feat: add Random Forest algorithm in machine learning

[Dcyaprogrammer]

Pull Request Template

Description

A Random Forest is an ensemble learning algorithm that combines multiple decision trees to improve prediction accuracy and reduce overfitting. It uses bagging (bootstrap aggregating) to train each tree on a random subset of the training data and random feature selection at each split. Predictions are made by majority voting across all trees, providing better generalization than individual decision trees.

Type of change

-✅ New feature (non-breaking change which adds functionality)

Checklist:

• ✅ I ran bellow commands using the latest version of rust nightly.
• ✅ I ran cargo clippy --all -- -D warnings just before my last commit and fixed any issue that was found.
• ✅ I ran cargo fmt just before my last commit.
• ✅ I ran cargo test just before my last commit and all tests passed.
• ✅ I added my algorithm to the corresponding mod.rs file within its own folder, and in any parent folder(s).
• ✅ I added my algorithm to DIRECTORY.md with the correct link.
• ✅ I checked COUNTRIBUTING.md and my code follows its guidelines.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 87.70227% with 38 lines in your changes missing coverage. Please review.
✅ Project coverage is 95.96%. Comparing base (a6f9ffd) to head (6422a62).

Files with missing lines	Patch %	Lines
src/machine_learning/random_forest.rs	87.70%	38 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1007      +/-   ##
==========================================
- Coverage   96.09%   95.96%   -0.14%     
==========================================
  Files         375      376       +1     
  Lines       26674    26983     +309     
==========================================
+ Hits        25632    25893     +261     
- Misses       1042     1090      +48     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[Dcyaprogrammer]

Hi @siriak, would you please take a look to your convenience. I believe this is ready to be merged. Thank you! Lines missing coverage in the report are sub-modules and inner logic , hard to cover with test case. Normal and boundary cases have been tested. If requested I can try add more test cases simply for coverage.