Standardize Dataset Handling Across All ML Modules

Description:
Right now, different subfolders load and process datasets in inconsistent ways. Create a unified, reusable Python module to handle dataset loading and basic preprocessing (e.g., scaling, splitting). This ensures maintainability and reduces repeated code across ML scripts.

Expected Tasks:

• Create a Python utility (e.g., data_utils.py) with functions like:
  • load_csv(path)
  • train_test_split(X, y, test_size=0.2)
  • standardize(X)
• Save this file in a utils/ folder.
• Refactor at least two existing implementations (e.g., KNN and Perceptron) to use this utility.
• Update documentation in the root README.md and/or affected folders.

Stretch Goal:

• Add optional support for downloading public datasets (e.g., from UCI or sklearn).