Machine Learning Tutorial Python – 8: Logistic Regression (Binary Classification)

Logistic regression is employed for classification troubles in machine learning. This tutorial will demonstrate you how to use sklearn logisticregression class to fix binary classification problem to predict if a shopper would invest in a everyday living insurance coverage. At the stop we have an fascinating workout for you to resolve.
Ordinarily there are two varieties of machine learning challenges (1) Linear regression where prediction worth is continuous (2) Classification the place predicted price is categorical. Logistic regression is applied for classification troubles predominantly.

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Code: https://github.com/codebasics/py/blob/learn/ML/7_logistic_reg/7_logistic_regression.ipynb
Physical exercise: Open over notebook from github and go to the close.
Work out option: https://github.com/codebasics/py/blob/learn/ML/7_logistic_reg/Workout/7_logistic_regression_exercise.ipynb

Matters that are included in this Video clip:
:00 – Principle (Reveal distinction amongst logic regression and classification)
1:18 – What is logistic regression?
1:26 – Classification kinds (Binary vs multiclass classification)
1:53 – Clarification of logistic regression employing the case in point of if man or woman will invest in insurance primarily based on his age
5:38 – Sigmoid or Logit purpose
8:18 – Coding (for coding we are making use of an instance of if a man or woman will obtain insurance policies or not dependent on his age)
14:36 – sklearn forecast_proba() functionality
15:49 – Exercising (Resolve a dilemma of predicting personnel retention based mostly on wage, distance to do the job, marketing, department and so on)

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Subsequent Online video:
Machine Learning Tutorial Python – 8 Logistic Regression (Multiclass Classification): https://www.youtube.com/view?v=J5bXOOmkopc&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=9

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