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.