Refactor project description and update installation instructions

readme.md

@@ -7,15 +7,14 @@
 ## Overview
 This project leverages advanced machine learning algorithms to detect and classify malicious emails, focusing on spam and phishing threats. As email threats grow more sophisticated, accurate detection is critical to ensuring the security and privacy of both individuals and organizations.
 
-Our solution uses a machine learning pipeline incorporating data preprocessing, feature engineering, and model training to identify phishing and spam emails. With a focus on handling real-world challenges like imbalanced datasets, the project utilizes datasets from SpamAssassin and CEAS to train and evaluate the models, enhancing overall email filtering.
+Our solution applies a combination of processes such as data preprocessing, feature engineering, and model training techniques to identify spam and phishing emails. While we implemented specific steps for data cleaning and feature extraction, these were not integrated into a single machine learning pipeline. The project addresses real-world challenges like imbalanced datasets by utilizing SpamAssassin and CEAS datasets for training and evaluation, ultimately enhancing the model's ability to filter phishing and spam emails effectively.
 
 Key Technologies:
-- BERT for Feature Extraction: We use Bidirectional Encoder Representations from Transformers (BERT) to enhance contextual understanding of email content.
-- Stacked Ensemble Learning: The model ensemble combines XGBoost, Bagged SVM, and Logistic Regression, providing a robust solution for detecting phishing emails.
-- Optuna for Hyperparameter Tuning: Optuna optimizes the model's performance by fine-tuning key parameters.
-- SMOTE for Imbalanced Data: The Synthetic Minority Over-sampling Technique (SMOTE) addresses the imbalance between "Safe" and "Not Safe" emails.
+- **BERT for Feature Extraction**: We use Bidirectional Encoder Representations from Transformers (BERT) to enhance contextual understanding of email content.
+- **Stacked Ensemble Learning**: The model ensemble combines XGBoost, Bagged SVM, and Logistic Regression, providing a robust solution for detecting phishing emails.
+- **Optuna for Hyperparameter Tuning**: Optuna optimizes the model's performance by fine-tuning key parameters.
 
-The project also includes a Flask web application that serves as a user interface, enabling users to upload email content and receive real-time classifications on whether emails are "Safe" or "Not Safe."
+The project also includes a Flask web application that serves as a user interface, enabling users to receive real-time classifications on their inboxes on whether emails are "Safe" or "Not Safe."
 
