saro0307 / Email-Spam-Detection

Phase 2 of Data Science project (program) to Analysis Email spam Detection using Python On Google Colab for Coderscave Internship sept 2023

Email Spam Prediction Program

This repository contains a Python program for predicting email spam using a Kaggle dataset. The program is designed to run in Google Colab and serves as a starting point for building email spam prediction models. In this README, you will find instructions on how to set up and run the program, as well as an overview of its functionality.

Table of Contents

• Introduction
• Prerequisites
• Getting Started
• Usage
• Contributing
• License

Introduction

Email spam prediction is a common problem in the field of machine learning and natural language processing (NLP). This program provides a framework for developing and evaluating email spam prediction models using Python, a Kaggle dataset, and Google Colab.

Prerequisites

Before you begin, ensure you have the following prerequisites installed:

• Python (Python 3.6 or higher)
• Google Colab
• Jupyter Notebook (optional, if you prefer using Jupyter Notebooks)
• Kaggle Account

Getting Started

1. Clone or fork this repository to your local machine.

   git clone https://github.com/yourusername/email-spam-prediction.git

2. Navigate to the project directory.

   cd email-spam-prediction

3. Set up your Kaggle API credentials.

   To download datasets from Kaggle, you need to set up your Kaggle API credentials. You can do this by following the official Kaggle API documentation.

4. Install the required Python packages.

   pip install -r requirements.txt

5. Open the Jupyter Notebook or Python script in Google Colab.

   • If using Jupyter Notebook, open the notebook by running:

     jupyter notebook

     Then, navigate to the notebook file and open it.

   • If using a Python script, simply open it in Google Colab.

Usage

This program provides a basic structure for building and evaluating email spam prediction models. Here's how to use it:

1. Load your email spam dataset into the Google Colab environment. You can use the Kaggle API to download the dataset directly into Colab.

2. Modify the provided Jupyter Notebook or Python script to preprocess the data, build and train your email spam prediction model.

3. Evaluate the model's performance using appropriate metrics (e.g., accuracy, precision, recall, F1-score).

4. Fine-tune the model, experiment with different algorithms, or try various NLP techniques to improve prediction accuracy.

5. Document your findings, insights, and the performance of your model in the notebook or script.

6. Save and share your work with others on GitHub or other platforms.

Contributing

If you would like to contribute to this project or improve the email spam prediction model, please follow these guidelines:

1. Fork the repository.

2. Create a new branch for your feature or improvement.

3. Make your changes and thoroughly test them.

4. Create a pull request with a clear description of your changes and any relevant documentation updates.

5. Your contribution will be reviewed, and if it meets the project's standards, it will be merged.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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