Netflix Movie Recommender

Project Overview: Building and Validating a Recommendation Model Using Netflix Prize Data

Project Description:

This project aims to develop and validate a simple recommendation system using the Netflix Prize dataset.

Notebooks:

The workflow is structured across two main notebooks:

Exploration.ipynb

• Objective: Perform data exploration and preprocessing to understand the characteristics and distributions within the Netflix Prize dataset.
• Activities:
  • Develop an approach to handle large datasets effectively.
  • Analyze data distributions, such as movie ratings and movie popularity.
  • Sample data to create representative subsets for model development and validation.

Modeling.ipynb

• Objective: Implement and evaluate the model.
• Activities:
  • Select appropriate recommendation algorithms.
  • Tune model parameters using grid search.
  • Validate models using accuracy metrics such as RMSE and MAE and user-centric metrics like hit rate, diversity, or novelty.
  • Generate sample recommendations to assess the effectiveness of the selected model.

Other elements:

data: Samples drawn from the original data for model training.
models: Serialized models and parameters.
scripts: Utility functions and classes to facilitate the data processing and analysis.
test: Basic tests for MovieSampler class.

Additional Notes:

Data Source: The Netflix Prize dataset (Kaggle)
Tools: Python, Numpy, Pandas, Seaborn, surprise