Linear Regression with Python

1. Simple Linear Regression

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

What is Linear Regression

Finding the Parameters

Building Linear Regression Using NumPy

Building Linear Regression Using Statsmodels

Predict House Prices

2. Multiple Linear Regression

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

Linear Regression with Two Features

Linear Regression with n Features

Building Multiple Linear Regression

Choosing the Features

Predict Prices Using Two Features

3. Polynomial Regression

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Quadratic Regression

Polynomial Regression

Building Polynomial Regression

Interpolation vs Extrapolation

Evaluate the Model

4. Choosing The Best Model

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Metrics

Overfitting

R-squared

Predict Prices Using Polynomial Regression

Linear Regression with n Features

n-feature Linear Regression Equation

As we have seen, adding the new feature to the linear regression model is as easy as adding it along with the new parameter to the model's equation. We can add much more than two parameters that way.

Note
Consider n to be a whole number greater than two.

Normal Equation

The only problem is the visualization. If we have two parameters, we need to build a 3D plot. But if we have more than two parameters, the plot will be more than three-dimensional. But we live in a 3-dimensional world and cannot imagine higher-dimensional plots. However, it is not necessary to visualize the result. We only need to find the parameters for the model to work. Luckily, it is relatively easy to find them. The good old Normal Equation will help us:

X̃ Matrix

Notice that only the X̃ matrix has changed. Let's take a closer look at this matrix. You can think of the columns of this matrix as each responsible for its β parameter. The following video explains what I mean.

The first column of 1s is needed to find the β₀ parameter.

Tudo estava claro?

Seção 2. Capítulo 2

Conteúdo do Curso

Linear Regression with Python

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building Linear Regression Using NumPy Building Linear Regression Using Statsmodels Predict House Prices

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Choosing the Features Predict Prices Using Two Features

3. Polynomial Regression

Quadratic Regression Polynomial Regression Building Polynomial Regression Interpolation vs Extrapolation Evaluate the Model

4. Choosing The Best Model

Metrics Overfitting R-squared Predict Prices Using Polynomial Regression

Linear Regression with Python

Linear Regression with n Features

n-feature Linear Regression Equation

As we have seen, adding the new feature to the linear regression model is as easy as adding it along with the new parameter to the model's equation. We can add much more than two parameters that way.

Note
Consider n to be a whole number greater than two.

Normal Equation

X̃ Matrix

The first column of 1s is needed to find the β₀ parameter.

Tudo estava claro?

Seção 2. Capítulo 2