Implement a Cross-Domain ML Benchmark Suite

Description:
Create a benchmark script to run and compare ML models from different folders on a shared dataset. Helps understand model performance across implementations.

Expected Tasks:

• Choose or create a simple classification dataset (e.g., .csv).
• Write a script (e.g., benchmark.py) to:
  • Import models like Perceptron, Linear Regression, etc.
  • Train and evaluate on the same dataset.
  • Output metrics like accuracy or F1 score.
• Store benchmark results in a benchmarks/ folder.
• Include summary output to console and optionally .csv.

Stretch Goal:

• Add timing or memory comparisons.
• Plot results using matplotlib or seaborn.