# Introduction to Classification

In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia. Hope you're hungry!

Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with Regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classfication.

Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?

Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.

[![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")

> 🎥 Click the image above for a video: MIT's John Guttag introduces Classification

## [Pre-lecture quiz](link-to-quiz-app)
### Introduction

Before working to clean the data and prepare it for analysis, it's useful to understand several of the algorithms that you will use. 

- Support-vector machines
- Naive Bayes
- Decision trees
- K-nearest neighbor algorithm



✅ Knowledge Check - use this moment to stretch students' knowledge with open questions


## 🚀Challenge

## [Post-lecture quiz](link-to-quiz-app)

## Review & Self Study

## Assignment 

[Assignment Name](assignment.md)