Data analysis and hypothesis testing to identify key factors influencing Yulu bike rental demand and support business decision-making.

This project explores the demand patterns for shared electric cycles offered by Yulu, India’s leading micro-mobility provider. With recent revenue dips, Yulu aims to understand what factors influence cycle rentals and how these variables interact to drive user demand.

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Statistical analysis of Yulu bike rentals using hypothesis testing to uncover how weather, season, and working days influence ride demand.

End-to-end data analytics on the Yulu Bike Sharing dataset from Kaggle, employing techniques such as data cleaning, exploratory data analysis, and predictive modelling. Leveraged ML algorithms to forecast bike rides and uncovered key insights into user behaviour and demand patterns.

Statistical analysis of Yulu bike rental demand using hypothesis testing including T-Test, ANOVA, Kruskal-Wallis, and Chi-Square tests to identify factors affecting demand.

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Performed hypothesis testing (t-test, ANOVA, Chi-square) on Yulu bike rental data to identify the impact of working days, seasons, and weather on demand and provided business recommendations.

Exploratory data analysis and statistical hypothesis testing to identify key factors influencing Yulu electric bike rental demand.

"Investigating factors affecting shared electric cycle demand in India using EDA and Hypothesis Testing to drive revenue recovery."

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Yulu Bike Demand Analysis: Exploratory Data Analysis (EDA)

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A data-driven analysis to forecast and optimize Yulu bike rental demand based on historical usage, weather patterns, and seasonal trends.

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An in-depth analysis of a bike-sharing dataset to uncover how factors like weather, seasons, holidays, and working days influence bike rental demand. Leveraged data visualization and hypothesis testing to extract actionable insights for better resource management.

This project performs an end-to-end exploratory data analysis (EDA) and hypothesis testing on Yulu’s historical rental data to identify the key factors influencing bike rental demand and to derive actionable business insights.

Case study on Yulu’s bike rental demand analysis.

This case study helped understand how statistical tools can be applied to real-world business problems. The ability to interpret results despite some violations of assumptions is critical in business analytics. Yulu can now use these findings to better predict and influence user demand, especially under varying seasonal and weather conditions.

This project analyzes over 10,000+ bike rental records to understand the impact of weather, season, and working days on rental demand. It includes Exploratory Data Analysis (EDA), Hypothesis Testing (t-test, ANOVA, Chi-Square), and Business Recommendations.

This project analyzes over 10,000+ bike rental records to understand the impact of weather, season, and working days on rental demand. It includes Exploratory Data Analysis (EDA), Hypothesis Testing (t-test, ANOVA, Chi-Square), and Business Recommendations.

This project analyzes the factors influencing the demand for Yulu’s shared electric bikes in India. Using statistical techniques like t-tests, ANOVA, and Chi-square tests, the study explores how variables such as season, weather, working days, and temperature affect bike rentals.

Hypothesis testing case study for Yulu bike sharing — statistical analysis of rental demand factors

Demand analysis of Yulu bike rentals using exploratory data analysis and statistical techniques to derive pricing and operational insights.

Statistical analysis of Yulu bike rentals using Python to study how weather, seasons, and working days affect demand.