TMDB Graph-Based Movie Recommender & Visual Dashboard

This project combines rich data storytelling and graph machine learning to analyze and recommend movies using the TMDB 5000 Movie Dataset from Kaggle.

Built as a research portfolio project for applying to the DICE Group at the University of Paderborn.

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Features

Visual Analytics Dashboard

Explore patterns in global movie production with 10 interactive visualizations:

• Movie Releases Per Year
• Top 10 Genres
• Top 10 Directors
• Top 10 Actors
• Movies by Production Country (Choropleth)
• Genre Distribution (Treemap)
• Spoken Languages (Bar)
• Language Distribution (Donut)
• Budget vs Revenue (Raw)
• Budget vs Revenue (Log)

Graph-Based Movie Recommendation Engine

• Movies, directors, genres, keywords form a heterogeneous knowledge graph
• Node embeddings learned using Node2Vec or KarateClub
• Recommendations generated via cosine similarity in embedding space
• Fully interactive search using Streamlit dropdown

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Tech Stack

• Python 3.10
• pandas, numpy, networkx, matplotlib, scikit-learn
• plotly, streamlit
• node2vec / karateclub
• TMDB 5000 dataset (Kaggle)

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Setup Instructions

1. Clone the repo:

git clone https://github.com/yourusername/tmdb-recommender-graphml.git
cd tmdb-recommender-graphml

2.Create a virtual environment: python3 -m venv graphenv source graphenv/bin/activate

3.Install dependencies:

pip install -r requirements.txt

4. Run the app:

   streamlit run app/app.py

Dataset Source

TMDB 5000 Movie Dataset: https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata