📧 Spam Mail Prediction using Machine Learning

A simple yet powerful machine learning project to classify emails as Spam or Not Spam based on their content.

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📌 Project Overview

This project demonstrates the use of Natural Language Processing (NLP) and Machine Learning to detect spam messages. It transforms text data into numeric vectors and applies ML models for classification.

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🗂️ Dataset

• Source: UCI SMS Spam Collection Dataset

• Size: ~5,500 SMS messages

• Labels:

  • spam: Unwanted messages
  • ham: Genuine/legit messages

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⚙️ Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• NLTK (Natural Language Toolkit)
• Jupyter Notebook / Google Colab

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🧠 ML Models Used

• Naive Bayes Classifier
• Support Vector Machine (SVM)
• Logistic Regression
• Decision Tree
• Random Forest

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🧪 NLP Techniques

• Tokenization
• Stopword Removal
• Lemmatization
• TF-IDF Vectorization

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📊 Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

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▶️ How to Run

git clone https://github.com/saheli56/Spam-Mail-Prediction-using-Machine-Learning.git
cd Spam-Mail-Prediction-using-Machine-Learning
Open the .ipynb file in Jupyter Notebook or Google Colab
Run all the cells sequentially

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✅ Key Features

• Cleaned and preprocessed text data
• Built and evaluated multiple ML models
• Visualized confusion matrix and performance metrics
• Identified most effective model for spam classification