https://github.com/niteshchawla/yulu-hypothesistesting

Yulu 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 cycles depends. Specifically, they want to understand the factors affecting the demand for these shared electric cycles in the Indian market.
https://github.com/niteshchawla/yulu-hypothesistesting

hypothesis-testing matplot numpy pandas-library scipy-stats seaborn

Last synced: 8 days ago
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Yulu 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 cycles depends. Specifically, they want to understand the factors affecting the demand for these shared electric cycles in the Indian market.

• Host: GitHub
• URL: https://github.com/niteshchawla/yulu-hypothesistesting
• Owner: Niteshchawla
• Created: 2024-06-28T09:58:55.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-28T09:59:59.000Z (5 months ago)
• Last Synced: 2024-06-28T11:24:23.067Z (5 months ago)
• Topics: hypothesis-testing, matplot, numpy, pandas-library, scipy-stats, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Yulu-HypothesisTesting

Introduction:

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

Objective:

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 a

Dataset:

Dataset Link: yulu_data.csv

Column Profiling:

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.

weather:

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

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: count of total rental bikes including both casual and registered

Concept Used:

Bi-Variate Analysis

2-sample t-test: testing for difference across populations

ANNOVA

Chi-square