Movie Recommendation System

Overview

The Movie Recommendation System is a Python-based web application developed using Streamlit to recommend movies based on user input. The project uses TMDB (The Movie Database) API to fetch movie posters and Cosine Similarity to suggest movies that are similar to a selected title. The recommendation system processes a dataset containing over 5,000 movies and provides users with five movie suggestions along with their posters.

Features

• Movie Recommendation: Recommends 5 similar movies based on the selected movie title using a content-based filtering approach.
• Poster Fetching: Automatically fetches movie posters from the TMDB API to enhance user experience.
• Interactive Web Interface: Built with Streamlit, offering an easy-to-use and interactive interface for users to select movies and view recommendations.

Technology Stack

Libraries and Tools

• Python: Core programming language for the application.
• Streamlit: For building the interactive web-based interface.
• TMDB API: For fetching movie posters based on movie IDs.
• Pandas: Data processing and manipulation.
• Sklearn (Scikit-learn): For implementing Cosine Similarity and text vectorization.
• Pickle: For saving the movie list and similarity matrix for quick future access.

Dataset

The dataset used consists of over 5,000 movies with the following fields:

budget,genres,homepage,id,keywords,original_language,original_title,overview,popularity,production_companies,production_countries,release_date,revenue,runtime,spoken_languages,status,tagline,title,vote_average,vote_count

Example Data:

237000000,"[{""id"": 28, ""name"": ""Action""}, {""id"": 12, ""name"": ""Adventure""}, {""id"": 14, ""name"": ""Fantasy""}, {""id"": 878, ""name"": ""Science Fiction""}]",http://www.avatarmovie.com/,19995,"[{""id"": 1463, ""name"": ""culture clash""}, {""id"": 2964, ""name"": ""future""}]",en,Avatar,"In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission.",150.437577,"[{""name"": ""Ingenious Film Partners"", ""id"": 289}]",2009-12-10,2787965087,162,"[{""iso_639_1"": ""en"", ""name"": ""English""}]"

Setup Instructions

Prerequisites

Before setting up the project, ensure you have the following installed:

• Python 3.x
• Pip (Python package installer)

Installation Steps

1. Clone the Repository:

   git clone https://github.com/jeevangowda0711/MoviesRecommendation.git
   cd MoviesRecommendation

2. Create and Activate a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the Required Dependencies:

   pip install -r requirements.txt

4. Run the Application:

   streamlit run app.py

   This will launch the web application on your local server. Open your browser and go to the provided URL (usually http://localhost:8501/).

Usage

1. Select a Movie: Use the dropdown menu to select a movie from the list of available titles.
2. Show Recommendations: Click the "Show Recommendation" button to view 5 recommended movies along with their posters.
3. View Results: The recommendations will be displayed in a grid, showing the movie titles and posters fetched from the TMDB API.

How the Recommendation Works

• Content-Based Filtering: The system uses Count Vectorization to convert movie metadata (title, genres, cast, crew, keywords) into vectors.
• Cosine Similarity: The cosine similarity of these vectors is then computed to identify movies that are most similar to the selected title.
• Recommendations: The system suggests the top 5 movies that are most similar to the user's selection based on the calculated similarity.

Example Movies in Dataset

• Avatar
• Pirates of the Caribbean: At World's End
• The Dark Knight Rises
• John Carter
• Spectre

Project Files

• app.py: Main file containing the Streamlit app code and recommendation logic.
• notebook.ipynb: Jupyter notebook used for data preprocessing and model training.
• movie_list.pkl: Pickled file containing the list of movies.
• similarity.pkl: Pickled file containing the precomputed similarity matrix.
• tmdb_5000_movies.csv: Movie dataset used for recommendations.
• requirements.txt: List of required Python packages for running the project.

Future Enhancements

• Improve recommendation accuracy by incorporating user ratings and reviews into the model.
• Add more advanced filtering options such as genre-based recommendations.
• Integrate a more robust dataset with a larger number of movies.

License

This project is licensed under the MIT License.

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Acknowledgments

• TMDB API for providing access to movie metadata and posters.
• Streamlit for providing an easy-to-use framework for building web applications.
• Scikit-learn for offering a comprehensive set of machine learning tools used for similarity calculations.