This is a movie recommendation system based on the Movie Dataset available on Kaggle. The system uses a content-based recommendation algorithm to recommend movies based on the features of movies that the user has liked in the past.
This movie recommendation system is built using Python and the scikit-learn library. The system uses a content-based recommendation algorithm to recommend movies to users based on the features of movies that they have liked in the past.
The dataset used in this project is the Movie Dataset available on Kaggle. The dataset contains metadata for over 45,000 movies, including information on title, release date, budget, revenue, genres, and ratings.
The movie recommendation system is based on a content-based recommendation algorithm. The system uses the following steps to recommend movies:
- Exploratory Data Analysis (EDA): The movie dataset is analyzed to get insights into the data and identify any potential issues.
- Data preprocessing: The movie dataset is cleaned and preprocessed to remove missing values and outliers.
- Feature engineering: The movie features are engineered to create a set of movie attributes that can be used for recommendation. The features include genre, director, actor, and release year.
- Machine Learning: The system trains a machine learning algorithm to predict the movies that the user might like based on their past preferences.
- Recommendation generation: The system recommends movies based on the predictions of the machine learning algorithm.
The movie recommendation system provides an easy and efficient way to recommend movies to users based on their past preferences. The system can be further improved by incorporating additional features and algorithms.
Bhavya Pandya