Machine Learning Feature Engineering Tool

To predict the global box office of a movie based on its cast, year of release, and production budget, we can add the following features to the dataset: 1. Genre of the movie - This feature would help us understand the type of movie and its target audience, which could impact its box office performance. 2. Director of the movie - A well-known and successful director could have a positive impact on a movie's box office performance. 3. Rating of the movie - The rating of the movie could influence its target audience and therefore its box office performance. 4. Marketing budget - The amount of money spent on marketing the movie could influence its box office performance. 5. Duration of the movie - The length of the movie could impact its appeal to audiences and therefore its box office performance. After adding these features to the dataset, we can perform feature engineering by creating new features that could help improve the accuracy of our model. For example, we could create a feature that combines the production budget and marketing budget to give us the total amount spent on making and promoting the movie. Additionally, we could create a feature that calculates the difference between the year of release and the current year, which would give us the age of the movie and could impact its box office performance. By adding these features and performing feature engineering, we can create a more comprehensive dataset that would help us make more accurate predictions for the global box office of a movie based on its cast, year of release, and production budget.