Linear Regression with Python

Linear Regression with Python

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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.

Predict Prices Using Polynomial Regression

Contenido del Curso

Linear Regression with Python

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

Finding the Parameters

We now know that Linear Regression is just a line that best fits data. But how can you tell which is the right one?

Well, you can calculate the difference between the predicted value and the actual target value for each data point in the training set.
These differences are called residuals(or errors). And the goal is to make the residuals as small as possible.

Ordinary Least Squares

The default approach is the Ordinary Least Squares(OLS) method:
Take each residual, square it (mainly to eliminate the sign of a residual), and sum all of them.
That is called SSR(Sum of squared residuals). And the task is to find the parameters that minimize the SSR.

Normal Equation

Fortunately, we do not need to try all the lines and calculate SSR for them. The task of minimizing SSR has a mathematical solution that is not very computationally expensive.
This solution is called the Normal Equation.

This equation gives us the parameters of a line with the least SSR.
Did you not understand how it works? No worries! It is pretty complex maths. But you don't have to calculate the parameters with your hands. Many libraries have already implemented linear regression.

So hop into the following chapters. They will show you how to build the linear regression model using those libraries.

Quiz

1. Consider the image above. Which regression line is better?

2. y_true - y_predicted is called

¿Todo estuvo claro?

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Sección 1. Capítulo 2

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