Overview
This project focuses on scraping properties for student accommodation across UK and their associated metrics from Student Accommodation UK, performing exploratory data analysis to generate insights and visualize them with the help of Power BI.
The repository directory structure is as follows:
UK-Student-Accommodation-Analysis
├─ 01_WEBSCRAPING
├─ 02_ETL
├─ 03_DATA
├─ 04_ANALYSIS
├─ 05_DASHBOARD
├─ 06_RESOURCES
The type of content present in the directories is as follows:
01_WEBSCRAPING
This directory contains the python script to scrape data from the website along with flat file that has the scraped data.
02_ETL
This directory contains the ETL script that takes the scraped dataset as input, transforms it and exports an analysis-ready dataset into the 03_DATA directory.
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
05_DASHBOARD
This directory contains the python notebook with an embedded Power BI report 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
Prerequisites
The major skills that are required as prerequisite to fully understand this project are as follows:
- Basics of Python
- Python libraries: Requests-HTML, Pandas, DateTime, concurrent_futures
- Basics of Python Notebooks
- 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.
