sms-spam-classifier

SMS Spam Detector

Project Overview

A machine learning-based web application that classifies SMS messages as spam or not spam. This project demonstrates the full pipeline of a machine learning project, from data preprocessing to model deployment via streamlit.

Live demo: SMS Spam Detector App

Features

• Text input for SMS messages
• Real-time classification of messages as spam or not spam
• Simple and intuitive user interface

Technologies Used

• Python
• Scikit-learn for handling machine learning models
• NLTK for natural language processing
• Streamlit for web application development
• Pickle for model serialization and vectorization

Project Structure

• app.py: Main Streamlit application file
• model.pkl: Serialized machine learning model
• vectorizer.pkl: Serialized text vectorizer
• requirements.txt: List of Python dependencies
• email_spam_classifier.ipynb: Additional exploratory data analysis and model training and evaluating.

How to Run Locally

1. Clone this repository
2. Install dependencies: pip install -r requirements.txt
3. Run the Streamlit app: streamlit run app.py

Machine Learning Pipeline

1. Data Preprocessing:
   • Removed special characters and numbers
   • Converted text to lowercase
   • Tokenized messages
   • Removed stop words
   • Applied stemming to reduce words to their root form
2. Exploratory Data Analysis:
   • Cleaned data: removed unnecessary columns and duplicates
   • Graphs and Heatmap: Learnt about trends and its corelations using graphs and heatmaps
   • WordClouds: Used wordclouds to visualise which were the most common words
   • Found class imbalance: more ham than spam
   • Key insight: Spam messages typically longer than ham
3. Feature Engineering:
   • Used TF-IDF (Term Frequency-Inverse Document Frequency) vectorization
   • Experimented with different max_features settings for TF-IDF
4. Model Selection:
   • Tested models:
     • SVC
     • KNeighbors
     • MultinomialNB
     • DecisionTree
     • LogisticRegression
     • RandomForest
     • AdaBoost
     • BaggingClassifier
     • ExtraTrees
     • GradientBoosting
     • XGBoost
   • Also experimented with ensemble methods:
     • VotingClassifier
     • StackingClassifier
   • Final model chosen: MultinomialNB
5. Model Evaluation:
   • Used accuracy and precision as key metrics
   • Compared performance across different models and configurations
   • Used train-test split for evaluation (80% train, 20% test)

Future Improvements

1. Hyperparameter tuning: Systematically tune hyperparameters for top-performing models

2. Advanced NLP techniques: Incorporate word embeddings or transformer-based models for potentially improved performance

3. Multilingual: Develop the ability to classify data from different languages.

Contributing

Contributions to improve the project are welcome. Please feel free to submit a Pull Request.

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Project Link: GitHub Repository URL