DataScience-Task4-EmailSpamDetection

📧 Email Spam Detection Project – Task 4

Project Outline

This project focuses on building an intelligent system to classify messages as either Spam or Ham using Natural Language Processing (NLP) and Machine Learning.

1. Objective

• Detect spam messages automatically.
• Apply text preprocessing techniques to clean and prepare the data.
• Train a machine learning model to classify new messages.
• Deploy a simple prediction app using Streamlit.

2. Dataset

• Source: SMS Spam Collection Dataset
• Contains labeled SMS messages
• Classes:
  • Ham (Not Spam)
  • Spam

3. Workflow

1. Load the dataset
2. Clean the data
   • remove unnecessary columns
   • rename columns
   • handle missing values and duplicates
3. Explore the data
   • check class distribution
   • analyze message length and word patterns
4. Preprocess text
   • lowercase text
   • remove punctuation and special characters
   • tokenize words
   • remove stopwords
   • apply stemming
5. Convert text into features
   • use TF-IDF vectorization
6. Train the model
   • use Multinomial Naive Bayes
7. Evaluate performance
   • accuracy
   • precision
   • recall
   • F1-score
   • confusion matrix
8. Build a prediction app
   • create a Streamlit interface for real-time testing

4. Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• NLTK
• Matplotlib
• Seaborn
• Streamlit
• Jupyter Notebook

5. Project Structure

• Email_Spam_Detection.ipynb — main notebook for analysis and modeling
• app/app.py — Streamlit web app
• app/requirements.txt — Python dependencies
• dataset/spam.csv — dataset file
• outputs/ — saved model metrics and reports

6. How to Run

cd DataScience-Task4-EmailSpamDetection
pip install -r app/requirements.txt
streamlit run app/app.py

7. Expected Outcome

• The model should classify incoming messages as spam or ham.
• The web app should provide a quick and interactive prediction experience.