# main.py

# A demonstration of the Gaussian Naive Bayes classification algorithm.

#

# Before running, you may need to install scikit-learn and pandas:

# pip install scikit-learn pandas

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import GaussianNB

from sklearn.metrics import accuracy_score, classification_report

import pandas as pd

print("--- Starting Gaussian Naive Bayes Classification Demonstration ---")

# --- Section 1: Load and Prepare the Dataset ---

# We will use the Iris dataset. Gaussian Naive Bayes works well with

# continuous features like the measurements in this dataset.

print("\n--- 1. Loading the Iris Dataset ---")

iris = load_iris()

X = iris.data  # The features

y = iris.target # The target classes

# For clarity, let's see the feature and target names

print(f"Features: {iris.feature_names}")

print(f"Target Classes: {iris.target_names}")

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(

    X, y, test_size=0.3, random_state=42

)

print(f"\nData split into {len(X_train)} training samples and {len(X_test)} testing samples.")

# --- Section 2: Train the Gaussian Naive Bayes Model ---

# The core of the program is creating an instance of the GaussianNB classifier

# and fitting it to our training data.

print("\n--- 2. Training the Gaussian Naive Bayes Model ---")

gnb = GaussianNB()

gnb.fit(X_train, y_train)

print("Model training complete.")

# --- Section 3: Make Predictions and Evaluate the Model ---

# Now we use the trained model to make predictions on the unseen test data

# and evaluate how well it performed.

print("\n--- 3. Evaluating the Model ---")

y_pred = gnb.predict(X_test)

# Calculate the accuracy of the model

accuracy = accuracy_score(y_test, y_pred)

print(f"\nModel Accuracy: {accuracy:.4f}")

# Print a detailed classification report

print("\nClassification Report:")

# This report shows key metrics like precision, recall, and F1-score for each class.

print(classification_report(y_test, y_pred, target_names=iris.target_names))

print("\n--- End of Demonstration ---")