Added Random Forest Algorithm Implementation

[manishkumar00208]

Describe your change:

Implementation of Random Forest Algorithm (from scratch) using Python - A Machine Learning Model/Algorithm.

• [✔️] Add an algorithm? Implemented Random Forest Classifier for Machine Learning using Python (No Libraries are used).
• [x] Fix a bug or typo in an existing algorithm?
• [x] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• [x] 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.

Screenshot:

Note: Directly run the program for the execution. It provides the robust functionality of RandomForest Classifier. This program will build 10 decision tree models to use as a ensemble in RandomForest classifer, and is tested with all 3 datasets provided. The accuracy of the classifier is almost same (sometimes a bit high, sometimes a bit low) as we obtain from the Weka toolkit.