🎬 Movie Recommendation System (MRS)

A content-based Movie Recommendation System built using Python, Streamlit, and scikit-learn.
It recommends movies similar to the one selected by the user based on movie genres, keywords, cast, and crew.

🌐 Live App: Movie Recommendation System on Streamlit
📊 Dataset: TMDB Movie Metadata (Kaggle)

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🚀 Features

• Uses TF-IDF Vectorization and Cosine Similarity for movie similarity.
• Recommends top 10 most similar movies to the selected title.
• Dynamically displays movie posters from the TMDB API.
• Simple, clean, and interactive Streamlit web interface.

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🧠 How It Works

1. The dataset (tmdb_5000_movies.csv and tmdb_5000_credits.csv) is loaded and merged in MRS.ipynb.
2. Extracts relevant features like genres, keywords, cast, and crew.
3. Combines them into a single text field called tags.
4. Applies TF-IDF Vectorization to convert text into numerical vectors.
5. Calculates Cosine Similarity to find related movies.
6. Saves the processed data into a pickle file (movie_data.pkl).
7. The Streamlit app (app.py) uses this file to recommend and display movie posters.

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🧰 Tools & Technologies Used

Category	Tools
Programming Language	Python
Libraries	pandas, numpy, scikit-learn, streamlit, requests
Machine Learning	Cosine Similarity, CountVectorizer
Data Source	TMDB Movie Metadata (Kaggle)
Deployment	Streamlit Cloud
Environment	Jupyter Notebook, VS Code

🖥️ Project Structure

• MRS.ipynb # Data preprocessing and feature extraction
• app.py # Streamlit web app
• movie_data.pkl # Preprocessed movie dataset
• requirements.txt # Dependencies
• tmdb_5000_movies.csv # Movies dataset
• tmdb_5000_credits.csv # Credits dataset
• README.md # Project documentation