Movie Recommendation System UI

Project Information

  • Category: Machine Learning Project
  • Team: Solo Project
  • Project Date: 2023
  • Project Type: Content-Based Recommendation System
  • Deployment: Streamlit Web App
  • Live Demo: movierecommendation.streamlit.app

Movie Recommendation System

This project is a content-based movie recommendation system designed to deliver personalized movie suggestions based on user-selected preferences. The system analyzes movie metadata and computes similarity scores to recommend the most relevant films.

Key Features:

  • Content-based recommendation using cosine similarity
  • Personalized top 5 movie recommendations
  • Processed and analyzed a dataset of 4,800+ movies
  • Integrated TMDB API for real-time movie posters and metadata
  • Interactive and user-friendly Streamlit interface
  • Efficient data preprocessing and feature extraction pipeline

Tech Stack:

Python, Pandas, NumPy, scikit-learn, Streamlit, TMDB API

The project demonstrates practical application of machine learning concepts such as vectorization, similarity measurement, and real-world data handling. It highlights strong problem-solving skills, algorithmic thinking, and the ability to deploy ML models into interactive applications.