Here’s how I used Python to build a regression model using an e-commerce dataset
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Here's how I used Python to build a regression model using an e-commerce dataset
The programming language of Python is gaining popularity among SEOs for its ease of use to automate daily, routine tasks. It can save time and generate some fancy machine learning to solve more significant problems that can ultimately help your brand and your career. Apart from automations, this article will assist those who want to learn more about data science and how Python can help.
In the example below, I use an e-commerce data set to build a regression model. I also explain how to determine if the model reveals anything statistically significant, as well as how outliers may skew your results.
I use Python 3 and Jupyter Notebooks to generate plots and equations with linear 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 building my model, I want to step back to offer an easy-to-understand definition of linear regression and why it's vital to analyzing data.
What is linear regression?
Linear regression is a basic machine learning algorithm that is used for predicting 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 against x values. ''a'' is coefficient of ''x'' and also the slope of the line, ''b'' is intercept which means when x = 0, b = y.
My e-commerce dataset
I used this dataset from Kaggle. It is not a very complicated or detailed one but enough to study linear regression concept.
If you are new and didn't use Jupyter Notebook before, here is