so lucid and clear. Thanks

[cpmcmanaman]

This was fantastic. The content and the presenter were flawless. Been looking at logistic regression introductions for a week now. This is by far the best. Thank you so much!

[vimala]

Tq so much for your very understandable lecture sir😍😍😍

[rajivgupta]

You can achieve accuracy upto ~97.5%

Do this,

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size = 0.2, random_state = 30)

model = LogisticRegression(solver = 'lbfgs', max_iter = 10000)

Output
print("Accuracy score: {}".format(accuracy_score(y_test, y_pred)))
Accuracy score: 0.975

[tejaseemakurthi]

Upto now I got more confused with this algorithms... but after this video it's very easy😊

[yashpandey]

Thank you so much you nailed the logistic regression topic❤❤❤❤

[hamzawi]

Let me say thank you so much for this huge valuable work. It is so easy, you are a professional teacher.

[tarunkumarpatel]

Nice explanation on Logistic Regression for beginners!!! Thank you :)

[strugglingprogrammer]

sir i have started Learning machine Learning from your YouTube channel and special thanks for linear regression class now please share logistic regression dataset with me

[debanitadasgupta]

Brilliant Mohan. Your sessions are my favourite.

[shubhamsarafo]

super video !! really appreciate the contributors.

[sambithdas]

thnaks for the video. Just have a question at 9:53, the graph which was showing that above the threshold the car will break down (i.e 1) and below the threshold(.50) the car will not breakdown(i.e 0). My question is what the model will predict at the threshold, means at exactly 3.5 years will this model predict that the car will breakdown or not.
Thanks in advance!!

[vaishalibisht]

Love this series. You guys have made machine learning so simple.

[indugula_Ashish_Hardini]

its really wonderful session . Thanks a lot.

[rahulds]

Hello Simplilearn. Your content is super nice. It made a novice like me who has a little knowledge about programming learn Machine Learning in an effortless manner. A request I would like to make that I am watching all of your videos to learn about all the different algorithms like Logistic Regression, SVM, RF, so every time asking about the dataset breaks the consistency cause it takes time for you to send also, so can you keep my request of sending the datasets for all the models in once so that my continuity doesn't break.

[maYYidtS]

excellent teaching....i have a doubt i.e.
where we use hypothesis testing and a/b testing in this problem. and what is the purpose of them?
and where we use regularization method in this model?

[anikatasnim]

Thank you so much. I was really having a hard time understanding machine learning algorithms. but now after watching this I can confidently give a presentation on it. Thank you. Is there any pdf regarding this video u may provide? It would be really helpful. Again Thank you so much.

[sudhagarraghavan]

Thanks for the video, well presented. One suggestion - you might want to have a more realistic example. The car breakdown example is not realistic at all as any car owner will attest to. No car will be able to run without service even for a year. Though I know it is for illustrative purposes, it would be good if the example could be a bit more realistic. Another suggestion is to explain more on the difference between linear and logistic regression by providing more more insight into data types and independent/dependent variables. That'll be more helpful for newcomers.