The Academy Awards, also known as the Oscars, are a set of awards for artistic and technical merit in the film industry. The various category winners are awarded a copy of a golden statuette, officially called the "Academy Award of Merit", although more commonly referred to by its nickname "Oscar". February is that time of the year where movie enthusiasts are eagerly waiting to see if their favorite movie, actor and actress win the Oscar’s or not. Although few of the results might not be as expected, but what makes them hopeful is the person’s performance and a prediction which says they might win.
- For artists associated with movie making all around the around, winning an Oscar is the most prestigious thing and a proud moment which symbolizes that they have made something worth watching with technical brilliance and passion.
- For people who are movie lovers, eagerly await hoping that their favorite actor or actress wins an Oscar for their performance.
- We aim to predict the Oscar award winners for the main category i.e Academy Award for Best Picture for 2019
- Any prediction problem will involve two possible outcomes – Yes or No.
- In our case as well, we’re trying to predict which movie will win the Best Picture award that involves two possibilities – Winner or not.
- Supervised learning is a machine learning concept that allows data scientists to understand the relationship between one output and a lot of inputs. In this case, it helps us understand past outcomes (who won Best Picture previously and why) so we can better predict future outcomes (who will win this year and why).
We use the following models to predict Oscar
- Logistic Regression
- Decision Trees
- Random Forests
- Artificial Neural Networks We evaluate each of the models to see accuracy and try to better the prediction results
data_csv -> https://datahub.io/rufuspollock/oscars-nominees-and-winners ratings_movieslens, movies_movieslens -> http://files.grouplens.org/datasets/movielens/ml-latest- README.html title.ratings, title.basics -> https://www.imdb.com/interfaces/ https://archive.ics.uci.edu/ml/datasets/Movie https://en.wikipedia.org/wiki/Academy_Awards https://oscar.go.com/ https://towardsdatascience.com/approximating-the-minds-of-2019-oscars-voters-using-neural- networks-b922f3d6864c