Implementasi Exploratory Data Analysis Pada Dataset Video Trending Harian YouTube
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
YouTube is a video sharing website that allows its users to interact through videos created by video creators (YouTubers).Videos on YouTube can go to the 'Trending' tab that shows videos that are considered trending by YouTube. The YouTube Helpwebsite says that they use many parameters to determine trends. However, YouTube does not specify exact parameters and numbers.Therefore, data analysis was performed on video datasets in three countries namely Canada, the United Kingdom and the UnitedStates using the Exploratory Data Analysis method. Data processing was carried out with Pandas and data was visualized with theMatplotlib, Seaborn, Bokeh, and WordCloud libraries. Work starts from normalizing categorical data, changing the shape of the datainto the desired form, visualizing the data, and taking meaning from the information generated from exploration and visualizationresults. The results of exploration and visualization of data in the form of boxplots, bar charts, line plots, and word clouds showpatterns in the categories and tags contained in videos that discuss trends in the three countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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