Skip to content

Latest commit

 

History

History
32 lines (26 loc) · 1.88 KB

README.md

File metadata and controls

32 lines (26 loc) · 1.88 KB
Raw

Project-4.-Data-Engineering

Goal

To determine the ideal locations for e-scooters in major cities across Germany.

Overview

GANS is a new e-scooter rental company aiming to operate in cities across Germany. Leveraging web scraping, API data collection, MySQL databases, and AWS Lambda automation, GANS hopes to optimize their fleet deployment strategy.

Task:

  • Use webscraping for information about cities incl.: their size, population, attractiveness to tourists
  • Use API's to get flight and weather information - as e-scooters are popular among tourists, especially budget travellers
  • Use MySQL database to connect all insights in a logical way
  • Use Jupyter notebooks to build a local pipeline
  • Use AWS Lambda automation to deploy in the Cloud

Deliverables

Overview article on Medium found here that succintly summarizes the findings and the process. Python code with the clean notebooks are found here, and the code for creating a database in SQL is found here.

Skills & Tools

  1. Webscraping
  2. API's
  3. MySQL
  4. Jupyter Notebooks
  5. AWS Lambda

Furher Considerations (limited due to the time allocated for the project)

  1. Consideration of budget airlines, define and select budget airlines
  2. Research cities that are not major cities ex.: Berlin, but get a lot of tourists for some specific attraction
  3. Take into consideration seasonality or some special event ex.: Octoberfest or Rock Im Ring.
  4. Incentivize e-scooter use in bad weather
  5. Incentivize the parking of e-scooters in areas where they will be more likely to be rented. Ex.: More likely to rent at the bottom of a hill to go up than the other way around.