🎬 Movie Recommendation System

A content-based movie recommendation engine that suggests similar movies based on metadata like genres, cast, crew, and keywords — powered by Cosine Similarity and deployed as an interactive Streamlit web app.

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🚀 Live Demo

🔗 [Coming Soon — Streamlit Deployment]

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📌 Overview

Ever wondered how Netflix or Spotify knows what to suggest next? This project replicates that core idea using Content-Based Filtering — analyzing what a movie is rather than what users rated it.

Given any movie title, the system finds the Top 5 most similar movies by computing similarity across key metadata features from the TMDB dataset.

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✨ Features

• 🔍 Search any movie and get 5 instant recommendations
• 🧠 Content-based filtering using NLP-style feature extraction
• ⚡ Fast results via precomputed similarity matrix (Pickle)
• 🎨 Clean and interactive Streamlit UI
• 🎥 Movie poster fetching via TMDB API

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🛠️ Tech Stack

Tool	Purpose
Python	Core language
Pandas & NumPy	Data processing
Scikit-learn	Vectorization & Cosine Similarity
TMDB Dataset	Movie metadata
Pickle	Model serialization
Streamlit	Web app deployment

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🧠 How It Works

User inputs a movie title
        ↓
Fetch movie's feature vector (genres + cast + crew + keywords + overview)
        ↓
Compute Cosine Similarity against all movies in dataset
        ↓
Return Top 5 most similar movies

1. Data Preprocessing — Combined genres, cast, crew, keywords into a single tags column
2. Vectorization — Converted tags into numerical vectors using Count Vectorizer
3. Similarity Computation — Applied Cosine Similarity to find closest matches
4. Serialization — Saved model & similarity matrix using Pickle for fast loading
5. Deployment — Built interactive UI with Streamlit

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📂 Project Structure

Movie-recommendation-system/
│
├── app.py                  # Streamlit web app
├── movie_recommendation.ipynb  # Model building notebook
├── movies.pkl              # Serialized movie data
├── similarity.pkl          # Precomputed similarity matrix
├── requirements.txt        # Dependencies
└── README.md

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⚙️ Installation & Setup

# 1. Clone the repository
git clone https://github.com/byteephantom/Movie-recommendation-system.git
cd Movie-recommendation-system

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the app
streamlit run app.py

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📦 Requirements

pandas
numpy
scikit-learn
streamlit
requests
pickle-mixin

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📊 Dataset

• Source: TMDB Movie Dataset — Kaggle
• Size: 5000+ movies
• Features used: title, genres, keywords, cast, crew, overview

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🙋‍♂️ Author

Ayush Kumar

• GitHub: @byteephantom

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📄 License

This project is licensed under the MIT License.

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