Introducing Data Science

List of Listings

Chapter 2. The data science process

Listing 2.1. Executing a linear prediction model on semi-random data

Listing 2.2. Executing k-nearest neighbor classification on semi-random data

Chapter 3. Machine learning

Listing 3.1. Step 2 of the data science process: fetching the digital image data

Listing 3.2. Step 4 of the data science process: using Scikit-learn

Listing 3.3. Image data classification problem on images of digits

Listing 3.4. Inspecting predictions vs actual numbers

Listing 3.5. Data acquisition and variable standardization

Listing 3.6. Executing the principal component analysis

Listing 3.7. Showing PCA components in a Pandas data frame

Listing 3.8. Wine score prediction before principal component analysis

Listing 3.9. ...