Readme.md

Naive Bayes Classifier (From Scratch)

This project implements a Naive Bayes Classifier in Python without using machine learning libraries like scikit-learn.

It allows the user to:

• Load data from a CSV file
• Select input features manually
• Calculate prior probabilities and conditional probabilities
• Predict the most likely class using Bayes' Theorem

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📁 Project Structure

naive-bayes-classifier-python/ │ ├── naive_bayes.py # Main program ├── data/ │ └── data3.csv # Dataset used for predictions ├── README.md # Project documentation ├── requirements.txt # Required Python libraries └── .gitignore # Ignore temporary/system files

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🚀 How to Run

# Step 1: Install dependencies
pip install -r requirements.txt

# Step 2: Run the program
python naive_bayes.py
✅ Features

Calculates P(Class) and P(Feature | Class)

Uses Laplace Smoothing to avoid zero probabilities

Supports any CSV dataset with categorical values

Pure Python implementation (no ML libraries)

📌 Requirements
pandas
numpy

⚠ Note

Make sure your dataset file is placed inside the data/ folder.

Modify the path in pd.read_csv() if needed.

✨ Future Improvements

Add GUI using Tkinter or Streamlit

Save outputs to a text file or JSON

Convert to scikit-learn compatible model