 
 ## Installation
@@ -27,19 +26,21 @@ git clone https://github.com/yourusername/phishing-email-detection.git
 cd phishing-email-detection
 pip install -r requirements.txt
 ```
+Once the dependencies are installed, you can run the phishing email detection program using the following command:
+```bash
+python SpamAndPhishingDetection.py
+```
 
 ## Data
 The project utilizes merged datasets from SpamAssassin (Hugging Face) and CEAS (Kaggle) to enhance email threat detection:
 
-- SpamAssassin: Contains real-world spam and legitimate emails.
-- CEAS: Specially curated for anti-spam research, with a focus on phishing examples.
-### Preprocessing and Feature Engineering:
-- Label Standardization: Emails are labeled as "Safe" or "Not Safe."
-- Data Cleaning: HTML tags, punctuation, URLs, and special symbols are removed, while tokenization and stop-word removal are performed.
-- Feature Extraction: Includes sender and receiver addresses, blacklisted keywords, URL count, IP addresses, and BERT embeddings for deep textual understanding.
+- **SpamAssassin**: Contains real-world spam and legitimate emails.
+- **CEAS 2008**: Specially curated for anti-spam research, with a focus on phishing examples.
 
+## Merging Datasets
+TThe project integrates the **[Spam Assassin](https://huggingface.co/datasets/talby/spamassassin)** and **[CEAS 2008](https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset?select=CEAS_08.csv)** datasets, aligning them by columns and ensuring label consistency. This creates a robust, well-labeled dataset that improves phishing and spam detection accuracy.
 
-The dataset consists of labeled emails, which are preprocessed and stored in the following structure:
+### File Structure for Storing Results
 - Data Cleaning/
 - Data Integration/
 - Data Preprocessing/
@@ -49,55 +50,74 @@ The dataset consists of labeled emails, which are preprocessed and stored in the
 - Feature Extraction/
 - Models & Parameters/
 
-## Merging Datasets
-TThe project integrates the **[Spam Assassin](https://huggingface.co/datasets/talby/spamassassin)** and **[CEAS_08](https://www.kaggle.com/datasets/naserabdullahalam/phishing-email-dataset?select=CEAS_08.csv)** datasets, aligning them by columns and ensuring label consistency. This creates a robust, well-labeled dataset that improves phishing and spam detection accuracy.
-
-
 ## Model Training Methodologies
 The following methodologies were employed to enhance the model's performance:
 
-- **BERT Feature Extraction:** Leveraging BERT to extract contextual features from email content, which improves the model's understanding of the text.
+- **BERT Feature Extraction**: Leveraging BERT to extract contextual features from email content, which improves the model's understanding of the text.
 
-- **Ensemble Learning:** Combining multiple models (e.g., Logistic Regression, SVM, XGBoost) using meta-modeling techniques to improve predictive performance and robustness.'
+- **Ensemble Learning**: Combining multiple models (e.g., Logistic Regression, SVM, XGBoost) using meta-modeling techniques to improve predictive performance and robustness.'
 
 - **Noise Injection**: Adding controlled random variations to features to improve model generalization and reduce overfitting.
 
-- **Hyperparameter Optimization with Optuna:** Using Optuna to systematically tune model hyperparameters, ensuring optimal settings for better accuracy and reduced overfitting.
+- **Hyperparameter Optimization with Optuna**: Using Optuna to systematically tune model hyperparameters, ensuring optimal settings for better accuracy and reduced overfitting.
 
-- **Cross-Validation:** Implementing stratified K-fold cross-validation to ensure the model generalizes well across different subsets of data.
+- **Cross-Validation**: Implementing stratified K-fold cross-validation to ensure the model generalizes well across different subsets of data.
 
-- **Preprocessing Pipelines:** Utilizing pipelines that include:
+- **Preprocessing Pipelines**: Utilizing pipelines that include:
 
-    - Rare Category Removal: To eliminate categories with very few occurrences, enhancing model performance.
-    - Imputation: Filling missing values in the dataset.
-    - Encoding: Applying One-Hot Encoding for categorical variables.
-    - Standard Scaling: Normalizing numerical features for improved model performance.
+    - **Rare Category Removal**: To eliminate categories with very few occurrences, enhancing model performance.
+    - **Imputation**: Filling missing values in the dataset.
+    - **Encoding**: Applying One-Hot Encoding for categorical variables.
+    - **Standard Scaling**: Normalizing numerical features for improved model performance.
 
 ## Evaluation
-- **Accuracy:** Measures overall prediction correctness.
+- **Accuracy**: Measures overall prediction correctness.
 - **Precision, Recall, F1-Score:** Evaluates the balance between correct and incorrect classifications.
-- **Confusion Matrix:** Displays the performance of each model in predicting "Safe" vs. "Not Safe" emails.
+- **Confusion Matrix**: Displays the performance of each model in predicting "Safe" vs. "Not Safe" emails.
+- **Learning Curve**: A plot showing model performance (accuracy/loss) as a function of training data size, helping to visualize overfitting, underfitting, and the effectiveness of adding more training data.
+
+
 
 
 ### Example Output
 ```
 Training Accuracy: XX.XX%
-Test Accuracy: XX.XX%
+Test Accuracy:    XX.XX%
+
 Confusion Matrix:
-[[TN FP]
- [FN TP]]
+[[ TN  FP ]
+ [ FN  TP ]]
+Classification Report for Training Data:
+              precision    recall  f1-score   support
+       Safe       0.XX      0.XX      0.XX      XX
+   Not Safe       0.XX      0.XX      0.XX      XX
+
+   accuracy                           0.XX      XX
+  macro avg       0.XX      0.XX      0.XX      XX
+weighted avg      0.XX      0.XX      0.XX      XX
+
+
 Classification Report for Test Data:
               precision    recall  f1-score   support
        Safe       0.XX      0.XX      0.XX      XX
    Not Safe       0.XX      0.XX      0.XX      XX
+
+   accuracy                           0.XX      XX
+  macro avg       0.XX      0.XX      0.XX      XX
+weighted avg      0.XX      0.XX      0.XX      XX
 ```
 
 ## Flask Application
 The accompanying Flask application provides a user-friendly interface where users can input email content for real-time phishing detection. The system returns an analysis of whether an email is "Safe" or "Not Safe."
 
 ### Key Features:
-- User Interface: A simple email input form that allows users to upload or paste email content.
-- Instant Feedback: Provides immediate results, flagging malicious content.
-- Integration: The app communicates with the machine learning model backend for classification.
+- **User Interface**: A simple email input form that allows users to upload or paste email content.
+- **Instant Feedback**: Provides immediate results, flagging malicious content.
+- **Integration**: The app communicates with the machine learning model backend for classification.
+
 ### Future Enhancements
-The project will continue evolving with the goal of improving model scalability and enhancing integration with platforms like Microsoft Outlook for automatic phishing email detection and flagging.
+The project will continue evolving with the goal of improving model scalability and enhancing integration with platforms like Microsoft Outlook for automatic phishing email detection and flagging.
+
+## Additional Files
+- **Dataset Evaluation**: Utilized for assessing the model on an additional dataset.
+- **Multi-Model Evaluation**: Employed for batch testing models on sample emails.