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📘This repository provides a detailed exploration of `Yulu` Bike rentals data using Hypothesis testing, employing statistical techniques, we delve into the nuances of customer behavior, E-bikes rental patterns offering insights & key metrics to enhance understanding and inform strategic decisions

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🚲🚴‍♂️YULU_Bikes - Business CaseStudy🚴‍♂️🚲

Hypothesis Testing

Analysed by : KASI

yulu-bike

🚲Yulu: Zipping Through Indian Cities with Green Scooters

Yulu, India's pioneering micro-mobility service provider, has embarked on a mission to revolutionize daily commutes by offering unique, sustainable transportation solutions. However, recent revenue setbacks have prompted Yulu to seek the expertise of a consulting company to delve into the factors influencing the demand for their shared electric cycles, specifically in the Indian market. India's leading micromobility platform, transforming urban commutes. Founded in 2017, Yulu isn't just about rides, it's about a sustainable future.

🌐 Website : www.yulu.bike

The Yulu Way:

  • E-scooters and e-bikes: Ditch cars, hail a Yulu! These dockless rides, accessed through a slick app, are scattered across Bengaluru, Mumbai, Delhi, and more.
  • Convenience reigns: Find, unlock, and park your Yulu anywhere within designated zones. No docking dramas, just hop on and off!
  • Green warriors: Every Yulu ride cuts traffic congestion and carbon emissions, breathing new life into city air.

Impact beyond rides:

  • Over 25,000 Yulu scoots and millions of happy riders: More than just a fun ride, Yulu is a movement.
  • Boosting the local economy: Yulu creates jobs, supports businesses, and revitalizes cityscapes.
  • Yulu Wynn: Introducing India's first truly keyless electric scooter, offering personal e-mobility options.

Yulu's not just a company; it's a vision:

A vision of urban streets buzzing with eco-friendly rides, a vision of cleaner air, and a vision of a future where getting around is effortless and green. So, next time you're in an Indian city, ditch the cab, skip the bus, and Yulu your way to a better future.

In a nutshell:

Yulu = Green Mobility. Indian cities = Grateful (and scooting!).

Why this case study?

  • From Yulu's Perspective:

    • Strategic Expansion: Yulu's decision to enter the Indian market is a strategic move to expand its global footprint. Understanding the demand factors in this new market is essential to tailor their services and strategies accordingly.
    • Revenue Recovery: Yulu's recent revenue decline is a pressing concern. By analyzing the factors affecting demand for shared electric cycles in the Indian market, they can make informed adjustments to regain profitability.
  • From Learners' Perspective:

    • Real-World Problem-Solving: It presents an opportunity to apply machine learning and data analysis techniques to address a real-world business problem.
    • Market Insights: Analyzing factors affecting demand in the Indian market equips learners with market research skills. This knowledge is transferable to various industries.
    • Consulting Skills: Learners can develop their ability to act as consultants, providing data-driven insights to organizations

Business Problem:

  • Which variables are significant in predicting the demand for shared electric cycles in the Indian market ?

  • How well those variables describe the electric cycle demands.


📃 Features of the dataset:

  • Column Profiling:
Feature Description
datetime datetime
season season (1: spring, 2: summer, 3: fall, 4: winter)
holiday whether day is a holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
workingday if day is neither weekend nor holiday is 1, otherwise is 0.
temp temperature in Celsius
atemp feeling temperature in Celsius
humidity humidity
windspeed wind speed
casual count of casual users
registered count of registered users
count - Total_riders count of total rental bikes including both casual and registered
  • weather
Category Details
1 Clear, Few clouds, partly cloudy, partly cloudy
2 Mist + Cloudy, Mist + Broken 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

📝 Case Report

  • You can access the complete Case python file here - Python
  • You can access the complete Casestudy in pdf format here - Report

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📘This repository provides a detailed exploration of `Yulu` Bike rentals data using Hypothesis testing, employing statistical techniques, we delve into the nuances of customer behavior, E-bikes rental patterns offering insights & key metrics to enhance understanding and inform strategic decisions

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