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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
5
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

Feature creation

Sometimes, you'll find yourself in a situation where features and target variables are not really related. In this case, you can modify the input dataset, apply linear or nonlinear transformations that can improve the accuracy of the system, and so on. It's a very important step of the process because it completely depends on the skills of the data scientist, who is the one responsible for artificially changing the dataset and shaping the input data for a better fit with the classification model.

For example, if you're trying to predict the value of a house and you just know the height, width, and the length of each room, you can artificially build a feature that represents the volume of the house. This is strictly not an observed feature, but it's a feature built on the top of the existing ones. Let's start with some code:

In: import numpy as np
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import mean_squared_error...
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Python Data Science Essentials
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