Email Spam Classification

A machine learning-based email spam classification system using Logistic Regression. It preprocesses email content with TF-IDF and achieves high accuracy in detecting spam. The model is deployed as a Streamlit app for easy testing and interaction.

Project Overview:

The system uses the Logistic Regression model, built and trained on email data. It preprocesses the raw email text using TF-IDF to extract meaningful features, making the classifier capable of achieving impressive performance in detecting spam. With an accuracy of 94.32%, the model is fine-tuned for precision and recall, reducing false positives and negatives.

Key Features:

• Input: Raw email text (message content).
• Output: Prediction label ("Spam" or "Not Spam").
• Preprocessing: The raw email text is transformed into numerical features using TF-IDF Vectorizer, making it ready for model input.
• Model: Logistic Regression optimized for high precision and recall on imbalanced datasets.
• Performance: 94.32% accuracy, with strong precision and recall, ensuring minimized errors.

Logistic Regression Model:

Logistic Regression is a binary classifier that predicts the probability of a particular outcome (spam or not spam). It uses the sigmoid function to transform predicted values into a range of 0 to 1, which is then used to classify the email.

Why Logistic Regression?

• Efficiency: It is computationally light and quick to train, making it ideal for real-time classification.
• Interpretability: The model's coefficients are easy to interpret, giving insights into which features are most influential in the classification process.
• Scalability: Works well with large datasets when combined with efficient feature extraction like TF-IDF.

Technologies Used:

• Python: Programming language.
• Scikit-learn: For building and training the Logistic Regression model.
• Streamlit: For creating the web-based interface for deploying the model.
• Pandas: For data handling and manipulation.
• Numpy: For numerical operations.
• TF-IDF: For transforming text data into numerical format suitable for the model.

Installation and Setup:

1. Clone the repository:

   git clone https://github.com/your-username/email-spam-classification.git

2. Navigate into the project folder:

   cd email-spam-classification

3. Install the required libraries:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run Streamlit_app.py

Deployed App

Contributing:

Feel free to fork this project and make contributions. Pull requests are welcome!