Spam Message Detection Using Machine Learning

Project Overview

This project implements a Spam Message Detection System using Machine Learning techniques in Python.
The system classifies SMS messages as Spam or Ham (Not Spam) using text classification methods.

This project is developed purely for learning and understanding Machine Learning fundamentals and is intended for academic use.

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Academic Context

• Degree: MSc (Computer Science / Data Science)
• Project Type: Academic / Learning Project
• Focus Area: Machine Learning, Text Classification

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Machine Learning Approach

The following concepts are used in this project:

• Text preprocessing
• TF-IDF (Term Frequency–Inverse Document Frequency)
• Logistic Regression classifier
• Train–Test split
• Model evaluation metrics

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Dataset Information

• File Name: spam.csv
• Type: SMS message dataset
• Labels:
  • ham → Not Spam
  • spam → Spam

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Technologies & Libraries Used

• Python
• Pandas
• Scikit-learn

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Project Workflow

1. Load and explore the dataset
2. Convert text labels (ham/spam) into numerical values
3. Transform text messages into numerical vectors using TF-IDF
4. Split dataset into training and testing sets
5. Train a Logistic Regression model
6. Evaluate the model using accuracy and classification report

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How to Run the Project

1. Clone the Repository

git clone https://github.com/Dikshakamalaskar003/Spam-Message-Detection-Using-ML.git
cd Spam-Message-Detection-Using-ML