vishalgt3078 / email_spam_detector

Email_spam_detector

Overview

This project aims to classify emails as spam or ham (non-spam) using the Naive Bayes classifier. It utilizes the sklearn library in Python for data preprocessing and modeling.

Dependencies

Make sure you have the following dependencies installed:

• pandas
• numpy
• scikit-learn (sklearn)

You can install them using pip:

pip install pandas numpy scikit-learn

Usage

1. Clone the Repository: Clone this repository to your local machine.
2. Install Dependencies: Ensure that all required dependencies are installed as mentioned above.
3. Data Preparation:
   • Prepare your dataset in CSV format. This code assumes you have a dataset named "spam.csv" with a 'Message' column containing email text and a 'Category' column indicating whether the email is spam or ham.
4. Model Training and Testing:
   • Run the provided Python script.
   • The script will read the dataset, preprocess the data, train the Naive Bayes classifier, and test the model's accuracy.
5. Real-time Prediction:
   • You can test the trained model with new email examples.
   • Modify the email_ham and email_spam lists with your own email examples and run the corresponding prediction code.
6. Experiment and Evaluation:
   • Experiment with different feature extraction techniques or classifiers to improve the model's performance.
   • Evaluate the model's accuracy on the test set to assess its effectiveness.

Example

Here's a simple example of how to use the provided code:

# Run the provided Python script spam_classifier.py
python spam_classifier.py