Blockbuster or art film?

1. Set up a data science project structure in a new git repository in your GitHub account
2. Download the one of the MovieLens datasets from https://grouplens.org/datasets/movielens/
3. Load the data set into panda data frames
4. Formulate one or two ideas on how the combination of ratings and tags by users helps the data set to establish additional value using exploratory data analysis
5. Build one or more clustering models to determine similar movies to recommend using the other ratings and tags of movies by other users as features
6. Document your process and results

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

# Movie-Recommender