Design a Unified ML Experiment Tracking Framework

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Description:
Develop a lightweight and consistent way to log and visualize machine learning experiment results across all subfolders (e.g., neural-networks, supervised-learning, anomaly-detection). This helps contributors compare results over time and improve reproducibility.

Expected Tasks:

• Create a Python utility (e.g., experiment_logger.py) that logs metrics like accuracy, loss, and hyperparameters to a .csv file.
• Add basic plotting functionality using matplotlib or seaborn.
• Place the utility in a new folder like utils/ or tools/.
• Create a sample log for an existing implementation (e.g., Perceptron).
• Write a README.md in the root directory explaining:
  • How to use the logger.
  • Required libraries.
  • How to integrate it into a new or existing ML script.

Stretch Goal:

• Explore integration with lightweight experiment trackers like MLflow or Weights & Biases, while keeping setup minimal.

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