🛡️ Phishing Email Detection Model Overview Welcome to the Phishing Email Detection Model 🌐, a cutting-edge solution employing PyTorch, CUDA, and Hugging Face for identifying potential phishing threats in emails. This model is adept at processing and analyzing URLs to detect phishing activities with high accuracy.

Workflow

The model operates through a structured workflow, encompassing several key stages:

1. Data Preprocessing 📊 Loading Data: Initiate by loading the dataset from a specified directory. Handling Missing Values: Ensures data integrity by managing any missing values. Tokenizing Data: Converts URLs into a tokenized format suitable for model processing.
2. Data Splitting 🔀 Train-Test Split: Divides the dataset into training, validation, and testing segments for a comprehensive evaluation.
3. Model Training 🏋️‍♂️ Model Initialization: Sets up the phishing detection model using PyTorch. Training: The model undergoes training with specified parameters, learning to distinguish between phishing and legitimate URLs. Model Saving: Post-training, the model is saved for future evaluation and deployment.
4. Model Evaluation 📝 Model Loading: Retrieves the trained model for performance assessment. Testing: Evaluates the model's effectiveness on the test dataset. Metrics Calculation: Computes various metrics to gauge the model's accuracy and reliability. Key Functions 🗝️ To operate this model in a terminal environment, the following functions are integral:

load_data(): Initiates the data loading process. handle_missing_values(): Manages and rectifies missing data points. tokenize_data(): Transforms URL data into a tokenized format. split_data(): Segregates the dataset for effective training and testing. create_datasets(): Prepares PyTorch datasets. initialize_model(): Sets up the phishing detection model. train_model(): Engages in the training of the model. save_model(): Archives the trained model. load_trained_model(): Retrieves the trained model for evaluation. evaluate_model(): Conducts an assessment of the model on test data. calculate_metrics(): Computes and presents key performance metrics. Execution Guide 🚀 To execute the model, follow these steps:

Open your terminal and navigate to the project directory. Run the main.py script. Ensure all dependencies are installed, including PyTorch, CUDA, and Hugging Face libraries. Dependencies 📌 PyTorch: For model building and training. CUDA: Utilized for GPU acceleration, enhancing performance. Hugging Face: Provides additional tools and libraries for model optimization.