Using Python and sci-kit learn to build a simple k-nearest neighbor classification.

The k-nearest neighbor algorithm is applied to different classification and regression problems. The closest k training samples are used to predict the class of new input data, i.e., the most similar samples already known are used to classify an unknown data sample. Since the sci-kit library provides all the necessary tools to work on this algorithm, you can use these 5 steps to build your own KNN classifier in Python!

1. Import Libraries & Get the Data
2. Standardization
3. Train-Test Split
4. Build the Model
5. Evaluate the Model

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