You can use linear regression to model your advertising data, discover the relation between advertising data and your revenue, and predict how much revenue your business will generate depending on how much you spend on ads in a given month. raw materials expenses, stock prices, labor costs, etc.), you can use linear regression to predict what the prices of these items are going to be in the future. If your business is buying items or services (e.g. Linear regression is used for a wide array of business prediction problems: 1.2 What are business applications of linear regression?
Hyperplan in linear regression download#
Download the entire modeling process with this Jupyter Notebook. Linear regression is not limited to real-estate problems: it can also be applied to a variety of business use cases.īonus material: Deep dive into the data science behind linear regression. This is because linear regression tries to find a straight line that best fits the data.
In this case, we would use a dataset containing historic records of house purchases in the form of (“number of bedrooms”, “selling price”): We obtain the equation by training it on pairs of (X, Y) values. Linear regression creates an equation in which you input your given numbers (X) and it outputs the target variable that you want to find out (Y). This is Y - the price in $ that you can sell your house for.
And you want to know how much your estate could be worth on the market. You have information about your house, for instance, the number of bedrooms is 2 - this is your X. You might be wondering how much you can sell your house for. You can think of linear regression as the answer to the question “How can I use X to predict Y?”, where X is some information that you have and Y is some information that you want to know. 1.1 How would linear regression be described in layman’s terms? Thanks to its simplicity, interpretability and speed, linear regression is one of the most widely used artificial intelligence algorithms in real-life machine learning problems.
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