Yulu is India’s leading micro-mobility service provider, offering shared electric cycles for short-distance urban travel. Recently, Yulu experienced a decline in revenue, signaling a potential shift in demand patterns.
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.
Yulu wants to understand:
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Which variables significantly influence electric cycle demand?
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Does demand differ on working days vs non-working days?
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How do season and weather conditions impact rentals?
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Are weather conditions dependent on seasons?
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How can these insights help improve revenue and operational efficiency?
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Python
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Libraries: Pandas, NumPy, Matplotlib, Seaborn, SciPy, StatsModels
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Statistical Methods:
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Independent t-test
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ANOVA
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Kruskal-Wallis test
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Chi-Square test
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Shapiro-Wilk & Levene’s tests
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Seasonality and weather are the strongest drivers of demand.
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Fall and Summer record the highest bike rentals.
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Clear weather leads to maximum usage, while rain/snow suppresses demand.
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Working days and holidays do not significantly change average demand — indicating Yulu is used for general urban mobility, not just office commuting.
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Peak demand consistently occurs during 7–9 AM and 5–7 PM, aligning with commute hours.
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Implement weather-aware demand forecasting for fleet optimization
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Increase bike availability during high-demand seasons (Summer & Fall)
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Focus operational planning on peak hours, not just day type
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Avoid reducing fleet size on holidays
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Introduce weather-based promotions and off-season incentives
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Improve data collection for extreme weather scenarios
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Leverage sustainability-focused campaigns for brand growth
Tableau Link :- https://public.tableau.com/app/profile/jiyansh.garg/viz/YuluBikeDemandAnalysis/Dashboard1
