youtube-views-prediction

Developing a regression model to predict the number of views on a YouTube video based on the selected parameters.

Team members:

• Artem Mozol (411021320)

Project overview

Developing a regression model to predict the number of views on the next video of a YouTube channel based on the selected parameters.

Dataset

Source: YouTube API-provided channel and video statistics of a given channel, able to be obtained via the application from an online source (aforementioned API) and able to be refreshed for added new information daily.

Analysis method categories:

Regression Analysis;

Tools/programming languages:

Programming language: Python;

Python libraries: pandas, scikit-learn, matplotlib/seaborn, Google API Python Client, google-auth (possible to be expanded);

Tools: Jupyter Notebook (documentation/visualisation), PyCharm (editing/debugging);

Goals/objectives:

Goal: Develop a regression model using YouTube API data to predict the number of views on the next video on a selected channel.

Intermediary objectives:

• Data collection: Get video data, channel statistics, and viewer engagement using the YouTube API.
• Data analysis and parameter selection: conduct exploratory data analysis (EDA) and select parameters that influence the number of views.
• Model creation and training:
o Generate a dataset with the selected parameters and number of views.
o Divide the data into training and test sets.
o Develop and train a regression model.
• Model performance evaluation: Use metrics such as mean absolute error (MAE) or root mean square error (RMSE) to evaluate model quality.
• Interpretation of results and preparation of report:
o Analyze the model coefficients and their impact on the number of views.
o Prepare a report containing key findings and recommendations for the content creator.