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Python Data Science Essentials

Python Data Science Essentials

By : Alberto Boschetti
4.2 (6)
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Python Data Science Essentials

Python Data Science Essentials

4.2 (6)
By: Alberto Boschetti
Buy this Book

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. First Steps
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1. First Steps
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Introducing data science and Python
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Installing Python
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Scientific distributions
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Introducing IPython
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Summary
2
2. Data Munging
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2. Data Munging
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The data science process
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Data loading and preprocessing with pandas
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Working with categorical and textual data
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Data processing with NumPy
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Creating NumPy arrays
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NumPy fast operation and computations
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Summary
3
3. The Data Science Pipeline
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3. The Data Science Pipeline
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Introducing EDA
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Feature creation
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Dimensionality reduction
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The detection and treatment of outliers
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Scoring functions
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Testing and validating
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Cross-validation
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Hyper-parameters' optimization
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Feature selection
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Summary
4
4. Machine Learning
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4. Machine Learning
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Linear and logistic regression
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Naive Bayes
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The k-Nearest Neighbors
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Advanced nonlinear algorithms
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Ensemble strategies
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A peek into Natural Language Processing (NLP)
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An overview of unsupervised learning
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Summary
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5. Social Network Analysis
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5. Social Network Analysis
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Introduction to graph theory
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Graph algorithms
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Graph loading, dumping, and sampling
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Summary
6
6. Visualization
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6. Visualization
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Introducing the basics of matplotlib
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Selected graphical examples with pandas
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Advanced data learning representation
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Summary
7
Index
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Index

Linear and logistic regression

Linear and logistic regressions are the two methods that can be used to linearly predict a target value and target class respectively. Let's start with an example of linear regression.

In this section, we will use the Boston dataset, which contains 506 samples, 13 features (all real numbers), and a (real) numerical target. We will divide our dataset into two sections by using a so-called train/test split cross-validation to test our methodology (in the example, 80 percent of our dataset goes in training, and 20 percent in the test):

In: from sklearn.datasets import load_boston
boston = load_boston()
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=0)

The dataset is now loaded and the train/test pairs have been created. In the next few steps, we're going to train and fit the regressor in the training set and predict the target variable in the test...

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Programming languages
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Python Data Science Essentials
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