CinemAI: A Personalized Movie Recommendation System 🎥

CinemAI is a machine learning-based movie recommendation system designed to help users discover movies tailored to their preferences. This project leverages content-based filtering, cosine similarity, and text vectorization to deliver highly relevant recommendations through an intuitive user interface built with Streamlit, HTML, and CSS.

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Features ✨

• Personalized Recommendations: Suggests movies based on user input and preferences.
• Content-Based Filtering: Uses attributes like genres, directors, and actors to calculate similarity.
• Interactive UI: A user-friendly interface to search for movies and get recommendations instantly.
• TMDb Dataset: Data-driven insights using the popular TMDb 5000 Movie Dataset.
• Cosine Similarity: Measures the relevance of movies to suggest the closest matches.
• Model Persistence: Utilizes the Pickle library to save and load machine learning models efficiently.

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Demo 🎬

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Libraries Used

• NumPy: For numerical computations.
• Pandas: For data manipulation and analysis.
• Scikit-Learn: For building and evaluating the recommendation engine.
• Streamlit: For creating the web-based interface.
• Pickle: For saving and loading models.

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Data & Resources 📊

Dataset can be found on Kaggle https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata

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How to Get TMDb API Key & Access Token 🔑

This project integrates TMDb API to fetch movie details. Follow these steps to obtain your API Key and Access Token:

1. Sign up on TMDb

   • Go to The Movie Database (TMDb)
   • Create an account if you don’t already have one.

2. Request an API Key

   • Navigate to your profile by clicking on your avatar (top-right corner).
   • Go to Settings > API.
   • Click on Create a new API key.
   • Choose "Developer" and provide the required details.
   • You will receive a TMDB API Key.

3. Generate an Access Token

   • On the same API page, request a Bearer Token.
   • TMDb will generate an access token that you can use for authentication.

4. Store API Credentials Securely

   • Create a .env file in your project directory and add the following:

       TMDB_API_KEY=your_tmdb_api_key_here
       TMDB_ACCESS_TOKEN=your_tmdb_access_token_here
     

   • Do NOT share this file or upload it to GitHub.

   • Add .env to your .gitignore file to keep it private.

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How It Works 🚀

1. Data Preprocessing:

   • Cleans the dataset by handling missing values and duplicates.
   • Extracts relevant features like genres, directors, and actors.

2. Text Vectorization:

   • Converts textual data into numerical format using techniques like TF-IDF.

3. Similarity Calculation:

   • Computes cosine similarity between movies based on their features.

4. Recommendation Engine:

   • Suggests movies based on similarity scores.

5. Web Application:

   • Built using Streamlit for seamless user interaction.

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Installation 🛠️

1. Clone this repository:

    git clone https://github.com/your-username/cinemai.git 
    cd cinemai
   

2. Install the required libraries:

    pip install -r requirements.txt
   

3. Run the application:

    streamlit run main.py