This project aims to create a deep learning trained model to accurately distinguish between spam and legitimate SMS messages. The main objective is to analyze SMS message features and develop a software solution that automatically detects and filters out spam. Additionally, Dataset Description These messages are characterized as engaging and attention-grabbing, making the dataset a valuable tool for research into mobile phone spam. The dataset used for this project typically consists of labeled emails, with each email marked as spam or not spam. The dataset can be obtained from various sources, such as:

• TREC 2007 Spam Track Public Corpus
• Enron Spam Dataset
• SpamAssassin Corpus The dataset should be preprocessed to remove unwanted characters, punctuation, and stop words. Model Architecture Explanation The model architecture used for this project is based on natural language processing (NLP) techniques. Here's a high-level overview of the architecture:

1. Text Preprocessing: The input email text is preprocessed to remove unwanted characters, punctuation, and stop words.
2. Feature Extraction: The preprocessed text is then converted into numerical features using techniques such as:
   • Bag-of-Words (BoW)
   • Term Frequency-Inverse Document Frequency (TF-IDF)
3. Model Training: The extracted features are used to train a machine learning model, such as:
   • Naive Bayes (NB)
   • Support Vector Machines (SVM)
   • Random Forest (RF)
   • Convolutional Neural Networks (CNN)
4. Model Evaluation: The trained model is evaluated using metrics such as accuracy, precision, recall, and F1-score.

Details

• Programming Language: Python
• Libraries: scikit-learn, pandas, NumPy, NLTK, TensorFlow (optional)
• Model: The model can be trained using various algorithms, such as NB, SVM, RF, or CNN.

API Reference The API for this project can be built using Flask or Django. The API can have endpoints for:hMy

• Training: Train the model using a dataset
• Prediction: Classify an email as spam or not spam
• Evaluation: Evaluate the model's performance using metrics

Use Cases

1. Email Filtering: The model can be used to filter out spam emails from a user's inbox.
2. Spam Detection: The model can be used to detect spam emails in real-time.
3. Email Classification: The model can be used to classify emails into different categories, such as spam, promotional, or personal.

Programming Code example code using scikit-learn and NLTK: import pandas as pd import nltk from nltk.tokenize import word_tokenize from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score