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I am new to data mining concepts and trying to learn the differences between supervised and unsupervised learning. So far what i know is that supervised means getting the information from labeled datasets and unsupervised means clustering the data without any labels given.

I kinda understand what they are but can't really apply them in real life (can't really apply the concepts to ask real time questions). I found the following example question on one of the machine-learning web forums and was wondering if someone could help me with it so i can use it as an example to understand the concept a little better. The question is:

Given the following dataset on different cars, make up 2 questions based on supervised and unsupervised learning.

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Any kind of help is appreciated.

Thanks :)

Community
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Taran Rai
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1 Answers1

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Supervised Learning:

So the above dataset has 11 attributes. From the 11 attributes we can see that am column is something that classifies the car with manual transmission if it is 0 and car with automatic transmission if it is 1. So usually in supervised learning we are given a training data with a response variable , so in this case if you treat this is a data given for supervised learning, you can train your model using appropriate algorithm and then for any test data predict what will be the corresponding am(Manual or Automatic).

Unsupervised Learning :

Assume that the transmission column is not given , and try to group the cars into clusters based on any Unsupervised learning Algorithm, see if you can come with two clusters which would be one cluster consisting of manual transmission cars and one cluster consisting of automatic transmission cars.

You may check the below links , these are two videos from Andrew Ng lecture They are very short videos and will help you to get an better understanding.

https://www.youtube.com/watch?v=ls7Ke48jCt8&index=3&list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW

https://www.youtube.com/watch?v=qHfUlFHGG08&list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW&index=4

Shailendra
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