Subscribers Forecasting of Netflix Based on Multiple Linear Models
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
Netflix is one of the world's leading entertainment services, with millions of subscribers all over the world. To make a prediction of the expected number of future subscribers is a meaningful and valuable research topic. However, there is no authoritative model to help make predictions. Therefore, this study will explore the relationship between possible factors and the number of future subscribers in terms of the data from Netflix over the years. Subsequently, some candidate multiple linear regression models are constructed and the “best” model is selected to help make predictions. The final model shows that the number of subscribers is related to four variables, i.e., a negative relationship with the price of the basic plan, a positive relationship with the price of the standard plan, the number of countries where Netflix is available and the medium level of annual world income. The data on the number of subscribers over the years shows an increase in subscribers every year, as well as the amount of growth varies from year to year. In other words, the increase in the price of the basic plan may lead to a decrease in the number of subscribers, while the increase in the price of the standard plan, in the number of countries where Netflix is available and in the medium level of annual world income may lead to an incline in it. These results shed light on guiding further exploration of having a practical method to predict the number of future subscribers for Netflix.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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