Custom Random Forest Implementation from Scratch

This repository contains a Jupyter Notebook explaining and testing a custom implementation of the Random Forest algorithm. The implementation is built from scratch using Python, without relying on external libraries for the Random Forest algorithm itself.

Overview

Random Forest is an ensemble learning algorithm that operates by constructing a multitude of decision trees during training and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

In this project, we have created a custom implementation of the Random Forest algorithm to gain a deeper understanding of its inner workings. The implementation covers key aspects such as:

• Decision Tree Construction
• Bootstrap Sampling
• Feature Randomization
• Aggregation of Predictions

Dataset

The algorithm is tested on a custom dataset created specifically for this project. The dataset showcases the algorithm's performance in various scenarios and allows for a comprehensive evaluation of its accuracy, precision, and recall.

Jupyter Notebook

The Jupyter Notebook in this repository provides a step-by-step explanation of the custom Random Forest implementation. It includes:

1. Data Loading and Preprocessing
2. Decision Tree Construction
3. Random Forest Training
4. Model Evaluation on the Custom Dataset

Feel free to explore the notebook to gain insights into the details of the Random Forest algorithm and how it performs on the provided dataset.

                                                               it'sZoral1 peace out ^_^