Email and SMS Spam Classifier

This project aims to classify emails and SMS messages as spam or not spam using machine learning and nlp techniques.

Features

• Train a spam classifier using a labeled dataset.
• Evaluate the performance of the model.
• Deploy the model as a web application for real-time spam detection.

Deployment

The app is deployed and can be accessed here.

Files

• app.py: Streamlit application for running the classifier.
• requirements.txt: Lists required Python packages.
• sms_spam_detection.ipynb: Jupyter Notebook for training and evaluating the model and for data understanding using Exploratory Data Analysis.
• spam.csv: Dataset containing labeled messages.
• model_files/: Directory containing vectorizer and trained model files.

Installation

1. Clone the repository:

   git clone https://github.com/Phani943/Email_Sms_Spam_Classifier.git

2. Navigate to the project directory:

   cd Email_Sms_Spam_Classifier

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Training the Model:

   • Open and run the sms_spam_detection.ipynb notebook to train and evaluate the spam classifier.

2. Running the Web Application:

   • Start the Streamlit app:

     streamlit run app.py

   • Open your web browser and go to url provided there, to use the spam classifier.

Dataset

The spam.csv file contains the labeled messages used for training. Each message is labeled as either "spam" or "ham" (not spam).

Acknowledgements

• The dataset used in this project is sourced from the Kaggle/UCI Machine Learning.