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RecipeForReviews

An exploratory data project analyzing restaurant reviews to gain insights into ratings, sentiment, trends and more.

Project Description

This project aims to leverage SQL and Python to analyze Yelp restaurant reviews. The goal is to extract meaningful insights from restaurant reviews data with a focus on ratings, sentiments, trends, and other potentially interesting factors.

Dataset

The data for this project is obtains from the Yelp Fusion API. The API provides access to various Yelp businessess and reviews data.

Tools & Libraries

  • Python
  • SQL
  • Jupyter Notebook
  • Git & Github

Setup & Installation

  • Installed Anaconda distribution which includes Python and Jupyter Notebook.
  • Created a GitHub repository to manage and track versions of the project.

Methods Used

  • Run Jupyter Notebook server using Anaconda Prompt and navigate to the project folder.
  • Data Extraction - used the Yelp Fusion API to obtain data about restaurants in Toronto
  • Data Normalization - used pandas to normalize the data from the API, which included flattening the nested 'location' field

Project Steps

Project Conceptualization (05-25-2023)

  • Identified the objective of the project - analyzing restaurant reviews to gain insights into various aspects of the reviews such as restaurant ratings, sentiment of revies, trends over time, etc.
  • Selected Yelp Fusion API as the data source for the project

GitHub Repo Creation (05-25-2023)

  • Created a new GitHub repo named "RecipeForReviews"
  • Initialized the repo with a README file for documenting the project steps and methodologies.

Jupyter Notebook Setup (05-25-2023)

  • Installed and setup Anaconda and Jupyter Notebook on local machine
  • Created a new Jupyter notebook titled "Data_Extraction_Preprocessing.ipynb" in the project directory

Data extraction using the Yelp Fusion API (05-27-2023)

  • Created a Python code block to fetch data using the Yelp Fusion API related to restaurants in Toronto.
  • Stored the fetched data in a pandas dataframe.

Data Normalization (05-27-2023)

  • Tried to flatten the nested 'location' field in the data using 'pd.json_normalize()', but encountered a TypeError.
  • Resolved the error by accessing the 'location' field directly instead of trying to flatten it.
  • Merged the original dataframe and the location details into a new dataframe.

Yelp API Usage

This project utilizes the Yelp Fusion API to collect and analyze restaurant reviews. By accessing and using the yelp API, I adhere to the terms and conditions outlined in the Yelp API Terms of Use.

Please note that my usage of the Yelp API is subject to the restrictions and guidelines specified in the API Terms of Use. I acknowledge Yelp as the provider of the data and services used in this project.

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This project leverages the Yelp Fusion API to analyze restaurant data

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