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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Classification

As a healthcare data analyst, your job is to identify patients or sufferers that have a higher chance of a particular disease, for example, diabetes or cancer. These predictions will help you to treat patients before the disease occurs. Similarly, a sales and marketing manager wants to predict potential customers who have more of a chance of buying a product. This is the process of categorizing customers into two or more categories known as classification. The classification model predicts the categorical class label, such as whether the customer is potential or not. In the classification process, the model is trained on available data, makes predictions, and evaluates the model performance. Developed models are called classifiers. This means it has three stages: training, prediction, and evaluation. The trained model is evaluated using parameters such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC). Classification has a variety of applications in various...

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