https://github.com/a-solo/sms_spam_detection_using_nlp

A supervised deep learning project using Natural Language Processing(NLP) that classifies a given SMS as a spam/ham message.
https://github.com/a-solo/sms_spam_detection_using_nlp

machine-learning nlp nltk-tokenizer pandas sklearn spam-detection

Last synced: about 15 hours ago
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A supervised deep learning project using Natural Language Processing(NLP) that classifies a given SMS as a spam/ham message.

• Host: GitHub
• URL: https://github.com/a-solo/sms_spam_detection_using_nlp
• Owner: A-SOLO
• License: apache-2.0
• Created: 2024-08-29T19:36:43.000Z (29 days ago)
• Default Branch: main
• Last Pushed: 2024-08-29T21:32:36.000Z (29 days ago)
• Last Synced: 2024-09-23T06:32:27.224Z (4 days ago)
• Topics: machine-learning, nlp, nltk-tokenizer, pandas, sklearn, spam-detection
• Language: Jupyter Notebook
• Homepage:
• Size: 608 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SMS_Spam_Detection_using_NLP

A supervised deep learning project using Natural Language Processing(NLP) that classifies a given SMS as a spam/ham message.

![spam_ham_5](https://github.com/user-attachments/assets/78297550-6ec2-4cd3-ad4c-2e50645195be)

### Dependencies/Libraries used:

* Pandas

* Numpy

* Seaborn

* Sklearn

* NLTK

* Pickle

* Streamlit

### Input data:

Dataset - [https://www.kaggle.com/datasets/bagavathypriya/spam-ham-dataset](https://www.kaggle.com/datasets/bagavathypriya/spam-ham-dataset)

### Building Model:

* ### Imbalanced Data

 ![spam_ham_M1](https://github.com/user-attachments/assets/26bbc461-fa27-46de-ade0-6500ae798e3e)

* ### Visualizing the trend of number of characters, number of words and number of sentences in a spam/ ham SMS

 ![spam_ham_M2](https://github.com/user-attachments/assets/0d707bc2-d0e7-4359-a5bf-38f6e6db78f2)

* ### Plotting the trend as a Heatmap

 ![spam_ham_M3](https://github.com/user-attachments/assets/6a45fc9f-898a-4912-af99-4cc3446a06e3)

* ### Most common words in a Spam Corpus

 ![spam_ham_M4](https://github.com/user-attachments/assets/34427830-51b1-4a10-ac4c-1d5c2f5c891a)

* ### Performance of Various Models

 ![spam_ham_M5](https://github.com/user-attachments/assets/6f942ba8-cddf-4e79-99b8-550acbbaa3fe)

# Results:

* Applied NLP techniques like tokenization, lemmatization, and stop words and punctuation removal using NLTK and regex.

* Performed feature engineering with handcrafted features such as digit count and email length.

* Implemented various classification models, Naive Bayes, SVC, ETC to find the best performer.

* Created an ensemble model, improving the accuracy from 97.87% to 98.25%.

* Designed & deployed a basic UI with Streamlit for classifying new inputs as spam or ham.

# Top Performing Model

Combined Support Vector Classifier, Multinomial Naive Bayes and Extra Trees Classifier to build an Stacking Classifier Model( Ensemble Model) whose:

* ### Accuracy: 0.9825918762088974

* ### Precision: 0.9736842105263158

# Testing Examples:

* Example 1:![spam_ham_2](https://github.com/user-attachments/assets/8dd33e66-a2c5-46fe-ab4f-61c8d02d9f3d)

* Example 2:![spam_ham_4](https://github.com/user-attachments/assets/1ef23986-224a-498d-9de6-248c8b4e59ad)

* Example 3:![spam_ham_3](https://github.com/user-attachments/assets/642a163a-3892-4e5c-a237-48d8581c5df4)