Prediction of YouTube View Count using Supervised and Ensemble Machine Learning Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The social media platform named YouTube is an American based online video sharing and it is headquartered in California. It also provides various services to users such as watching and uploading their own videos through their laptop, mobile and PC’s. The goal of this research work is to analyze the YouTube view count for five different countries namely India, Britain, Russia, Canada and United states. The key goal is to investigate the view count of the video with influencing factors up on YouTube such as likes, dislikes, published date, trending date, Country, Category of the video and other ten variables. This has also indicated the relationship between dependent variable "view count "with all other independent variable by the regression analysis. This data analysis helps the users for better understand of their video, channel performance and reports in YouTube. Through the results of YouTube analysis, it is helpful for the users to identify the key metrics such as video content, duration of the video and liked or disliked. These metrics helps the users to make their video trending. The data is collected from the Kaggle repository, where the data will be updated on the daily basis. Various machine learning regression models such as Multiple Linear Regression (MLR), Random Forest Regressor (RFR), Decision Tree Regressor (DTR), XGBoost Regressor (XGB), Gradient Boost Regressor (GBR) has been used to predict the view count of the video. The results of each of these algorithms are noted and compared in order to determine which method is best suited for the view count prediction. The experimental results inferred that the Random Forest technique performs better than the other machine learning models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it