https://github.com/toscdom/spam_detection

This repository contains a project focused on analyzing and classifying emails to detect SPAM. It includes: Training a machine learning classifier for SPAM detection. Identifying key topics in SPAM emails using NLP techniques. Calculating semantic distances to evaluate topic similarity. Tools used include Python libraries like nlp frameworks
https://github.com/toscdom/spam_detection

classifier nlp nltk scikit-learn semantic-analysis spam-detection

Last synced: about 1 month ago
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This repository contains a project focused on analyzing and classifying emails to detect SPAM. It includes: Training a machine learning classifier for SPAM detection. Identifying key topics in SPAM emails using NLP techniques. Calculating semantic distances to evaluate topic similarity. Tools used include Python libraries like nlp frameworks

• Host: GitHub
• URL: https://github.com/toscdom/spam_detection
• Owner: ToscDom
• Created: 2024-12-12T07:37:23.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-12T07:43:37.000Z (about 1 month ago)
• Last Synced: 2024-12-12T08:29:44.682Z (about 1 month ago)
• Topics: classifier, nlp, nltk, scikit-learn, semantic-analysis, spam-detection
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Email Analysis and Classification for SPAM Detection ##

This project focuses on analyzing and classifying emails to identify SPAM and extract key insights from the data. 

The main objectives of the project are:

1. __Train a Classifier__ : Develop a machine learning model to accurately classify emails as SPAM or NOT SPAM.

2. __Topic Identification__: Analyze the content of SPAM emails to identify the main topics or recurring themes

3. __Semantic Analysis__: Calculate the semantic distance between identified topics to evaluate their similarity.

__Features__

- *Data Preparation*: Load, clean, and preprocess email datasets to prepare them for analysis.

- *Model Training*: Utilize supervised learning techniques to build a robust SPAM detection classifier.

- *Topic Modeling*: Use natural language processing (NLP) methods to identify and group similar topics within SPAM emails.

- *Semantic Distance Calculation*: Employ techniques to measure the similarity between topics, aiding in deeper understanding of email patterns.

__Tools and Libraries__

- *Core Libraries*: pandas, numpy, and collections for data manipulation and analysis.

- *Visualization*: Libraries like matplotlib and seaborn for exploratory data analysis and visual representation of findings.

- *Machine Learning and NLP*: Frameworks for building and evaluating the SPAM classifier and topic models.