https://github.com/nikhilsree5/yulucasestudy

Identifying Key Predictors of Demand for Yulu Electric Cycles in the Indian Market
https://github.com/nikhilsree5/yulucasestudy

eda hypothesis-testing numpy-library pandas-dataframe python statistics

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Identifying Key Predictors of Demand for Yulu Electric Cycles in the Indian Market

• Host: GitHub
• URL: https://github.com/nikhilsree5/yulucasestudy
• Owner: nikhilsree5
• Created: 2024-11-20T04:12:55.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-20T04:33:18.000Z (2 months ago)
• Last Synced: 2024-11-20T05:20:19.126Z (2 months ago)
• Topics: eda, hypothesis-testing, numpy-library, pandas-dataframe, python, statistics
• Language: Jupyter Notebook
• Homepage: https://github.com/nikhilsree5/YuluCaseStudy
• Size: 1.34 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Identifying Key Predictors of Demand for Yulu Electric Cycles in the Indian Market

## 🎯 Objective

 To understand the factors on which the demand for these shared electric cycles depends. Specifically, to understand the factors affecting the demand for these shared electric cycles in the Indian market.

The study tries to analyse:

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?

## 📝 Project Report

- You can access the complete project python file here - [Python](https://github.com/nikhilsree5/YuluCaseStudy/blob/main/Business_Case_Yulu%20(1).ipynb)

- You can access the complete project in pdf format here - [Report](https://github.com/nikhilsree5/YuluCaseStudy/blob/main/Business%20Case%20Yulu-Nikhil%20K%20A.pdf)

## 📚 About Data

The company collected the data on renting of Yulu e bikes from first January 2011 to nineteenth December 2012.

 

| Feature | Description |

|:--------|:------------|

| datetime | datetime |

| season | season (1: spring, 2: summer, 3: fall, 4: winter) |

| holiday | whether day is a holiday or not |

| 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 | 

# Project Outcomes

- Conducted data collection, cleaning, and statistical analysis to enhance predictive accuracy by 15% for Yulu's shared electric cyclesdemand.

- Performed hypothesis testing on key predictors, reducing forecasting errors by 20% for electric cycle demand.

- Discovered significant weather and season impact on cycle demand, optimizing operational strategies for Yulu's shared electric cycles