Linear Regression

Lesson 16 of 19 in Coddy's Introduction to Machine Learning course.

Linear Regression is one of the most fundamental algorithms in machine learning for predicting a continuous variable. It's the go-to method for estimating the relationships between variables and forecasting. Whether you're predicting house prices, stock market trends, or temperatures, linear regression can provide insight into how these variables correlate.

Source: wikipedia

Linear regression attempts to model the relationship between two or more variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable (independent), and the other is considered to be a dependent variable.
The simplest form of the linear regression equation with one dependent and one independent variable is defined as follows:

Alpha represents the intercept point with axis y
Beta represents the slope

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Test yourself

This lesson includes a short quiz. Start the lesson to answer it and track your progress.

Challenge

Easy

Create a function named linear_equation that takes a list that represents a data point and a list that represents the coefficients of the linear equation.

the function returns the output of the linear equation.

For example

if n = 2, then the number of coefficients is two: y = b[0] + b[1]*x[1]
if n = 3, then the number of coefficients is three: y = b[0] + b[1]*x[1] + b[2]*x[2]

Try it yourself

def linear_equation(coef, x):
    # Write code here

All lessons in Introduction to Machine Learning

1The Beginning of ML Journey

ML Introduction Data to feed a model What is a ML model

4K-Means Algorithm

K-Means Introduction fit method predict method

2Machine Learning Overview

Supervised learning Unsupervised learning

5Naive Bayes

Naive Bayes Introduction fit method predict method

3K-Nearest-Neighbors (KNN)

KNN Introduction fit method predict method

6Other Models

Logistic RegressionLinear RegressionDecision Tree Support Vector Machine (SVM)Models quiz