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A Python and Streamlit-based web app that recommends movies using content-based filtering and Integrates the OMDb API to show real-time posters and movie details.

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RohitManvar/Movie-Recommender-System

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Movie Recommendation System | Content-Based Recommender

A machine learning-powered Movie Recommendation System built with Python and Streamlit.
This web app suggests movies based on user input using content-based filtering and dynamically fetches movie posters & details from the OMDb API.


Deployment

You can access the live app here: Movie Recommendation System


Features

Smart Recommendations – Suggests top similar movies based on your selection
Content-Based Filtering – Uses Vectorization
Dynamic Posters – Fetches posters, descriptions, and ratings from OMDb API
Simple & Elegant UI – Built with Streamlit for an interactive experience
Real Dataset – Based on the Kaggle TMDB Movie Dataset


Tech Stack

  • Python 3
  • Streamlit (Frontend & UI)
  • Pandas, NumPy (Data Processing)
  • Scikit-learn (Vectorization + Similarity)
  • OMDb API (Posters, Descriptions)

How to Run Locally

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

# 2️ Install dependencies
pip install -r requirements.txt

# 3️ Run the Streamlit app
streamlit run app.py

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A Python and Streamlit-based web app that recommends movies using content-based filtering and Integrates the OMDb API to show real-time posters and movie details.

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