OLA Ride Analysis Dashboard

A comprehensive Power BI dashboard for Ola ride analysis, tracking over 93.61K bookings.

Project Overview

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Developed a comprehensive Power BI dashboard for Ola ride analysis, tracking over 93.61K bookings and $33.46M in booking value to provide key business insights.

Key Deliverables: - Used MySQL queries for fast retrieval of data for stakeholder requirements on fast decision making. - Analyzed and visualized ride-sharing performance by vehicle type, identifying Prime Plus as the top revenue generator with $4.84M in booking value. - Built a detailed cancellation analysis module, highlighting that 19% of rides were customer-canceled versus 3.39% driver-canceled. - Monitored and reported on booking success rates (61.88%) and failure rates (38.12%). - Designed series of visuals including line charts for daily trends and pie charts for segmentation.

Demonstrating Success Rates with Python

To satisfy the Quarto environment requirements, I am demonstrating the Booking Success vs Failure rate logic using an embedded Python chunk.

import matplotlib.pyplot as plt
import numpy as np

labels = ['Success Rate', 'Failure Rate']
rates = [61.88, 38.12]
colors = ['#27ae60', '#e74c3c']

fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.barh(labels, rates, color=colors, edgecolor='black')

# Labeling bars
for bar in bars:
    width = bar.get_width()
    ax.text(width + 1, bar.get_y() + bar.get_height()/2, 
            f'{width}%', ha='left', va='center', weight='bold')

ax.set_xlim(0, 80)
ax.set_xlabel('Percentage (%)')
ax.set_title('Ride Booking Success vs Failure Rates', fontsize=14, pad=15)
plt.tight_layout()
plt.show()

Figure 1: OLA Ride Booking Outcomes

This visualization reinforces the finding that there is a significant margin for improving operational efficiency by targeting the 38.12% failure segment.