ZeusSama0001 / youtube_classification

YouTube Text Classification Project

YouTube Text Classification Project

This project consists of two Jupyter Notebook files for scraping YouTube data (youtube_scraper.ipynb) and performing text classification (text_classification.ipynb). The scraped data is stored in youtube_data.csv.

Dependencies

Ensure you have the following dependencies installed to run the Jupyter Notebooks:

• google-api-python-client: Used for interacting with the YouTube Data API.
• pandas: Required for handling and manipulating data.
• scikit-learn: Used for machine learning and text classification tasks.
• matplotlib and seaborn: Used for visualizing evaluation metrics.
• python-dotenv: Used for loading environment variables from a .env file.

Install the dependencies using the following command:

pip install google-api-python-client pandas scikit-learn matplotlib seaborn python-dotenv

Setting Up API Key and Virtual Environment

1. Create a .env file in the root directory of the project.

2. Add your YouTube Data API key to the .env file:

   API_KEY=your_api_key_here

• Ensure there are no spaces around the equal sign.

3. Create and activate the virtual environment. If your virtual environment is named yt_scrape, use the following commands:

   python -m venv yt_scrape
   source yt_scrape/bin/activate

• On Windows, use yt_scrape\Scripts\activate

Running the Jupyter Notebooks

1. Open and run youtube_scraper.ipynb to scrape YouTube data and save it to youtube_data.csv.

2. Open and run text_classification.ipynb to perform text classification on the scraped data.

Model Evaluation Metrics

Display precision, recall, and F1 scores for each model:

| Model           | Precision | Recall | F1 Score |
| --------------- | --------- | ------ | -------- |
| SVM             |   0.90    |  0.89  |   0.89   |
| Random Forest   |   0.89    |  0.89  |   0.89   |
| Naive Bayes     |   0.85    |  0.81  |   0.80   |
| Neural Network  |   0.88    |  0.86  |   0.86   |

Confusion Matrices

Display confusion matrices for each model:

• SVM Confusion Matrix:

• Random Forest Confusion Matrix:

• Naive Bayes Confusion Matrix:

• Neural Network Confusion Matrix:

Author

Vaibhav Srivastava

GitHub: ZeusSama0001

License

This project is licensed under the MIT License. See the LICENSE.txt file for more details.