Spam_Classification

classification_model

📧 Spam Classification using XGBoost and Decision Tree

This project applies machine learning to classify SMS or email messages as spam or ham (not spam) using two algorithms:

• XGBoost Classifier
• Decision Tree Classifier

We compare their performance on the same dataset to evaluate which performs better for spam detection.

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🚀 Project Overview

The dataset is preprocessed and transformed into numerical features using TF-IDF vectorization.
Both models are trained and evaluated using accuracy, precision, recall, and F1-score.

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🧩 Steps

1. Data Loading & Cleaning

   • Lowercased text
   • Encoded target labels (ham = 0, spam = 1)

2. Feature Extraction

   • Used TfidfVectorizer (max 3000 features, English stopwords)

3. Model Training

   • Trained both XGBoostClassifier and DecisionTreeClassifier using an 80/20 train-test split

4. Evaluation

   • Compared performance with classification_report, confusion_matrix, and accuracy

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📊 Results Summary

Model	Accuracy
XGBoost	0.97
Decision Tree	0.96

XGBoost slightly outperformed Decision Tree.

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Visualizations

The notebook includes:

• Confusion matrices for both models
• Accuracy comparison bar chart

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Dataset

Dataset: SMS Spam Collection (Kaggle)

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