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Machine Learning with Python:

We have successfully completed the regression algorithms, we now move forward to classification algorithms.

STEP TEN:-

Understanding Logistic Regression:

Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary/categorical outcome, we use dummy variables. You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function.


What is logistic regression? An amazing answer by Thirumal Venkat https://www.quora.com/What-is-logistic-regression/answer/Thirumal-Venkat-1?share=b8bc2ce1&srid=tNaj

Here's a video explaining the idea (Watch First): https://youtu.be/pA90ubXxgGg

Here are videos explaining the code (Watch in sequence):

https://youtu.be/5BWv7U6x7Vo
https://youtu.be/jezkDyN19qI
https://youtu.be/isRPCMmEHsA
https://youtu.be/_BBl8rxwFAE
https://youtu.be/JpnsMtuOCsw

The code: https://github.com/priyanka-kasture/ML-Algorithms/blob/master/Classification/LogisticRegression/LogisticRegression.py

The dataset: https://github.com/priyanka-kasture/ML-Algorithms/blob/master/Classification/LogisticRegression/Social_Network_Ads.csv

Hope y'all find the content useful! Happy Machine Learning!

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