Why Linear Regression is Important for Data Scientists & How to Learn It?

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Linear regression is a powerful predictive modeling technique that enables the statistical analysis of continuous variables. It is the most popular technique for estimating relationships between inputs and outputs.

This post discusses linear regression, how to use it in data science, and why you need to know about it as a professional data scientist.  Now let's dive into the topic!

What is Linear Regression?

We start this section by defining linear regression. Here, in simple words, it is an approach to estimate the relationship between the input and output. It simplifies the modeling process and produces more interpretable results. When you need to make predictions on new data, Linear discriminant analysis is a better option for making predictions on new data points (i.e., test set) because of its solid statistical foundation and mathematical proofs of performance guarantees.

Why is Linear Regression Essential for Data science?

For a Data Scientist, it is essential to know and understand the concept of linear regression and how to use it. This section provides some reasons why it is critical for data scientists:

When you don't know which variables are important: In many real-world problems, no one tells you which input variable(s) affect the output variable. In cases where you have access to historical data, it is possible to find the relationship(s) between input and output variables (i.e., linear regression).

When your model needs linearity assumption: Incorporating nonlinearities in the prediction function requires complex modeling techniques like applying polynomial transformations or neural networks.

How can we use linear regression?

Here are some common scenarios where we use in the industry.

  • You can predict the price of a house/cars/robots etc., indicating loan eligibility for an individual based on his salary. How many items will you sell tomorrow? What time of the day am I likely to buy something?
    Estimating Expected Weight of a baby based on mother's weight during pregnancy, Estimating the passengers who will purchase tickets for an airline, etc.
  • Now you can solve all these real-world problems with linear regression!
  • Linear regression is a beautiful yet straightforward statistical technique to estimate the relationship between input and output variables. In other words, it helps you to find a function that best explains the relationship between input and output variables.

Input features = house size, car speed, age of a person, flight duration, etc

Output variable = price of a house/car/flight ticket etc

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