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Email-spam-detection

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# Email Spam Detection

A **scalable and efficient machine learning–based email spam detection system** built to demonstrate text preprocessing, feature engineering, and supervised learning techniques.  
This project focuses on **Natural Language Processing (NLP), classification accuracy, and clean modular code structure**.

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## Overview
This application automatically classifies email messages as **Spam** or **Ham (Not Spam)** by analyzing textual content.  
It uses NLP techniques and machine learning algorithms to identify spam patterns and reduce unwanted or malicious emails.

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## Features
This application allows users to:

- Detect spam and non-spam emails automatically
- Preprocess raw email text using NLP techniques
- Extract features using TF-IDF and Bag of Words
- Train and evaluate multiple machine learning models
- Measure performance using standard evaluation metrics
- Test new email samples for spam prediction

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## Tech Stack

### Programming Language
- Python

### Machine Learning & NLP
- Scikit-learn
- NLTK
- TF-IDF Vectorization
- Bag of Words

### Data Handling & Visualization
- NumPy
- Pandas
- Matplotlib / Seaborn

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## Dataset
- Publicly available **Email Spam Dataset**
- Labeled email messages:
  - Spam
  - Ham
- Used for training, validation, and testing

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## Methodology
- Data loading and exploratory analysis
- Text preprocessing:
  - Lowercasing
  - Removing punctuation and stopwords
  - Tokenization
  - Stemming / Lemmatization
- Feature extraction using TF-IDF
- Model training:
  - Multinomial Naive Bayes
  - Logistic Regression
- Model evaluation:
  - Accuracy
  - Precision
  - Recall
  - Confusion Matrix

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## Core Functionalities
- Automated email classification
- High-performance spam detection
- Reduced false positives
- Efficient handling of textual data
- Modular and reusable codebase

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Email-spam-detection

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