Restaurant-Recommendation-system-and-Analysis

An University Project with @markserver using Spark and MLlib - Big Data

Various data analysis tasks on the “Restaurants and Consumers” dataset by UCI ML.
Tasks range from simple statistical analysis to business-economical analysis.

The final task implies the use of ML to create a recommendation engine with the Collaborative Filtering algorithm.

Targets

1. Preliminary analysis on restaurants and consumers.
2. Relation between certain features of a restaurant and the received rating.
3. Supply and demand analysis for each kind of cuisine.
4. Analysis on the ratings.
5. Recommendation system based on the most liked restaurant and type of cuisine.
6. Creation of a recommendation system based on Collaborative Filtering technique.

Usage

All code are in the RestaurantsAndConsumers.ipynb a Jupyter Notebook.
Or use at this link for interactive jupyter report