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

Linear regression

Linear regression is a kind of curve-fitting and prediction algorithm. It is used to discover the linear association between a dependent (or target) column and one or more independent columns (or predictor variables). This relationship is deterministic, which means it predicts the dependent variable with some amount of error. In regression analysis, the dependent variable is continuous and independent variables of any type are continuous or discrete. Linear regression has been applied to various kinds of business and scientific problems, for example, stock price, crude oil price, sales, property price, and GDP growth rate predictions. In the following graph, we can see how linear regression can fit data in two-dimensional space:

The main objective is to find the best-fit line to understand the relationship between variables with minimum error. Error in regression is the difference between the forecasted and actual values. Coefficients of regression are estimated using...