Solves #1543 :- EV Battery Life Prediction

[PrAyAg9]

Description

It can be used in following cases:-
Predictive Maintenance:
Use Case: Fleet managers can use battery life predictions to schedule maintenance proactively, preventing unexpected failures and optimizing vehicle uptime.

Consumer Insights:
Use Case: Individual EV owners can receive insights about when to replace their battery or adjust their charging habits for optimal performance.

Battery Replacement Planning:
Use Case: Automakers can provide customers with accurate predictions for battery replacement timelines, enhancing customer satisfaction and loyalty.

Performance Optimization:
Use Case: EV manufacturers can use predictions to optimize battery management systems, improving overall vehicle performance based on user driving habits and environmental conditions.

Fixes #1543

Type of change

• Added a new machine learning frameworks, libraries or software.
• [ yes] Documentation update

Checklist:

• [yes ] My code follows the style guidelines of this project
• [yes ] I have performed a self-review of my own code
• [ yes] I have commented my code, particularly in hard-to-understand areas
• [yes ] I have made corresponding changes to the documentation
• [yes ] My changes generate no new warnings

[github-actions]

Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊

[PrAyAg9]

@sanjay-kv Could you please assign me this issue and add gssoc and lvl labels on it.

[PrAyAg9]

@sanjay-kv One last request, please attach a level label on it.

[sanjay-kv]

sorry bro ,missed it