Exploratory analysis of Movielens dataset

The purpose of this project is to employ exploratory analysis of movielens dataset(https://grouplens.org/datasets/movielens/1m/) in order to get interesting insights. The dataset contains 3 related data sources: ratings, users and movies in .dat format.

• ratings.dat contains attributes UserID, MovieID, Rating and Timestamp representing id of user, id of movie, rating given by user to the movie and timestamp of the rating.
• users.dat contains attributes UserID, Gender, Age, Occupation and Zip-code for each user.
• movies.dat contain attributes MovieID, Title and Genres.

Phases of analysis:

• Explore each data sources individually.
• Combine movies and users to the ratings data in order to get interesting insights.

Libraries

• Pandas: for data manipulation and analysis. Dataframe feature provided by this library is really flexible in handling the data.
• Numpy: provide flexibility in dealing with multi-dimensional arrays and complex mathematical functions.
• Matplotlib: Commonly used library for data visualization
• Searborn: visualization library based on matplotlib. It provides a high-level interface to attractive graphs. Run command: pip install -r requirements.txt to install required libraries.

To run the code

Common requirements: Python 3+(version used for the project: 3.5.3), Jupyter notebook

Data directory: /src/main/data(data needs to be downloaded from https://grouplens.org/datasets/movielens/1m/)
Extract the zip and copy ratings.dat, users.dat and movies.dat to this directory.

code directory: /src/main/code code file: exploratory_analysis.ipynb(jupyter notebook)
Run each cell of the jupyter in the order.