title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ ÑÐ°Ð·Ð²Ð»ÐµÐºÐ°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ YouTube-ÐºÐ°Ð½Ð°Ð»Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of methods for predicting the popularity of entertainment video content based on YouTube channel data
Creators: name: Ð¡Ð¾Ð»Ð¾Ð²ÑÐµÐ², ÐÐ»ÐµÐºÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÑ
Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½Ñ
youtube
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
random forest
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
python
forecasting
popularity
video content
machine learning
neural network
data analysis
python.

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ ÑÐ°Ð·Ð²Ð»ÐµÐºÐ°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ YouTube-ÐºÐ°Ð½Ð°Ð»Ð°Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½ÑÐ° Ð½Ð° Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼Ðµ YouTube. ÐÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ ÑÐµÐ»ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐ°Ð»Ð¾ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÑÐ¾Ð»Ð¸ÐºÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð´Ð°Ð½Ð½ÑÑ YouTube-ÐºÐ°Ð½Ð°Ð»Ð° Â«Stand-Up Club #1Â», ÑÑÐ¾ Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÑ Ð¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐµÑÐµÐ½Ñ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ðµ Ð·Ð°Ð´Ð°ÑÐ¸: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð°ÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð½Ð° Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐµ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÐ¸ÑÑÐ¾Ð²ÑÑ Ð¿Ð»Ð°ÑÑÐ¾ÑÐ¼. 2. ÐÐ±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÑÐ¾Ð»Ð¸ÐºÐ¾Ð². 3. Ð¤Ð¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð½Ð°Ð±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ, Ð²ÐºÐ»ÑÑÐ°ÑÑÐµÐ³Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð¿ÑÐ¾ÑÐ¼Ð¾ÑÑÐ¾Ð², Ð»Ð°Ð¹ÐºÐ¾Ð², ÐºÐ¾Ð¼Ð¼ÐµÐ½ÑÐ°ÑÐ¸ÐµÐ² Ð¸ ÑÐµÐºÑÑÐ¾Ð²ÑÐµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð·Ð°Ð³Ð¾Ð»Ð¾Ð²ÐºÐ¾Ð². 4. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð²ÐºÐ»ÑÑÐ°Ñ Ð°Ð½ÑÐ°Ð¼Ð±Ð»ÐµÐ²ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Random Forest Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸, Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸. 5. ÐÐ¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÐ°Ð±Ð¾ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð°Ð½Ð°Ð»Ð¸Ð· Ð¾ÑÐ¸Ð±Ð¾Ðº Ð¸ Ð¾ÑÐµÐ½ÐºÐ° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ. Ð Ð°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð½Ð° Ð±Ð°Ð·Ðµ Ð´Ð°Ð½Ð½ÑÑ YouTube-ÐºÐ°Ð½Ð°Ð»Ð° Â«Stand-Up Club #1Â», Ð² Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¸ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÑ Python, Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ (Pandas, NumPy), Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ (Matplotlib, Seaborn), Ð° ÑÐ°ÐºÐ¶Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (Scikit-learn, PyTorch). ÐÐ½Ð°Ð»Ð¸Ð· Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð², Ð²ÐºÐ»ÑÑÐ°Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÑ ÑÐµÐºÑÑÐ¾Ð²ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Bag of Words, ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ðµ ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼Ð°ÑÑÐ¸Ñ Ð¸ Ð¼Ð°ÑÑÑÐ°Ð±Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¸ÑÐ»Ð¾Ð²ÑÑ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð². Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÐ¸ Ð²Ð¸Ð´ÐµÐ¾ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð» Ð²ÑÑÐ¾ÐºÐ¸Ðµ Ð¼ÐµÑÑÐ¸ÐºÐ¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸, Ð¿Ð¾Ð»Ð½Ð¾ÑÑ Ð¸ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ ROC-AUC. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½ÑÐµ ÑÑÐ¿ÐµÑÐ¸ Ð² ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½ÑÑ Ð²Ð¸Ð´ÐµÐ¾ÑÐ¾Ð»Ð¸ÐºÐ¾Ð², Ð² ÑÐ¾ Ð²ÑÐµÐ¼Ñ ÐºÐ°Ðº Random Forest Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð» Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð¾ Ð² ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ Ðº Ð¸Ð·Ð¼ÐµÐ½ÐµÐ½Ð¸ÑÐ¼ Ð² Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ»Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ñ ÐºÐ»ÑÑÐµÐ²ÑÐµ ÑÐ°ÐºÑÐ¾ÑÑ, Ð²Ð»Ð¸ÑÑÑÐ¸Ðµ Ð½Ð° Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÑ ÐºÐ¾Ð½ÑÐµÐ½ÑÐ°, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº ÑÐµÐºÑÑÐ¾Ð²ÑÐµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð·Ð°Ð³Ð¾Ð»Ð¾Ð²ÐºÐ¾Ð² Ð¸ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð·ÑÐ¸ÑÐµÐ»ÐµÐ¹.
description: The topic of the final qualification work: Â«Development of methods for predicting the popularity of entertainment video content based on YouTube channel dataÂ». This work is devoted to the study of approaches to predicting the popularity of video content on the YouTube platform. The main goal of the study was to create effective methods for analyzing and predicting the popularity of video clips based on data from YouTube channel Â«Stand-Up Club #1Â», which includes the development and testing of machine learning algorithms. In the course of the research the following tasks were accomplished: 1. Studying the relevance of the problem of video content popularity prediction based on analytics of modern digital platforms. 2. Review of existing research and methods in the field of video popularity prediction. 3. Generating a dataset including metrics of views, likes, comments and textual features of titles. 4. Developing machine learning algorithms, including Random Forest ensemble method and neural networks, for popularity prediction. 5. Visualization of model results, error analysis and evaluation of prediction quality. The work was performed on the basis of data from the YouTube channel Â«Stand-Up Club #1Â», in the process Python tools, libraries for data analysis (Pandas, NumPy), visualization (Matplotlib, Seaborn), as well as machine learning methods (Scikit-learn, PyTorch) were used. The analysis was conducted using modern approaches including Bag of Words text data processing, creation of correlation matrices and scaling of numerical features. The study resulted in the development of an effective approach for predicting video content popularity, which demonstrated high metrics of accuracy, completeness and ROC-AUC values. The application of neural network showed significant success in categorizing popular videos, while Random Forest showed an advantage in resilience to changes in the data. Key factors affecting content popularity such as textual features of titles and viewer activity metrics were identified.