Problem: With the increasing use of mobile phones and messaging services, users are often bombarded with unwanted spam messages. Detecting and filtering out spam messages is critical for improving user experience and reducing the risks associated with malicious content.
Real-World Impact: An effective SMS spam detection system can prevent users from receiving unsolicited or harmful content, protecting them from phishing attempts, scams, and other harmful attacks. This solution can be integrated into messaging services or mobile apps to automatically filter spam, ensuring only important and relevant messages reach the user.
The system uses a Naive Bayes Classifier trained on a dataset of spam and non-spam messages. Here’s the approach:
Vectorization:
vector.pkl) performs this task by converting the text input into a bag-of-words or term frequency-inverse document frequency (TF-IDF) representation.Model:
nbmod.pkl) is used to classify SMS messages as either "spam" or "not spam." This model has been pre-trained on a labeled dataset of SMS messages to distinguish between spam and legitimate messages.Streamlit Interface:
predicto() function, which uses the vectorizer to transform the input and the Naive Bayes model to classify the message.The system accurately classifies SMS messages as either "spam" or "not spam" based on the trained model. Users can quickly test the classification by entering any message and receiving a real-time prediction.
Results: The app correctly identifies spam messages with a high level of accuracy. This functionality can help individuals and businesses automatically filter out unwanted or malicious messages.
Potential Impact: Incorporating such a system into mobile devices or messaging services can significantly reduce the nuisance of spam messages and protect users from potential security threats. This could lead to improved communication safety and user satisfaction.
The code for the SMS Spam Detection system is available on GitHub (https://github.com/Abhinav2k4/SMS_Spam_Detection). The dataset used to train the Naive Bayes model is a publicly available spam SMS dataset.
Dataset: The training data consists of SMS messages labeled as "spam" or "ham" (non-spam). The model learns from this labeled data to classify future messages.
Pre-trained Model and Vectorizer: Both the Naive Bayes model and the vectorizer have been pre-trained and saved as nbmod.pkl and vector.pkl respectively, and are loaded into the app for real-time classification.
Challenge: Preprocessing text data for machine learning can be challenging due to the need to clean the data and represent it in a form suitable for algorithms.
Lesson Learned: Using efficient text vectorization techniques, like TF-IDF, helped capture the essential features of the messages, improving the model's performance on detecting spam.
Model Enhancement: Future iterations could involve experimenting with more advanced models, such as deep learning techniques (e.g., LSTM or transformers), to further improve accuracy.
Scalability: Currently, the model operates on a pre-trained dataset, but it could be expanded to retrain on new data continuously, allowing it to adapt to new types of spam.
User Experience: Additional features such as highlighting suspicious keywords, providing reasons for the classification, or integrating with actual messaging platforms can make the tool more practical for end-users.