https://github.com/antim21/spamsense-ai
Classifying emails into Spam or Not Spam categories using Machine Learning techniques
https://github.com/antim21/spamsense-ai
machine-learning nlp python scikit-learn
Last synced: about 1 month ago
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Classifying emails into Spam or Not Spam categories using Machine Learning techniques
- Host: GitHub
- URL: https://github.com/antim21/spamsense-ai
- Owner: Antim21
- License: mit
- Created: 2025-01-24T14:12:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-15T11:13:56.000Z (over 1 year ago)
- Last Synced: 2025-03-15T12:22:19.911Z (over 1 year ago)
- Topics: machine-learning, nlp, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 308 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SpamSense-AI
This project focuses on classifying emails into Spam or Not Spam categories using Machine Learning techniques. It is implemented in a Jupyter Notebook and provides a step-by-step approach to building and evaluating the classification model.
**Key Features**
Preprocessing of raw email data (e.g., cleaning, tokenization, and vectorization).
Implementation of multiple classification algorithms like Naive Bayes, Logistic Regression, or SVM.
Performance evaluation using metrics like accuracy, precision, recall, and F1-score.
Visualization of results through plots and charts for better understanding.
**Tech Stack**
Python
Jupyter Notebook
Libraries Used:
scikit-learn
pandas
numpy
matplotlib
seaborn
# How It Works
**Data Preprocessing:**
Removal of special characters, stopwords, and unwanted symbols.
Conversion of text into numerical features using techniques like TF-IDF or Bag of Words.
**Model Training:**
Multiple models are trained on the processed data to classify emails into Spam or Not Spam categories.
**Evaluation:**
Models are evaluated using metrics and confusion matrix for performance analysis.
**Visualization:**
Insights are visualized with charts to show data distribution, feature importance, and model accuracy.