Email Spam Classification

Overview

This repository contains code for a machine learning model that classifies emails as spam or non-spam (ham). The model uses a Support Vector Machine (SVM) algorithm with a text classification approach.

Table of Contents

• Background
• Features
• Requirements
• Usage
• Data
• Model Training
• Evaluation
• Tuning
• Results

Background

This project aims to build a machine learning model for classifying emails as spam or non-spam. The SVM algorithm is chosen for its effectiveness in text classification tasks.

Features

• Text classification using Support Vector Machine (SVM)
• Model training, evaluation, and tuning scripts

Requirements

• Python 3.6+

Usage

• To use the Email Spam Classification code, follow these steps:

1. Prepare Your Data:

   Ensure your email data is in a suitable format for the model. For example, a CSV file with columns for email text and labels.

2. Train the Model:

   Use the train_model.py script to train the SVM model on your training data.

3. Make Predictions:

   After training, you can use the model to make predictions on new email data.

Data

• The dataset used for this project can be found at Dataset Source.

Model Training

• To train the SVM model on your data, use the train data. Ensure your data is properly formatted and split into training and testing sets.

Evaluation

• Evaluate the model's performance using the evaluation metric. This will provide insights into accuracy and other relevant metrics.

Tuning

• Experiment with hyperparameter tuning to improve model performance. Adjust parameters in the tuned_param dictionary within the train_model.py script.

  tuned_param = {'kernel': ['linear', 'rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000, 10000]}

Results

• The accuracy is 86%. We couldn't achieve higher accuracy because the dataset is little