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

KNN classification

KNN is a simple, easy-to-comprehend, and easy-to-implement classification algorithm. It can also be used for regression problems. KNN can be employed in lots of use cases, such as item recommendations and classification problems. Specifically, it can suggest movies on Netflix, articles on Medium, candidates on, products on eBay, and videos on YouTube. In classification, it can be used to classify instances such as, for example, banking institutes that can classify the loan of risky candidates, or political scientists can classify potential voters.

KNN has three basic properties, which are non-parametric, lazy learner, and instance-based learning. Non-parametric means the algorithm is distribution-free and there is no need for parameters such as mean and standard deviation. Lazy learner means KNN does not train the model; that is, the model is trained in the testing phase. This makes for faster training but slower testing. It is also more time- and memory...