Principal Component Analysis (PCA) Implementation
├── Introduction
│   └── Overview of PCA
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Implementing PCA
│   ├── Data Preparation
│   ├── Model Training
│   └── Transforming Data
├── Visualization
│   └── PCA Projection Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of PCA

• PCA is a dimensionality reduction technique that transforms the data into a new coordinate system, where the greatest variances lie on the first coordinates (the principal components).

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import make_classification

Generating the Dataset

# Python code to generate a sample dataset
X, _ = make_classification(n_samples=300, n_features=3, n_informative=3, n_redundant=0, n_clusters_per_class=1, random_state=42)

3. Implementing PCA

Data Preparation

• The data is prepared and scaled if necessary.

Model Training

# Python code to train the PCA model
pca = PCA(n_components=2)
pca.fit(X)

Transforming Data

# Python code to transform the data using PCA
X_pca = pca.transform(X)

4. Visualization

PCA Projection Visualization