Solution for a issue#206

[shashwata2003]

Reference Issues:

[EXE] Anomaly Detection Exercise #206

What does this implement?

This pull request addresses Issue #206 by providing a complete solution for anomaly detection using the EV Charging Stations dataset from Kaggle. The implementation emphasizes thorough data preprocessing, enhancement of feature quality, and the application of advanced clustering and anomaly detection techniques.

Key changes include:

• Handling missing values effectively, including replacing missing entries with -1.
• Converting the cost_per_unit column from a textual format (e.g., "₹10 per unit") to a numerical float format for compatibility with algorithms.
• Scaling features to improve clustering algorithm performance.
• Applying K-Means clustering and Isolation Forest, with an optimized set of parameters for improved anomaly detection.
• Evaluating the performance using metrics like Silhouette Score and Anomaly Detection Rate to provide actionable insights.

These changes improve data consistency, enhance clustering accuracy, and facilitate the detection of potential anomalies in the dataset.

Comments:

I have ensured adherence to the project's contribution guidelines. Please review and let me know if any further adjustments or refinements are required.