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Python Data Analysis

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
4.5 (13)
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Python Data Analysis

Python Data Analysis

4.5 (13)
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)
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1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing

Sentiment analysis using text classification

A business or data analyst needs to understand customer feedback and reviews about a specific product. What did customers like or dislike? And how are sales going? As a business analyst, you need to analyze these things with reasonable accuracy and quantify customer reviews, feedback, opinions, and tweets to understand the target audience. Sentiment analysis extracts the core information from the text and provides people's perception of products, services, brands, and political and social topics. Sentiment analysis is used to understand customers' and people's mindset. It is not only used in marketing, we can also use it in politics, public administration, policy-making, information security, and research. It helps us to understand the polarity of people's feedback. Sentiment analysis also covers words, tone, and writing style.

Text classification can be one of the approaches used for sentiment analysis. It is a supervised...

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Python Data Analysis
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