Email-based-Spam-Detection-Model-using-Machine-Learning

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Email-based-Spam-Detection-Model-using-Machine-Learning

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A machine learning project to detect spam emails using TF-IDF and logistic regression.

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# Email-based-Spam-Detection-Model-using-Machine-Learning
This project is focused on building a machine learning model that classifies email messages as spam or ham (not spam) using natural language processing and logistic regression.

🔍 Project Highlights

✅ Built using Python, scikit-learn, NLTK, Matplotlib, Seaborn

📁 Trained on a labeled dataset from Kaggle

📊 Performed EDA, text preprocessing, and TF-IDF vectorization

🤖 Developed a Logistic Regression model for binary classification

🧠 Evaluated model using accuracy score and confusion matrix

📈 Visualized top keywords in spam/ham messages using bar charts

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🧪 Tech Stack

Python 3.x

scikit-learn

NLTK

Pandas

NumPy

Matplotlib & Seaborn

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📌 How it Works

Load and clean the dataset

Preprocess text using NLTK

Transform text into TF-IDF features

Train a logistic regression model

Evaluate performance

Visualize results

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🚀 Future Improvements

Deep learning (LSTM, BERT)

Multilingual detection

Real-time filtering system

Feedback-based learning

IP-based spam flagging

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![Screenshot 2025-04-12 115125](https://github.com/user-attachments/assets/9358cdd1-1369-4715-8edb-99014808dc5c)
![Screenshot 2025-04-12 115137](https://github.com/user-attachments/assets/a6982b22-082d-4557-ac65-e01349d91950)
![Screenshot 2025-04-12 115153](https://github.com/user-attachments/assets/3b9771bd-63fc-44b2-a969-4ff415d875d0)

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