The purpose of this project is to predict the IMDB rating score for over 1000 popularized movies. The model will be built based on certain features such as title, released year, alphabetized certificate, runtime in minutes, genre, IMDB rating, overview of the movie, a metascore earned by the movie, and the director. Therefore, the film review machine we will design can find the best combination of the film information and accurately predict the IMDB rating based on the customer preferences. Moreover, our model can benefit the user by displaying the attributes and features they prefer to find a high ranking movie.
The dataset includes over 5000 tuples with the following features:
- title
- released year
- alphabetized certificate
- runtime in minutes
- genre
- IMDB rating
- overview of the movie
- a metascore earned by the movie
- director
- Yasin Zahir
- Ted Wu