yelp-analysis

Yelp Academic Dataset Analysis

Installation

To run the notebooks, you will need a standard installation of Anaconda with Python 3.x.

Additional libraries needed:

• sklearn
• s2sphere

Data

Data used in the analysis can be downloaded at https://www.yelp.com/dataset.

Project Motivation

This project was done as part of Udacity's Data Scientist Nanodegree. A yelp academic dataset was explored and the following questions were answered:

1. Are there differences in the star ratings by major categories (Restaurants, Shopping, Nightlife, ...)? Any differences in Restaurant categories?
2. What affects if a business will close?
3. Does vicinity to other similar business help or hurt ratings?

File Descriptions

There are four notebooks that contain the analysis of the dataset.

• Yelp Academic Dataset.ipynb: Start here. Contains the introduction, data exploration, and data preparation.
• Question1.ipynb
• Question2.ipynb
• Question3.ipynb

There is also a ProcessData.py file which contains functions for processing data which are needed by the notebooks.

Results

The results of the analysis are presented in a Medium Post.

Main findings:

• Restaurants have the lowest stars ratings compared to other types of businesses. Cafes have the highest average ratings within restaurants.
• The location of the business has the largest effect on whether it will stay open or not.
• You don’t need to be around other restaurants to get a good rating.