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Project Overview
The Spam Classifier is a machine learning model designed to classify incoming messages as spam or ham (non-spam). This project leverages natural language processing (NLP) techniques, including NLTK for text preprocessing and TfIdf Vectorizer to transform textual data into numerical features that are suitable for modeling. The classification model is built using the Naive Bayes algorithm, which is well-suited for text classification tasks.
Key Features:
- Message Classification: Accurately classifies messages into spam or ham using machine learning.
- Data Preprocessing: Utilizes NLTK for tokenization, stopword removal, and stemming, ensuring high-quality input data.
- Text Representation: Transforms text data into numerical form using TfIdf Vectorizer, improving the accuracy of the classification model.
- Modeling with Naive Bayes: Implements the Naive Bayes algorithm, known for its simplicity and effectiveness in spam detection.
- Data Visualization: Integrates Seaborn for visualizing dataset distributions and model performance.
- Wordcloud Insights: Generates wordclouds to visually highlight common terms in spam vs. ham messages, helping to understand data patterns.
Technologies Used:
- Natural Language Processing (NLP): For text analysis and preprocessing.
- NLTK: To tokenize, stem, and clean text data.
- TfIdf Vectorizer: For transforming text into numerical features for machine learning.
- Naive Bayes Classifier: For classifying messages as spam or ham.
- Seaborn & Matplotlib: For data visualization and model performance evaluation.
Benefits:
- Accurate Spam Detection: Efficiently classifies spam messages, helping users avoid unwanted content.
- Easy Integration: The model can be easily integrated into messaging platforms for real-time spam filtering.
- Insights into Data: The wordclouds and visualizations provide deeper insights into the content of spam and ham messages.