Machine Learning Fundamentals Logistic Regression

About

An introduction to Logistic Regression: what it is, how it works, and how to run a model using scikit-learn.

Learning Objectives

You will be able to:

Describe the principles of Logistic Regression.
Describe how logistic regression is similar to and different from linear regression.
Interpret the coefficients of a Logistic Regression model.
State the assumptions of a Logistic Regression model.
Fit, evaluate, and generate predictions from a Logistic Regression model in scikit-learn.

Content

Topic	About
Full Lesson Deck	- Describe the principles of Logistic Regression- - Interpret the coefficients of a Logistic Regression model.- - State the assumptions of a Logistic Regression model.
02 Logistic Regression	- Fit, evaluate, and generate predictions from a Logistic Regression model in `scikit-learn`
Practice	An optional additional exercise to help practice building logistic regression models

Prerequisites

Interpret and use Python dictionaries.
Build pandas DataFrames from dictionaries.
Perform simple data manipulation on pandas objects.
Build for and while loops in Python.
Use pip install for package management.
Create data visualizations in a noteboook using matplotlib and seaborn.
Build a linear regression model.
Understand bias / variance and implment train-test split.

References

📖 Reference Materials

Important note

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