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10 Best Open Source Datasets for Linear Regression
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10 Best Open Source Datasets for Linear Regression


Linear regression is a critical tool for data scientists and analysts in data analysis and machine learning. For those eager to deepen their understanding or engage in hands-on practice, we hope this guide will steer you through a curated list of open datasets for linear regression. Each data set is available for immediate download and provides valuable opportunities to practice linear regression tasks and predictive modelling.

Remember, machines require data to learn. Hence, the datasets listed here will enable you to conduct regression tasks and provide a platform for you to complete assigned challenges. So, let’s plunge into the world of data!

Our Favourite Open Datasets for Linear Regression

1. WHO Statistics on Life Expectancy

The WHO statistics on life expectancy dataset is a comprehensive dataset compiled by the World Health Organization and the United Nations. It tracks factors that affect life expectancy. The dataset contains 2938 rows and 22 columns, including country, year, developing status, adult mortality, life expectancy, infant deaths, alcohol consumption per capita, country’s expenditure on health, immunization coverage, BMI, deaths under five years old, deaths due to HIV/AIDS, GDP, population, body condition, income information, and education.

2. Fish Market Dataset

The Fish Market Dataset is an excellent resource for multiple linear regression and multivariate analysis.

At its core, the Fish Market Dataset goes beyond mere listings of fish species; it provides a nuanced portrait of each specimen, including crucial metrics such as weight, length, height, and width. This granular level of detail empowers researchers to delve deep into the multifaceted relationships between these variables, uncovering insights that can inform marketing strategies, pricing decisions, and supply chain optimizations within the fishing industry.

Whether exploring the relationships between fish dimensions and market prices or investigating the influence of environmental factors on fish size and abundance, the Fish Market Dataset offers a rich tapestry of data waiting to be explored.

Moreover, the availability of multivariate information within the dataset opens up avenues for sophisticated analysis, allowing researchers to discern patterns and correlations that might otherwise remain hidden. By leveraging multiple linear regression techniques, analysts can untangle the complex interplay between various factors, identifying key drivers of fish market dynamics and elucidating strategies for enhancing market competitiveness and sustainability.

Armed with empirical insights gleaned from rigorous analysis, stakeholders in the fishing industry can make informed decisions that optimize economic returns and promote responsible stewardship of marine resources for generations to come. The Fish Market Dataset goes beyond its simple role as a mere collection of data points; it emerges as a cornerstone for empirical research and evidence-based decision-making in fisheries management and aquatic commerce.

3. OLS Regression Challenge

The OLS regression challenge requires predicting cancer mortality rates for US counties. The CSV dataset includes data from cancer.gov, clinicaltrials.gov, and the American Community Survey. It provides information about cancer in the US, including death rates, reported cases, county name, income per county, population, demographics, and more.

4. Red Wine Quality

Sourced from the UCI Machine Learning Repository, the red wine quality dataset can be used for regression modelling and classification tasks. It provides information about the chemical properties of different types of wine and their correlation with overall quality.

The dataset provides a multifaceted portrait of each wine variant, encompassing crucial chemical attributes such as acidity levels, residual sugar content, pH, alcohol content, and more. These granular details lay the foundation for in-depth analyses to unravel the nuanced relationships between chemical composition and sensory perception, thereby shedding light on the factors underpinning wine quality.

Whether researchers endeavour to predict wine quality based on chemical profiles or classify wines into distinct quality categories, the Red Wine Quality dataset offers countless opportunities for exploration and discovery.

Moreover, the dataset’s suitability for regression modelling enables researchers to delve into predictive analytics, forecasting the quality of red wines based on their chemical composition with a high degree of accuracy. By harnessing advanced modelling techniques, analysts can identify key chemical markers associated with premium-quality wines, paving the way for enhanced quality control measures and targeted interventions within the red wine industry.

5. Vehicle Dataset from CarDekho

The vehicle dataset from CarDekho is ideal for price prediction. It provides information about cars and motorcycles listed on CarDekho.com. The data is in a CSV file, including columns for the model, year, selling price, showroom price, kilometres driven, fuel type, seller type, transmission, and the number of previous owners.

6. Cancer Linear Regression

The cancer linear regression dataset is an excellent starting point. This dataset, derived from cancer.gov, provides a comprehensive picture of cancer-related mortality in the United States.

What sets this dataset apart is its breadth of data and the meticulous documentation and guidance provided alongside it. Accompanied by a detailed walkthrough, it serves as more than just a collection of numbers; it becomes a roadmap for navigating the complexities of cancer data analysis. From the initial stages of data sourcing and preparation to the exploratory study, model selection, diagnostics, and interpretation, every step is carefully elucidated, ensuring that researchers can confidently extract meaningful insights.

Researchers leveraging this dataset are not merely crunching numbers; they are unravelling the intricate tapestry of cancer mortality trends, identifying potential risk factors, and exploring avenues for intervention and prevention. Whether examining the impact of demographic variables, environmental factors, or access to healthcare resources, this dataset provides a robust foundation for conducting rigorous analyses and developing predictive models to guide decision-making in public health policy and clinical practice.

As researchers delve deeper into this dataset, they uncover countless opportunities to advance our understanding of cancer epidemiology and inform targeted interventions to reduce cancer burden and improve patient outcomes. By leveraging the insights gleaned from this dataset, stakeholders across the healthcare continuum can work towards a future where cancer is not just treatable but preventable, ultimately leading to better health outcomes and improved quality of life for individuals and communities affected by this devastating disease.

7. Real Estate Price Prediction

The real estate price prediction dataset is designed for regression analysis, linear regression, multiple regression, and prediction models. It provides data on the date of purchase, house age, location, distance to the nearest MRT station, and house price per unit area.

8. Medical Insurance Costs

Inspired by Brett Lantz’s book Machine Learning with R, the medical insurance costs dataset contains medical information and costs billed by health insurance companies. With 1338 rows of data, it includes columns for age, gender, BMI, children, smoker, region, and insurance charges.

9. New York Stock Exchange Dataset

The New York Stock Exchange dataset is a veritable treasure trove for anyone seeking to unravel the complexities of one of the world’s most dynamic financial markets. Boasting an extensive array of historical data meticulously organized into four distinct CSV files – namely, prices, prices-split-adjusted, securities, and fundamentals – this dataset offers a panoramic view of market dynamics and corporate performance spanning significant periods. Its rich repository of information serves as a testament to the intricate interplay of myriad factors that shape stock market movements and investor sentiment.

For researchers and analysts alike, the New York Stock Exchange dataset represents more than just a collection of numbers; it embodies a gateway to understanding the fundamental principles and intricacies that underpin financial markets. This dataset presents an expansive canvas for empirical inquiries and data-driven investigations, from the predictive modelling of stock price movements to the nuanced exploration of rolling linear regression techniques.

10. CDC Data: Nutrition, Physical Activity, Obesity

The CDC data from the Behavioral Risk Factor Surveillance System presents information about physical activity, weight, and the average adult’s diet. This dataset is an invaluable resource for studies related to health and nutrition.

Lastly, we’ve compiled this fantastic compilation of the 65 best free datasets for machine learning. The list is a treasure trove of information for those seeking further data exploration.

We hope this list has not only shown you a little more of the power of linear regression and how it has become a critical tool for data analysis and machine learning experts but also helped equip you with the various datasets being used today.

Till next time, happy data exploring!