Implement the K-Nearest Neighbors Algorithm

Requirements Clarification & Assessment

1. Understanding the Problem:

   • Determine whether the task is a regression or classification problem.
   • Clarify the objective of using K-Nearest Neighbors (KNN) in the context of the dataset provided.
   • Identify the features and target variables in the dataset.

2. Data Exploration:

   • Assess the size and dimensionality of the dataset.
   • Examine the distribution of classes in the dataset to identify any class imbalance.
   • Check for missing values and outliers that may affect the performance of the KNN algorithm.

3. Dataset Preparation:

   • Ensure the dataset is split into training, validation, and test sets.
   • Decide on the preprocessing steps needed, such as normalization or standardization, to ensure all features are on the same scale.

4. Selection of Parameters:

   • Determine the range of 'k' values to explore.
   • Choose an appropriate distance metric (e.g., Euclidean, Manhattan, etc.) based on the feature types and problem context.