Random Forest Implementation
├── Introduction
│   └── Overview of Random Forest
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing Random Forest
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── Feature Importance Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Random Forest

• Random Forest is an ensemble learning method used for classification and regression. It builds multiple decision trees and merges them together to get a more accurate and stable prediction.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

Loading the Dataset

# Python code to load a sample dataset
iris = load_iris()
X = iris.data
y = iris.target

3. Implementing Random Forest

Data Preparation

# Python code to split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Model Training

# Python code to train the Random Forest model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

Model Evaluation

# Python code to evaluate the model
y_pred = model.predict(X_test)

4. Visualization

Feature Importance Visualization