Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Several multivariate linear models

As we mentioned at the beginning of the chapter, we could show several applications of multivariable linear models. The first one is a three-factor linear model. The general formula is quite similar to the one-factor linear model, shown here:

The definitions are the same as before. The only difference is that we have three independent variables instead of one. Our objective is to estimate four parameters, one intercept plus three coefficients:

For example, the equation of the famous Fama-French 3-factor model is given, where Ri is the stock i's return and Rm is the market return. SMB (Small Minus Big) is defined as the returns of the small portfolios minus the returns of the big portfolios and HML (High Minus Low) is the difference of returns of high book-to-market portfolios minus the returns of low book-to-market portfolios. (See the...