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

ROC curve and AUC

AUC-ROC curve is a tool to measure and assess the performance of classification models. ROC (Receiver Operating Characteristics) is a pictorial visualization of model performance. It plots a two-dimensional probability plot between the FP rate (or 1-specificity) and the TP rate (or sensitivity). We can also represent the area covered by a model with a single number using AUC:

Let's create the ROC curve using the scikit-learn module:

# import plot_roc_curve
from sklearn.metrics import plot_roc_curve

plot_roc_curve(logreg , feature_test, target_test)

This results in the following output:

In the preceding example, We have drawn the ROC plot plot_roc_curve() method with model object, testing feature set, and testing label set parameters.

In the ROC curve, the AUC is a measure of divisibility. It tells us about the model's class distinction capability. The higher the AUC value, the better the model is at distinguishing between "fraud" and "not fraud...