## Linear Regression with Python

INTERMEDIATE

#python

Author: Volodymyr Romanovych

Course description

Linear Regression is a crucial concept in predictive analytics. It is widely used by data scientists, data analytics, and statisticians as it is easy to build and interpret but powerful enough for many tasks.

Complete all chapters to get certificate

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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 The Linear Regression

Finding The Parameters

Building The Linear Regression Using NumPy

Building The Linear Regression Using Statsmodels

Multiple Linear Regression

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

Challenge

Linear Regression With Two Features

Linear Regression With n Features

Building Multiple Linear Regression

Choosing The Features

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.

Challenge

Quadratic Regression

Polynomial Regression

Building The Polynomial Regression

Interpolation vs Extrapolation

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.

Challenge 1

Metrics

Overfitting

R-squared

Challenge 2