# Modeling and Analysis

Get some hands-on experience modeling and analyzing in Python.

In the previous lesson, we discussed data preprocessing techniques to clean and prepare our dataset for modeling. In the end, we split our dataset into datasets for testing and training. In this lesson, we’ll build regression models to predict the tip amount based on various features. We’ll train two traditional ML models and one DL model and then evaluate their performance.

## ML models

We’ll use the scikit-learn library to train two ML models. We’ll use regression models because regression always outputs a continuous value.

### Linear regression

Let’s start with a simple linear regression model. Linear regression falls under the category of supervised ML algorithms and is utilized to forecast values within a continuous numerical range.

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