Movie Recommender System (Collaborative Filtering)

This project uses collaborative filtering to build a basic recommender system using movie rating data.

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📁 Dataset

Two files: Download them here - https://www.kaggle.com/datasets/ashukr/movie-rating-data?select=ratings.csv

• ratings.csv: User ratings for movies
• movies.csv: Metadata (title, genres)

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📊 Approach

• Merge ratings and metadata
• Compute average rating and number of ratings per movie
• Visualize distributions
• Create a utility matrix (userId vs movie titles)
• Use Pearson correlation to find movies similar to a target (e.g., xXx (2002))

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📈 Visualizations

• Histogram of number of ratings
• Histogram of average ratings
• Joint distribution of count vs average
• Heatmap of feature correlations

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📦 Project Structure

movie-recommender/
├── data/
│   ├── ratings.csv
│   └── movies.csv
├── notebooks/
│   └── movie_recommender.ipynb
├── README.md
├── requirements.txt
└── .gitignore

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🚀 How to Run

1. Clone the repo
2. Install dependenciesand run:

   pip install -r requirements.txt
   #Run
   jupyter notebook notebooks/movie_recommender.ipynb