K-Nearest-Neighbor

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.[1] In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

Input:
filename.csv
M rows and N columns
Nth column must be a class

training data (input training data need to be randomized)
testing data

Output:
List of actual and predicted class
Accuracy

More on supervised learnining:

Metaheuristic design of feedforward neural networks: A review of two decades of research

VK Ojha, A Abraham, V Snášel - Engineering Applications of Artificial Intelligence, 2017
https://doi.org/10.1016/j.engappai.2017.01.013