K-Nearest Neighbors (KNN) Implementation
├── Introduction
│   └── Overview of KNN
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing KNN
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── KNN Classification Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of KNN

• K-Nearest Neighbors (KNN) is a simple and widely used classification algorithm. It classifies a data point based on how its neighbors are classified.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
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 KNN

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 KNN model
model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)

Model Evaluation

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

4. Visualization

KNN Classification Visualization