📧 Email Spam Prediction

📄 Overview

This project aims to build a machine learning model to classify emails as either 'spam' or 'ham' (not spam) using Logistic Regression. The model uses textual features extracted from the emails to make predictions. We use the TF-IDF (Term Frequency-Inverse Document Frequency) method to transform the text into numerical features suitable for training a machine learning model.

📚 Table of Contents

• Overview
• [Screenshot]
• Installation
• Usage
• Dataset
• Feature Extraction
• Model Training
• Evaluation

Screenshot

Here's a glimpse of the project in action:

⚙️ Installation

Prerequisites

Ensure you have the following installed:

• 🐍 Python 3.12.2
• 📒 Jupyter Notebook or any other Python IDE

Data Preprocessing

Combining Columns: The 'Subject' and 'Body' are combined to form the full message content. Encoding Labels: 'spam' is encoded as 0 and 'ham' as 1.

🔍 Feature Extraction

We use TfidfVectorizer from scikit-learn to convert the email text into TF-IDF feature vectors. This process transforms the text data into a format suitable for machine learning models.

🧠 Model Training

Split the dataset into training and testing sets, then train a machine learning model using the training set and apply Logistic Regression Model.

📊 Evaluation

Evaluate the model's performance using the testing set. You can use metrics like accuracy score.