🎬 Movie Recommendation System using Collaborative Filtering

This project is a beginner-friendly Movie Recommendation System built using Python, pandas, and Google Colab. It uses Item-Based Collaborative Filtering to suggest movies similar to the ones a user already likes — just like Netflix or Amazon!

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📌 Project Highlights

• ✅ Simple and easy to understand — made for beginners
• ✅ Uses the popular MovieLens 100k dataset
• ✅ Built entirely in Google Colab
• ✅ No machine learning experience required
• ✅ Optional Streamlit web app version

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🚀 How It Works

The system finds movies that are most similar in user rating patterns to a movie you choose.

It follows these steps:

1. Load and merge the dataset
2. Create a user-movie rating matrix
3. Use Pearson correlation to find similar movies
4. Filter out movies with low rating counts
5. Return top 10 most similar movie recommendations

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🧠 Techniques Used

• Collaborative Filtering (Item-Based)
• Pivot Tables with pandas
• Correlation (Pearson) for similarity
• Filtering for reliability
• (Optional) streamlit for simple UI

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🗂️ Files Included

• movie_recommendation.ipynb – Google Colab notebook with full code and explanations
• u.data, u.item – MovieLens dataset files (downloaded manually)
• app.py (optional) – Streamlit web app (if implemented)
• README.md – Project description

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📥 Dataset Info

We use the MovieLens 100k dataset:

• 100,000 ratings
• 943 users
• 1,682 movies

🔗 Download MovieLens 100k Dataset

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📸 Sample Output

Example: Recommendations for "Star Wars (1977)"

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⚙️ How to Run

Option 1: Run in Google Colab (No setup needed)

1. Open the .ipynb notebook
2. Upload u.data and u.item files
3. Run the cells one by one

Option 2: Run Locally

1. Clone this repo
2. Install dependencies: