There is a vast amount of data available on-line. National Institute of Standards and Technology works to ensure the computational accuracy of statistical software for conducting descriptive, multiple regression, ANOVA and nonlinear regression analyses, by providing a library of statistical reference datasets.
University-Related Data Resources There are innumerable university, departmental and faculty sites, in the US and internationally, that provide data, including:. Manchester Metropolitan University provides examples of behavioral, biological, medical and weather data, suitable for principal components analysis, cluster analysis, multiple regression analysis, discriminant analysis, etc. German Rodriguez of Princeton University provides about 20 largely frequency well-documented datasets on issues like births, deaths, salaries of professors, time-to-doctorate, contraceptive use, ship damage, etc.
Government Sources of Data Many government departments, in the USA and elsewhere, provide access to enormous amounts of important data, both aggregated and disaggregated:. US Census Department HGSE Shortcuts Data from National Institutes. Edu Page last updated: May 31, In a recent post about Multiple Correlationyou learned how you can identify independent variables that are most relevant for the response variable.
For example, in a data set with eight numeric variables describing properties of a vehicle, through Multiple Correlation you figured that the four variables accelerationdistancehorsepower and weight contain best information to be able to predict the values of mpg miles per gallon. Multiple Regression is a technique where you now use these variables to learn a model that enables you to predict the value of the response variable, given a new record where you only know the values of the dependent variables but the value of mpg is unknown.
I will briefly explain the mathematical background of how to learn such a multiple regression model, walk through the details how this can be implemented in Datameer on big data with a set of custom linear algebra functions and show how the derived model can be used in Datameer to make predictions on new data.
Multiple Linear Regression attempts to fit a series of independent variables each denoted as X and a dependent variable Y in to a linear model. This means we want to find the best way to describe the Y variable as a linear combination of the X variables. Using matrix algebra, we can describe this problem as a general linear system:.
The large letters are the matrices and the smaller letters describe the dimensions of each term. We are solving for the beta vector. After some transformations, this can be expressed as:. This equation can then be used to make predictions on data where the values of Y are unknown. As input data in this example, we use an illustrative data set of records describing the properties distancehorsepowerweightacceleration and mpg miles per gallon of a vehicle.
Mpg represents the dependent variable Y. We also introduce a constant intercept column equal to 1 to initialize? To be able to compute the values of the?
The first step is to compute the inverse of the transpose product of the independent variables input data. This results in a list of lists represent a 5 by 5 matrix, with each inner list being a row of that matrix. This how the custom function formats its output as a matrix representation. Note that this scales on big data — the custom functions can deal with arbitrarily many rows of data in X.
Note that each inner list in this result matrix is single-element single column row vector. This final result contains the regression model consisting of five entries — the intercept and the factors that can now directly be used to make predictions on new data.
To use that model, we join it with each row of our new data set we want to apply it on, in order to predict the value of Y mpg in our running example for each record. The prediction itself is done by simply applying the formula described in the math section above:. Since the model is represented as a list of lists we apply the Listelement function in order to retrieve the intercept and the factor values. Note that in this example, we use the input data itself where we applied the model on.Learn about Springboard.
Completing your first project is a major milestone on the road to becoming a data scientist and helps to both reinforce your skills and provide something you can discuss during the interview process. The first step is to find an appropriate, interesting data set. These data sets cover a variety of sources: demographic data, economic data, text data, and corporate data. Need more? Check out our list of free data mining tools.
This post was originally published October 13, It was last updated August 21, You can follow him on Twitter tjdegroat. A Curated List of Data Science Interview Questions and Answers Preparing for an interview is not easy—there is significant uncertainty regarding the data science interview questions you will be asked.
No matter how much work experience or what data science certificate you have, an interviewer can throw you off with a set of questions that […]. Data mining and algorithms Data mining is the process of discovering predictive information from the analysis of large databases.
For a data scientist, data mining can be a vague and daunting task — it requires a diverse set of skills and knowledge of many data mining techniques to take raw data and successfully get insights […]. As part of that exercise, we dove deep into the different roles within data science. Around the world, organizations are creating more data every day, yet most […]. Census Bureau publishes reams of demographic data at the state, city, and even zip code level.
It is a fantastic data set for students interested in creating geographic data visualizations and can be accessed on the Census Bureau website. Alternatively, the data can be accessed via an API. One convenient way to use that API is through the choroplethr. In general, this data is very clean and very comprehensive.An important note to users with version 1. The Delve datasets and families are available from this page. Every dataset or family has a brief overview page and many also have detailed documentation.
You can download gzipped-tar files of the datasets, but you will require the delve software environment to get maximum benefit from them. Datasets are categorized as primarily assessmentdevelopment or historical according to their recommended use. Within each category we have distinguished datasets as regression or classification according to how their prototasks have been created.
Details on how to install the downloaded datasets are given below. There is also a summary table of the datasets. Datasets from this section are recommended to be used when reporting results for your learning method.
You should run your method once on each task and report the results from that run. That is, you should not use results from the testing data to modify your method, then re-run it. We recommend that you use datasets from this section while developing a new learning method, or fine-tuning parameters. That is, you can re-run your method several times on a dataset until you obtain the desired performance. If you do use a dataset in this manner, you should not use it when reporting your method's performance: you should use datasets from the Assessment section.
