🚗 Dynamic Pricing Analysis

Interactive Visualizations for Ride-Sharing Data Science Project

1. Ride Duration vs Cost Analysis

This scatter plot reveals the relationship between expected ride duration and historical cost, with a trend line showing the correlation strength.

🔍 Key Insights:

  • Strong Positive Correlation: Longer rides consistently cost more
  • Linear Relationship: Easy to predict costs based on duration
  • Few Outliers: Most data points follow the trend closely
  • Business Value: Duration is a reliable pricing factor

2. Vehicle Type Cost Distribution

Box plot comparing cost distributions between Economy and Premium vehicle types, showing pricing differences and variability.

🔍 Key Insights:

  • Premium vs Economy: Clear cost differentiation between vehicle types
  • Price Range: Premium vehicles have higher median costs
  • Variability: Both types show similar cost distribution patterns
  • Pricing Strategy: Vehicle type is a key pricing factor

3. Feature Correlation Matrix

Heatmap showing correlations between all numeric features, helping identify which factors most strongly influence ride costs.

🔍 Key Insights:

  • Duration-Cost Correlation: Strongest relationship for pricing
  • Feature Selection: Identifies most important variables
  • Model Building: Guides feature selection for ML models
  • Business Logic: Validates pricing strategy assumptions