Linear Discriminant Analysis (LDA) Implementation
├── Introduction
│   └── Overview of LDA
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing LDA
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── LDA Projection Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of LDA

• Linear Discriminant Analysis (LDA) is a classification and dimensionality reduction technique. It is used to project the features in higher-dimensional space onto a lower-dimensional space while maintaining the class-discriminatory information.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

Loading the Dataset

# Python code to load a sample dataset
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, n_clusters_per_class=1, random_state=6)

3. Implementing LDA

Data Preparation

# Python code to split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Training

pythonCopy code
# Python code to train the LDA model
model = LDA()
model.fit(X_train, y_train)

Model Evaluation

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

4. Visualization

LDA Projection Visualization