Movie Recommendation System

Overview

The Movie Recommendation System is a project designed to suggest movies to users based on their preferences. It uses a precomputed similarity matrix and a dataset of movies to provide personalized recommendations through an interactive web interface built with Streamlit. Additionally, the project includes a Jupyter Notebook for data preprocessing and model creation.

Features

• Personalized Recommendations: Suggests movies similar to the one selected by the user.
• Interactive Interface: Users can select a movie from a dropdown and get recommendations instantly.
• Streamlit Integration: A lightweight and user-friendly web application framework.
• Data Preprocessing Notebook: A detailed Jupyter Notebook for merging, cleaning, and preparing the dataset for recommendations.

Technologies Used

• Programming Language: Python
• Libraries:
  • Streamlit
  • Pandas
  • NumPy
  • Scikit-learn
  • NLTK
  • Pickle
• Data: TMDB 5000 Movies and Credits datasets.

Installation

1. Clone the repository:

   git clone https://github.com/your-username/Movie_Recommendation_System.git

2. Navigate to the project directory:

   cd Movie_Recommendation_System

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Streamlit Application

1. Ensure the required .pkl files (movie_dict.pkl and similarity.pkl) are present in the project directory.
2. Run the application:

   streamlit run 1.py

3. Open your browser and navigate to the URL provided by Streamlit (e.g., http://localhost:8501).
4. Select a movie from the dropdown menu and click the "Recommend" button to see the recommendations.

Jupyter Notebook

1. Open the Movie_Recomendation_System.ipynb file in Jupyter Notebook or JupyterLab.
2. Follow the steps in the notebook to:
   • Load and preprocess the TMDB datasets.
   • Merge, clean, and transform the data.
   • Create a similarity matrix using cosine similarity.
   • Save the processed data and similarity matrix as .pkl files.

Project Structure

Movie_Recommendation_System/
│
├── 1.py                             # Main Streamlit application file
├── Movie_Recomendation_System.ipynb # Jupyter Notebook for data preprocessing
├── tmdb_5000_movies.csv             # Movies dataset
├── tmdb_5000_credits.csv            # Credits dataset
├── movie_dict.pkl                   # Preprocessed movie dataset (dictionary format)
├── similarity.pkl                   # Precomputed similarity matrix
├── requirements.txt                 # Python dependencies
└── README.md                        # Project documentation

How It Works

Data Preprocessing

1. Dataset Loading: The TMDB 5000 Movies and Credits datasets are loaded into Pandas DataFrames.
2. Data Cleaning: Unnecessary columns are removed, missing values are handled, and duplicate entries are dropped.
3. Feature Engineering:
   • Columns like genres, keywords, cast, and crew are transformed into a unified format.
   • A new tags column is created by combining relevant features.
4. Vectorization: The tags column is vectorized using CountVectorizer to create numerical representations.
5. Similarity Matrix: Cosine similarity is computed between movie vectors to identify similar movies.

Recommendation Logic

1. When a user selects a movie, its index is retrieved from the dataset.
2. The similarity scores for the selected movie are sorted to find the top 5 most similar movies.
3. The recommendations are displayed in the Streamlit interface.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

• Thanks to the open-source community for providing useful libraries and tools.
• TMDB for providing the datasets used in this project.
• Inspiration from popular recommendation systems like Netflix and Amazon.