I used Catboost for training a model on the numerical features of every YouTube video (e.g., the number of views, comments, likes, etc.) along with sentiment analysis of the video descriptions and comments using the VADER sentiment analysis model.

Predicting YouTube Dislikes using Machine Learning

Machine learning project predicting YouTube video views using engagement metrics (likes, comments, dislikes) with regression models in Python.

It's basically a Machine Learning and Deep Learning project that can predict adview on a YouTube video using parameters like views, likes, dislikes etc.

To Predict whether video will trend or not on the trending Page of YouTube.The analysis is done using user features such as Views, Comments, Likes, Shares and Dislikes. Analysis can be performed using algorithms like Random Forest classification, other Machine learning models and python libraries like pandas, matplot library to classify the YouTube Data and obtain useful information.

To analyse YouTube trending data and find the most liked, disliked and viewed video overall and in each category using Big data(PySpark) and in addition to predict the most number of likes for a video using Machine learning(Mllib).

This project predicts how many views a YouTube ad might get based on its metadata — like the title length, video category, duration, likes, dislikes, comments, and when it was uploaded. It uses machine learning models to find patterns that influence ad performance, helping creators and marketers make smarter, data-driven decisions.

YouTube trending videos represent content that targets viewers’ attention over a relatively short time, this paper analyses YouTube trending data and finds the most liked, disliked and viewed video overall and in each category using Big data(PySpark) and in addition to predict the most number of likes for a video using Machine learning(Mllib).