Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
Table of Contents (20 chapters)
Section 1: Foundation for Data Analysis
Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Section 4: NLP, Image Analytics, and Parallel Computing

Feature scaling

In real life, most features have different ranges, magnitudes, and units, such as age being between 0-200 and salary being between 0 to thousands or millions. From a data analyst or data scientist's point of view, how can we compare these features when they are on different scales? High-magnitude features will weigh more on machine learning models than lower magnitude features. Thankfully, feature scaling or feature normalization can solve such issues.

Feature scaling brings all the features to the same level of magnitude. This is not compulsory for all kinds of algorithms; some algorithms clearly need scaled data, such as those that rely on Euclidean distance measures such as K-nearest neighbor and the K-means clustering algorithm.

Methods for feature scaling

Now, let's look at the various methods we can use for feature scaling:

  • Standard Scaling or Z-Score Normalization: This method computes the scaled values of a feature by using the mean and standard deviation...