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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Idris
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (16 chapters)
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13
A. Key Concepts
15
C. Online Resources

Autoregressive models


An autoregressive model can be used to represent a time series with the goal of forecasting future values. In such a model, a variable is assumed to depend on its previous values. The relation is also assumed to be linear and we are required to fit the data in order to find the parameters of the data. The mathematical formula for the autoregressive model is as follows:

In the preceding formula, c is a constant and the last term is a random component also known as white noise.

This presents us with the very common problem of linear regression. For practical reasons, it's important to keep the model simple and only involve necessary lagged components. In machine learning jargon, these are called features. For regression problems, the Python machine learning scikit-learn library is a good, if not the best, choice. We will work with this API in Chapter 10, Predictive Analytics and Machine Learning.

In regression setups, we frequently encounter the problem of overfitting...

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