Merge pull request #231 from SayantikaLaskar/spammail

Add spam mail detection

Spam Mail Classifier/README.md

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+# Spam Mail Detection Project
+
+## Overview
+
+Welcome to the Spam Mail Detection Project! This project aims to develop a machine learning model to classify emails as spam or not spam. The primary objective is to provide an efficient and accurate tool to filter out unwanted spam emails from the user's inbox. This project is a part of the Girl Script Summer of Code (GSSOC) 2024 initiative.
+
+## Table of Contents
+
+1. [Introduction](#introduction)
+2. [Features](#features)
+3. [Installation](#installation)
+4. [Usage](#usage)
+5. [Model Training](#model-training)
+6. [License](#license)
+
+## Introduction
+
+Spam emails are a significant issue for many users, leading to wasted time and potential security threats. Our project leverages machine learning techniques to identify and filter out spam emails. The model will be trained on a labeled dataset of emails and will use various features extracted from the email content and metadata.
+
+## Features
+
+- **Email Preprocessing**: Clean and preprocess email data for model training.
+- **Feature Extraction**: Extract relevant features from emails, such as word frequency, email headers, and metadata.
+- **Model Training**: Train a machine learning model using the preprocessed and feature-extracted data.
+- **Spam Detection**: Classify new emails as spam or not spam using the trained model.
+- **Evaluation Metrics**: Evaluate the performance of the model using accuracy, precision, recall, and F1-score.
+
+## Installation
+
+To set up the project locally, follow these steps:
+
+1. Clone the repository:
+    ```sh
+    git clone https://github.com/yourusername/spam-mail-detection.git
+    cd spam-mail-detection
+    ```
+
+2. Create a virtual environment and activate it:
+    ```sh
+    python -m venv venv
+    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+    ```
+
+3. Install the required dependencies:
+    ```sh
+    pip install -r requirements.txt
+    ```
+
+## Usage
+
+1. **Data Preprocessing**:
+    - Run the preprocessing script to clean and prepare the email data:
+      ```sh
+      python preprocess.py
+      ```
+
+2. **Feature Extraction**:
+    - Extract features from the preprocessed data:
+      ```sh
+      python feature_extraction.py
+      ```
+
+3. **Model Training**:
+    - Train the machine learning model:
+      ```sh
+      python train_model.py
+      ```
+
+4. **Spam Detection**:
+    - Use the trained model to classify new emails:
+      ```sh
+      python predict.py --email "path_to_email_file"
+      ```
+
+
+## Model Training
+
+The model training process involves the following steps:
+
+1. Load and preprocess the dataset.
+2. Extract features from the emails.
+3. Split the data into training and testing sets.
+4. Train a machine learning model (e.g., Naive Bayes, SVM, Random Forest).
+5. Evaluate the model using appropriate metrics.
+
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
+
+
+We hope you find this project useful and look forward to your contributions!
+
+---
+
+*This project is developed as part of the Girl Script Summer of Code (GSSOC) 2024 initiative.*