Added Random Forest Regressor as an additional prediction model.

[priyanshu-8789]

Describe your change:

Implemented RandomForestRegressor alongside SVR and SARIMAX

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[priyanshu-8789]

Implemented RandomForestRegressor alongside SVR and SARIMAX
It will help to improve robustness and accuracy in data safety checking
Updated the voting mechanism to include Random Forest predictions

[priyanshu-8789]

Added Random Forest Regressor to main voting

[priyanshu-8789]

Hi @poyea,
I've added the Random Forest Regressor as an additional prediction model. All checks have passed.
Could you please review and approve this pull request when convenient?
Thank you!

[priyanshu-8789]

Hi @TheAlgorithms,
I've added the Random Forest Regressor as an additional prediction model. All checks have passed.
Could you please review and approve this pull request when convenient?
Thank you!

[priyanshu-8789]

Hi @TheAlgorithms,
I've added the Random Forest Regressor as an additional prediction model and added matplotlib library for seeing predictions vs actuals which will help non-technical users validate data easily. All checks have passed.
Could you please review and approve this pull request when convenient?
Thank you!

[Copilot]

Pull request overview

This PR extends the existing forecasting “voting” script by adding a Random Forest–based regressor as an additional prediction option, and introduces a small visualization helper for comparing predictions. It also updates the repository’s directory index.

Changes:

• Add random_forest_regressor() (sklearn) and include it in the res_vote ensemble.
• Add plot_forecast() (matplotlib) and call it from the __main__ block to visualize results.
• Update DIRECTORY.md to add a Geometry index entry.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.

File	Description
machine_learning/forecasting/run.py	Adds RandomForestRegressor forecasting method and a matplotlib plotting helper; integrates both into the main execution path.
DIRECTORY.md	Adds a Geometry section index entry (“Segment Intersection”).

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[poyea]

Type hints required