📧 Spam Email Detection using NLP & Machine Learning

This notebook-based project demonstrates how to detect spam emails using Natural Language Processing and classic Machine Learning algorithms.

💡 Built entirely in a Jupyter Notebook using email.csv as the dataset.

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🔍 What’s Inside

• Exploratory Data Analysis (EDA) of spam vs. ham messages
• Data cleaning and duplicate removal
• Feature engineering: character count, word count, sentence count
• Text preprocessing using NLTK (tokenization, stopword removal, stemming)
• Label encoding (Spam = 0, Ham = 1)
• Vectorization using CountVectorizer and TF-IDF
• Model training with:
  • Logistic Regression
  • Support Vector Machine (SVM)
  • Random Forest
  • Decision Trees
  • Naive Bayes
  • AdaBoost, Bagging, Gradient Boosting
• Model comparison using accuracy, classification report, and confusion matrix

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🛠 Libraries Used

• Python 3.x
• pandas, numpy
• nltk
• matplotlib, seaborn
• scikit-learn

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🧪 How to Run

1. Open the notebook in Jupyter or Google Colab
2. Make sure email.csv is present in the same directory
3. Run the notebook cells step-by-step
4. You'll see preprocessing, training, and evaluation all inside one file

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📁 Files

Spam_Email_Detection/

├── Spam_Email_Detection.ipynb

└── email.csv

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🔑 Keywords

NLP · Spam Classification · Email Filtering · TF-IDF · CountVectorizer · Scikit-learn · NLTK · Logistic Regression · Random Forest · Text Preprocessing · Model Evaluation · Python

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📌 Note

All processing and training steps are performed inside the Jupyter Notebook itself — no external scripts or setup required.