# Your Guide to Linear Regression Models This article explains linear regression and how to program linear regression models in Python. In this post, I’ll focus on Linear Regression models that examine the linear relationship between a dependent variable and one (Simple Linear Regression) or more (Multiple Linear Regression) independent variables.

Interpretability is one of the biggest challenges in machine learning. A model has more interpretability than another one if its decisions are easier for a human to comprehend. Some models are so complex and are internally structured in such a way that it’s almost impossible to understand how they reached their final results. These black boxes seem to break the association between raw data and final output, since several processes happen in between.

But in the universe of machine learning algorithms, some models are more transparent than others. Decision Trees are definitely one of them, and Linear Regression models are another one. Their simplicity and straightforward approach turns them into an ideal tool to approach different problems. Let’s see how.

You can use Linear Regression models to analyze how salaries in a given place depend on features like experience, level of education, role, city they work in, and so on. Similarly, you can analyze if real estate prices depend on factors such as their areas, numbers of bedrooms, or distances to the city center.

In this post, I’ll focus on Linear Regression models that examine the linear relationship between a dependent variable *and one (Simple Linear Regression) or more (Multiple Linear Regression) *independent variables.

## A Deep Dive into Linear Regression

Lets begin our machine learning journey. A Deep Dive into Linear Regression. Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.

## 5 Regression algorithms: Explanation & Implementation in Python

What is regression analysis in simple words? How is it applied in practice for real-world problems?

## Python Tricks Every Developer Should Know

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

## How to Remove all Duplicate Files on your Drive via Python

Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.

## Which methods should be used for solving linear regression?

As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.ere are many different methods that we can apply to our linear regression model in order to make it more efficient. But we will discuss the most common of them here.