📩 SMS Spam Detection with Machine Learning

A Python-based SMS Spam Detection system using machine learning (SVM, Naive Bayes, or Logistic Regression). It uses TF-IDF vectorization to process messages and evaluates performance using accuracy, confusion matrix, classification report, and ROC-AUC curve.

---

📂 Dataset

• Dataset used: SMS Spam Collection
• File format: CSV (spam.csv)

---

⚙️ Features

• Text preprocessing & cleaning
• TF-IDF vectorization with bi-grams
• Model options:
  • svm (default & recommended)
  • nb (Naive Bayes baseline)
  • logreg (Logistic Regression)
• Visual evaluation: Confusion Matrix & ROC Curve
• Predict function for custom SMS input

---

🚀 How to Run

1. Set your CSV file path in the script:

file_path = r"E:\SMS_Spam_Detection\spam.csv"

MODEL_CHOICE = 'svm'  # Options: 'svm', 'nb', 'logreg'

## Run the script to:

Load and preprocess data

Train model

Evaluate results with confusion matrix and ROC curve

Predict on test data

## Sample Evaluation Output

Accuracy

Confusion Matrix (Visualized)

Classification Report

ROC Curve & AUC