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https://github.com/rayyan9477/youtube-spam-detection-with-flask-and-machine-learning
This is a web application built using Flask that detects spam comments on YouTube using a Naive Bayes classifier. It leverages techniques such as CountVectorizer for feature extraction and scikit-learn for machine learning. The application reads data from a CSV file and predicts whether a comment is spam or not.
https://github.com/rayyan9477/youtube-spam-detection-with-flask-and-machine-learning
data-analysis data-science machine-learning nlp-machine-learning spam-detection
Last synced: 4 days ago
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This is a web application built using Flask that detects spam comments on YouTube using a Naive Bayes classifier. It leverages techniques such as CountVectorizer for feature extraction and scikit-learn for machine learning. The application reads data from a CSV file and predicts whether a comment is spam or not.
- Host: GitHub
- URL: https://github.com/rayyan9477/youtube-spam-detection-with-flask-and-machine-learning
- Owner: Rayyan9477
- License: mit
- Created: 2024-08-07T17:55:57.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-21T18:21:42.000Z (3 months ago)
- Last Synced: 2024-08-21T20:43:27.535Z (3 months ago)
- Topics: data-analysis, data-science, machine-learning, nlp-machine-learning, spam-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 337 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# YouTube Spam Detection with Flask and Machine Learning
This is a web application built using Flask that detects spam comments on YouTube using a Naive Bayes classifier. It leverages techniques such as CountVectorizer for feature extraction and scikit-learn for machine learning. The application reads data from a CSV file and predicts whether a comment is spam or not.
## Installation and Run
1. **Clone the repository:**
```sh
git clone
cd
```
2. **Create a virtual environment and activate it:**
```sh
python -m venv venv
venv\Scripts\activate
```
3. **Install the dependencies:**
```sh
pip install -r requirements.txt
```
4. **Run the application:**
```sh
python app.py
```
## Project Structure
```
.
├── templates
│ └── home.html
├── app.py
├── YoutubeSpamMergedData.csv
├── requirements.txt
└── README.md
```
## Dependencies and Techniques Used
- **Flask:** Web framework for Python.
- **Pandas:** Data manipulation and analysis.
- **NumPy:** Numerical Computing
- **scikit-learn:** Machine learning library.
- **CountVectorizer:** Feature extraction technique.
- **Naive Bayes Classifier:** Machine learning algorithm.
- **joblib:** Serialization of Python objects.
For any queries, contact me at:
- **Email:** [email protected]
- **LinkedIn:** [Rayyan Ahmed](https://www.linkedin.com/in/rayyan-ahmed9477/)