Feature description

I would like to contribute implementations of both the Random Forest Classifier and Random Forest Regressor from scratch (without using libraries such as scikit-learn).

The implementation will include:

• Decision Tree base learners implemented from scratch
• Bootstrap sampling (bagging)
• Random feature selection at each split (feature bagging)
• Aggregation:
  • Majority voting for classification
  • Mean prediction for regression

Please let me know if this addition is acceptable for the machine_learning directory. I will start working after approval.