The main goal is to create a machine learning regression that can estimate the number of YouTube adviews based on other parameters. Advertisers on YouTube pay content creators based on how many times their ads are viewed and clicked. They want to estimate the adview based on other metrics like vidid, adviews, published, duration, views, comments, likes etc. CSV files are utilised for training and fitting, and then they are tested to get the best outcomes. As a result, the goal of the project is to train multiple regression models and select the best one for predicting the number of adviews. We validate datasets and packages like Numpy, Pandas, and Sklearn for their form and data type. Also, we visualise and clean the dataset followed by transforming attributes into numerical values. Using different regression algorithms, normalise the data and separate it into training and test sets. The model is saved and used to predict on the test set. To acquire better outcomes, data or information must be improved, filtered, and cleansed before being fed in based on numerous criteria.
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