9.1. Introduction

As we use data from multiple sources, we will find them stored in many formats. One of the most common ways of storing data is in a database. In this chapter, we will use SQLlite to send queries to different databases, and import data from those databases into a pandas DataFrame. Then we will use the data to model different situations and predict outcomes.

9.1.1. Learning Goals

• Manipulate data from a database using Structured Query Language.

• Use linear regression to model the relationship between predicted data and actual data.

• Create models that we can use to predict outcomes.

9.1.2. Learning Objectives

• Import a SQL database into a Pandas DataFrame.

• Retrieve, sort, and aggregate data from a database.

• Join and extract data from multiple databases.

• Visualize data over a time series.

• Use a Linear Regression model to identify Mean Squared Error and Mean Absolute Error.

• Use the train-test split method to create and test models.

• Add features and use machine learning libraries to refine models and predictions.

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