perf: ⚡️ Enhanced + updated code snippets & README doc

[yashksaini-coder]

✅ Closes: #447

This pull request includes extensive additions to the pysnippets/Scrubs directory, introducing several new Python scripts for data preprocessing and cleaning, along with detailed documentation. The changes are organized into different scripts, each serving a specific purpose in the data cleaning pipeline.

Documentation and Structure:

• pysnippets/Scrubs/README.md: Added a comprehensive README file that describes the purpose of the directory and details the functionality of each script.

New Scripts for Data Preprocessing:

• pysnippets/Scrubs/backup.py: Introduced a script to handle saving cleaned dataframes to disk, including functions for fixing dates, imputing categorical variables, and compressing numeric columns.
• pysnippets/Scrubs/clean.py: Added a script with functions to clean dataframes by removing unwanted columns and rows, handling zero and near-zero variance columns, and dropping columns with excessive missing values.
• pysnippets/Scrubs/clip.py: Introduced a script for managing categorical variables by reducing the number of levels based on frequency and coverage thresholds.
• pysnippets/Scrubs/compress.py: Added functions to compress numeric data types and encode categorical variables, with persistence of encoders.
• pysnippets/Scrubs/dummies.py: Created functions to generate dummy variables for categorical data and integrate them into dataframes.
• pysnippets/Scrubs/pipeline.py: Developed a script to orchestrate the entire data cleaning and preprocessing pipeline, including feature engineering and data aggregation.

Utility Functions:

• pysnippets/Scrubs/utils.py: Added utility functions, including a decorator to measure the execution time of functions.

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@UTSAVS26 kindly review this PR. I request you to please give level 3 on this PR.

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