The goal of this first notebook is to explore logistic regression and feature engineering with sklearn.
In this notebook you will use product review data from Amazon.com to predict whether the sentiments about a product (from its reviews) are positive or negative.
Use Pandas Dataframes to do feature engineering
- Train a logistic regression model to predict the sentiment of product reviews.
- Inspect the weights (coefficients) of a trained logistic regression model.
- Make a prediction (both class and probability) of sentiment for a new product review.
- Given a classifier, create a confusion matrix
- Compare multiple logistic regression models.
Any cell with #TODO is code from myself, Cecilis Barnes. All other code from this assignment belongs to the copyright below.
Copyright ©2021 Emily Fox, Hunter Schafer, Valentina Staneva. All rights reserved. Permission is hereby granted to students registered for University of Washington CSE/STAT 416 for use solely during Spring Quarter 2021 for purposes of the course. No other use, copying, distribution, or modification is permitted without prior written consent. Copyrights for third-party components of this work must be honored. Instructors interested in reusing these course materials should contact the author.