Add the email_spam_detection directory in NLP/projects

docs/NLP/projects/Email_Spam_Detection/README.md

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+
+# Email Spam Detection
+
+### AIM 
+To develop a machine learning-based system that classifies email content as spam or ham (not spam).
+
+### DATASET LINK 
+[https://www.kaggle.com/datasets/ashfakyeafi/spam-email-classification](https://www.kaggle.com/datasets/ashfakyeafi/spam-email-classification)
+
+
+### NOTEBOOK LINK 
+[https://www.kaggle.com/code/inshak9/email-spam-detection](https://www.kaggle.com/code/inshak9/email-spam-detection)
+
+
+### LIBRARIES NEEDED
+
+??? quote "LIBRARIES USED"
+
+    - pandas
+    - numpy
+    - scikit-learn
+    - matplotlib
+    - seaborn
+
+
+--- 
+
+### DESCRIPTION 
+!!! info "What is the requirement of the project?"
+    - A robust system to detect spam emails is essential to combat increasing spam content.
+    - It improves user experience by automatically filtering unwanted messages.
+
+??? info "Why is it necessary?"
+    - Spam emails consume resources, time, and may pose security risks like phishing.
+    - Helps organizations and individuals streamline their email communication.
+
+??? info "How is it beneficial and used?"
+    - Provides a quick and automated solution for spam classification.
+    - Used in email services, IT systems, and anti-spam software to filter messages.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+    - Analyzed the dataset and prepared features.
+    - Implemented various machine learning models for comparison.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+    - Documentation from [scikit-learn](https://scikit-learn.org)
+    - Blog: Introduction to Spam Classification with ML
+
+---
+
+### EXPLANATION
+
+#### DETAILS OF THE DIFFERENT FEATURES
+The dataset contains features like word frequency, capital letter counts, and others that help in distinguishing spam emails from ham.
+
+| Feature              | Description                                     |
+|----------------------|-------------------------------------------------|
+| `word_freq_x`        | Frequency of specific words in the email body  |
+| `capital_run_length` | Length of consecutive capital letters          |
+| `char_freq`          | Frequency of special characters like `;` and `$` |
+| `is_spam`            | Target variable (1 = Spam, 0 = Ham)            |
+
+---
+
+#### WHAT I HAVE DONE
+
+=== "Step 1"
+
+    Initial data exploration and understanding:
+      - Loaded the dataset using pandas.
+      - Explored dataset features and target variable distribution.
+
+=== "Step 2"
+
+    Data cleaning and preprocessing:
+      - Checked for missing values.
+      - Standardized features using scaling techniques.
+
+=== "Step 3"
+
+    Feature engineering and selection:
+      - Extracted relevant features for spam classification.
+      - Used correlation matrix to select significant features.
+
+=== "Step 4"
+
+    Model training and evaluation:
+      - Trained models: KNN, Naive Bayes, SVM, and Random Forest.
+      - Evaluated models using accuracy, precision, and recall.
+
+=== "Step 5"
+
+    Model optimization and fine-tuning:
+      - Tuned hyperparameters using GridSearchCV.
+
+=== "Step 6"
+
+    Validation and testing:
+      - Tested models on unseen data to check performance.
+
+---
+
+#### PROJECT TRADE-OFFS AND SOLUTIONS
+
+=== "Trade Off 1"
+    - **Accuracy vs. Training Time**:
+      - Models like Random Forest took longer to train but achieved higher accuracy compared to Naive Bayes.
+
+=== "Trade Off 2"
+    - **Complexity vs. Interpretability**:
+      - Simpler models like Naive Bayes were more interpretable but slightly less accurate.
+
+---
+
+### SCREENSHOTS
+<!-- Attach the screenshots and images -->
+
+!!! success "Project structure or tree diagram"
+
+    ``` mermaid
+      graph LR
+        A[Start] --> B[Load Dataset];
+        B --> C[Preprocessing];
+        C --> D[Train Models];
+        D --> E{Compare Performance};
+        E -->|Best Model| F[Deploy];
+        E -->|Retry| C;
+    ```
+
+??? tip "Visualizations and EDA of different features"
+
+    === "Feature Correlation Heatmap"
+        ![Correlation](images/correlation_heatmap.png)
+
+??? example "Model performance graphs"
+
+    === "Model Comparison"
+        ![Comparison](images/model_comparison.png)
+
+---
+
+### MODELS USED AND THEIR EVALUATION METRICS
+
+|    Model             | Accuracy | Precision | Recall |
+|----------------------|----------|-----------|--------|
+| KNN                  | 90%      | 89%       | 88%    |
+| Naive Bayes          | 92%      | 91%       | 90%    |
+| SVM                  | 94%      | 93%       | 91%    |
+| Random Forest        | 95%      | 94%       | 93%    |
+| AdaBoost             | 97%      | 97%       | 100%   |
+
+---
+
+#### MODELS COMPARISON GRAPHS
+
+!!! tip "Models Comparison Graphs"
+
+    === "Accuracy Comparison"
+        ![Accuracy Graph](images/accuracy_graph.png)
+
+---
+
+### CONCLUSION
+
+#### WHAT YOU HAVE LEARNED
+
+!!! tip "Insights gained from the data"
+    - Feature importance significantly impacts spam detection.
+    - Simple models like Naive Bayes can achieve competitive performance.
+
+??? tip "Improvements in understanding machine learning concepts"
+    - Gained hands-on experience with classification models and model evaluation techniques.
+
+??? tip "Challenges faced and how they were overcome"
+    - Balancing between accuracy and training time was challenging, solved using model tuning.
+
+---
+
+#### USE CASES OF THIS MODEL
+
+=== "Application 1"
+
+    **Email Service Providers**
+    - Automated filtering of spam emails for improved user experience.
+
+=== "Application 2"
+
+    **Enterprise Email Security**
+    - Used in enterprise software to detect phishing and spam emails.
+
+---
+
+### FEATURES PLANNED BUT NOT IMPLEMENTED
+
+=== "Feature 1"
+
+    - Integration of deep learning models (LSTM) for improved accuracy.
+
+---
+
+### **DEVELOPER**
+***Insha Khan***
+
+[LinkedIn](https://www.linkedin.com/in/insha-khan-4087532a4/){ .md-button }
+[GitHub](https://www.github.com/ikcod){ .md-button }
+
+##### Happy Coding 🤓
+#### Show some &nbsp;❤️&nbsp; by &nbsp;🌟&nbsp; this repository!
+
+
+
+
+