Hybrid AI Movie Recommendation System for Pre-2015 Films

Mohammad Umar

Completed work

Data Analyst

Data Scientist

ML Engineer

Python

scikit-learn

Streamlit

Arts & Entertainment

Hybrid AI Movie Recommendation System

Award-winning ML project for Hollywood films released before 2015

🏆 Project Highlights

🥇 Best BSc Project (Grade A) — Selected among top 1% of cohort

🎯 2 Competition Finalist — Showcased at:

Project Expo Chandigarh University 2025 (Finalist, offered direct interview by one of the judges)

National Science Day 2025 (Finalist)

🎓 Built with: Python, Streamlit, Scikit-learn, Pandas, MovieLens 20M

🕰️ Optimized for films released before 2015 (when user ratings are more consistent)

📖 Overview

This hybrid movie recommender system blends both collaborative and content-based filtering to recommend relevant Hollywood films.

It was trained on over 27,000 movies and 20 million user ratings, using:

🔗 KNN-based collaborative filtering for taste matching

🧠 TF-IDF + Cosine similarity for genre-based similarity

⚖️ Hybrid weighting to balance both approaches

“Particularly excels with pre-2015 classic cinema, where user behavior patterns are strongest.”

🧠 System Architecture

graph LR
    A[User Input] --> B(Title Matching)
    B --> C{Hybrid Engine}
    C --> D[KNN]
    C --> E[Cosine Sim]
    D --> F[Recommendations]
    E --> F

📊 Model Performance

Metric Score RMSE 0.87 Coverage 94% (pre-2015) Diversity 0.68

🚀 How to Run Locally

git clone https://github.com/UmarTests/AI-Movie-Recommendation-System.git
cd AI-Movie-Recommendation-System
pip install -r requirements.txt
streamlit run Movei_rec_app.py

🛠 Key Features

🎯 Personalized suggestions using hybrid ML techniques

🗃️ Trained on MovieLens 20M dataset

🧩 Robust against title misspellings

🖥️ Optional Streamlit UI for end-user interaction

🎥 Focused on films released before 2015 for rating strength

🎓 Academic Recognition

📌 Capstone Project for BSc in Computer Science

🏅 Grade A and Best Project Award

🏆 Finalist and awardee in multiple competitions

🧠 Demonstrated core ML techniques: matrix factorization, sparse vectors, cosine distance

✨ Why This Project Stands Out

+ Demonstrates mastery in applied ML and recommender systems  
+ Focused on pre-2015 cinema: real-world data quirks handled  
+ End-to-end ready: From notebook → deployable UI  
+ Git versioned, modular Python architecture

📂 File Structure

├── Movie_Rec_proj.py           # Recommender engine logic  
├── Movei_rec_app.py            # Streamlit UI (optional)  
├── recommendation_system.py    # Core similarity functions  
├── requirements.txt            # All dependencies  
├── README.md                   # You're reading it

📫 Let’s Connect

🔗 LinkedIn : www.linkedin.com/in/mohammad-umar-9127162b8

🌐 Portfolio : https://github.com/UmarTests

📧 umar.test.49@gmail.com

📄 License

MIT License – Free to use, fork, and learn from.

For detailed methodology and dataset processing, see the 📄 Project Report (PDF)

🌐 INTRO LANDING PAGE (For GitHub Pages or `index.html`)

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Hybrid Movie Recommendation System</title>
  <style>
    body { font-family: sans-serif; background: #111; color: #eee; padding: 2rem; text-align: center; }
    h1 { font-size: 2.5rem; }
    p { font-size: 1.2rem; }
    .gif { margin-top: 2rem; }
    a.button {
      display: inline-block; padding: 0.8rem 1.5rem; background: crimson;
      color: white; text-decoration: none; border-radius: 8px; margin-top: 1rem;
    }
  </style>
</head>
<body>
  <h1>🎬 Hybrid Movie Recommendation System</h1>
  <p>An award-winning AI engine for recommending movies released before 2015.</p>
  <p><strong>Built with:</strong> Python, Streamlit, scikit-learn, MovieLens</p>

  <div class="gif">
    <img src="https://media.giphy.com/media/3o7TKsrfldmm8cukWs/giphy.gif" width="480" />
  </div>

  <a href="https://github.com/UmarTests/AI-Movie-Recommendation-System" class="button">🔗 View on GitHub</a>
</body>
</html>

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Completed work

Posted Aug 30, 2025

Developed a hybrid AI movie recommendation system for pre-2015 films.

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Jan 1, 2025 - Feb 20, 2025