📧 Email Spam Detection System using Naive Bayes

This project is a simple yet effective spam detection system that utilises the naive Bayes classifier to classify emails as either spam or not spam. It utilizes text preprocessing techniques, TF-IDF vectorization, and machine learning to build a spam filter based on the popular spam.csv dataset.

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🧠 Machine Learning Algorithm

• Multinomial Naive Bayes is used for classification because it performs well with text data and discrete features (like word counts or frequencies).

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📂 Dataset

• Dataset: spam.csv

• Columns:

  • v1: Label (spam/ham)
  • v2: Text message

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🛠️ Features

• Text cleaning (punctuation removal, stopwords removal)

• Text stemming using PorterStemmer

• TF-IDF Vectorisation

• Train-Test Split

• Model Training & Evaluation

• Performance metrics:

  • Accuracy
  • Confusion Matrix
  • Classification Report

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🧪 Libraries Used

pandas
numpy
string
nltk
sklearn

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📊 Model Evaluation

The model is evaluated using:

• Accuracy Score
• Confusion Matrix
• Classification Report

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📝 How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/Email-Spam-Detection.git
   cd Email-Spam-Detection

2. Install required libraries:

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook Email_Spam_Detection_system_using_Naive_Bayes.ipynb

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📌 Project Structure

📁 Email-Spam-Detection/
│
├── Email_Spam_Detection_system_using_Naive_Bayes.ipynb
├── spam.csv
├── README.md
└── requirements.txt

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🤖 Sample Result

Metric	Value
Accuracy	97%+
Precision	High
Recall	High

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✅ Future Improvements

• Add GUI or Web Interface (using Streamlit/Flask)
• Deploy the model using FastAPI or Flask
• Add more datasets and deep learning models

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👨‍💻 Author

Asim Hanif GitHub | LinkedIn Software Engineering Student & ML Enthusiast