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k-nearest-neighbors

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Implementation of KNN algorithm in Python 3

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# k-nearest-neighbors

Implementation of KNN algorithm in Python 3

## Description

* K-Nearest-Neighbors algorithm is used for classification and regression problems.
* In this project, it is used for classification.
* The Iris data set is bundled for test, however you are free to use any data set of your choice provided that it follows the specified format.

## Data set format

* CSV (Comma Separated Values) format.
* Attributes can be integer or real values.
* List attributes first, and add response as the last parameter in each row.
    * E.g. [4.5, 7, 2.6, "Orange"], where the first 3 numbers are values of attributes and "Orange" is one of the response classes.
    * Another example can be [1.2, 4.3, 3], in this case there are 2 attributes while the response class is the integer 3.
    * The square brackets are shown for convenience in reading, don't put them in your CSV file.
* Responses can be integer, real or categorical.

## Using provided data sets

* The Iris data set is provided in the repository. 
* Enter 'iris-dataset.csv' when asked for training data file name.
* Enter 'iris-test.csv' when asked for test data file name.

## Notes

* Keep the data set files in the working directory of project as defined by the IDE configuration.
* When running in stand alone mode (E.g. command line), keep the data sets in the same directory as the script.

k-nearest-neighbors

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