32. Text: Recap

Recap

This concludes the practical statistics content! Much of what you saw in these last two lessons on Multiple Linear Regression and Logistic Regression begins to move towards more of a Data Science view of the world, and beyond what most Data Analysts perform on a day to day basis. However, I hope you enjoyed some of the challenges in these two lessons.

These lessons on Multiple Linear Regression and Logistic Regression were just a first glimpse of two methods that are a part of supervised machine learning. You can learn more from the free Udacity course or gain project reviews and the Udacity community as a part of the journey with the Machine Learning Nanodegree.

In this lesson, we looked at Logistic Regression. You learned:

1. How to use python to perform logistic regression to predict binary response values in both statsmodels and sklearn.
   
   

2. How to interpret coefficients from logistic regression output in statsmodels.
   
   

3. How to assess how well your model is performing using a variety of metrics.
   
   

4. How to assess model fit in python.

You have come a long way! Congrats, and good luck with the project!

The notebook solutions and data for sampling distributions through logistic regression lessons are available at the bottom of this page. You also can find them individually within the course should you want to go back and revisit specific ideas within any lesson.