"KNN/Machine learning" This is KNN project for soft computing course. The project steps are listed below:

A. The dataset provided in the file Inputs.xlsx is imported into the Python environment and split into a training and testing dataset with a ratio of 60:40.

B. Standardize the training dataset and then normalize both the training and testing datasets using the Z-score method.

C. KNN model was bulit, RMSE value is calculated, and the value of K is reported (type of model).

D. The K parameter si set equal to 1, 5, and 20, and RMSE values is reported for each with a comparison of results.

E. I Build a KNN model for each K and determine the optimal distance metric. The optimal value for K with the corresponding distance metric is also reported.