This is a data science capstone project aimed at building a movie genre recommendation system using machine learning techniques. The goal of the project is to recommend movie genres based on their synopsis.
The aim of this project is to build a recommendation system that can predict the genre of a movie based on its synopsis. The system will use machine learning techniques to analyze the synopsis of a movie and predict its genre.
The dataset used in this project is an IMDB movie dataset, which includes information such as the movie title, genre, synopsis, cast, director, rating, and poster link.
The following machine learning algorithms were used in this project:
- RandomForestClassifier
- Support Vector Machine Classifier
- Linear Regression
- Decision Tree Classifier
The data was preprocessed using techniques such as text cleaning, tokenization, and TF-IDF vectorization to prepare it for the machine learning algorithms.
The RandomForestClassifier and Decision Tree Classifier algorithms were trained on the preprocessed data to build the recommendation system. These algorithms were chosen for their ability to handle multi-class classification tasks.
The final output of this project is a recommendation system that can predict the genre of a movie based on its synopsis.
The GenreOracle app is a movie genre recommendation system that uses machine learning techniques to predict the genre of a movie based on its synopsis. The app takes in the synopsis of a movie as input and predicts its genre, providing users with a list of suggested movie titles based on the predicted genre. With the GenreOracle app, users can easily discover new movies based on their interests and preferences. The app is simple to use and provides accurate predictions, making it an essential tool for movie enthusiasts.
