Analyzing & Visualizing Restaurants listed in Zomato across Bengaluru City of India with Python and Power BI
Overview • Prerequisites • Architecture • Demo • Support • License
This project focuses on analyzing and visualizing restaurants listed in Zomato across Bengaluru city of India.
The repository directory structure is as follows:
Zomato-Restaurants-Analysis
├─ 01_SOURCE
├─ 02_ETL
├─ 03_DATA
├─ 04_ANALYSIS
├─ 05_DASHBOARD
├─ 06_RESOURCES
The type of content present in the directories is as follows:
01_SOURCE
This directory contains the the received/downloaded raw data that needs to be cleaned and organized to ease out the data analysis and visualization process.
02_ETL
This directory contains the ETL script that takes the raw dataset as an input, transforms it and exports an analysis-ready dataset into the 03_DATA directory.
In this project; we have also performed geocoding of suburbs with Bing API & GeoPy library of Python.
03_DATA
This directory contains the data that can be directly used for exploratory data analysis and data visualization purposes.
04_ANALYSIS
This directory contains the python notebooks that analyzes the clean dataset to generate insights.
For analyzing the data with Jupyter Notebook; we have used the clean dataset present in the SQLite database.
05_DASHBOARD
This directory contains the markdown file with an embedded Power BI report link that visualizes the data.
The Power BI dashboard contains slicers, cross-filtering and other advance capabilities that end user can play with to visualize a specific facet of the data or, to get additional insights.
06_RESOURCES
This directory contains images, icons, layouts, etc. that are used in this project.
The major skills that are required as prerequisite to fully understand this project are as follows:
- Basics of Python & Jupyter Notebook
- Basics of Power BI
In order to complete the project, I've used the following applications and libraries
- Python
- Python libraries mentioned in requirements.txt file
- Jupyter Notebook
- Visual Studio Code
- Microsoft Power BI
The choice of applications & their installation might vary based on individual preferences & system settings.
The project architecture is quite straight forward and can be explained through the below image:
As shown in the above workflow; we are first performing necessary cleaning and transformation in the received raw dataset using Python and exporting the clean dataset as a comma-separated flat file
Finally; we leverage the clean & analysis-ready dataset for exploratory data analysis (EDA) using Jupyter Notebook and creating an insightful report using Power BI.
The interactive Power BI dashboard can be viewed here:
If you have any doubts, queries or, suggestions then, please connect with me in any of the following platforms:
If you like my work then, you may support me at Patreon:
This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.