Analysis streaming application viewership with EDA

Abstract

Streaming applications require a high amount of bandwidth to deliver high-quality media content to users. However, bandwidth is not always available or consistent, especially in remote or congested areas. This can result in buffering, lagging, or poor quality of the streaming content, which can frustrate users and affect their satisfaction and retention. Streaming applications need to minimize the delay between the source and the destination of the media content, especially for live or interactive streaming. However, latency can be affected by many factors, such as network congestion, server load, routing, encoding, etc. Predictive analysis can help to forecast the future outcomes or behaviors of the streaming data, such as the demand, the popularity, the retention, the churn, etc. For example, one can use predictive analysis to estimate the optimal pricing strategy for a streaming service, or to predict the likelihood of a viewer to cancel their subscription. Streaming application with EDA can also help to detect and resolve any issues or errors that may affect the streaming quality, such as network congestion, server load, device compatibility, etc. Streaming application with EDA can help to understand and predict the user behavior, such as the viewing duration, frequency, preference, rating, feedback, etc., of the media content consumed by the users.