Naive Bayes Classifier Implementation
├── Introduction
│   └── Overview of Naive Bayes Classifier
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing Naive Bayes Classifier
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── Model Prediction Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Naive Bayes Classifier

• The Naive Bayes Classifier is a probabilistic classifier based on applying Bayes' theorem with strong independence assumptions between the features.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
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 Naive Bayes Classifier

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 Naive Bayes Classifier
model = GaussianNB()
model.fit(X_train, y_train)

Model Evaluation

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

4. Visualization

Model Prediction Visualization