Python's programming language is gaining popularity among SEOs for its ease of use in automating daily routine tasks. It can save time and generate sophisticated machine learning Industry Email List to solve bigger problems that can ultimately help your brand and your career. Besides automations, this article will help those who want to learn more about data Industry Email List science and how Python can help. In the example below, I'm using an e-commerce dataset to create a regression model.
I also explain how to determine if the model is Industry Email List revealing something statistically significant, as well as how outliers can skew your results. I'm using Python 3 and Jupyter Notebooks to generate graphs and equations with linear Industry Email List regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset. With this setup, I now have an equation to predict my target variable. Before I create my model, I want to take a step back to provide an easy-to-understand definition of linear regression and explain why it's vital to data analysis.
What is Linear Regression? Linear regression is a Industry Email List basic machine learning algorithm used to predict a variable based on its linear relationship between other independent variables. Let's see a simple linear regression graph: If you know the equation here, you can also know y-values versus x-values. 'a' is the coefficient of 'x' and also the slope of the line, 'b' is an Industry Email List intersection which means when x = 0, b = y. My e-commerce dataset I used this dataset from Kaggle. It is not very