Demand for Electric Bicycles

Lyft: Decode e-bike demand via non-parametric hypothesis tests and effect size analysis. Python, Scipy, Scikit-learn, Matplotlib.

Lyft is a leading North American transportation company, which offers shared bikes and scooters alongside its rideshare service for the daily commute. Starting off as a mission to reduce traffic congestion in cities, Lyft provides a commute solution through a user-friendly mobile app to enable shared, solo and sustainable commuting. Lyft's bike stations are located at all the appropriate locations (including transit stations, bus stops, office spaces, residential areas, corporate offices, etc) to make those first and last miles smooth, affordable, and convenient.

Assignment

Lyft has recently suffered considerable dips in its revenues. They have contracted a consulting company to understand the factors on which the demand for these shared electric bicycles depends. Specifically, they want to understand the factors affecting the demand for these shared electric bicycles in the US market.

The company wants to know:

• Which variables are significant in predicting the demand for shared electric bicycles in the US market?
• How well do those variables describe the electric bicycle demand.

Data Description

The file bike_sharing.csv contains the usage data aggregated by the hour with the following columns:

• datetime - beginning of an hour for which the data are aggregated;
• season
  • 1: spring,
  • 2: summer,
  • 3: fall,
  • 4: winter;
• holiday - whether a day is a holiday or not
• workingday - if a day is neither weekend nor holiday is 1, otherwise is 0.
• weather
  • 1: clear, few clouds, scattered clouds
  • 2: mist + cloudy, mist + scattered clouds, mist + few clouds, mist
  • 3: light snow, light rain + thunderstorm + scattered clouds, light rain + scattered clouds
  • 4: heavy rain + ice pallets + thunderstorm + mist, snow + fog
• temp - air temperature in degrees Celsius
• atemp - feeling temperature in degrees Celsius
• humidity - air humidity in percent
• windspeed - wind speed in km/h
• casual - number of bikes rented by casual users
• registered - number of bikes rented by registered users
• count - total number of rented bikes including both casual and registered users

Practicalities

Analyze the provided data and provide insights to the best of your abilities. Use statistical tests to support your claims. Include the relevant tables/graphs/visualization to explain what you have learned about the market. Make sure that the solution reflects your entire thought process including the preparation of data - it is more important how the code is structured rather than just the final result or plot.