September 2014
Intermediate to advanced
512 pages
12h 39m
English
Content preview from IPython Interactive Computing and Visualization CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Predicting who will survive on the Titanic with logistic regression
In this recipe, we will introduce logistic regression, a basic classifier. We will also show how to perform a grid search with cross-validation.
We will apply these techniques on a Kaggle dataset where the goal is to predict survival on the Titanic based on real data.
Tip
Kaggle (www.kaggle.com/competitions) hosts machine learning competitions where anyone can download a dataset, train a model, and test the predictions on the website. The author of the best model might even win a prize! It is a fun way to get started with machine learning.
Getting ready
Download the Titanic dataset from the book's GitHub repository at https://github.com/ipython-books/cookbook-data.
The dataset has been ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.