Naive Bayes

 Perfect. Since this is for your course, we'll keep every algorithm in the exact same format for consistency.

For every Machine Learning algorithm, we'll use only these sections:

1. Definition

2. Purpose

3. When to Use

4. Complete Python Code (using Pandas DataFrame with dummy data)

---

Chapter 9 — Naive Bayes

1. Definition

Naive Bayes is a Supervised Machine Learning Classification Algorithm that predicts the category (class) of new data using probability.

It is based on Bayes Theorem and assumes that all input features are independent of each other.

It is mainly used for classification problems, such as predicting Yes/No, Spam/Not Spam, or Positive/Negative.

Example

A company wants to predict whether a customer will purchase a product.

Input:

• Age

• Monthly Income

• Website Visits

• Previous Purchases

Output:

Purchased = Yes

or

Purchased = No

---

2. Purpose

The main purpose of Naive Bayes is to classify data into different categories based on probability.

It helps computers answer questions like:

• Will the customer buy the product?

• Is the email spam?

• Is the review positive?

• Does the patient have a disease?

Naive Bayes is widely used because it is:

• Very fast

• Easy to train

• Easy to understand

• Works well on small datasets

• Excellent for text classification

---

3. When to Use

Use Naive Bayes when:

• The problem is Classification.

• The output is categorical (Yes/No, Pass/Fail, Spam/Not Spam).

• The dataset contains numerical or text features.

• Fast prediction is required.

• The features are mostly independent.

Real-Life Applications

• Email Spam Detection

• Sentiment Analysis

• News Classification

• Disease Prediction

• Product Purchase Prediction

• Customer Classification

---

4. Complete Python Example (Using Pandas DataFrame)

import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB

from sklearn.metrics import (
    confusion_matrix,
    accuracy_score,
    precision_score,
    recall_score,
    f1_score
)

# -----------------------------
# Create Dataset
# -----------------------------

data = {
    "Age": [
        22,25,28,30,35,
        40,45,50,27,32,
        24,29,36,42,48,
        23,31,38,44,52
    ],

    "MonthlyIncome": [
        25000,30000,35000,42000,50000,
        56000,62000,70000,32000,45000,
        28000,38000,52000,60000,68000,
        26000,43000,55000,63000,72000
    ],

    "WebsiteVisits": [
        2,3,4,5,7,
        8,9,10,3,6,
        2,4,7,8,9,
        2,5,7,9,10
    ],

    "PreviousPurchases": [
        0,1,1,2,3,
        4,5,6,1,2,
        0,1,3,4,5,
        0,2,3,5,6
    ],

    "Purchased": [
        0,0,0,0,1,
        1,1,1,0,1,
        0,0,1,1,1,
        0,1,1,1,1
    ]
}

df = pd.DataFrame(data)

print("="*50)
print("DATASET")
print("="*50)
print(df.head())

# -----------------------------
# Features and Target
# -----------------------------

X = df[
    [
        "Age",
        "MonthlyIncome",
        "WebsiteVisits",
        "PreviousPurchases"
    ]
]

y = df["Purchased"]

# -----------------------------
# Train-Test Split
# -----------------------------

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.30,
    random_state=42
)

# -----------------------------
# Train Model
# -----------------------------

model = GaussianNB()

model.fit(X_train, y_train)

# -----------------------------
# Prediction
# -----------------------------

y_pred = model.predict(X_test)

probability = model.predict_proba(X_test)

# -----------------------------
# Evaluation
# -----------------------------

cm = confusion_matrix(y_test, y_pred)

accuracy = accuracy_score(y_test, y_pred)

precision = precision_score(y_test, y_pred)

recall = recall_score(y_test, y_pred)

f1 = f1_score(y_test, y_pred)

print("\nConfusion Matrix")
print(cm)

print("\nAccuracy :", round(accuracy,4))
print("Precision:", round(precision,4))
print("Recall   :", round(recall,4))
print("F1 Score :", round(f1,4))

print("\nPrediction Probabilities")
print(probability)

# -----------------------------
# New Customer
# -----------------------------

new_customer = [[34,50000,7,3]]

prediction = model.predict(new_customer)

new_probability = model.predict_proba(new_customer)

print("\nNew Customer")

if prediction[0] == 1:
    print("Prediction : PURCHASE")
else:
    print("Prediction : NOT PURCHASE")

print("Not Purchase Probability:",
      round(new_probability[0][0]*100,2),"%")

print("Purchase Probability:",
      round(new_probability[0][1]*100,2),"%")

---

Summary

Section	Description
Definition	Naive Bayes is a supervised classification algorithm based on Bayes Theorem.
Purpose	Predict the category (class) of new data using probabilities.
When to Use	Use for classification problems such as spam detection, sentiment analysis, disease prediction, and purchase prediction.
Code Example	Uses GaussianNB, a Pandas DataFrame, train-test split, evaluation metrics, prediction probabilities, and new customer prediction.