Email Spam Detection 📨

Overview

This project focuses on detecting whether an email is spam or not using machine learning techniques. The main objective is to classify incoming emails into two categories — Spam and Ham (Not Spam) — based on the email content.
The project demonstrates the process of data preprocessing, feature extraction, model building, and evaluation using Python.

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Features

• Cleans and preprocesses raw email text
• Converts text data into numerical form using TF-IDF vectorization
• Implements machine learning models such as Logistic Regression for classification
• Evaluates model performance using metrics like accuracy, precision, recall, and F1 score
• Includes exploratory data analysis (EDA) for better understanding of the dataset

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Technologies Used

• Language: Python
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, nltk
• Environment: Jupyter Notebook

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Dataset

The dataset used in this project is mail_data.csv, which contains email messages and their corresponding labels (spam or ham).
Each record includes:

• Email text – The actual content of the email
• Label – Indicates whether the email is spam or not

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Project Structure

Email_Spam_Detection/
│
├── mail_data.csv              # Dataset file
├── detection.ipynb            # Main Jupyter notebook (data preprocessing, training, evaluation)
├── detection-checkpoint.ipynb # Backup notebook file
└── README.md                  # Project documentation

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Steps to Run the Project

1. Clone the repository

git clone https://github.com/Pallabi26313/Email_Spam_Detection.git
cd Email_Spam_Detection

2. Install dependencies

Make sure you have Python installed, then install required libraries:

pip install pandas numpy scikit-learn matplotlib seaborn nltk

3. Run the notebook

jupyter notebook detection.ipynb

4. Execute the cells sequentially to:

• Load and explore the dataset
• Preprocess the text data
• Train and test the model
• Evaluate model performance

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Results

After training and testing, the model achieved:

• Accuracy: [96%]

You can further improve accuracy by trying other models such as SVM, Random Forest, or XGBoost.

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Future Enhancements

• Add a web app interface using Streamlit or Flask
• Try deep learning models (LSTM or BERT) for better text understanding
• Improve text preprocessing using advanced NLP techniques
• Add visualization dashboards for real-time spam classification

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Conclusion

This project successfully demonstrates how machine learning can be applied to classify emails as spam or not spam. It covers end-to-end development — from data preprocessing to model evaluation — making it a useful beginner project for NLP and text classification.

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Author

Pallabi Ghosh

• GitHub: Pallabi26313
• Email: [pallabighosh7142@gmail.com]