  In the contemporary film industry, accurately predicting a movie's earnings
is paramount for maximizing profitability. This project aims to develop a
machine learning model for predicting movie earnings based on input features
like the movie name, the MPAA rating of the movie, the genre of the movie, the
year of release of the movie, the IMDb Rating, the votes by the watchers, the
director, the writer and the leading cast, the country of production of the
movie, the budget of the movie, the production company and the runtime of the
movie. Through a structured methodology involving data collection,
preprocessing, analysis, model selection, evaluation, and improvement, a robust
predictive model is constructed. Linear Regression, Decision Trees, Random
Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained
and tested. Model improvement strategies include hyperparameter tuning and
cross-validation. The resulting model offers promising accuracy and
generalization, facilitating informed decision-making in the film industry to
maximize profits.
