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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Decision trees - Predicting hiring decisions using Python

Turns out that it's easy to make decision trees; in fact it's crazy just how easy it is, with just a few lines of Python code. So let's give it a try.

I've included a PastHires.csv file with your book materials, and that just includes some fabricated data, that I made up, about people that either got a job offer or not based on the attributes of those candidates.

import numpy as np 
import pandas as pd 
from sklearn import tree 
 
input_file = "c:/spark/DataScience/PastHires.csv" 
df = pd.read_csv(input_file, header = 0) 

You'll want to please immediately change that path I used here for my own system (c:/spark/DataScience/PastHires.csv) to wherever you have installed the materials for this book. I'm not sure where you put it, but it's almost certainly not there.

We will use...

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