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#### 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.
Preface
Section 1: Foundation for Data Analysis
Free Chapter
Getting Started with Python Libraries
Section 2: Exploratory Data Analysis and Data Cleaning
Data Visualization
Cleaning Messy Data
Signal Processing and Time Series
Section 3: Deep Dive into Machine Learning
Supervised Learning - Regression Analysis
Supervised Learning - Classification Techniques
Unsupervised Learning - PCA and Clustering
Section 4: NLP, Image Analytics, and Parallel Computing
Analyzing Textual Data
Analyzing Image Data
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# Understanding multicollinearity

Multicollinearity represents the very high intercorrelations or inter-association among the independent (or predictor) variables.

Multicollinearity takes place when independent variables of multiple regression analysis are highly associated with each other. This association is caused by a high correlation among independent variables. This high correlation will trigger a problem in the linear regression model prediction results. It's the basic assumption of linear regression analysis to avoid multicollinearity for better results:

• It occurs due to the inappropriate use of dummy variables.
• It also occurs due to the repetition of similar variables.
• It is also caused due to synthesized variables from other variables in the data.
• It can occur due to high correlation among variables.

Multicollinearity causes the following problems:

• It causes difficulty in estimating the regression coefficients precisely and coefficients become more susceptible to minor...