Design a Unified ML Experiment Tracking Framework

**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](https://mlflow.org/) or [Weights & Biases](https://wandb.ai/), while keeping setup minimal.

---

Start by reviewing the existing implementations in the neural-networks, supervised-learning, and anomaly-detection subfolders, including the Perceptron example. Define the shared logging and plotting utility in utils/ or tools/, then add a sample log and document usage, dependencies, and integration in the root README.md. Done means the utility records metrics and hyperparameters to CSV, produces basic plots, and is demonstrated by an existing implementation.

Written by the indexing model from the issue text.