Introduction

0:00 The topic for today is linear regression with Scikit-Learn. This is the first topic we are covering in machine learning with Python. And in fact, one of the things we'll try and do today is to apply linear regression to a real world problem. And we'll try to predict medical expenses using this technique. So here's an overview of what we are going to cover today. Just a quick outline. So first we start by stating a typical problem statement for machine learning.

0:52 Then we will download and explore a dataset. Then we will perform linear regression with one variable using Scikit-Learn. Then we'll perform linear regression with multiple variables. And don't worry if these terms don't make sense right now. They will by the end of this session. Then we'll use categorical features for machine learning. Learn how to apply that in general, not just for linear regression. We will also talk about regression coefficients and feature importance. How do you interpret the outputs of a model that you create?

1:14 And then we will apply linear regression to some other datasets. This tutorial is an executable Jupyter notebook hosted on Jovian. And you can run this online on binder or you can run this on your computer as well. You can also run this on Google collab and that's perfectly fine too. So here's the problem that we will solve today. And using this problem, we will define the terms machine learning and linear regression in the context of this problem.

2:10 And then later we'll generalize their definitions. Let's read the problem carefully. And then we'll think about how we can approach this problem. Acme insurance incorporated offers affordable health insurance to thousands of customers all over the United States. And as the lead data scientist at Acme, you are tasked with creating an automated system to estimate the annual medical expenditure for new customers using information such as their age, sex, BMI, children, smoking habits, region of residents, et cetera.

2:40 Why are you asked to do that? Because estimates from your system will be used to determine the annual insurance premium, which is the amount paid every month or every year. That is offered to the customer. So your job is to figure out when a new customer applies for medical insurance based on information like their age, sex, BMI, et cetera, how much they might incur in medical bills in an average year. If you can find that information along with some other information can be used to determine what you should charge the customer for insurance.

3:06 And this is a typical problem that you will face and it's a typical example