📧 MailGuard AI – Email Spam Detection

🛡️ An intelligent spam email classifier built with Machine Learning and Streamlit

🚀 Features

.🧠 ML Model: Trained using Naive Bayes classifier on a TF-IDF feature vector.
.📥 User Input: Paste email content or upload a .txt file.
.⚙️ Instant Predictions: Detect whether an email is SPAM or NOT SPAM.
.📊 Model Dashboard: View Accuracy, Precision, Recall, and F1-Score.
.📁 Prediction History: View past predictions within the session.

🖼️ Interface Preview

🧠 How It Works

1.Input email via text box or .txt file.
2.The app vectorizes the text using a TF-IDF vectorizer.
3.The trained Naive Bayes model predicts if it’s SPAM or NOT SPAM.
4.Displays prediction 
5.Tracks recent predictions in session history.

🛠️ Tech Stack

.Python 3.12
.Scikit-learn (for model & vectorization)
.Streamlit (for frontend)
.Seaborn, Plotly (for visualization)

📦 Installation

1.Clone the repository:-

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

2.Install dependencies:-
    pip install -r requirements.txt

3.Run the app:-
    streamlit run app.py

📈 Model Performance:-

Metric	    Score
Accuracy	96.80%
Precision	97.10%
Recall	    97.10%
F1-Score	97.10%

🗂 Dataset https://www.kaggle.com/datasets/purusinghvi/email-spam-classification-dataset?select=combined_data.csv