Implement Automated Data Cleaning and Preprocessing for Student Metrics

[Anushka-Pote]

Currently, the student metrics data (study hours, participation rate, assignment completion) is directly analyzed without automated cleaning or preprocessing. To improve data reliability and maintain analysis accuracy, let's introduce automated data cleaning steps to handle inconsistent or extreme values.

What will be automated-
Create functions to preprocess and clean student metrics data before analysis, ensuring:
Normalization: All participation rates are standardized on a 0-1 scale.
Capping and Flooring: Set reasonable upper and lower bounds for each metric (e.g., study hours should not exceed 100).
Handling Missing Values: Fill or interpolate any missing data points to avoid calculation errors.

[Sankalp13353]

I would be grateful if you could assign this issue to me. I am eager to contribute to this project and look forward to working on it. Please let me know if you need any additional information from my side.