🎬 Movie Recommender System

A Content-Based Movie Recommendation System built with Python, Streamlit, and the TMDb API. This web app recommends movies based on their similarity to a selected title using cosine similarity computed from movie metadata.

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🖼️ Preview

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🧠 How It Works

This app uses content-based filtering to suggest movies similar to the one you select by analyzing:

• 🎞️ Movie overviews
• 🧩 Genres & keywords
• 🧑‍🎤 Cast and crew

Cosine similarity is calculated on vectorized features to find and rank the most similar titles.

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🛠️ Built With

• 🐍 Python
• 📊 Pandas / NumPy
• 🤖 Scikit-learn
• 🌐 Streamlit – for the interactive web interface
• 🎥 TMDb API – for fetching movie posters and metadata
• 🧃 Pickle – for saving and loading processed data & similarity matrix

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✨ Features

• 🔍 Select any movie and get top 5 similar recommendations
• 📸 Fetch movie posters and titles via TMDb API
• ⚡ Efficient cosine similarity-based ranking
• 🧑‍💻 Clean and interactive UI built with Streamlit

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📦 Installation

🔁 Clone the Repository

git clone https://github.com/your-username/movie-recommender-system.git
cd movie-recommender-system

🧱 Install Requirements

pip install -r requirements.txt

▶️ Run the App

streamlit run app.py

📁 Project Structure

├── app.py                  # Streamlit app
├── movies.csv              # Movie metadata
├── similarity.pkl          # Precomputed similarity matrix
├── poster_fetch.py         # TMDb API calls for poster images
├── requirements.txt
└── README.md

🔑 API Key

This project uses the TMDb API to fetch movie posters.