Dataset for Linear Regression

Last Updated : 10 Mar, 2026

Linear regression is a machine learning technique used for predicting continuous outcome variable based on one or more input variables. It assumes a linear relationship between the input variables and the target variable which make it simple and easy for beginners. In this article, we will see some datasets widely used for regression.

1. Boston Housing Dataset

Boston Housing Dataset contains information collected by U.S Census Service about houses in the Boston area. It includes various attributes such as the crime rate, average number of rooms, proportion of non-retail business, etc.

  • Dataset Source: Boston Housing Dataset
  • Labels: Continuous values representing median value of owner-occupied homes (in $1000s).
  • Size: 506 samples each with 14 attributes.
  • Data: Numerical data

2. Advertising Dataset

This dataset contains data about the sales of a product in relation to the advertising budgets spent on TV, radio and newspaper. It's commonly used to explore the relationship between advertising efforts and sales.

  • Dataset Source: Advertising Dataset
  • Labels: Continuous values representing sales of the product (in thousands of units).
  • Size: 200 samples each with 4 attributes.
  • Data: Numerical data

3. California Housing Dataset

It is collected by U.S. Census in 1990 and this dataset includes various attributes for California districts such as median house value, median income, housing median age, total rooms, total bedrooms, population, households, latitude and longitude.

  • Dataset Source: California Housing Dataset
  • Labels: Continuous values representing the median house value (in $1000s).
  • Size: 20,640 samples each with 9 attributes.
  • Data: Numerical data

4. Auto MPG Dataset

This dataset contains data on the fuel consumption (miles per gallon) of various car models along with other attributes like engine displacement, horsepower, weight, acceleration and model year.

  • Dataset Source: Auto MPG Dataset
  • Labels: Continuous values representing miles per gallon (mpg).
  • Size: 398 samples, each with 8 attributes.
  • Data: Numerical data

5. Diabetes Dataset

This Pima Indians Diabetes dataset contains medical diagnostic measurements such as glucose level, blood pressure, BMI, insulin, age and number of pregnancies. It is used to study the relationship between these health indicators and the presence of diabetes.

  • Dataset Source: Diabetes Dataset
  • Labels: Binary values indicating whether a patient has diabetes (1) or not (0).
  • Size: 768 samples with 8 input attributes and 1 target variable.
  • Data: Numerical data

6. Fish Market Dataset

This dataset includes data on the common fish species in fish market sales. Attributes include weight, length, height and width of fish used to predict fish weight based on these physical characteristics.

  • Dataset Source: Fish Market Dataset
  • Labels: Continuous values representing the weight of the fish (in grams).
  • Size: 159 samples each with 7 attributes.
  • Data: Numerical data

7. Wine Quality Dataset

This dataset contains various chemical properties of wine such as acidity, residual sugar, chlorides and sulfur dioxide levels and quality ratings. It is often used to predict wine quality based on these chemical properties.

8. Insurance Charges Dataset

This dataset includes information about medical charges billed by health insurance companies with features like age, sex, BMI, children, smoker status, region and the charges billed.

  • Dataset Source: Insurance Charges Dataset
  • Labels: Continuous values representing individual medical costs.
  • Size: 1,338 samples each with 7 attributes.
  • Data: Numerical data

9. Salary Dataset

This dataset contains information on years of experience and the corresponding salary which is useful for predicting salary based on experience.

  • Dataset Source: Salary Dataset
  • Labels: Continuous values representing salary.
  • Size: 30 samples each with 2 attributes.
  • Data: Numerical data

10. Energy Efficiency Dataset

This dataset provides data on the energy efficiency of buildings including features such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and more. It is used to predict the heating and cooling load requirements of buildings.

  • Dataset Source: Energy Efficiency Dataset
  • Labels: Continuous values representing heating and cooling loads (energy efficiency measures).
  • Size: 768 samples each with 8 attributes.
  • Data: Numerical data

11. Stock Market Dataset

This dataset contains historical stock market data for various companies including attributes such as opening price, closing price, high, low, trading volume and other financial indicators. It is useful for financial market predictions and trend analysis.

  • Dataset Source: Stock Market Dataset
  • Labels: Continuous values for attributes like closing price (could be a target variable).
  • Size: Typically contains thousands to millions of records depending on the dataset.
  • Data: Numerical data

12. Customer Churn Dataset

This dataset includes information about customers of a business with labels indicating whether the customer churned (stopped using the service) or not. Attributes include demographics, usage patterns and customer satisfaction scores which can be used to predict churn.

13. Student Performance Dataset

This dataset contains information about students' academic performance including attributes such as study time, previous grades, socioeconomic status and demographic factors. It is useful for predicting student performance based on these factors.

  • Dataset Source: Student Performance Dataset
  • Labels: Continuous values representing grades or binary labels for passing/failing.
  • Size: 1,000 samples each with 33 attributes.
  • Data: Numerical and Categorical data.

With these datasets you can begin with machine learning using linear regression.

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