This project is a Movie Recommendation System designed to provide users with personalized movie recommendations based on a given input movie. The system leverages machine learning techniques, including TF-IDF Vectorizer and cosine similarity, to identify and suggest movies that are similar in content to the user's input.
The system is built on a huge collection of movies, ensuring diverse and accurate recommendations.
The recommendation system uses movie descriptions, genres, and other textual data to determine similarity between movies.
By using TF-IDF (Term Frequency-Inverse Document Frequency) Vectorization, the textual data is transformed into numerical values representing the importance of words in each movie. Cosine similarity is then applied to measure the closeness between movie vectors.
Users can simply provide the name of a movie, and the system will return a list of similar movies ranked by relevance.
Movie details such as title, genres, and overviews are preprocessed (cleaned, tokenized, etc.).
TF-IDF Vectorizer is applied to transform the text data into feature vectors.
Cosine similarity is used to compute the similarity between the input movie and all other movies in the dataset.
Based on the similarity scores, the system identifies and recommends the top movies most similar to the input.
Python: Programming language for implementation.
Pandas: For data manipulation and cleaning.
Scikit-learn: For TF-IDF vectorization and cosine similarity computation.
Jupyter Notebook: For developing and testing the model.