yebaffoe1/yulu-electric-cycle-demand-analysis

Yulu Electric Cycle Demand Analysis Readme

Business Problem

The objective of this analysis is to understand the significant variables influencing the demand for shared electric cycles in the Indian market. Key questions include identifying the variables predicting demand, assessing how well these variables describe the demand, and providing actionable insights for optimizing operations and marketing strategies.

Approach

Data Exploration and Analysis

1. Import and Data Analysis: Conducted a thorough analysis of the dataset structure and characteristics.
2. Variable Relations: Established relationships between the dependent variable (Count) and independent variables (Workingday, Weather, Season).
3. Statistical Tests: Utilized tests to check the effects of Working Day, Seasons, and Weather on the number of electric cycles rented.

Problem Solving

1. Hypothesis Testing: Set up null and alternate hypotheses, checking assumptions, and computing test statistics.
2. Data Cleaning: Addressed null values, duplicates, and outliers.
3. Visualization: Employed visualizations to enhance understanding and interpretation of data patterns.

Problems Encountered and Solutions

Assumption Check

• Problem: Some assumptions for statistical tests were not met, such as normal distribution.
• Solution: Utilized non-parametric tests and transformations to accommodate non-normality.

Data Quality

• Problem: Outliers in humidity, windspeed, casual, registered, and count columns.
• Solution: Identified and handled outliers appropriately to ensure data integrity.

Seasonal Dependency

• Problem: Dependency of weather and season on the number of bikes rented.
• Solution: Conducted Chi-square tests and removed less representative data points to ensure accurate results.

Correlation Analysis

• Problem: High correlation observed between certain variables.
• Solution: Analyzed correlations to understand relationships and made recommendations based on findings.

Observations from Results

1. Seasonal Impact: Demand is higher in the fall season, followed by summer and winter, while it is lower in spring.
2. Weather Impact: Clear and cloudy weather sees higher demand, followed by misty and rainy weather.
3. Working Day Effect: No significant difference in the mean hourly count between working and non-working days.
4. Holiday Effect: Similar counts of rented electric cycles observed on both holidays and non-holidays.
5. Weather and Season Dependency: Statistically significant dependency of weather and season on the number of bikes rented.
6. Weather Impact on Rental Bikes: Significant differences in the number of cycles rented based on weather conditions.
7. Seasonal Impact on Rental Bikes: Significant variations in the number of cycles rented across different seasons.

Recommendations

1. Seasonal Marketing: Adjust marketing strategies based on seasonal patterns, focusing on promotions during high-demand months.
2. Time-based Pricing: Implement time-based pricing to balance demand, offering lower rates during off-peak hours.
3. Weather-based Promotions: Create weather-specific promotions, targeting clear and cloudy weather conditions.
4. User Segmentation: Tailor marketing for registered and casual users, offering loyalty programs and personalized recommendations.
5. Optimize Inventory: Adjust inventory based on demand patterns to optimize resources and prevent excess bikes during low-demand months.
6. Improve Weather Data Collection: Enhance data collection for extreme weather conditions to better understand customer behavior.
7. Customer Comfort: Provide amenities like umbrellas or rain jackets to enhance the customer experience during moist conditions.
8. Collaborate with Weather Services: Partner with weather services to provide real-time updates and forecasts, integrating weather information into marketing strategies.
9. Seasonal Bike Maintenance: Conduct seasonal maintenance checks to ensure bikes are in top condition during peak seasons.
10. Customer Feedback and Reviews: Encourage customer feedback to identify areas for improvement and understand preferences.

Conclusion

The analysis provides valuable insights into factors influencing electric cycle demand, enabling Yulu to make informed decisions. By implementing the recommendations, Yulu can optimize operations, enhance customer experience, and formulate targeted marketing strategies for different demographic and seasonal segments.