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I am new to machine learning and looking for some datasets through which i can compare and contrasts the differences between different machine learning algorithms (Decision Trees, Boosting, SVM and Neural Networks)

Where can I find such datasets ? What should I be looking for while considering a dataset ?

It would be great if you can point to some good datasets and also tell me what makes them a good dataset?

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The data sets in the following sites are available for free. These data sets have been used to teach ML algorithms to students because for most there are descriptions with the data sets. Also, it's been mentioned which kind of algorithms are applicable.

  1. UCI- Machine Learning repository
  2. ML Comp
  3. Mammo Image
  4. Mulan
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Kaggle has a whole host of datasets you can use to practice with.

(I'm surprised it wasn't mentioned so far!)

It's got two things (among many others) that make it a highly invaluable resource:

  • Lots of clean datasets. While noise-free datasets aren't really representative of real-world datasets, they're especially suited for your purpose - deploying ML algorithms.
  • You can also view others' ML models for the same dataset, which could be a fun way to pick up some hacks along the way. It goes without saying that the kind of exposure you get from learning from the best practitioners is, like for anything else, super helpful.
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    $\begingroup$ This really should be the top answer because in addition to an enormous variety of datasets, the forums for each challenge are an invaluable resource for picking up techniques and tricks, along with code examples. $\endgroup$
    – Alex R.
    Commented Apr 14, 2017 at 18:18
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First, I'd recommend starting with the sample data that is provided with the software. Most software distributions include example data that you can use to get familiar with the algorithm without dealing with data types and wrestling the data into the right format for the algorithm. Even if you are building an algorithm from scratch, you can start with the sample from a similar implementation and compare the performance.

Second, I'd recommend experimenting with synthetic data sets to get a feel for how the algorithm performs when you know how the data was generated and the signal to noise ratio.

In R, you can list all dataset in the currently installed packages with this command:

data(package = installed.packages()[, 1])

The R package mlbench has real datasets and can generate synthetic datasets that are useful for studying algorithm performance.

Python's scikit-learn has sample data and generates synthetic/toy dataset too.

SAS has training dataset available for download and the SPSS sample data is installed with the software at C:\Program Files\IBM\SPSS\Statistics\22\Samples

Lastly, I'd look at data in the wild. I'd compare the performance of different algorithms and tuning parameters on real data sets. This usually requires a lot more work because you will rarely find dataset with data types and structures that you can drop right into your algorithms.

For data in the wild, I'd recommend:

reddit's Dataset Archive

KDnugget's list

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    $\begingroup$ For those who don't have R, & don't want to download it just to get access to these datasets, the datasets & descriptions are available online here. $\endgroup$
    – gung - Reinstate Monica
    Commented Apr 17, 2017 at 20:08
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The Iris data set hands down. It's in base R as well.

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    $\begingroup$ Please respond to the substantive part of the question: "... also tell me what makes them a good dataset?" $\endgroup$
    – whuber ♦
    Commented Apr 5, 2017 at 20:42
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In my opinion, you can should start with small datasets which do not have too many features.

One example would be the Iris dataset (for classification). It has 3 classes, 50 samples for each class totaling 150 data points. One excellent resource to help you explore this dataset is this video series by Data School.

Another dataset to checkout is the Wine Quality data set from UCI -ML repository. It has 4898 data points with 12 attributes.

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