Spam-Detection-Text-Processing

This repository contains a Jupyter notebook pipeline for Mail spam detection using classical text preprocessing + feature extraction + classification.

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Contents

• spam_NLP.csv — Original Mail messages labelled “spam” or “ham”.
• spam_NLP_cleaned.csv — Cleaned version of the text (lowercasing, punctuation removed, etc.).
• TEST_DATA_spam.csv — Smaller dataset used for quick testing/validation.
• main.ipynb — Main notebook: loads data, cleans, extracts features, trains classifier, evaluates.
• logic.ipynb — Supporting notebook for experiments / exploring alternative preprocessing and feature setups.

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Features & What It Does

• Text preprocessing: tokenization, lower-case conversion, stopword removal, punctuation removal, possibly more cleaning steps (stemming / lemmatization if included).
• Feature extraction:
  • Bag of Words (count vectorization)
  • TF-IDF vectorization
• Classification:
  • Multinomial Naive Bayes classifier
• Evaluation:
  • Accuracy
  • Precision, Recall, F1-score
  • Confusion matrix

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Requirements

• Python 3.8+ (ideally)
• Required packages:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib

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Dataset

The dataset (spam_NLP.csv, spam_NLP_cleaned.csv, TEST_DATA_spam.csv) contains email messages labeled as spam or ham.
It is a processed version of a mail spam dataset (5,796 rows).

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Run

1. Clone the repository

   git clone https://github.com/Splendorius/Spam-Detection-Text-Processing.git
   cd Spam-Detection-Text-Processing

2. Install dependencies

pip install pandas numpy scikit-learn matplotlib

3. Launch Jupyter Notebook

jupyter notebook

4. Open the notebook main.ipynb and run the cells top-to-bottom.