Yulu - Demand for Shared Electric Cycles

This project investigates factors influencing the demand for Yulu's shared electric cycles in the Indian market.
By analyzing rental data, the project identifies key variables impacting usage and provides recommendations to optimize service offerings.

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📌 Business Problem

Yulu has experienced revenue dips and seeks to understand:

• What factors influence rental demand
• How seasonality, weather, and working days impact usage
• Data-driven strategies to boost utilization and revenue

This project aims to:

• Identify key demand factors
• Perform statistical analysis on influencing variables
• Provide actionable insights for service optimization

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📊 Dataset

The dataset: yulu_data.csv contains detailed rental information.
Key features include:

• Datetime Variables:

  • datetime: Date and time of rental
  • season: Season (1 = Spring, 2 = Summer, 3 = Fall, 4 = Winter)
  • holiday: 0 = Not a holiday, 1 = Holiday
  • workingday: 0 = Weekend/Holiday, 1 = Workday

• Weather & Environmental Factors:

  • weather: (1 = Clear, 2 = Mist, 3 = Light Rain/Snow, 4 = Heavy Rain/Snow)
  • temp: Temperature (°C)
  • atemp: Feeling temperature (°C)
  • humidity: Humidity (%)
  • windspeed: Wind speed

• Rental Counts:

  • casual: Number of rentals by casual users
  • registered: Number of rentals by registered users
  • count: Total rentals (casual + registered)

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🏗️ Project Structure

The case study covers the following steps:

1. Data Exploration

   • Inspect dataset structure and characteristics
   • Handle missing values and outliers (if present)

2. Feature Relationships

   • Analyze relationships between rental demand (count) and independent variables (season, weather, workingday, etc.)

3. Statistical Testing

   • Apply t-tests, ANOVA, and Chi-square tests
   • Assess the significance of demand differences across categories

4. Insights & Recommendations

   • Identify patterns in rental demand
   • Suggest strategies for improving service offerings

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📈 Outcome

• Key Demand Factors

  • Identification of significant variables influencing shared cycle usage

• Statistical Analysis

  • Hypothesis testing results on the influence of working days, seasons, and weather conditions

• Actionable Insights

  • Recommendations for optimizing availability and pricing based on demand patterns

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🚀 Future Work

• Incorporate time-series forecasting models (ARIMA, Prophet, LSTMs)
• Use machine learning regression models for demand prediction
• Build a dashboard for real-time demand monitoring

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📂 Repository Contents

• notebooks/ → Data exploration, statistical tests, and visualization
• scripts/ → Python scripts for preprocessing and analysis
• README.md → Project documentation

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🤝 Contributions

Contributions, issues, and feature requests are welcome!
Feel free to fork the repo and submit a pull request.

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📜 License

This project is licensed under the MIT License.