Dataset for Linear Regression

In this article, we will explore the Dataset for Linear Regression (LR). Linear regression is a fundamental statistical and machine learning technique used for predicting a continuous outcome variable based on one or more explanatory variables.

It assumes a linear relationship between the input variables and the target variable, making it a simple yet powerful tool for modeling and understanding data. Linear regression datasets play a crucial role in training and evaluating linear regression models.

We will examine the list of top Linear Regression datasets in this article.

The Boston Housing Dataset contains information collected by the U.S Census Service concerning housing in the Boston area. It includes various attributes such as the crime rate, the average number of rooms per dwelling, the proportion of non-retail business acres per town, and the pupil-teacher ratio by town.

• Dataset Source: Boston Housing Dataset
• Labels: Continuous values representing the median value of owner-occupied homes (in $1000s).
• Scope: Covers multiple neighborhoods in Boston with diverse attributes like crime rate, average number of rooms, and proximity to employment centers.
• Size: 506 samples, each with 14 attributes.
• Language: N/A (numerical data)

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).
• Scope: Covers advertising spends on TV, radio, and newspaper across 200 markets.
• Size: 200 samples, each with 4 attributes.
• Language: N/A (numerical data).

Derived from the 1990 U.S. Census, 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).
• Scope: Includes various attributes of districts in California such as median income, house age, and geographical coordinates.
• Size: 20,640 samples, each with 9 attributes.
• Language: N/A (numerical data).

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).
• Scope: Covers different car models with attributes such as engine displacement, horsepower, and weight.
• Size: 398 samples, each with 8 attributes.
• Language: N/A (numerical data).

This dataset includes medical predictor variables and one target variable, a quantitative measure of disease progression one year after baseline. It is used to predict the progression of diabetes based on factors such as age, sex, BMI, blood pressure, and six blood serum measurements.

• Dataset Source: Diabetes Dataset
• Labels: Continuous values representing disease progression after one year.
• Scope: Includes attributes like age, sex, BMI, blood pressure, and blood serum measurements.
• Size: 442 samples, each with 10 attributes.

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

• Dataset Source: Fish Market Dataset
• Labels: Continuous values representing the weight of the fish (in grams).
• Scope: Includes various species of fish with attributes like length, height, and width.
• Size: 159 samples, each with 7 attributes.
• Language: N/A (numerical data).

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.

• Dataset Source: Wine Quality Red Dataset, Wine Quality White dataset
• Labels: Continuous values representing the quality score of wine.
• Scope: Includes two datasets (red and white wine) with attributes such as acidity, residual sugar, and alcohol content.
• Size: Red wine: 1,599 samples; White wine: 4,898 samples. Each with 12 attributes.
• Language: N/A (numerical data).

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.
• Scope: Covers attributes like age, sex, BMI, number of children, smoker status, and region.
• Size: 1,338 samples, each with 7 attributes.
• Language: N/A (numerical data).

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.
• Scope: Covers attributes like years of experience.
• Size: 30 samples, each with 2 attributes.
• Language: N/A (numerical data).

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. These datasets provide a variety of attributes and target variables suitable for practicing linear regression.

• Dataset Source: Energy Efficiency Dataset
• Labels: Continuous values representing heating and cooling loads (energy efficiency measures).
• Scope: Covers attributes such as relative compactness, surface area, wall area, and roof area.
• Size: 768 samples, each with 8 attributes.
• Language: N/A (numerical data).

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).
• Scope: Covers multiple companies with attributes like opening price, highest price, lowest price, closing price, trading volume, and adjusted closing price.
• Size: Typically, contains thousands to millions of records depending on the dataset.
• Language: N/A (numerical data).

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

• Dataset Source: Customer Churn Dataset: Training, Customer Churn Dataset: Testing
• Labels: Binary values indicating whether a customer has churned (yes/no).
• Scope: Covers attributes like customer ID, gender, age, tenure, balance, and product usage.
• Size: Varies, typically contains thousands of records.
• Language: N/A (numerical and categorical data).

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.
• Scope: Covers attributes such as study time, previous grades, parental education level, and socioeconomic status.
• Size: 1,000 samples, each with 33 attributes.
• Language: N/A (numerical and categorical data).

We provided a list of commonly used linear regression datasets, such as the Boston Housing, Advertising, California Housing, Auto MPG, Diabetes, Fish Market, Wine Quality, Insurance Charges, Salary, and Energy Efficiency datasets. These datasets cover a range of domains and provide diverse data for practicing linear regression modeling.

What criteria should I consider when selecting a dataset for linear regression analysis?

When selecting a data set for linear regression, consider such factors as the nature of the outcome variable (continuous or categorical), the presence and relevance of explanatory variables, the size of the data set, and its representation of a real-world phenomenon as your’ enjoy modeling. Ensure that the data set satisfies the assumptions of linear regression, such as linearity, independence, and homogeneity.

How can I handle missing data in a linear regression dataset?

Dealing with missing data is deficlut for maintaining the integrity of your analysis. Depending on the extent of missing value and the nature of the data, strategies such as imputation (replacing missing values with estimated values), deletion of incomplete cases, or advanced techniques like multiple imputation can be employed. It’s essential to assess the impact of missing data handling methods on the results of your regression analysis.

What diagnostic tests should I perform to evaluate the validity of a linear regression model?

Several diagnostic tests help assess the assumptions and validity of a linear regression model. These include checking for linearity and homoscedasticity of residuals, checking normality of residuals, detecting multicollinearity among explanatory variables, and identifying influential outliers. Additionally, measures like R-squared, adjusted R-squared, and significance tests for coefficients provide insights into the overall goodness-of-fit and significance of the model.

How can I interpret the results of a linear regression analysis?

Finding the results of a linear regression analysis involves understanding the coefficients of the explanatory variables, their significance levels for p-values, and their effect sizes. Coefficients represent the change in the outcome variable associated with a one unit change in the corresponding explanatory variable, holding other variables constant. Significant coefficients indicate variables that have a statistically significant impact on the outcome. Additionally, diagnostic plots and statistics help assess the overall fit and assumptions of the model.