Sales Prediction with Linear Regression

Linear regression is a fundamental statistical and machine learning technique used to model the relationship between a dependent variable (target) and one or more independent variables (features). In this tutorial, we’ll walk through the process of predicting sales using linear regression.

Before we get started, we need to install and import the required libraries. We will be using the following libraries:

You can install these libraries using pip by running the following commands in your command prompt or terminal:

Let’s begin by importing the necessary libraries:

We need to convert categorical columns into numerical values for the linear regression model.

We’ll split the data into a training set (80%) and a testing set (20%), then train the model and predict.

We’ll evaluate the model’s performance using the Mean Squared Error (MSE) and R2 score.

We’ll create a 2x2 grid of subplots to visualize the model’s performance.

That’s it! We’ve built a linear regression model to predict sales using various features from the sales data. This tutorial demonstrated the process of loading data, preprocessing it, training a model, making predictions, evaluating performance, and visualizing results. You can now apply these techniques to your own sales prediction projects or other linear regression tasks.

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