Feb 20, 2024 · We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known …

In the paper titled ‘TV Show Popularity Analysis using Social Media, Data Mining’, a predictive model to predict the popularity of TV shows based on user comments from social media is …

Feb 20, 2024 · We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known …

Use the machine learning-based predictive models such as linear regression, K nearest Neighbors, Stochastic Gradient Descent, Decision Trees and Forests, Neural Networks, …

This project involves harnessing the power of data mining and Python to analyze the popularity of TV shows. The process includes collecting TV show data, cleaning and transforming it, …

We have K-Means and Incremental K-Means algorithms to compare the TRP. The comparison between the two algorithms is very clear on histograms. It is the easiest way of predicting TV …

In this study, we propose a predictive model that uses sentiment analysis to determine the popularity of a given TV show. This enables accurate decisions to be made based on the …

Apr 1, 2021 · With such data available, we can find out what features the most successful shows have in common and the shows of which genre are likely to be more successful with the help …

“general” approach for predicting the success of a TV show in terms of its ratings in the USA market, based on well-known machine learning regression models; to the best of our …

Simple predictions of a trending TV show based on individual ratings can be made based on the target audience (Age, Year of Release, Rotten Tomatoes, etc.). This project uses machine …