🔍 Spam SMS Detection Using Machine Learning in Python
📄 Executive Summary:
This project aims to develop a predictive model to classify Spam SMS using machine learning in Python. The model will focus on identifying and filtering spam messages, enhancing communication security and efficiency.
🎯 Problem Statement:
Background: The increasing volume of SMS communications includes a significant number of spam messages, which can lead to reduced efficiency and potential security risks.
Objective: Develop a predictive model to accurately classify and filter out spam SMS messages.
Scope: Initial focus on building and training the model using historical SMS data, followed by testing and validation to ensure high accuracy in spam detection.
🛠 Methodology:
Data Cleaning: Collect and clean SMS data to remove duplicates, inconsistencies, and irrelevant information.
EDA: Perform EDA to understand the distribution, patterns, and key characteristics of the data.
Data Preprocessing: Transform and preprocess the data, including tokenization, text normalization, and feature extraction.
Model Building: Develop and train machine learning models using various algorithms to classify spam and non-spam SMS messages.
📈 Expected Outcomes:
Accurate classification of spam and non-spam SMS messages.
Enhanced communication security by effectively filtering out spam messages.
Improved efficiency in handling SMS communications for better resource allocation.
Comprehensive evaluation report detailing model performance and potential areas for further enhancement.
🛠 Tools and Technologies:
Python, pandas, NumPy, Scikit-learn for data processing, model development, and evaluation.
Data Quality: Ensuring clean SMS data.
Imbalanced Data: Handling the imbalance between spam and non-spam messages.
Model Generalization: Ensuring the model generalizes well to new, unseen data.
Computational Resources: Managing computational requirements for training complex models.
Security and Privacy: Maintaining security and privacy of SMS data.
🏁 Conclusion:
This project aims to develop a robust spam detection system, enhancing communication security and efficiency. By leveraging advanced machine learning techniques, the predictive model will accurately classify spam SMS messages, reducing potential risks and improving overall operational effectiveness. The implementation of this solution will foster a more secure and efficient communication environment, supporting a commitment to leveraging technology for improved processes.
Demo :-
To try out the SMS Spam Detection model, visit https://sonali-email-spam-detect.streamlit.app/