K-Nearest Neighbors (KNN) Algorithm Implementation in Machine Learning - Code Help Needed

Lakshit

In my current machine learning project, I intend to classify data using the K-Nearest Neighbours (KNN) technique. Although I have a fundamental knowledge of the algorithm's premise and have looked at the Scalers KNN algorithm topic but am having trouble implementing the method in my favorite programming language.

I would like to seek guidance from the community on how to effectively implement the KNN algorithm in [insert your programming language here, e.g., Python, R, etc.]. Specifically, I need help with the following aspects:

1. How to properly preprocess and prepare my dataset for KNN?
2. Choosing the optimal value of 'k' for my dataset.
3. Writing the code to calculate distances (Euclidean, Manhattan, etc.) between data points.
4. Efficiently selecting the 'k' nearest neighbors.
5. Handling ties and choosing the class label in case of equal votes.
6. Evaluating the performance of the KNN model using suitable metrics (accuracy, precision, recall, etc.).

If it's feasible, would you kindly offer a step-by-step code example demonstrating how to construct KNN using a sample dataset in [your programming language]? Additionally, it would be really appreciated if you had any recommendations or best practices for enhancing the algorithm's effectiveness and accuracy.

Please accept my gratitude for your assistance and knowledge.