YouTube Views Prediction is an innovative tool designed for content creators to estimate the view count of their YouTube videos which will be uploaded in the future. The application utilizes a detailed form where users input various parameters such as channel name, video title, category, and more. This data is then used to forecast the potential performance of the video on YouTube, aiding creators in strategizing their content.
- User Input Form: Detailed form capturing essential information about the video.
- Predictive Analysis: Uses input data to predict the future view count of YouTube videos.
- Interactive Interface: Easy-to-use web interface for inputting video details.
- Dynamic Data Processing: Analyzes various factors like video category, tags, and engagement options.
- Data-Driven Insights: Offers creators insights into potential video performance.
In here datasets are used from: Kaggle
- Frontend: HTML, CSS, JavaScript
- Framework: TensorFlow, Keras
- Preprocessing and Model Training: Python, Pandas, NumPy, Matplotlib, using IBM Watson for advanced machine learning capabilities
- Prediction Engine: IBM Watson for generating accurate and reliable video view predictions
To getting up and running the Dataset_Creation.ipynb file you have to download USVideos.csv file from USVideos.csv and add it to the datasets folder.
The research paper for this project is available at: Machine Learning enabled models for YouTube Ranking Mechanism and Views Prediction
Contributions are welcome. We welcome contributions to the Youtube Views Predictor project. Please read our CONTRIBUTING.md for guidelines on how to contribute.
This project is licensed under the MIT LICENSE.
- Our team: Vandit Gupta, Akshit Diwan, and Arpit Jain
- All contributors and supporters of the Yotube Views Predictor project
For any inquiries or contributions, please contact us at gupta.vandi@northeastern.edu or akshitdiwan05@gmail.com or arpit.arpit.jain5@gmail.com