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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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Product type Paperback
Published in Sep 2014
Publisher
ISBN-13 9781783284818
Length 512 pages
Edition 1st Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Toc

Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Notebook 4. Profiling and Optimization 5. High-performance Computing 6. Advanced Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

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.

Note

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 obtained from www.kaggle.com/c/titanic-gettingStarted.

How to do it...

  1. We import the standard packages:

    In [1]: import numpy as np
            import pandas as pd
            import sklearn
            import sklearn.linear_model as lm
            import sklearn...
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