Support Vector Machine (SVM) Implementation
├── Introduction
│   └── Overview of SVM
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing SVM
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── SVM Decision Boundary Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of SVM

• Support Vector Machine (SVM) is a powerful supervised learning algorithm used for both classification and regression, but it is more commonly used in classification problems.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
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 SVM

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

# Python code to train the SVM model
model = svm.SVC(kernel='linear')
model.fit(X_train, y_train)

Model Evaluation

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

4. Visualization

SVM Decision Boundary Visualization