All of us love watching movies, a go-to for most people when they get bored. We all know that a lot of money goes into the making of a movie, so if it fares well at the box office there's nothing better than that. But if at all it turns out to be a flop movie, it's a huge setback for the entire team. Already, the current state of the world has led to millions of dollars worth of loss in the movie industry. Hence we have come up with a project that predicts the success outcome of a movie even before its production.
One of the foremost challenges we faced was finding a suitable dataset for our project. Though a lot of datasets were available, finding a clean dataset with fewer null values was difficult. We used a lot of data cleaning and feature engineering techniques to overcome this challenge.
Deploying the model was another challenge that we faced.
Technologies used
Discussion