Regression is a machine learning technique used in finance, investing, and other related fields to determine the relationship between a dependent variable and a series of independent variables. Simply put, when we want to predict the future values of a variable by analyzing the independent variables on which our target variable depends, it is nothing more than a task of regression analysis. If youâ€™re looking for data science case studies on regression, this article is for you. In this article, Iâ€™m going to introduce you to some of the best data science case studies on regression.

## Data Science Case Studies on Regression

#### House Price Prediction:

Forecasting house prices is one of the most popular use cases of regression among the data science community. Here you have to predict the future prices of houses in California based on the location of the houses, the number of rooms, the number of bedrooms, the income of the people, and many other factors that affect the prices of a property.

Predicting the value of House Prices is based on the problem of regression because here we want to predict the value of house prices (which is a dependent variable) by analyzing a series of independent variables on which the value of house prices is dependent. So you can find this data science case study on regression solved and explained using Python fromÂ ** here**.

#### Profit Prediction:

A company should always set a goal that should be achievable, otherwise, employees will not be able to work to their best potential if they find that the goal set by the company is unachievable. The task of profit prediction for a particular period is the same as setting goals. If you know how much profit you can make with the amount of R&D and marketing you do, then a business can make more than the predicted profit provided the predicted value is achievable.

The task of predicting profit is an important task for every business to set an achievable goal. For example, if the business spends $500 on marketing, it canâ€™t expect a profit of $20,000. Likewise, there are many other factors on which the profit of a business depends. A company must therefore set a goal that can be achieved. So here you have to analyze the factors affecting the profitability of a company so that you can predict the future profit of a company. You can find this data science case study on regression solved and explained using Python fromÂ ** here**.Â

#### Instagram Reach Analysis:

Predicting the reach of a social media post is one of the most important tasks for any business that relies heavily on social media for its services. So these businesses need to predict the reach of their social media posts based on the headline they write, the description they use, or the tags they use.

So, analyzing the reach of an Instagram post can help a business predict how many people it can reach from its post and how many products it can sell. You can find this Data Science Case Study on regression Solved and Explained using Python fromÂ ** here**.

Also, Read â€“ Data Science Case Studies on Classification.

### Summary

So these were some of the best data science case studies based on regression analysis. When we want to predict the future values of a variable by analyzing the independent variables on which our target variable depends, it is nothing more than a task of regression analysis. I hope you liked this article on data science case studies based on regression solved and explained using *Python*. Feel free to ask your valuable questions in the comments section below.