Datasets from this section have been included because they are established in the literature. We have attempted to reproduce the original usage as closely as possible to facilitate comparisons. Before you can install the datasets, you must build and install the Delve utilities. Once you've done that, you can install the datasets. This involves simply extracting the files from their tape archives into the proper directory: the installed top-level Delve data directory.
Each tape archive will create a directory with the same base name as the archive file. This directory will contain all the data and specification files Delve needs to generate the tasks. Delve Datasets Collections of data for developing, evaluating, and comparing learning methods.Tech Support Advice Links. Facebook LinkedIn. Examples of regression data and analysis The Excel files whose links are given below provide examples of linear and logistic regression analysis illustrated with RegressIt.
Most of them include detailed notes that explain the analysis and are useful for teaching purposes. Links for examples of analysis performed with other add-ins are at the bottom of the page. If you normally use Excel's own Data Analysis Toolpak for regression, you should stop right now and visit this link first.
Datasets Available Online
Its analysis is described in detail on the Features pages, in the User Manualand on the Statistical Forecasting site. The objective is to predict a car's fuel consumption from its physical attributes and its country of origin.
Baseball batting averages are particularly good raw material for this kind of analysis because they are averages of almost-independent and almost-identically distributed random variables with large sample sizes, and they measure a skill that needs to be exhibited within acceptable limits by all players, not merely specialists at a position.
A thorough discussion of this example can be found on the Statistical Forecasting site. Daily web site visitors: This data set consists of 3 months of daily visitor counts on an educational web site.
There is a very strong day-of-week effect that provides a good opportunity for using dummy variables to capture a repeating time pattern. The analysis of this series is illustrated on the Forecasting page on this site. It's an extension of the standard model that is used in the fishery literature and provides another nice example of the use of dummy variables and the natural log transformation.
Monthly natural gas consumption in North Carolina: This data set consists of monthly natural gas consumption by end use commercial, residential, etc. See which ones you like. If you have some examples of data analysis with RegressIt that you would like to share, please send them to feedback regressit.Last Updated on December 13, In this short post you will discover how you can load standard classification and regression datasets in R.
This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R. Discover how to prepare data, fit machine learning models and evaluate their predictions in R with my new bookincluding 14 step-by-step tutorials, 3 projects, and full source code.
There are hundreds of standard test datasets that you can use to practice and get better at machine learning. These datasets are useful because they are well understood, they are well behaved and they are small.
There is a more convenient approach to loading the standard dataset. In this section you will discover the libraries that you can use to get access to standard machine learning datasets. You will also discover specific classification and regression that you can load and use to practice machine learning in R.
The datasets library comes with base R which means you do not need to explicitly load the library. It includes a large number of datasets that you can use.
A collection of artificial and real-world machine learning benchmark problems, including, e. Many books that use R also include their own R library that provides all of the code and datasets used in the book.
In this post you discovered that you do not need to collect or load your own data in order to practice machine learning in R.Multiple Linear Regression using python and sklearn
You learned about 3 different libraries that provide sample machine learning datasets that you can use:. You also discovered 10 specific standard machine learning datasets that you can use to practice classification and regression machine learning techniques.
Covers self-study tutorials and end-to-end projects like: Loading data, visualization, build models, tuning, and much more But in one place you wrote that the set is for regression and in another place you wrote is for classification. It is misleading. Please suggest. Pima Indians Diabetes Database binary classification. Not same Pima Indians Diabetes Database binary classification. This has got to be the only post of yours where the pictures actually match the topic.
The rest seem to be random. Name required. Email will not be published required. Tweet Share Share. Length Sepal. Width Petal. Length Petal. Width Species 1 5. Width Species. Longley's Economic Regression Data data longley dim longley head longley. Longley's Economic Regression Data. Forces Population Year Employed Forces Population Year Employed.
Boston Housing Data. Wisconsin Breast Cancer Database.If you work with statistical programming long enough, you're going ta want to find more data to work with, either to practice on or to augment your own research. Here are a handful of sources for data to work with.
All of the datasets listed here are free for download. If you want more, it's easy enough to do a search. World Bank Data - Literally hundreds of datasets spanning many decades, sortable by topic or country. This is an outstanding resource. Gapminder - Hundreds of datasets on world health, economics, population, etc.
All of it is viewable online within Google Docs, and downloadable as spreadsheets. Most of these datasets come from the government. Kaggle - Kaggle is a site that hosts data mining competitions. Each competition provides a data set that's free for download.
This list has several datasets related to social networking.
Predictive Analytics Through Multiple Regression – With Datameer!
Lots of fun in here! Million Song Dataset - This is a collection of audio features and metadata for a million contemporary popular music tracks. Energy Information Administration - This site offers a number of datasets on energy production, consumption, sources, etc.
Reddit Datasets - This last one isn't a dataset itself, but rather a social news site devoted to datasets. It's updated regularly with news about newly available datasets. Quandl - This is a web-based front end to a number of public data sets. What's nice about this website is that it allows for the combination of data from a number of sources, and can export the data in a number of formats.
There's not much organization here, but there really are a LOT of datasets. Dive in and have fun. Webscope - A reference library of interesting and scientifically useful datasets for non-commercial use by academics and other scientists. Time Series Data Library - Curated by Professor Rob Hyndman of Monash University in Australia, this is a collection of over datasets containing time-series data, organized by category. Awesome Public Datasets - Curated list of hundreds of public datasets, organized by topic.
Common Crawl - Massive dataset of billions of pages scraped from the web. The dataset is updated with a new scrape about once per month. E-Books Tutorials Courses Books.